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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one.

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount.

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions.

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
Interviews: In-depth conversations with participants, offering qualitative insights.
Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions.

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
Random Assignment: Participants are randomly assigned to different experimental conditions.
Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
Large Sample Size: Surveys often target a large and diverse group of participants.
Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

Observation and Data Collection: This involves observing and documenting without manipulating variables.
Objective Description: Aim to provide an unbiased and factual account of the subject.
Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
Contextual Understanding: Aims to understand the context and unique characteristics of the case.
Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

Participant Observation: Researchers actively participate in the community or setting.
Holistic Perspective: Focuses on the interconnectedness of cultural elements.
Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

Constant Comparison: Data is continually compared and analyzed during the research process.
Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

Flexibility: Allows for flexibility in data collection and analysis.
Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

Structured Data Collection: Involves systematic and structured data collection methods.
Objective Presentation: Aims to provide an unbiased and factual account of the subject.
Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

Causal Relationships: Seeks to establish causal relationships between variables.
Controlled Variables : Often involves controlling certain variables to isolate causal factors.
Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

Snapshot View: Provides a snapshot of a population at a specific moment.
Efficiency: More efficient in terms of time and resources.
Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

Temporal Sequence: Allows for the examination of changes over time.
Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

Causality Inference: Enables the inference of cause-and-effect relationships.
Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

Natural Settings: Data is often collected in natural settings without intervention.
Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour.

Designing Effective Surveys

Objective Design:

Clearly define the research objectives to guide the survey design.
Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

Use a structured format with standardized questions for consistency.
Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

Conduct pilot tests to identify and rectify potential issues with survey design.
Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

Develop a robust sampling strategy to ensure a representative participant group.
Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

Build rapport with participants to create a comfortable and open environment.
Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

Frame open-ended questions to encourage detailed responses.
Allow participants to express their thoughts and perspectives freely.

Active Listening:

Practice active listening to understand areas and gather rich data.
Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

Obtain informed consent and assure participants of confidentiality.
Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

Actively immerse yourself in the setting or group being observed.
Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

Maintain detailed and reflective field notes during observations.
Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

Be conscious of ethical considerations, ensuring respect for participants.
Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

Maintain a more detached and objective stance during non-participant observation.
Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

Enhance the reliability of data by reducing observer bias.
Develop clear observation protocols and guidelines.

Contextual Understanding:

Strive for a thorough understanding of the observed context.
Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

Locate and access archives relevant to the research topic.
Collaborate with institutions or repositories holding valuable data.

Data Verification:

Verify the accuracy and reliability of archived data.
Cross-reference with other sources to ensure data integrity.

Ethical Use:

Adhere to ethical guidelines when using existing data.
Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

Address the possibility of incomplete or inaccurate archival records.
Acknowledge limitations and uncertainties in the data.

Temporal Bias:

Recognize potential temporal biases in archived data.
Consider the historical context and changes that may impact interpretation.

Access Limitations:

Address potential limitations in accessing certain archives.
Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

Limited timeframes can compromise the depth and thoroughness of the research process.
Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

Inadequate resources, whether financial or human, can impede the execution of research activities.
Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

Selecting participants in a way that systematically skews the sample can introduce selection bias.
Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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What is a framework? Understanding their purpose, value, development and use

Articles with Attitude
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Published: 14 April 2023
Volume 13 , pages 510–519, ( 2023 )

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Stefan Partelow ORCID: orcid.org/0000-0002-7751-4005 1 , 2

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Many frameworks exist across the sciences and science-policy interface, but it is not always clear how they are developed or can be applied. It is also often vague how new or existing frameworks are positioned in a theory of science to advance a specific theory or paradigm. This article examines these questions and positions the role of frameworks as integral but often vague scientific tools, highlighting benefits and critiques. While frameworks can be useful for synthesizing and communicating core concepts in a field, they often lack transparency in how they were developed and how they can be applied. Positioning frameworks within a theory of science can aid in knowing the purpose and value of framework use. This article provides a meta-framework for visualizing and engaging the four mediating processes for framework development and application: (1) empirical generalization, (2) theoretical fitting, (3) application, and (4) hypothesizing. Guiding points for scholars and policymakers using or developing frameworks in their research are provided in closing.

Introduction

Reflections on Methodological Issues

Beyond qualitative/quantitative structuralism: the positivist qualitative research and the paradigmatic disclaimer, explore related subjects.

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The development of ‘frameworks’ is at present probably the most common strategy in the field of natural resources management to achieve integration and interdisciplinarity. Mollinga , 2008

…it is not clear what the role of a scientific framework should be, and relatedly, what makes for a successful scientific framework. Ban and Cox, 2017

Frameworks are important research tools across nearly all fields of science. They are critically important for structuring empirical inquiry and theoretical development in the environmental social sciences, governance research and practice, the sustainability sciences and fields of social-ecological systems research in tangent with the associated disciplines of those fields (Binder et al. 2013 ; Pulver et al. 2018 ; Colding and Barthel 2019 ). Many well-established frameworks are regularly applied to collect new data or to structure entire research programs such as the Ecosystem Services (ES) framework (Potschin-Young et al. 2018 ), the Social-Ecological Systems Framework (SESF) (McGinnis and Ostrom 2014a ), Earth Systems Governance (ESG) (Biermann et al. 2010 ), the Driver-Impact-Pressure-State-Response (DIPSR) framework, and the Life Cycle Assessment (LCA) framework. Frameworks are also put forth by major scientific organizing bodies to steer scientific and policy agendas at regional and global levels such as the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (Díaz et al. 2015 ) and the Global Sustainable Development Report’s transformational levers and fields (UN 2019 ).

Despite the countless frameworks, it is not always clear how a framework can be developed or applied (Ban and Cox 2017 ; Partelow 2018 ; Nagel and Partelow 2022 ). Development may occur through empirically backed synthesis or by scholars based on their own knowledge, values, or interests. These diverse development pathways do, however, result in common trends. The structure of most frameworks is the identification of a set of concepts and their general relationships — often in the form box-and-arrow diagrams — that are loosely defined or unspecified. This hallmark has both benefits and challenges. On one hand, this is arguably the purpose of frameworks, to structure the basic ideas of theory or conceptual thinking, and if they were more detailed they would be models. On the other hand, there is often a “black box” nature to frameworks. It is often unclear why some sets of concepts and relationships are chosen for integration into frameworks, and others not. As argued below, these choices are often the result of the positionality of the framework’s creators. Publications of frameworks, furthermore, often lack descriptions of their value and potential uses compared to other frameworks or analytical tools that exist in the field.

Now shifting focus to how frameworks are applied. Some frameworks provide measureable indicators as the key variables in the framework, but many only suggest general concepts. This creates the need to link concepts and their relationships to data through other more tangible indicators. Methods to measure such indicators will also be needed in new empirical studies. These methodological and study design steps necessary to associate data to framework concepts is often referred to as “operationalizing” a framework. However, without guidance on how to do this, scholars are often left with developing their own strategies, which can lead to heterogeneous and idiosyncratic methods and data. These challenges can be referred to as methodological gaps (Partelow 2018 ), where the details of how to move from concept to indicator to measurement to data transformation, are not always detailed in a way that welcomes replicability or learning. This is not necessarily a problem if the purpose of a framework is to only guide the analysis of individual cases or synthesis activities in isolation, for example to inform local management, but it hinders meta-analyses, cross-case learning and data interpretability for others.

In this article, a brief overview of framework definitions and current synthesis literature are reviewed in the “ What is a framework? ” section. This is coupled with the argument that frameworks often lack clarity in their development and application because their positioning within a theory of science is unclear. In the “ Mechanisms of framework development and use: a meta-framework ” section, a meta-framework is proposed to assist in clarifying the four major levers with which frameworks are developed and applied: (1) empirical generalization, (2) theoretical fitting, (3) hypothesizing, and (4) application. The meta-framework aims to position individual frameworks into a theory of science, which can enable scholars to take a conceptual “step back” in order to view how their engagement with a framework contributes to their broader scientific goal and field. Two case studies of different frameworks are provided to explore how the meta-framework can aid in comparing them. This is followed by a discussion of what makes a good framework, along with explicit guiding points for the use of frameworks in research and policy practice.

What is a framework?

The definition and purpose of a framework is likely to vary across disciplines and thematic fields (Cox et al. 2016 ). There is no universal definition of a framework, but it is useful to provide a brief overview of different definitions for orientation. The Cambridge Dictionary states that frameworks are “a supporting structure around which something can be built; a system of rules, ideas, or beliefs that is used to plan or decide something.” Schlager ( 2007 , 293) states that “frameworks provide a foundation for inquiry,” and Cumming ( 2014 , 5) adds that this “does not necessarily depend on deductive logic to connect different ideas.” Importantly, Binder et al., ( 2013 , 2) note that “a framework provides a set of assumptions, concepts, values and practices,” emphasizing the normative or inherently subjective logic to framework development. A core theme being plurality and connectivity. Similarly, McGinnis and Ostrom ( 2014a , 1) define frameworks as “the basic vocabulary of concepts and terms that may be used to construct the kinds of causal explanations expected of a theory. Frameworks organize diagnostic, descriptive, and prescriptive inquiry.” In a review comparing ten commonly used frameworks in social-ecological systems (SES) research, Binder et al., ( 2013 , 1) state that frameworks are useful for developing “a common language, to structure research on SES, and to provide guidance toward a more sustainable development of SES.” In a similar review, Pulver et al., ( 2018 , 1) suggest that frameworks “assist scholars and practitioners to analyze the complex, nonlinear interdependencies that characterize interactions between biophysical and social arenas and to navigate the new epistemological, ontological, analytical, and practical horizons of integrating knowledge for sustainability solutions.” It is important to recognize that the above claims often suggest the dualistic or bridging positions held by frameworks, in both theory building and for guiding empirical observations. However, there is relatively little discussion in the above literature on how frameworks act as bridging tools within a theory of science or how frameworks add value as positioning tools in a field.

Every framework has a position, meaning it is located within a specific context of a scientific field. As positioning tools, frameworks seem to “populate the scientist’s world with a set of conceptual objects and (non-causal) relationships among them,” shaping (and sometimes limiting) the way we think about problems and potential solutions (Cox et al. 2016 , 47). Thus, using a specific framework helps in part to position the work of a researcher in a field and its related concepts, theories and paradigms.

Four factors can be considered to evaluate the positioning of a framework: (a) who developed it, (b) the values being put forth by those researchers, (c) the research questions engaged with, and (d) the field in which it is embedded. For example, the Social-Ecological Systems Framework (SESF) (Ostrom 2009 ) was developed by (a) Elinor Ostrom who developed the framework studying common-pool resource and public goods governance from the 1960s until the 2000s. Ostrom’s overall goal was (b) to examine the hindering and enabling conditions for governance to guide the use and provision common goods towards sustainability outcomes. Her primary research questions (c) related to collective action theory, unpacking how and why people cooperate with each other or not. The field her work is embedded in (d) is an interdisciplinary mix between public policy, behavioral and institutional economics. Scholars who use Ostrom’s SESF today, carry this history with them and therefore position themselves, whether implicitly or explicitly, as part of this research landscape as systems thinkers and interdisciplinarians, even if they have other scholarly positions.

Frameworks are positioned within a theory of science. Understanding this positioning can guide scholars in comprehending how their engagement with frameworks contributes to the overall advancement of their field. To do this, taking a conceptual “step back” is necessary, to distinguish between different levels of theory in science. From the conceptually broadest to the most empirically specific, we can identify the following levels of theory: paradigms, frameworks, specific theories, models/archetypes and cases (Table 1 ). Knowledge production processes flow up and down these levels of theory. For example, as argued by Kuhn ( 1962 ), the purpose of a scientific field is to advance its paradigm. Thus, the study of empirical observations (e.g., case studies) — and the development of models or theories resulting from those data — are aimed at advancing the overarching paradigm. Such paradigms could be conservation, democracy, sustainable development or social-ecological systems.

There is a need to connect cases, models and specific theory up to the overall paradigms of a field to make aggregate knowledge gains. Here, the role of frameworks becomes more clear, as bridging tools that enable connections between levels of knowledge. From the top down, frameworks can specify paradigms with more tangible conceptual features and relationships, which can then guide empirical inquiry. For example, the Driver-Pressure-State-Impact-Response (DPSIR) framework (Smeets and Weterings 1999 ; Ness, Anderberg, and Olsson 2010 ) specifies how to evaluate policy options and their effects by focusing on the five embedded concepts in a relational order. Scholars can then generate more specific indicators and methods to measure the five specified features of the framework, and their relationships, to generate empirical insights that now have a direct link to the paradigm of sustainable policy development via the framework.

Furthermore, frameworks can also emerge from the bottom up, by distilling empirical data across cases and thus creating a knowledge bridge of more specified conceptual features and relationships that connect to a paradigm. In both top-down and bottom-up mechanism, frameworks can play a vital role in synthesizing and communicating ideas among scholars in a field — from empirical data to a paradigm. A challenge may be, however, that multiple frameworks have emerged attempting to specify the core conceptual features and relationships in a paradigm. A mature scientific field is likely to have many frameworks to guide research and debate. There is, however, a lack of research and tools available to compare frameworks and their added value.

Beyond their use as positioning tools, frameworks make day-to-day science easier. They can guide researchers in designing new empirical research by indicating which core concepts and relationships are of interest to be measured and compared. Scientific fields also need common fires to huddle around, meaning that we need reference points to initiate scholarly debates, coordinate disparate empirical efforts and to communicate findings and novel advancements through a common language (McGinnis and Ostrom 2014a ; Ban and Cox 2017 ). As such, frameworks are useful for synthesis research, focusing the attention of reviews and meta-analyses around core sets of concepts and relationships.

There is, however, a tension between frameworks that aim to capture complexity and those that aim to simplify core principles. Complexity oriented frameworks often advance systems thinking at the risk of including too many variables. They often have long lists of variables which makes empirical orientation and synthesis difficult. On the other hand, simplification frameworks face the challenge of leaving important things out, with the benefit of clarifying what may be important and giving clear direction.

From a more critical perspective, the “criteria for comparing frameworks are not well developed,” (Schlager, 2007 , 312), and the positionality of frameworks has not been rigorously explored outside of smaller studies. Nonetheless, numerous classifications or typologies of frameworks within specific fields have been suggested (Table 2 ), although not with reference to positionality (Spangenberg 2011 ; Binder et al. 2013 ; Cumming 2014 ; Schlager 2007 ; Ness et al. 2007 ; Potschin-Young et al. 2018 ; Cox et al. 2021 ; Louder et al. 2021 ; Chofreh and Goni 2017 ; Alaoui et al. 2022 ; Tapio and Willamo 2008 ). These studies point to the question of: what makes a good framework? Are there certain quality criteria that make some frameworks more useful than others? There has undoubtedly been a rise in the number of frameworks, but as expressed by Ban and Cox ( 2017 , 2), “it is not clear what the role of a scientific framework should be, and relatedly, what makes for a successful scientific framework. Although there are many frameworks […] there is little discussion on what their scientific role ought to be, other than providing a common scientific language.” The meta-framework presented below serves as a tool for answering these questions and provides guidance for developing and implementing frameworks in a range of settings.

Mechanisms of framework development and use: a meta-framework

This section presents a meta-framework detailing the mechanisms of framework development and use (Fig. 1 ). The meta-framework illustrates the role of frameworks as bridging tools for knowledge synthesis and communication. Therefore, the purpose of the meta-framework is to demonstrate how the mechanisms of framework development and use act as levers of knowledge flow across levels within a theory of science, doing so by enabling the communication and synthesis of knowledge. Introducing the meta-framework has two parts, outlined below.

A meta-framework outlining the central role frameworks play in scientific advancement through their development and use. In the center, frameworks provide two core bridging values: knowledge synthesis and knowledge communication. Three modes of logical reasoning contribute to framework development: induction, deduction and abduction. Frameworks are used and developed through four mediating processes: (1) empirical generalization, (2) theoretical fitting, (3) application, and (4) hypothesizing

First, the meta-framework visualizes the levels along the scale of scientific theory including paradigms, frameworks, specific theory and empirical observations, introduced above. Along this scale, three mechanisms of logical reasoning are typical: induction, deduction, and abduction. Induction is a mode of logical reasoning based on sets of empirical observations, which, when patterns within those observations emerge, can inform more generalized theory formation. Induction, in its pure form, is reasoning without prior assumptions about what we think is happening. In contrast, deduction is a mode of logical reasoning based on testing a claim or hypothesis, often based on a body of theory, against an observation to infer whether or not a claim is true. In contrast to induction, which always leads to probable or fuzzy conclusions, deductive logic provides true or false conclusions. A third mode of logical reasoning is abduction. Abduction starts with a single or limited set of observations, and assumes the most likely cause as a conclusion. Abduction can only provide probable conclusions. Knowledge claims from all three modes of logical reasoning are part of the nexus of potential framework creation or modification.

Second, the meta-framework has four iterative mediating processes that directly enable the development and/or application of frameworks (Fig. 1 ). Two of the four mediating processes relate to framework development: (1) empirical generalization and (2) theoretical fitting. The other two relate to framework application: (3) hypothesizing, and (4) application (Fig. 1 , Table 3 ). The details of the specific mediating pathways are outlined in Table 3 , including the processes involved in each. There are numerous potential benefits and challenges associated with each (Table 3 ).

The value of a meta-framework

The presented meta-framework (Fig. 1 ) allows us to assess the values different frameworks can provide. If a framework provides a novel synthesis of key ideas or new developments in a field, and communicates those insights well in its composition, it likely adds notable value. If a framework coordinates scientific inquiry across the 1 or more of the four mediating processes, it likely acts as an important gatekeeper and boundary object for what may otherwise be disparate or tangential research. If it contributes substantial advances in 3 or 4 of the mediating processes, the value of the framework is likely higher.

The meta-framework can further help identify the positioning of framework such as the type of logical reasoning processes used to create it, as well as help clarify the role of a framework along the scale of knowledge production (i.e., from data to paradigm). It might be clear, for example, what paradigm or specific theory a framework contributes to. The meta-framework can add value by guiding the assessment of how frameworks fit into the bigger picture of knowledge contribution in their field. Furthermore, many scholars and practitioners are interested in developing new frameworks. The meta-framework outlines the mechanisms that can be considered in creating the framework as well as help developers of new frameworks communicate how their frameworks add value. For example, to link empirical data collection to theoretical work in their field.

The meta-framework can help compare frameworks, to assess strengths and weaknesses in terms of their positioning and knowledge production mechanisms. It can also help elucidate the need for, or value of, new frameworks. This challenge is noted by Cumming ( 2014 , 18) in the field of social-ecological systems, reflecting that “the tendency of researchers to develop “new” frameworks without fully explaining how they relate to other existing frameworks and what new elements they bring to the problem is another obvious reason for the lack of a single dominant, unifying framework.” To showcase such as comparison, two brief examples are provided. The first example features the Driver-Pressure-State-Impact-Response (DPSIR) framework developed by the European Environmental Agency (EEA) (Box 1 ) (Smeets and Weterings 1999 ; Ness, Anderberg, and Olsson 2010 ). The DPSIR framework exemplifies a framework developed from the top-down (theoretical fitting) approach, to better organize the policy goal and paradigm of environmental sustainability to the indicators collected by EU member states. The second example highlights the Social-Ecological Systems Framework (SESF) developed by Elinor Ostrom (Box 2 ) (Ostrom 2009 ; McGinnis and Ostrom 2014a ). The SESF exemplifies a framework developed from the bottom up (empirical generalization) to aggregate data into common variables to enable data standardization and comparison towards theory building to improve environmental governance. In the case examples (Box 1 ; Box 2 ), we can see the value of both frameworks from different perspectives. The examples briefly illustrate how the positionality of each framework dictates how others use them to produce knowledge towards a paradigm. In the case of the DPSIR framework, from the top-down towards a policy goal, and with the SESF, from the bottom-up towards a theoretical goal.

Drivers – Pressures – State – Impact - Response (DPSIR) framework

Social-Ecological Systems Framework (SESF)

Discussion and directions forward

Frameworks are commons objects to huddle around in academic and practitioner communities, providing identity and guiding our effort. They focus scholarly attention on important issues, stimulate cognitive energy and provide fodder for discussion. However, reflection on the role and purpose of the frameworks we use needs to be a more common practice in science. The proposed meta-framework aims to showcase the role of frameworks as boundary objects that connect ideas and concepts to data in constructive and actionable ways, enabling knowledge to be built up and aggregated within scientific fields through using common languages and concepts (Mollinga 2008 ; Klein 1996 ).

Boundary objects such as frameworks can be especially important for inter- and transdisciplinary collaboration, where there may be few prior shared points of conceptual understanding or terminology beyond a problem context. Mollinga ( 2008 , 33) reflects that “frameworks are typical examples of boundary objects, building connections between the worlds of science and that of policy, and between different knowledge domains,” and that “the development of frameworks is at present probably the most common strategy in the field of natural resources management to achieve integration and interdisciplinarity,” (Mollinga, 2008 , 31). They are, however, critically important for both disciplinary specific fundamental research, as well as for bridging science-society gaps through translating often esoteric academic concepts and findings into digestible and often visual objects. For example, the DPSIR framework (Box 1 ) attempts to better organize the analysis of environmental indicators for policy evaluation processes in the EU. Furthermore, Partelow et al., ( 2019 ) and Gurney et al., ( 2019 ) both use Ostrom’s SESF (Box 2 ) as a boundary object at the science-society interface to visually communicate systems thinking and social-ecological interactions to fishers and coastal stakeholders involved in local management decision-making.

An important feature of frameworks is that the very contestation over their nature is perhaps their main value. A framework can only be an effective boundary object if it catalyzes deliberation and scholarly debate — thus contestation over what it is and its value is seeded into the toolbox and identity of a scholarly field. Although most frameworks are likely to have shortcomings, flaws or controversial features, the fact that they motivate engagement around common problems and stimulate scholarly engagement is a value of its own. In doing so, frameworks often become symbols of individual and community identity in contested spaces. This is evidenced in how frameworks are often used to stamp our research as valid, relevant and important to the field, even if done passively. Citing a framework both communicates the general purpose of what a scholar is attempting to achieve to others, and orients science towards a common synthetic object for future knowledge synthesis and debate. These positioning actions are essential for science and practitioner communities to understand a research or policy project, its aims and assumptions. Historically, disciplines have provided this value – signaling the problems, methods and theories one is likely to engage with. Frameworks can act as tools for bridging disciplines, helping to catalyze interdisciplinary engagement (Mollinga 2008 ; Klein 1996 ). As many scientific communities shift focus towards solving real-world problems (e.g., climate change, gender equality), tools that can help scientists’ cooperate and communicate, such as a framework, will continue to play a vital role in achieving knowledge co-production goals.

Guiding points for framework engagement

An aim of this article is not only to reflect on the purpose, value and positioning of frameworks, but to provide some take-away advice for engaging with frameworks in current or future work. Over the course of this article, the question of “What makes a good framework?” has been explored. The meta-framework outlines mechanisms of useful frameworks and can help understand the positioning of frameworks. Nonetheless, more detailed guiding points can be specified for both the use and development of frameworks going forward. A series of guiding points are outlined in Table 4 , generated from the literature cited throughout this article, feedback from colleagues and personal experiences applying and developing numerous frameworks. The guiding points focus on the two types of mediating processes, framework development and use (Table 4 ).

In conclusion, we need to know our academic tools in order make the best use of them in our own research, practice and knowledge communities. Frameworks have gained substantial popularity for the communication and synthesis of academic ideas, and as tools we all have the ability to create and perhaps the responsibility to steward. However, frameworks have struggled to find roots in a theory of science which grounds their contributions in relation to other scientific tools such as models, specific theories and empirical data. There is also a lack of discussion about what makes a good framework and how to apply frameworks in a way to makes those applications of integrative value to an overall community of scholars positioned around it. The meta-framework provided in this article offers insights into how to understand the purpose and positionality of frameworks, as well as the mechanisms for understanding the creation and application of frameworks. The meta-framework further allows for the comparison of frameworks to assess their value.

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Acknowledgements

I would like to thank Michael Cox and Achim Schlüter for their helpful feedback on previous versions of the manuscript and the ideas within it. I am grateful to the Leibniz Centre for Tropical Marine Research (ZMT) in Bremen, and the Center for Life Ethics at the University of Bonn for support.

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Partelow, S. What is a framework? Understanding their purpose, value, development and use. J Environ Stud Sci 13 , 510–519 (2023). https://doi.org/10.1007/s13412-023-00833-w

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How methodological frameworks are being developed: evidence from a scoping review

Affiliations.

1 Health Economics and Health Technology Assessment (HEHTA), Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8RZ, UK. [email protected].
2 Health Economics and Health Technology Assessment (HEHTA), Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8RZ, UK.
PMID: 32605535
PMCID: PMC7325096
DOI: 10.1186/s12874-020-01061-4

Background: Although the benefits of using methodological frameworks are increasingly recognised, to date, there is no formal definition of what constitutes a 'methodological framework', nor is there any published guidance on how to develop one. For the purposes of this study we have defined a methodological framework as a structured guide to completing a process or procedure. This study's aims are to: (a) map the existing landscape on the use of methodological frameworks; (b) identify approaches used for the development of methodological frameworks and terminology used; and (c) provide suggestions for developing future methodological frameworks. We took a broad view and did not limit our study to methodological frameworks in research and academia.

Methods: A scoping review was conducted, drawing on Arksey and O'Malley's methods and more recent guidance. We systematically searched two major electronic databases (MEDLINE and Web of Science), as well as grey literature sources and the reference lists and citations of all relevant papers. Study characteristics and approaches used for development of methodological frameworks were extracted from included studies. Descriptive analysis was conducted.

Results: We included a total of 30 studies, representing a wide range of subject areas. The most commonly reported approach for developing a methodological framework was 'Based on existing methods and guidelines' (66.7%), followed by 'Refined and validated' (33.3%), 'Experience and expertise' (30.0%), 'Literature review' (26.7%), 'Data synthesis and amalgamation' (23.3%), 'Data extraction' (10.0%), 'Iteratively developed' (6.7%) and 'Lab work results' (3.3%). There was no consistent use of terminology; diverse terms for methodological framework were used across and, interchangeably, within studies.

Conclusions: Although no formal guidance exists on how to develop a methodological framework, this scoping review found an overall consensus in approaches used, which can be broadly divided into three phases: (a) identifying data to inform the methodological framework; (b) developing the methodological framework; and (c) validating, testing and refining the methodological framework. Based on these phases, we provide suggestions to facilitate the development of future methodological frameworks.

Keywords: Framework; Methodological framework; Methodology; Scoping review.

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What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach
Whether you’ll rely on primary research or secondary research
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach	Quantitative approach
	and describe frequencies, averages, and correlations about relationships between variables

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
Descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental	relationships effect on a
Quasi-experimental	)
Correlational
Descriptive

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design	Purpose and characteristics


Grounded theory
Phenomenology

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling	Non-probability sampling

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Questionnaires	Interviews
	)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability	Validity
	) )

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

Approach	Characteristics
Thematic analysis
Discourse analysis

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Here's What You Need to Understand About Research Methodology

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

Analyze and understand research methodologies faster with SciSpace Copilot

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

When someone tries to duplicate or expand upon your research after few years.
If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

Observation
Experimental
Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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Literature Review and Theoretical Framework: Understanding the Differences

Using AI for research: A beginner’s guide

Home » Conceptual Framework – Types, Methodology and Examples

Conceptual Framework – Types, Methodology and Examples

Table of Contents

Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

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Having a good research methodology in place has the following advantages: 3

Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

Sampling: The most common type is probability sampling.
Interviews: Commonly telephonic or done in-person.
Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review: Reviewing existing research or documents to collect evidence for supporting the research.
Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

Measures of frequency (count, percent, frequency)
Measures of central tendency (mean, median, mode)
Measures of dispersion or variation (range, variance, standard deviation)
Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

Correlation: To understand the relationship between two or more variables.
Cross-tabulation: Analyze the relationship between multiple variables.
Regression analysis: Study the impact of independent variables on the dependent variable.
Frequency tables: To understand the frequency of data.
Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

Research objectives, aims, and questions —these would help structure the research design.
Review existing literature to identify any gaps in knowledge.
Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
Research method —this can be quantitative, qualitative, or mixed-method.
Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
Sampling —this involves selecting a representative subset of the population being studied.
Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
Data analysis —describe the data analysis methods you will use once you’ve collected the data.
Research limitations —mention any limitations you foresee while conducting your research.
Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

Participants should not be subjected to harm.
Respect for the dignity of participants should be prioritized.
Full consent should be obtained from participants before the study.
Participants’ privacy should be ensured.
Confidentiality of the research data should be ensured.
Anonymity of individuals and organizations participating in the research should be maintained.
The aims and objectives of the research should not be exaggerated.
Affiliations, sources of funding, and any possible conflicts of interest should be declared.
Communication in relation to the research should be honest and transparent.
Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Mastering Research Frameworks: 8 Step-by-Step Guide To A Success Academic Writing!

Introduction.

Research is a fundamental aspect of any academic or scientific endeavor. It involves the systematic investigation of a particular topic or problem to generate new knowledge or validate existing theories. However, conducting research can be a complex and challenging process, requiring careful planning and organization. This is where research frameworks come into play.

A research framework is important because it provides a structured approach to guide and organize the entire research process, ensuring that studies are methodical, coherent, and aligned with established objectives. (Researchmate.net)

In this comprehensive guide, we will explore the concept of research frameworks and how they can help researchers in their work. We will discuss the components of a research framework, the different types of frameworks, and the methodology behind developing and implementing a research framework. Additionally, we will provide examples of research frameworks as samples to guide researchers in designing their own projects. For researchers looking to collaborate and enhance their research framework strategies, platforms like Researchmate.net offer valuable resources and networking opportunities.

What is a Research Framework?

A research framework refers to the overall structure, approach, and theoretical underpinnings that guide a research study. It is a systematic and organized plan that outlines the key elements of a research project, including the research questions , objectives, methodology, data collection methods, and data analysis techniques.

A research framework provides researchers with a roadmap to follow throughout the research process, ensuring that the study is conducted in a logical and coherent manner. It helps researchers to organize their thoughts, identify gaps in existing knowledge, and develop a clear research plan. By establishing a research framework, researchers can ensure that their study is rigorous, valid, and reliable, and that it contributes to the existing body of knowledge in their field. Overall, a research framework serves as a foundation for the research study, guiding the researcher in every step of the research process.

Components of a Research Framework

A research framework consists of several key components that work together to guide the research process. It is essentially a structured outline that serves as a guide for researchers to organize their thoughts, define research objectives, and plan the research process comprehensively. While there are various research framework templates available, they typically include the following components:

Problem Statement

The problem statement defines the research problem or question that the study aims to address. It provides a clear and concise statement of the issue that needs to be investigated. This often emerges from identifying a research gap in the existing literature, highlighting areas that lack sufficient study or have not been explored at all.

The research objectives outline the specific goals and outcomes that the study aims to achieve. These objectives help to focus the research and provide a clear direction for the study. The objectives should be measurable and aligned with the research question to ensure that the study is targeted and relevant.

Literature Review

The literature review is a critical component of a research framework. It involves reviewing existing research and literature related to the research topic. This helps to identify gaps in the current knowledge and provides a foundation for the study.

Theoretical or Conceptual Framework

The phrases ‘ conceptual framework ‘ and ‘ theoretical framework ‘ are often used to describe the overall structure that defines and outlines a research project. These frameworks are composed of theories, concepts, and models that serve as the foundation and guide for the research process.

Methodology

The research methodology outlines the methods and techniques that will be used to collect and analyze data. It includes details on the research design, data collection methods, and data analysis techniques.

Data Collection

Data collection method is a component of research methodology which involves collecting data from various sources, such as surveys, interviews , observations, or existing datasets. The data collected should be relevant to the research objectives and provide insights into the research problem.

Data Analysis

Data analysis involves organizing, interpreting, and analyzing the collected data. This can include statistical analysis, qualitative analysis, or a combination of both, depending on the research objectives and data collected.

Findings and Conclusion

The findings and conclusion section presents the results of the data analysis and discusses the implications of the findings. It summarizes the key findings, draws conclusions, and provides recommendations for future research or practical applications. It highlights the contribution of the study to the existing body of knowledge and suggests areas for further investigation.

These components work together to provide a comprehensive framework for conducting research. Each component plays a crucial role in guiding the research process and ensuring that the study is rigorous and valid.

Types of Research Frameworks

There are two types of research frameworks: theoretical and conceptual.

A theoretical framework is a single formal theory that is used as the basis for a study. It provides a set of concepts and principles that guide the research process. On the other hand, a conceptual framework is a broader framework that includes multiple concepts and theories. It provides a unified framework for understanding and analyzing a particular research problem. The two types of frameworks relate differently to the research question and design. The theoretical framework often inspires the research question based on the existing theory, while the conceptual framework helps in organizing and structuring the research process.

Both types of frameworks have their advantages and limitations. A theoretical framework provides a solid foundation for research and allows for the testing of specific hypotheses. However, it may be limited in its applicability to a specific research problem. On the other hand, a conceptual framework allows for a more holistic and comprehensive understanding of the research problem. It provides a framework for exploring multiple perspectives and theories. However, it may lack the specificity and precision of a theoretical framework.

In practice, researchers often use a combination of theoretical and conceptual frameworks to guide their research. They may start with a theoretical framework to establish a foundation and then use a conceptual framework to explore and analyze the research problem from different angles. The choice of research framework depends on the nature of the research problem, the research question, and the goals of the study. Researchers should carefully consider the advantages and limitations of each type of framework and select the most appropriate one for their specific research context.

Research Framework Methodology

Methodology is an essential component of a research framework as it provides a structured approach to conducting research projects. The methodology section of a research framework includes the research design, sampling design, data collection techniques, analysis, and interpretation of the data. These elements are crucial in ensuring the validity and reliability of the research finding as follows:

The research design refers to the overall plan or strategy that researchers adopt to answer their research questions. It includes decisions about the type of research, the research approach, and the research paradigm. The research design provides a roadmap for the entire research process.
Sampling design is another important aspect of the methodology. It involves selecting a representative sample from the target population. The sample should be chosen in such a way that it accurately represents the characteristics of the population and allows for generalization of the findings.
Data collection techniques are the methods used to gather data for the research. These can include surveys, interviews, observations, experiments, or the analysis of existing data. The choice of data collection techniques depends on the research questions and the nature of the data being collected. Once the data is collected, it needs to be analyzed and interpreted. This involves organizing and summarizing the data, identifying patterns and trends, and drawing conclusions based on the findings.
The analysis and interpretation of data are crucial in generating meaningful insights and answering the research questions.

Research Framework Examples

Example 1: Tourism Research Framework

One example of a research framework is a tourism research framework. This framework includes various components such as tourism systems and development models, the political economy and political ecology of tourism, and community involvement in tourism. By using this framework, researchers can analyze and understand the complex dynamics of tourism and its impact on communities and the environment.

Example 2: Educational Research Framework

Another example of a research framework is an educational research framework. This framework focuses on studying various aspects of education, such as teaching methods, curriculum development, and student learning outcomes. It may include components like educational theories, pedagogical approaches, and assessment methods. Researchers can use this framework to guide their studies and gain insights into improving educational practices and policies.

Example 3: Health Research Framework

A health research framework is another common example. This framework is used to investigate different aspects of health, such as disease prevention, healthcare delivery, and patient outcomes. It may include components like epidemiological models, healthcare systems analysis, and health behavior theories. Researchers can utilize this framework to design studies that contribute to the understanding and improvement of healthcare practices and policies.

Example 4: Business Research Framework

In the field of business, a research framework can be developed to study various aspects of business operations, management strategies, and market dynamics. This framework may include components like organizational theories, market analysis models, and strategic planning frameworks. Researchers can apply this framework to investigate business-related phenomena and provide valuable insights for decision-making and industry development.

Example 5: Social Science Research Framework

A social science research framework is designed to study human behavior, social structures, and societal issues. It may include components like sociological theories, psychological models, and qualitative research methods. Researchers in the social sciences can use this framework to explore and analyze various social phenomena, contributing to the understanding and improvement of society as a whole.

In conclusion, a research framework provides a structured approach to organizing and analyzing research data, allowing researchers to make informed decisions and draw meaningful conclusions. Throughout this guide, we have delved into the nature of research frameworks, including their components, types, methodologies, and practical examples. These frameworks are essential tools for conducting effective and efficient research, helping researchers streamline processes, enhance the quality of findings, and contribute significantly to their fields.

However, it is important to recognize that research frameworks are not a one-size-fits-all solution; they may need to be tailored to suit the specific objectives, scope, and context of individual research projects. While these frameworks provide essential structure, they should not replace critical thinking and creativity. Researchers are encouraged to remain open to new ideas and perspectives, adapting frameworks to meet their unique needs and navigate the complexities of the research process, thereby advancing knowledge within their disciplines.

How to Formulate Research Questions in a Research Proposal? Discover The No. 1 Easiest Template Here!

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POLS 3700: Research Methods in Criminal Justice: Prompt Engineering

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Prompt Engineering

Writing and Citing

What is prompt engineering?

"Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs)... Prompt engineering is the process of structuring text using tactics to get better results. Knowing the capabilities and limitations of a tool can help you receive more accurate and relevant responses."

Source: Prompt Engineering Guide (DAIR.AI):

Microsoft CoPilot . UGA has a campus-wide license for Microsoft CoPilot, which provides some great tips on the "art of prompts ." Microsoft suggests including four elements in your prompts: the goal, context, expectations, and source.

Recommended Resources

What is Prompt Engineering? An article from the consulting form McKinsey & Company.
"Skill Up on Prompts" in the Students' Guides to Navigating College in the Artificial Intelligence Era - by Elon University & AACUS
Prompt engineering for ChatGPT A Coursera MOOC by Dr. Jules White, Vanderbilt University.
Prompt Engineering Guide A project by DAIR.AI that aims to educate researchers and practitioners about prompt engineering. DAIR.AI seeks to democratize AI research, education, and technologies. Their mission "is to enable the next-generation of AI innovators and creators."
How to use AI to do practical stuff: A new guide A resource page from Professor Ethan Mollick, Wharton School of Business, University of Pennsylvania, which is full of useful tips and examples.
Three ways to leverage ChatGPT and other generative AI in research A guide created by Times Higher Education focusing on "three key uses of generative AI tools like ChatGPT in developing and enhancing research."

CLEAR Framework for Prompt Engineering

The CLEAR Framework provides guidance for creating effective prompts. The following tips and examples are excerpted from the following article:

Article Citation: Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. Journal of Academic Librarianship , 49 (4), Article 102720. https://doi.org/10.1016/j.acalib.2023.102720.

Concise: brevity and clarity in prompts

Use a more concise and explicit prompt such as “Explain the process of photosynthesis and its significance” instead of “Can you provide me with a detailed explanation of the process of photosynthesis and its significance?”
Instead of requesting, “Please provide me with an extensive discussion on the factors that contributed to the economic growth of China during the last few decades”, use a concise prompt like, “Identify factors behind China's recent economic growth.”

Logical: structured and coherent prompts

“List the steps to write a research paper, beginning with selecting a topic and ending with proofreading the final draft” is a logically structured question.
A logically structured prompt could be, “Describe the steps in the scientific method, starting with forming a hypothesis and ending with drawing conclusions.”

Explicit: clear output specifications

Instead of, “Tell me about the French Revolution,” an explicit prompt would be, “Provide a concise overview of the French Revolution, emphasizing its causes, major events, and consequences.”
Rather than prompting, “What are some renewable energy sources ?”, opt for a more explicit version like, “Identify five renewable energy sources and explain how each works.”

Adaptive flexibility and customization in prompts

If an initial prompt such as “Discuss the impact of social media on mental health” elicits responses that are too general, consider a more focused and adaptable prompt such as “Examine the relationship between social media usage and anxiety in adolescents.”
If asking, “What are some ways to conserve water?” leads to generic responses, try a more targeted and adaptive prompt like, “List household practices for conserving water and their potential impact.”
If a prompt such as, “Describe the history of computers” yields too much information, use a more specific and adaptive prompt like, “Explain the development of personal computers from the 1970s to the 1990s.”

Reflective: continuous evaluation and improvement of prompts

After receiving AI-generated content on the benefits of a plant-based diet, evaluate the response's accuracy, relevance, and completeness. Use insights from the evaluation to refine future prompts, such as asking for more specific benefits or focusing on certain aspects of a plant-based diet.
After acquiring an AI-generated list of strategies for effective time management , evaluate the relevance and applicability of each strategy. Consider the target audience's needs, and use this information to tailor future prompts to generate content that better addresses specific challenges or contexts.
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Open access
Published: 04 September 2024

Digital Microbe: a genome-informed data integration framework for team science on emerging model organisms

Iva Veseli ORCID: orcid.org/0000-0003-2390-5286 1 , 2 na1 ,
Michelle A. DeMers 3 na1 ,
Zachary S. Cooper 4 na1 ,
Matthew S. Schechter 5 ,
Samuel Miller 6 ,
Laura Weber 7 ,
Christa B. Smith ORCID: orcid.org/0000-0001-7736-3166 4 ,
Lidimarie T. Rodriguez 8 ,
William F. Schroer ORCID: orcid.org/0000-0002-5675-2848 4 ,
Matthew R. McIlvin 7 ,
Paloma Z. Lopez 7 ,
Makoto Saito 7 ,
Sonya Dyhrman 9 ,
A. Murat Eren ORCID: orcid.org/0000-0001-9013-4827 1 , 2 , 6 , 10 , 11 ,
Mary Ann Moran ORCID: orcid.org/0000-0002-0702-8167 4 &
Rogier Braakman 3

Scientific Data volume 11 , Article number: 967 ( 2024 ) Cite this article

Metrics details

Bioinformatics
Computational platforms and environments
Data integration
Water microbiology

The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. “Digital Microbes” are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacterium Ruegeria pomeroyi DSS-3 with > 100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotroph Alteromonas containing 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings.

Introduction

Expanded access to the genomic data of microbial organisms has been transforming the way we approach microbiology research. Genome sequences are subsequently enhanced with knowledge from experimental, modeling, and field studies (e.g. 1 , 2 , 3 , 4 ) with the goal of yielding insights into microbial physiology, ecology, and biogeochemistry. Yet because different research teams independently consolidate and curate genome-related information via ad hoc solutions, these diverse data streams have created challenges for interoperable analyses, especially in collaborative work. More generally, the lack of a framework for establishing consensus versions of genome-linked reference data hinders community coalescence around shared datasets. To extend the impact of curated and collated microbial data beyond a single research group, requirements are: (1) an established reference dataset 5 , which provides existing and updated knowledge in a standardized format; and (2) open access to these data, which allows multiple groups to collaboratively analyze and update the same genome and genome-linked information. The power of establishing a strategy for the open exchange of consensus microbial data linked to reference genomes for emerging model organisms, whether they are laboratory cultures or those reconstructed from metagenomes, is increasing as team science takes on growing roles in environmental and life sciences research.

Contemporary software solutions for the analysis and exchange of microbial genomes and associated ‘omics survey data can be broadly characterized into three groups: (1) online portals that provide a centralized location for uploading or downloading genomes and/or ‘omics datasets; (2) online portals with embedded applications that allow the user to choose from pre-selected genomes and/or ‘omics datasets or, in some cases, upload their own data for analysis; and (3) downloadable tools that enable local analysis of genomes and/or ‘omics data (Table 1 ). While they provide important services for individual research groups, these solutions do not necessarily maximize the efficiency of collaborative team science efforts. Typically, datasets are provided either as raw data or as highly-polished summaries, and intermediate data products for coordination of downstream analyses are not maintained. Moreover, most existing solutions are centralized, in which case data curation and platform maintenance falls on a single entity vulnerable to loss of funding, while data format, updates, and accessibility are not fully under the control of researchers. An alternative solution that partially solves the data sharing needs of collaborative team science efforts is anvi’o 6 ( https://anvio.org ), an open-source software platform that can integrate a variety of data streams into interoperable, standalone SQL databases that can serve as collaborative data products 6 ; however, anvi’o data products are not version-controlled. Inspired by the state-of-the-art technical opportunities offered by anvi’o, here we propose a general framework for the distribution and collaborative analysis of ‘omics datasets that is conducive to team science efforts. The ‘Digital Microbe’ (DM) concept describes features of a data product (#1–3) and a data implementation framework (#4–5) that:

Stores a genome sequence with sequence-linked information (e.g., curated gene calls, user-defined functional annotations, etc).

Supports additional layers of genome-associated data (e.g., genomic regions of particular interest, mutant strain availability, protein structures, etc).

Supports additional layers of experimental or environmental survey data, including intermediate analysis results of value to the research team (e.g., transcriptomic or proteomic activity across different experimental conditions, environmental distribution patterns through metagenomic or metatranscriptomic read recruitment analyses, etc).

Enables version-controlled addition of new data layers or curation of existing ones iteratively by any researcher.

Stores and enables the export of information in a universal format that is accessible to other programs and centralized or decentralized analysis platforms.

We developed the Digital Microbe concept and its implementation in the National Science Foundation (NSF) Science and Technology Center for Chemical Currencies of a Microbial Planet (C-CoMP; https://ccomp-stc.org ) consisting of a research team geographically distributed across 12 institutions. Our construction of Digital Microbes enabled Center members to simultaneously access, analyze and update experimental and environmental datasets for the Center’s two model marine bacterial species, Ruegeria pomeroyi DSS-3 and Alteromonas macleodii MIT1002, including diverse data types ranging from ‘omics surveys and environmental parameters to metabolic models and metabolomes. Here we demonstrate the feasibility of the Digital Microbe concept as a solution addressing widespread needs in the microbiology community for reproducible, integrated data products and we describe Digital Microbe data packages for each of C-CoMP’s model bacteria. The first Digital Microbe compiles knowledge of transcriptional response by Ruegeria pomeroyi DSS-3 gathered from 8 independent studies carried out between 2014 and 2023 ( https://doi.org/10.5281/zenodo.7304959 ); the second describes an Alteromonas pangenome created by merging data from 339 isolate and metagenome-assembled genomes ( https://doi.org/10.5281/zenodo.7430118 ).

Results and Discussion

Digital microbe: concept and implementation.

At its core, a Digital Microbe is a curated and versioned public data package that is (1) ‘self-contained’ (i.e., it can explain itself and its contents) and (2) ‘extensible’ (i.e., others can extend a Digital Microbe data package with additional layers of information coming from new experiments). The package consists of multiple datasets organized and linked through reference to the genome of a single microbe or the pangenome of a group of microbes (Fig. 1 ). Data collection consolidates information such as gene annotations, coverage and other read-mapping statistics, and sample metadata. These data types can be flexible in scope and the extensibility of Digital Microbes via the programmatic addition of new ‘omics data types make them future-proof.

Architecture of a Digital Microbe. The genome of a model bacterium is ( a ) sequenced and ( b ) assembled and serves as the foundation of a Digital Microbe, a self-contained data package for a collaborative research team or a science community. ( c ) Alternatively, a pangenomic data package is assembled. ( d ) Intermediate datasets useful for downstream analyses are stored and reused, and ( e ) various data files and tables can be exported. ( f ) The Digital Microbe is iteratively populated with data layers referenced to individual genes, including mapped proteomes, transcriptomes, or gene-specific metadata types such as inventories of mutants or new annotations. Each Digital Microbe can be assigned a DOI (digital object identifier) and be versioned as new gene- or genome-referenced data are added.

The Digital Microbe framework utilizes a model organism’s genome or a clade’s pangenome as the foundation of a database file describing the DNA sequences (Fig. 2 ). This database file is hosted in a central data repository where it can be accessed by collaborators and community members. A software platform was needed for collaborative analyses, and we chose the open-source software platform anvi’o 6 , which implements many of the Digital Microbe features described above (Fig. 2 ). The concept behind the Digital Microbe framework, however, is independent of any one software platform. Similarly, C-CoMP hosts its Digital Microbe files on the data-sharing platform Zenodo ( https://zenodo.org/ ), but other version-controlled storage solutions are available. As team science progresses, other genome- or gene-linked datasets (including both raw data and analysis results) can be added to the database by various groups, who update the publicly-hosted file to a new version that disseminates their data and findings to the team or community.

Situating the Digital Microbe concept in the existing computational environment. The Digital Microbe approach facilitates collaborative science by: establishing a version-controlled (pan)genomic reference; consolidating and cross-referencing collections of experimental and environmental data associated with a genome or pangenome; facilitating access to reusable intermediate analyses; and providing data export capabilities for transitioning to other programs or analysis software. While each of these features could be established by generating new software, we chose to use the existing open-source software platform anvi’o 6 , which implements several aspects of a Digital Microbe via (pan)genomic data storage in programmatically-queryable SQLite databases. The concept behind the Digital Microbe framework, however, is independent of any one software platform.

Here, we present two examples of Digital Microbes – one for the model organism Ruegeria pomeroyi and another for the pangenome of Alteromonas spp. – as well as case studies that exemplify how they can be used.

The Ruegeria pomeroyi digital microbe

Ruegeria pomeroyi DSS-3 is a representative of the Roseobacteraceae family, an important bacterial group in marine microbial communities 7 with its members among the most metabolically active bacterial cells in algal blooms and coastal environments 8 . R. pomeroyi has been well studied in the laboratory and field 9 , 10 , 11 ; it grows well in both defined and rich media; and it is amenable to genetic alteration 12 , 13 .

The R. pomeroyi Digital Microbe (Fig. 3 ) is built on a well-curated genome assembly ( DM feature 1 ) first annotated in 2004 14 , reannotated in 2014 15 , and enhanced with information from NCBI Clusters of Orthologous Groups (COG) 16 , Pfam 17 , and KEGG Kofam 18 . The Digital Microbe annotation is also continually updated ( DM feature 4 ) with new experimental verifications of R. pomeroyi genes (e.g. 15 , 19 , 20 , 21 , 22 ) that have not been captured in standardized genome annotation repositories (e.g., RefSeq GCF_000011965.2). The R. pomeroyi DSS-3 Digital Microbe is available on Zenodo 23 .

Contents of the R. pomeroyi Digital Microbe. As visualized in anvi’o ‘gene mode’, each item on the inner tree corresponds to one gene call in the R. pomeroyi genome, and the blue concentric circles display the coverage of each gene in a given transcriptome sample. The outermost red concentric circles correspond to normalized protein abundances from proteome samples (raw files available in the Proteomics Identifications Database (PRIDE) via Project PXD045824). Samples are grouped by their study of origin, with the data source indicated in text of the same color as the samples. The brown bar plot indicates the total number of reads that mapped from each transcriptome to the R. pomeroyi genome. This figure was generated from version 5.0 of the R. pomeroyi Digital Microbe databases on Zenodo.

A use case: exploring the substrate landscape of R. pomeroyi

Our team is leveraging R. pomeroyi as a whole-cell biosensor of labile components of the marine dissolved organic carbon (DOC) pool. A recent study using R. pomeroyi knockout mutants definitively identified the cognate substrate of 18 organic compound transporters 24 which were added to the Digital Microbe ( DM feature 2 ). Previous homology-based annotations of most of these transporter systems were either incorrect or vague, and therefore of minimal ecological value. Although representing only a subset of the ~126 organic carbon influx transporter systems in the R. pomeroyi genome, the presence or expression of these 18 is unequivocally linked to a known metabolite. With the new annotations in hand, we undertook a meta-analysis of transporter expression across 133 previously sequenced R. pomeroyi transcriptomes from laboratory and field studies between 2014 and 2023 to gain insights into the availability of these 18 metabolites in marine environments.

We added transcriptomes of R. pomeroyi to the Digital Microbe by mapping them onto the genome as individual data layers (Fig. 3 , Table S1 ) ( DM feature 3 ). Using the anvi’o interactive interface, we established a custom dataset that consisted of the 62 protein components of the 18 experimentally annotated transporters ( DM feature 2 ). We normalized the read counts for each protein to transcripts per million (TPM) and clustered the resulting data (Euclidean distance and Ward clustering). To generate a heatmap of transporter expression, we extracted the data from anvi’o and visualized it using python ( DM feature 5 ).

This meta-analysis captured responses by R. pomeroyi to available substrates under 43 different ecological conditions (Fig. 4 ), including during co-culture growth with phytoplankton 25 , 26 , 27 , 28 , on defined single or mixed substrates 20 , and after introduction into a natural phytoplankton bloom 10 . At the broadest scale, the transporters enabling organic acid uptake (acetate, citrate, fumarate, and 3-hydroxybutyrate) had the highest relative expression across conditions, together accounting for an average of 48% (range: 9.7–86%) of the transcripts for transporters with confirmed substrates. Recent studies have indeed discovered that Roseobacteraceae members are organic acid catabolic specialists 29 , 30 . Transporter transcription patterns also revealed the differences in substrate availability across environments. Introduced into a natural dinoflagellate bloom 10 , the citrate transporter had the highest relative expression; in a diatom co-culture, the acetate transporter was the most highly expressed; co-cultured with a green alga, transporter genes indicated that taurine, glycerol, carnitine, and dimethylsulfoniopropionate (DMSP) were on the menu. The organic acid transporter that enables R. pomeroyi uptake of 3-hydroxybutyrate 24 was expressed across most growth conditions, yet this metabolite, also a precursor to the bacterium’s storage polymer polyhydroxybutyrate (PHB), has not previously been identified as a relevant currency in bacterially-mediated carbon flux. The meta-analysis also showed a pattern in expression for transporters that contain a substrate binding protein gene (i.e., the ABC and TRAP transporter classes): the gene is expressed at consistently higher levels than other genes in the same transporter (i.e., higher than permeases and ATP-binding proteins) despite all having membership in the same operon. Additional layers of gene regulation are therefore occurring either as within-operon differential expression or as post-transcriptional selective degradation. Regardless, this regulatory strategy would benefit a bacterium in an environment where substrate acquisition is the growth-limiting step.

Clustered heatmap of relative gene expression for 18 experimentally annotated R. pomeroyi transporters compiled in a Digital Microbe. Each row represents a single transcriptome from the Digital Microbe dataset, and each column represents all transporter proteins with experimentally confirmed cognate substrates. Row labels indicate study and sample name (Table S1 ). Brighter colors indicate higher proportional expression (the scale maximum is ≥5% of the sum of the 18 transporter transcriptomes) while darker colors indicate lower. Arrows point out transcriptomes collected when substrates were derived from dinoflagellate-rich natural communities (red) or diatom co-cultures (brown); significant differences in transporter protein expression between these two substrate sources are indicated with asterisks colored red (enriched with dinoflagellates) or brown (with diatoms) (T-test, p ≤ 0.05).

Three additional genome-linked data types recently added to the R. pomeroyi Digital Microbe include the locations of insertion sites of knockout mutants (covering 3,570 genes of 4,288 genes) 13 , 24 ; proteomic data collected concurrently with one of the transcriptomic studies 31 , 32 , and TnSeq mutant fitness measurements in synthetic microbial communities 11 ( DM feature 2 ); these are enhancing collaborations among team members.

The Alteromonas digital microbe

Alteromonas is a genus of marine Gammaproteobacteria whose members associate with particles and can contribute significantly to heterotrophic activity of phytoplankton blooms, sometimes in the role of helper bacteria that provide benefits to the phytoplankton 33 , 34 , 35 . Bacteria in this genus are distinguished by genomes encoding an average of 4,000 genes that enable use of a broad spectrum of substrates 36 , provide protection from reactive oxygen species to community members 33 , and mediate polysaccharide degradation 37 . The type species of the genus is Alteromonas macleodii 34 , 38 , with other notable species including A. mediterranea 39 , A. australica 40 , and A. stellipolaris 41 . While no single species has emerged as the primary model organism for this genus, the many genomes available for study provide an opportunity for pangenomic analysis to improve understanding of the evolution and diversity of this ubiquitous marine clade 42 .

The assembled pangenome consists of 336 Alteromonas genomes with genes called and annotated using one standardized pipeline (Fig. 5 ) ( DM feature 1 ). Of these, 78 are isolate genomes 43 , 44 , 45 and 258 are metagenome-assembled genomes (MAGs) obtained from a variety of marine environments in the global ocean 46 . Genomes represent members of the closely related ‘surface’ species A. macleodii (n = 139) and ‘deep’ species A. mediterranea (n = 25) 39 , and the widely distributed A. australica (n = 63) 47 . The 34,390 gene clusters of the pangenome are linked to an imported phylogenetic tree assembled from single-copy core genes (see Methods), annotated using NCBI COGs 16 , KEGG KOfams 18 , CAZyme HMMs 48 and orthology predictions from EggNOG-mapper 49 , 50 , 51 (DM feature 2 ), and assigned as core or accessory genes for the genus ( DM feature 3 ) based on a Bayesian approach available in anvi’o 52 . The Alteromonas Digital Microbe with relevant pangenome and phylogeny files is accessible on Zenodo 53 . Examples of future versioned enhancements of this Digital Microbe might include additions of new Alteromonas genomes and improved annotations from culture studies and novel annotation programs.

The Alteromonas Digital Microbe. Each concentric ring represents one Alteromonas genome, with colored rings identifying genomes from five clades of interest ( A. macleodii , A. mediterranea , A. austalica , A. stellioolaris , and A. naphthalenivorans ). The outermost green rings depict annotation sources applied to all genomes. Each spoke in the figure represents one gene cluster in the pangenome, with presence/absence denoted by darker/lighter colors, respectively. Genome metadata are shown next to each ring and include total genome length, GC content, completion, number of genes per kbp, and number of gene clusters per genome. The red heatmap above the metadata shows the average nucleotide identity (ANI) percentage scores between genomes. The tree above the ANI heatmap shows the imported phylogenomic tree, with clades of interest color-referenced in the circular portion of the figure. This figure was generated using the anvi’o ‘anvi-display-pan’ from a version of the Alteromonas digital microbe without singleton genes, which is available on Zenodo under https://doi.org/10.5281/zenodo.10421034 .

A use case: evolutionary patterns of Alteromonas carbohydrate use

We leveraged the information contained within the Digital Microbe to examine diversity in the ability of this opportunistic marine genus to use poly-/oligosaccharides 36 . Sugars and sugar polymers are an abundant and diverse component of the ocean’s dissolved organic carbon inventory 54 , and differences in how microbes use them provide important clues on the evolutionary diversification of their roles in the oceanic carbon cycle. Moreover, the ability to annotate genes with the Carbohydrate-Active enZYme (CAZyme) Database 48 was recently added to anvi’o, allowing augmentation of the Digital Microbe with CAZyme annotations. The results indicate distinct CAZyme distributions across Alteromonas clades (Fig. 6 ). For example, the A. australica and A. stellipolaris clades have more polysaccharide lyases than neighboring clades, while the A. stellipolaris clade is enriched in several other CAZyme categories as well. As patterns of diversity in CAZyme inventories are most distinct at the clade level compared to the within-clade level, carbohydrate utilization emerges as a potentially key driver of the large-scale niche partitioning of Alteromonas species.

Distribution of CAZYme annotations across a phylogeny of 336 isolate and MAG genomes from the genus Alteromonas . The phylogeny of the genus is displayed on the left side of the figure, with genomes represented by points on the tree and five of the clades ( A. macleodii , A. mediterranea , A. australica , A. napthalenivorans , and A. stellipolaris ) highlighted. Each row on the right side of the figure represents one genome. Completeness and type of genome are shown in the two heatmaps to the right of the phylogeny. The horizontal bar plots of different colors show the proportion of CAZymes in each genome relative to the maximum number of all categories of CAZymes as indicated in the legend in the inset at the upper left. The maximum number for each CAZyme category is represented by the vertical bar plot at the top of the figure.

We also gained insights into how carbohydrate usage has shaped Alteromonas evolution and ecology from gene phylogenies of selected CAZymes (Supplementary Figure 1 ). The topology of several CAZyme phylogenies broadly recapitulates the topology of the genome phylogeny built from single-copy core genes (Supplementary Figure 2 ), suggesting that vertical descent has dominated the evolution of these genes. However, the topologies of other CAZyme phylogenies have significant discordance with the genome phylogeny (Supplementary Figure 2 ), suggesting that horizontal transfer has also had an important role in the evolution of carbohydrate utilization strategies in Alteromonas . The divergent evolutionary trajectories of different CAZymes highlight selective pressures acting on the metabolic diversification of Alteromonas , and may offer clues on how this diversification has in turn impacted the evolution of carbon cycling in the ocean.

Future directions

Digital Microbe data packages furnish an architecture for reproducible, open, and extensible collaborative work in microbiology and its many derivative fields. While we present here a specific architecture tailored to our research focus, it is only one manifestation of the broader digital microbe concept: that decentralized taxon-specific databases are key mechanisms for capturing knowledge accumulating from genome-informed data that are now so vast and distributed as to be intractable to synthesize 55 . Digital Microbe packages allow one-stop shopping for data spread across multiple public archives, allow coordinated selection and documentation of genome structure and annotations within and between research teams, and are extensible to new data types. While the framework presented here is designed for bacterial and archaeal data, the development of digital microbes for eukaryotic model organisms is an important future application 56 . One enhancement under development by C-CoMP is an integrated toolkit for metabolic modeling, but the nature and scope of future applications can be defined by any research group that uses a digital microbe framework for their research. Organized and open access to taxon-explicit data is an essential foundation for modern microbiology.

Both Digital Microbes were generated and analyzed using v7.1-dev or later versions of anvi’o 6 .

Creation of the Ruegeria pomeroyi digital microbe

We created the Ruegeria pomeroyi Digital Microbe from the R. pomeroyi DSS-3 complete genome and megaplasmid sequences 14 and (meta)transcriptome samples from 10 , 27 , 28 , 32 , 57 , 58 , 59 . We generated a contigs database from the genome and megaplasmid sequences with ‘anvi-gen-contigs-database’ and annotated the resulting Prodigal 60 gene calls with de novo annotations from NCBI Clusters of Orthologous Genes (COGs) 16 , KEGG KOfams 18 , and Pfam 17 via the associated anvi’o program for each annotation source. We also identified single-copy core genes using ‘anvi-run-hmms’ and associated these genes with taxonomy data from the GTDB 61 using ‘anvi-run-scg-taxonomy’. We imported manually curated gene annotations, including annotations indicating which genes have available mutants 13 , using the program ‘anvi-import-functions’.

To process the (meta)transcriptomes, we quality-filtered the samples using FASTX-toolkit 62 with the parameters described in 25 . We mapped the reads to the DSS-3 genome using Bowtie 2 63 and samtools 64 . Each sample’s read mapping data were converted into an anvi’o profile database using ‘anvi-profile’, and all samples were merged into a single database with ‘anvi-merge’. To add proteomic data 31 , we normalized spectral abundance counts with a normalized spectral abundance factor to make data comparable across all proteomes. We generated a ‘genes database’ to store gene-level information by running ‘anvi-interactive’ on the established contigs and profile databases with the ‘–gene-mode’ flag, and we imported the normalized abundances for each gene into the genes database using the program ‘anvi-import-misc-data’. We also used this program to import fitness data associated with gene mutants from 11 into the same genes database.

Transporter expression analysis for Ruegeria pomeroyi

The genes database in the R. pomeroyi Digital Microbe contains gene-level transcript coverage information from > 100 samples. To assess the proportional expression of substrate-confirmed transporter genes, we used the anvi’o interactive interface to create a bin containing the transporter genes, and generated a static summary page with the “init-gene-coverages” box checked to export annotation and coverage data for each contig region where our genes of interest were located. After reading the exported data into dataframes using python v3.7.8 and pandas 65 , 66 , we extracted the coverage data for our specific genes of interest, normalized the coverages to TPM using the total number of reads per sample, and relativized these data to represent the proportional expression of each gene. We visualized these data as a clustermap using the seaborn package 67 and assessed statistical differences in the mean gene expression using the a t-test implemented in the scipy stats package 68 .

Creation of the Alteromonas digital microbe

To create the Alteromonas Digital Microbe, we collected 78 isolate genomes and 258 MAGs from the Joint Genome Institute’s Integrated Microbial Genomes (IMG) project 43 , NCBI 69 , and 46 . We converted each genome into an anvi’o contigs database using ‘anvi-gen-contigs-database’. For the genomes from IMG and NCBI, we determined completion and contamination statistics using CheckM v1.0.18 70 ; for the MAGs that were taken from 46 , we used the mean completeness and mean contamination statistics reported in that publication. We annotated the genes in each contigs database with the NCBI Clusters of Orthologous Genes (COGs) 16 , KEGG KOfams 18 , and Carbohydrate-Active enZYme (CAZyme) HMMs 48 via the associated anvi’o program for each annotation source, and imported externally-run annotations from EggNOG-mapper 49 , 50 , 51 and KEGG GhostKOALA 71 into the databases using ‘anvi-import-functions’.

We ran ‘anvi-pan-genome’ to create the pangenome and computed the average nucleotide identity (ANI) between all pairs of genomes using ‘anvi-compute-genome-similarity’. To extract the core genome from the pangenome (i.e., genes found in all genomes), we used a Bayesian statistical method 52 implemented in ‘anvi-script-compute-bayesian-pan-core’. This method employs mOTUpan.py to determine the gene clusters likely to be core based on individual genome completeness scores.

Phylogenomic analysis of the Alteromonas genomes

To build the phylogeny of Alteromonas , we aligned and concatenated the sequences from 110 single-copy core gene clusters using ‘anvi-get-sequences-for-gene-clusters’. We imported these sequences into the tree building software RAxML, version 8.2.12 72 , and built the tree under the “PROTGAMMAAUTO” model setting. We used FigTree v1.4.4 73 to midpoint root the tree and save it in newick file format. To incorporate the tree into the pangenome, we imported the newick tree with the program ‘anvi-import-misc-data’. For the phylogenomic CAZyme analysis, we used ‘anvi-split’ to subset gene clusters with known CAZyme functions into a new pangenome database and ran ‘anvi-summarize’ on this smaller pangenome to count the number of CAZymes per category, per genome. We visualized these data as a function of the previously-determined phylogeny in R v4.1.1 74 using the packages aplot v0.1.9 75 , BiocManager v1.30.20 76 , dplyr v1.1.0 77 , ggnewscale v0.4.8 78 , ggplot2 v3.4.1 79 , ggstance v0.3.6.9000 80 , ggtree v3.7.1.003 81 , 82 , 83 , 84 , 85 , ggtreeExtra v1.9.1.992 81 , 86 , nationalparkcolors v0.1.0 87 , plyr v1.8.8 88 , RColorBrewer v1.1-3 89 , scales v1.2.1 90 , and tidyr v1.3.0 91 .

We then repeated the initial steps above to generate a phylogeny for the subset of isolate genomes (n = 78), which resulted in a tree built from 111 single-copy core gene clusters. After subsetting the gene clusters with known CAZyme annotations into a smaller pangenome, we identified eight CAZyme-related gene clusters that were part of the single-copy core genome. We then generated an individual phylogeny from each of these CAZymes. We used R to compare the CAZyme phylogenies with the overall core genome phylogeny for these isolate genomes, with the packages listed above in addition to colorBlindness v0.1.9 92 , easyalluvial v0.3.1 93 , and gridExtra v2.3 94 .

Data availability

The Ruegeria pomeroyi Digital Microbe is available via https://doi.org/10.5281/zenodo.7304959 and the Alteromonas Digital Microbe is available via https://doi.org/10.5281/zenodo.7430118 . The raw proteomics data included in the Ruegeria pomeroyi Digital Microbe is available on the Proteomics Identifications Database (PRIDE) project PXD045824 with accompanying metadata and processed data available in Biological and Chemical Oceanography Data Management Office (BCO-DMO) dataset 927507 via https://doi.org/10.26008/1912/bco-dmo.927507.1 . The accompanying raw transcriptomic expression data to the proteomics data is available under the National Center for Biotechnology Information (NCBI) BioProject PRJNA972985 with metadata available in BCO-DMO dataset 916134 via https://doi.org/10.26008/1912/bco-dmo.916134.1 .

Code availability

Reproducible workflows for the generation of the Digital Microbes and the analyses described in this work can be accessed at https://github.com/C-CoMP-STC/digital-microbe . In particular, the Jupyter notebook for the Ruegeria pomeroyi use-case analysis can be found at https://github.com/C-CoMP-STC/digital-microbe/blob/main/rpom/rpom_dig_micro_transporter_expression_use_case.ipynb and the workflow for the Alteromonas use-case analysis can be found at the following link: https://github.com/C-CoMP-STC/digital-microbe/blob/main/alteromonas/useCase/alteromonasUseCases.md .

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Acknowledgements

This work was supported under NSF grant OCE-2019589 to the Center for Chemical Currencies of a Microbial Planet. IV acknowledges support by the National Science Foundation Graduate Research Fellowship under Grant No. 1746045. MAM acknowledges support by Simons Foundation Grant 542391 within the Principles of Microbial Ecosystems Collaborative. This is C-CoMP publication #024.

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These authors contributed equally: Iva Veseli, Michelle A. DeMers, Zachary S. Cooper.

Authors and Affiliations

Helmholtz Institute for Functional Marine Biodiversity, 26129, Oldenburg, Germany

Iva Veseli & A. Murat Eren

Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 27570, Bremerhaven, Germany

Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

Michelle A. DeMers & Rogier Braakman

Department of Marine Sciences, University of Georgia, Athens, GA, 30602, USA

Zachary S. Cooper, Christa B. Smith, William F. Schroer & Mary Ann Moran

Committee on Microbiology, The University of Chicago, Chicago, IL, 60637, USA

Matthew S. Schechter

Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, 02543, USA

Samuel Miller & A. Murat Eren

Woods Hole Oceanographic Institution, Falmouth, MA, 02543, USA

Laura Weber, Matthew R. McIlvin, Paloma Z. Lopez & Makoto Saito

Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32611-0180, USA

Lidimarie T. Rodriguez

Lamont-Doherty Earth Observatory, and the Department of Earth and Environmental Sciences, Columbia University, New York, NY, 10032, USA

Sonya Dyhrman

Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg, Germany

A. Murat Eren

Marine ‘Omics Bridging Group, Max Planck Institute for Marine Microbiology, 28359, Bremen, Germany

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Z.S.C., M.S.S., L.T.R., S.D., A.M.E., M.A.M. and R.B. conceptualized the study. Z.S.C., M.A.D., I.V., S.M., C.B.S., L.T.R., W.F.S., M.R.M., P.Z.L. and M.S. curated data. Z.S.C., M.A.D., M.A.M. and R.B. performed formal analyses. Z.S.C., M.A.D., C.B.S., L.T.R., W.F.S., M.R.M., P.Z.L. and M.S. conducted investigations. I.V., M.S.S., S.M. and A.M.E. developed methodology. L.W. and A.M.E. administered the project. A.M.E. and M.A.M. provided resources. I.V., M.S.S., S.M. and A.M.E. developed software. L.W., A.M.E., M.A.M. and R.B. supervised the project. Z.S.C., M.A.D. and I.V. validated the results. Z.S.C., M.A.D., L.W., M.A.M. and R.B. worked on visualization. Z.S.C., M.A.D., I.V., M.S.S., S.M., L.W., S.D., A.M.E., M.A.M. and R.B. wrote the paper with critical input from all authors.

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Correspondence to A. Murat Eren , Mary Ann Moran or Rogier Braakman .

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Veseli, I., DeMers, M.A., Cooper, Z.S. et al. Digital Microbe: a genome-informed data integration framework for team science on emerging model organisms. Sci Data 11 , 967 (2024). https://doi.org/10.1038/s41597-024-03778-z

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CBE Life Sci Educ
v.21(3); Fall 2022

Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

	Literature reviews	Theoretical frameworks	Conceptual frameworks
Purpose	To point out the need for the study in BER and connection to the field.	To state the assumptions and orientations of the researcher regarding the topic of study	To describe the researcher’s understanding of the main concepts under investigation
Aims	A literature review examines current and relevant research associated with the study question. It is comprehensive, critical, and purposeful.	A theoretical framework illuminates the phenomenon of study and the corresponding assumptions adopted by the researcher. Frameworks can take on different orientations.	The conceptual framework is created by the researcher(s), includes the presumed relationships among concepts, and addresses needed areas of study discovered in literature reviews.
Connection to the manuscript	A literature review should connect to the study question, guide the study methodology, and be central in the discussion by indicating how the analyzed data advances what is known in the field.	A theoretical framework drives the question, guides the types of methods for data collection and analysis, informs the discussion of the findings, and reveals the subjectivities of the researcher.	The conceptual framework is informed by literature reviews, experiences, or experiments. It may include emergent ideas that are not yet grounded in the literature. It should be coherent with the paper’s theoretical framing.
Additional points	A literature review may reach beyond BER and include other education research fields.	A theoretical framework does not rationalize the need for the study, and a theoretical framework can come from different fields.	A conceptual framework articulates the phenomenon under study through written descriptions and/or visual representations.

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

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Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

Supplementary Material

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Open access
Published: 04 September 2024

Targeting emotion dysregulation in depression: an intervention mapping protocol augmented by participatory action research

Myungjoo Lee ORCID: orcid.org/0000-0002-8301-7996 1 ,
Han Choi ORCID: orcid.org/0000-0003-0406-5605 2 &
Young Tak Jo ORCID: orcid.org/0000-0002-0561-2503 1

BMC Psychiatry volume 24 , Article number: 595 ( 2024 ) Cite this article

Metrics details

Depression is a highly prevalent and often recurrent condition; however, treatment is not always accessible or effective in addressing abnormalities in emotional processing. Given the high prevalence of depression worldwide, identifying and mapping out effective and sustainable interventions is crucial. Emotion dysregulation in depression is not readily amenable to improvement due to the complex, time-dynamic nature of emotion; however, systematic planning frameworks for programs addressing behavioral changes can provide guidelines for the development of a rational intervention that tackles these difficulties. This study proposes an empirical and theoretical art-based emotion regulation (ER) intervention using an integrated approach that combines intervention mapping (IM) with participatory action research (PAR).

We used the IM protocol to identify strategies and develop an intervention for patients with major depressive disorder (MDD). As applied in this study, IM comprises six steps: (a) determining the need for new treatments and determinants of risk; (b) identifying changeable determinants and assigning specific intervention targets; (c) selecting strategies to improve ER across relevant theories and research disciplines; (d) creating a treatment program and refining it based on consultations with an advisory group; (e) developing the implementation plan and conducting a PAR study to pilot-test it; and (f) planning evaluation strategies and conducting a PAR study for feedback on the initial testing.

Following the steps of IM, we developed two frameworks for an art-based ER intervention: an individual and an integrative framework. The programs include four theory- and evidence-based ER strategies aimed mainly at decreasing depressive symptoms and improving ER in patients with MDD. We also developed a plan for evaluating the proposed intervention. Based on our preliminary PAR studies, the intervention was feasible and acceptable for adoption and implementation in primary care settings.

The application of IM incorporated with PAR has resulted in an intervention for improving ER in depression. While changing behavior is perceived as a challenging and elaborate task, this method can be useful in offering a clear structure for developing rational interventions. Further refinement is necessary through rigorous research.

Peer Review reports

Depression is a highly prevalent and often recurrent condition that severely impairs psychological functioning and quality of life. According to the Global Health Data Exchange, depression affects 3.8% of the world’s population and, as “a major contributor to the overall global burden of disease,” is associated with substantial societal and personal costs [ 1 , 2 ]. Due to its enormous impact on public health, the World Health Organization (WHO) predicts that depression will rank first among all causes of the burden of disease by 2030 [ 3 ]. As depression is frequently comorbid with other mental and physical disorders, it is particularly challenging to identify risk factors and develop effective interventions.

Depression is a disorder of emotion. Disordered affect is a hallmark of depressive episodes, characterized by complex but apparent abnormalities of emotional functioning [ 4 , 5 ]. Many factors may be associated with the disorder; however, its symptoms evidently indicate failures in emotional self-regulation [ 6 ]. Emotion regulation (ER) refers to an individual’s ability to modulate the intensity, frequency, and duration of emotional responses [ 7 , 8 ]. Decades of empirical research have shown that depression is associated with increases in unpleasant emotions and decreases in positive emotions [ 9 , 10 ]. It has been proposed that difficulties in ER in depression significantly contribute to dysfunctional emotions [ 10 , 11 ].

The complexity and time-dynamic nature of emotion make emotion dysregulation in depression particularly challenging to tackle. Most situations in daily life that evoke emotions are ambiguous. It remains unclear how patients can enhance their ER abilities in treatment [ 12 ]. Dysfunctional ER is a fundamental risk factor for the onset of depression and a range of psychiatric disorders [ 13 , 14 ]; however, the evidence base is diffuse and broad, as its mechanisms remain poorly specified [ 12 , 15 , 16 ]. Although some studies have developed psychological interventions to improve ER, research in this area remains limited [ 12 , 17 , 18 ]. Some have argued that teaching a wide range of ER strategies might not be effective in enhancing patients’ emotional functioning [ 12 , 17 ]. Of note, there is a lack of research on the use of art psychotherapy in this context.

An intervention mapping (IM) study systematically rooted in the evidence and theories of basic affective science is required to increase the likelihood of changing behaviors in ER. To target emotional dysregulation, a systematic, participatory, and integrated approach that benefits from efficient behavior change is crucial [ 19 ]. Accordingly, this study determines effective ways of enhancing patients’ ER capacities and developing an optimized art-based psychotherapy intervention for depression. For this purpose, we followed the standard IM protocol [ 20 ]. While developing a treatment may be time-consuming and burdensome, this study provides a straightforward, stepwise decision-making procedure. Along with its use of participatory action research (PAR), this study aims to benefit from the engagement of patients and mental health professionals in a collaborative manner. This type of collaboration is a practical and powerful tool for developing specialized interventions.

Intervention mapping protocol

This study mapped out the process of development based on IM, a program-planning framework. IM provides a step-by-step process for planning theory/evidence-based interventions from the needs to potential methods addressing those needs [ 20 , 21 ]. Since its development in the healthcare field in 1998, IM has been widely used and applications have emerged in other fields, including health promotion. It has been used to develop intervention programs to better target specific behaviors, including health, discrimination, and safety behaviors [ 22 ]. In particular, mental health researchers have largely applied the IM approach for either creating new interventions or adapting existing ones: strategies have been developed for the treatment and prevention of depression through IM, such as an internet-based intervention for postpartum depression [ 23 ], an online-coaching blended program for depression in middle-aged couples [ 24 ], a return-to-work intervention for depression [ 25 ], music therapy for depressive symptoms in young adults [ 26 ], and life-review therapy for depressive symptoms in nursing home residents [ 27 ]. The use of IM has proven to be a useful instrument for the development and optimization of treatments for depression that are tailored to different contexts and target populations.

Over the course of the development of the entire program, four perspectives characterizing IM are applied: (a) a participation approach that engages intended participants, allies, and implementers in program development, implementation, and evaluation; (b) a systems approach acknowledging that an intervention is an event occurring in a system that includes other factors influencing the intervention; (c) a multi-theory approach that stimulates the use of multiple theories; and (d) an ecological approach recognizing the relevance of social, physical, and political environmental conditions.

The IM protocol includes six core steps: (i) justifying the rationale for developing a new treatment; (ii) selecting targeted determinants and setting treatment goals; (iii) determining theoretical and empirical methods for behavior change; (iv) developing a treatment and program materials; (v) planning for adoption and implementation; and (vi) specifying the evaluation design [ 20 , 21 , 28 ]. The development process is cumulative: subsequent steps are based on completed tasks from the previous step. Figure 1 presents the six steps of IM. This article presents the details of our study methods and the results as the six steps of the IM process.

Overview of the intervention mapping (IM) process [ 20 ]

Steps 1–3 of IM: Literature review

To address Steps 1, 2, and 3, we conducted a literature review using PubMed, ProQuest, Scopus, PsycArticles, and Google Scholar. Search strategies were devised using subject headings such as “emotion regulation,” “depression,” “emotional psychopathology,” “emotion regulation therapy,” and “art psychotherapy” as appropriate for each database. Furthermore, the program planners identified and included additional free text words. Due to the heterogeneity of emotion-related processes, the search strategies for Steps 1–3 were broad [ 15 ]. Additionally, we conducted an inclusive literature review of relevant databases to identify articles related to art-based interventions for ER, limited to published articles in English. This literature study identified effective ER strategies for improving regulatory capacities in depression. We describe the theoretical details related to ER and ER strategies in the Results section.

Steps 4–6 of IM: Participatory action research combined

Steps 4–6 of IM occasionally incorporate further studies for pilot testing and refining the intervention under development. As such, our study added participatory components to the IM process. PAR is “a participatory and consensual approach towards investigating problems and developing plans to deal with them.” [ 29 ] PAR empowers research participants compared with other approaches, where study participants are often considered subjects who passively follow directions [ 30 ]. The involvement of patients, care providers, and health professionals in research design is increasingly recognized as an essential approach for improving the quality of primary care [ 31 ] and bridging the gap between research and health care [ 32 ]. Indeed, PAR has been applied in many fields and achieved successful results, particularly in the field of mental health [ 33 ].

In particular, patient involvement is a meaningful partnership with stakeholders, including patients, carers, and public stakeholders, who actively participate in improving healthcare practices [ 31 ]. Involvement can occur at different levels and commonly includes patient engagement and advisory boards [ 32 ]. We conducted participatory action studies to combine systematic studies with the development of practical treatments [ 33 ] and anticipated the benefits of experiential knowledge. Figure 2 elaborates on how we incorporated PAR in the IM framework. It also presents our strategies to address the IM protocol and the results from each step. As described in Fig. 2 , the PAR in this current study comprises three phases:(a) consultation with an advisory board; (b) initial testing of intervention; and (c) mixed methods feedback studies using focus group interviews and survey research.

Study procedure combined with PAR, strategies applied for each step, and results for each step

Noted. The figure specifies strategies to adopt in addressing the six steps of IM protocol and the actions for each step. It represents how IM can be applied and how it can augment its protocols through PAR. In the application of IM, this study relied on literature research and empirical studies: we conducted a literature study to address Steps 1–3 and combined the participatory action approach with IM methodology to address Steps 4–6

(a) PAR 1: Consultation with the advisory board

First, we established an advisory board that included a psychiatrist, an expert on methodology, a trained integrative medicine professional, and a professor in a graduate art psychotherapy program. The advisory board provided feedback at the individual level and comments during subsequent consultations. We engaged and managed the advisory board throughout Step 4, the intervention development process.

(b) PAR 2: Initial testing of intervention being developed

In addition, we conducted a participatory action study to facilitate patient engagement and elicit their voices in a collaborative relationship with researchers. Based on voluntary participation, this study aimed to pretest art-based ER strategies and treatment designs. We conducted an art therapy program as part of routine inpatient therapeutic programs involving willing patients. The participants’ reports of their experiences during the sessions were obtained using structured questionnaires and unstructured interviews. For research purposes, we conducted a retrospective chart review for therapeutic sessions between February 2023 and February 2024. This review was approved by the Institutional Review Board of Kangdong Sacred Heart Hospital (IRB no. 2024–02-019) and exempted from requiring patients’ informed consent because it was part of a routine clinical practice.

(c) PAR 3: A mixed-method approach

In this study, we employed a mixed-methods approach to plan evaluation strategies by combining a quantitative online survey with focus group interviews. The primary aim of this study is to ensure that the intervention developed in Step 4 can be adopted and maintained over time. For this purpose, we are gathering feedback regarding the initial interventions from clinic staff, consisting of nurses and psychiatrists. This PAR study is currently ongoing and will last for four months. At the environmental level of the organization, the process will be managed to best leverage the intervention in primary care settings. This study was approved by the Institutional Review Board of Kangdong Sacred Heart Hospital (IRB no. 2023–12-002). PAR 2 and PAR 3 are currently being conducted; the results of those studies will be available after their completion.

This section focuses on the explanation of outputs obtained through the IM protocol. The details of the theoretical and empirical bases, designed frameworks, and strategies for the implementation and evaluation of the program are categorized into six steps:

Step 1. Needs and Logic for the Program

For the first step, we identified the target group and analyzed their determinants. This step included determining the rationale and need for a new art-based ER intervention for depression. The target population comprised patients diagnosed with major depressive disorder (MDD). Predefined behaviors targeted were core symptoms of major depression, namely, consistent depressed mood and anhedonia [ 6 ].

Theoretical evidence

Prior research has highlighted difficulties in ER contributing to the etiology and maintenance of numerous psychiatric symptoms, such as depression, chronic anxiety, post-traumatic stress disorder, eating disorders, and worry [ 15 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. In particular, research on depression has emphasized that apparent failure to modulate emotions is a hallmark of this disorder [ 6 ] and has attempted to link it to emotional abnormalities in depression [ 10 , 11 ]. ER, which influences the onset, magnitude, and duration of emotional response [ 41 ], is a distinct and differentiated higher-order construct from emotion itself (i.e., fear, anxiety, and depression) at different levels of analysis (e.g., behavioral or neural) [ 42 , 43 ]. From this perspective, ER is an important determinant affecting lower-order factor variability, whereas emotion determines variance downwards in the lower-order indicators [ 42 ].

A literature review revealed that ER difficulties play a role in understanding psychological health in major depression. This suggests the importance of altering problematic patterns of emotional reactivity in depression and identifies emotion dysregulation as a determinant of the predefined target behaviors [ 17 , 44 , 45 , 46 , 47 ]. According to imaging studies utilizing functional magnetic resonance imaging (fMRI), functional abnormalities in specific neural systems support the processing of emotion and ER in patients with depressive disorders [ 6 ]. Moreover, decades of empirical evidence supports the notion that depressive symptoms, characterized by consistently elevated depressed mood and relatively low positive mood, are associated with difficulties in ER [ 9 , 10 , 16 ]. Our review allowed us to analyze and specify the determinants of depressive symptoms (Fig. 3 ). Without this analysis, it would be challenging for psychological treatments to address emotion dysregulation in MDD.

Summary of the determinants influencing symptoms of major depression

Needs assessment for a new intervention

Although emotion dysregulation is a critical target in psychological treatments, intervention research examining ER is limited [ 18 , 48 ]. Psychotherapeutic approaches, including cognitive-behavioral and acceptance-based behavioral treatments, have positive effects on overall ER, and studies suggest that these improvements may mediate further improvements for psychiatric outcomes [ 18 , 48 ]: examples include cognitive behavioral therapy approaches (CBT) [ 49 , 50 ], acceptance and commitment therapy (ACT) [ 51 ], dialectical behavioral therapy (DBT) [ 52 , 53 ], and acceptance-based behavioral therapy (ABBT) [ 54 ]. However, most research assessing treatment efficacy precludes making any decisions about clinical mechanisms essential for improving ER. This is because they examine the impact of non-ER-focused interventions or interventions to target ER as part of a comprehensive program [ 18 , 48 ]. Due to the multi-component nature of the interventions, the specific components contributing to changes in ER remains unclear and whether the changes underlie improvements in other distressing symptoms has not yet been clarified. Thus, efforts to identify and inform the development of interventions leading to adaptive ER based on these studies are limited.

At present, patients who have distress disorders, such as generalized anxiety disorder (GAD), MDD, and particularly GAD diagnosed along with comorbid depression, often fail to respond well or experience sufficient gains from treatments: however, the reason for their lack of response is unknown [ 17 , 55 ]. Between 50 and 80% of patients receiving interventions for emotional disorders achieve the status of “responder.” [ 17 ] Between 50 and 60% of GAD patients showed meaningful improvement in response to treatment with traditional CBT [ 55 ]. While ER-focused interventions, such as the Unified Protocol (UP) [ 56 ], Emotion Regulation Group Therapy (ERGT) [ 57 ], and enhanced CBT emphasizing ER [ 58 ] were found to be effective in improving ER, research investigating these remains limited [ 18 , 59 , 60 ]. No substantial changes were found in the essential dimensions of ER after the application of several ER-focused interventions, implying that these were not present in a sufficient dose to promote ER [ 53 , 61 , 62 ]. Further, recent research identifying treatment response predictors for ERGT showed relatively few significant predictors [ 63 ]. In particular, the findings from a study that examined a treatment designed to enhance inpatient CBT for depression suggest that the addition of ER skills to CBT may not sufficiently change ER, although improvements were noted in ER strategies and depressive symptoms [ 58 ]. Another problem arises from the manualized CBT protocols, which are distinct and complex to use [ 17 , 64 ]. These protocols make it difficult to access and use CBT.

The limitations of the current interventions suggest the need for developing an ER-specific treatment. Designing more effective and targeted interventions requires a specific understanding of affective science to provide a broad framework for ER treatments. For example, recently, it has been identified that emotions can be generated and regulated not only through a top-down process but also through a bottom-up process: [ 65 ] current models of emotion generation and its regulation are based on these two processes, which are opposed but interactive [ 66 ]. The top-down mechanism is based on a view that focuses on cognition, where either individuals’ goal states or cognitive evaluations are thought to influence the variations in their emotional responses [ 67 ]. These processes are mapped to prefrontal cortical areas. Meanwhile, bottom-up mechanisms refer to processes based on a stimuli-focused view: in this mode of processing, emotions are mostly elicited by perceptions [ 68 ]. In everyday life, emotion can be processed through interactions between the bottom-up and top-down mechanisms [ 69 ].

Most research to date, however, has focused on top-down ER strategies, and few studies have focused on bottom-up regulation procedures [ 65 ]. In particular, CBT-based treatments, which are mainstream psychotherapies, focus on instruction in an array of cognitive means of coping with emotions; CBT traditionally tends to deal more directly with cognitive rather than emotional processes. One top-down strategy is cognitive reappraisal, an active component of most CBT-based treatments [ 70 ]. However, studies suggest that relying primarily on this strategy may be less effective for certain disorders, including depression, than treatments employing a flexible approach [ 65 ]. Such an approach would be straightforward and essential for researchers as they synthesize different research results, such as findings concerning bottom-up ER and its clinical implications for the investigation of interventions.

One intervention approach to bottom-up experiential ER is art psychotherapy. This type of treatment, which targets emotion dysregulation, may hold promise for improving ER in cases of depression. Patients with depression can benefit from experiential ER that emphasizes bottom-up means of coping with their emotional experiences over the course of art-based ER intervention. This perspective is supported by behavioral and neurocognitive findings indicating difficulties in top-down regulatory processes in individuals with depression [ 71 , 72 , 73 , 74 ]. Research examining neural activities between individuals with and without depression indicated different patterns between them: when downregulating negative emotions, individuals with depression show bilateral prefrontal cortex (PFC) activation, whereas individuals without depression show left-lateralized activation [ 74 ]. When given an effortful reappraisal task, moreover, the relationship patterns of individuals with depression between activation in the left ventrolateral PFC and the amygdala are different from those of individuals without depression. These findings indicate that the pathophysiology of depression underlies struggles of downregulation [ 74 ].

Thus, it is vital to design a new intervention for depression that focuses not only on top-down ER but also on bottom-up ER. In particular, this study examines art-based ER in the form of a client-centered and experiential psychotherapeutic approach allowing patients to attempt top-down and bottom-up regulation. While pursuing active engagement in art-based ER practices, patients can process their emotional experiences in a way that produces greater fine-tuning and depth. Art-based treatment is open and non-interventional as well as less demanding cognitively, enabling it to reach a diverse population with depressive symptoms. More promisingly, art-based ER primarily deals with visible and tangible works leading to visual representations. Emotional memory is perceptual [ 75 ], implying that art-based practices can influence its retrieval and manipulative process: the artworks that patients make in treatments are visual representations that are identical or similar to their emotional experiences. Importantly, creation involves colors, images, and spaces acting as new stimuli, allowing patients to manipulate and generate new emotions through a bottom-up process. As processes of emotion generation interact with those of ER [ 67 ], an art-based experiential approach can facilitate adaptive ER, potentially benefiting individuals who have emotional dysfunction.

However, few studies have explored ER in depression within the field of art psychotherapy [ 76 ]. The therapeutic strategies applied in relevant studies [ 77 , 78 , 79 ] are not explicitly identified or targeted with respect to the mechanisms of ER. For instance, earlier literature tested the effects of art therapy on ER in psychiatric disorders; most of these approaches focused on improving psychopathological symptoms related to specific disorders and considered ER to be a secondary therapeutic outcome. Thus, we identified a need to develop an effective art-based intervention specifically targeting emotion dysregulation in major depression.

Step 2: Formulation of change objectives

The second step required the specification of intervention goals, which involved moving from understanding what influences depressive symptoms, especially in terms of emotional abnormalities in depression, to clarifying what needs to be changed. Based on the needs assessment, the overall expected outcome was “a decrease in depressive symptoms and an improvement in ER.” In this process, the analysis of the determinants in Step 1 resulted in selecting key determinants to target, which were provided by a comprehensive review of the empirical literature and research evidence. It is difficult to understand generative and regulatory emotion processes that are enacted internally without the instigation of extrinsic stimuli [ 80 ]. Thus, it can be challenging to identify the right determinants to target and design an effective treatment that addresses problems related to ER. Based on our review, we determined and chose four important and changeable determinants and further divided them into five key determinants (see Table 1 ).

To apply IM, the construction of matrices of change consisting of performance and change objectives forms the basis for program development [ 20 , 81 ]. Overall, the program objectives were subdivided into performance objectives expected to be accomplished by the target group in the proposed intervention. While drawing on the key determinants and performance objectives, more general objectives, namely, change objectives, were formulated. The result of Step 2 is this change matrix, which further forms the basic factors for designing the intervention for major depression.

Step 3: Theory- and evidence-based strategies selection

In IM, Step 3 entails selecting theoretically grounded and evidence-based methods and strategies. For this process, we first conducted a comprehensive review of theories and empirical studies for therapeutic strategies, including the following characteristics: (i) they need to be confirmed as an efficient ER strategy based on empirical research evidence; (ii) they need to be effective not only in decreasing depressive symptoms but also in improving ER capacities of patients; and (iii) they can be translated into art-based practices. In iterations of reviewing theories related to and research evidence with regard to emotion regulatory strategies, we identified appropriate, theoretically sound therapeutic strategies for at least one program target.

Once an ER method was selected, we translated this method into art-based emotion regulation (ABER) strategies for practical applications. Practical applications refer to the practical translation of the chosen behavior change methods [ 19 , 20 , 21 , 81 ]. The end product of Step 3 is an initial set of theory- and evidence-based strategies selected and translated to address emotion dysregulation in major depression. Table 2 lists the strategies with supporting evidence and applications: art-based distraction, art-based positive rumination, art-based self-distancing (SD), and art-based acceptance. Based on an integrative view of emotional processing, which posits interactions between top-down and bottom-up systems [ 67 , 69 , 82 , 83 ], these strategies aim to modulate emotions through the use of top-down and bottom-up mechanisms.

In particular, as art-based ER involves visual-spatial processing that could exert influence as new sets of stimuli, this approach could lead to a more experiential bottom-up ER. For instance, distraction and cognitive defusion are usually considered cognitive forms of ER; however, both are translated and applied to art-based strategies. Individuals’ performance in art-based ER would differ from that on a given cognitive task, as their immersion experiences in the artistic and creative process involve the generation of colors, images, and spatial features, which may elicit new bottom-up processing. This may be associated with the superior ER effects of art-based distraction, as shown in some studies that compared the ER effects of artistic activities with those of non-artistic activities, such as completing verbal puzzles [ 98 , 99 , 100 ].

In addition, art-based SD promotes intuitive and experiential ER. Individuals are trained to adopt a self-distanced perspective in some treatments while reflecting on their emotions, such as mindfulness-based stress reduction (MBSR) and ERT. They meditate to take a decentered stance. Art-based SD may help those who have difficulty creating an internal distance. As individuals create visual forms of their inner feelings and thoughts, a spatially generated distance from the artworks representing their experiences allows them to adopt and maintain a more self-distanced perspective. As such, art-based SD is more intuitive but requires less mental energy. Importantly, this art-based experiential distancing may reconstrue individuals’ appraisals by facilitating a bottom-up mechanism.

Step 4: Program development

Step 4 concerns creating an actual program plan, leading to the ABER intervention model proposed in the current study. The intervention's elementary components, organization, and structure were created based on the findings of the preparation steps (Steps 1–3). Once the list of therapeutic strategies and their practical applications was generated, we designed a structured intervention framework that would be feasible and realistic to deliver in primary care settings.

The intervention framework developed in Step 4 is based on the process model of ER [ 7 ], supported by considerable empirical research [ 101 , 102 , 103 ]. Based on the extended model, a series of steps involved in the process of regulation with different ER strategies are considered while designing the conceptual framework. Accordingly, the primary areas of the intervention involve emotion perception, attention, and cognition. We developed specific art-based ER strategies, focusing primarily on antecedent- rather than response-focused regulation. Further, this intervention is meant to complement the process model in a framework that is designed to apply one or more strategies in a single session: this would be ideal for improving ER in real life, as current research on ER has found that people generally try multiple strategies simultaneously [ 104 ], whereas the process model examines a within-situation context, within which a single ER strategy is utilized [ 12 ]. In addition, we find that this treatment will be effective in improving ER as it attempts both top-down and bottom-up ER: actively engaging in artworks through the use of the body, a patient can apply experiential self-focus [ 64 ]. In treatment with art-making, patients can be provided with sufficient time and space to find personal meaning in their experiences and process emotions, which enables them to achieve change.

Table 3 presents an overview of the proposed intervention frameworks. As shown, we designed two frameworks to guide the intervention: an individual framework for short-term intervention and an integrative framework for long-term intervention. Each style of the ABER model draws on a different implementation design to build the framework, and each model has slightly different aims. In Step 4, the advisory board reviewed the draft frameworks, including the determinants, performance and change objectives, and therapeutic strategies. The advisory board acted as a support group throughout the review process, helping tailor the program to the target population. In response to the board’s reviews, supplementary resources were added.

Individual framework

First, a plan for an individual framework was devised that accounted for the scope and phase of a short-term intervention. As shown in Fig. 4 , this framework focuses on producing initial or short-term behavioral changes pertaining to achieving short-term clinical efficacy. That is, the individual model does not aim only at emotional changes in patients, such as increases or decreases in specific emotions. The therapeutic aim is not set in an emotion-specific manner, but in terms of effectiveness, it relates to the use of regulatory strategies [ 105 ]. Accordingly, an expected outcome is to increase the quantity and frequency of adaptive ER strategies. Patients are trained in rudimentary ER skills, including one or several combination ABER strategies, as designed in the previous step. These practices aim to enhance attentional, followed by cognitive control. The expected duration of individual sessions is around 1–1.5 h.

Individual intervention model diagram. Noted. The panel shows the individual intervention model in an inpatient setting as an example: each patient (patient i ) has a different time of admission (t 0 ) and inpatient discharge (t d ). Thus, the number of participating patients can differ per session. During the hospital stay, patients are trained in rudimentary emotion regulation (ER) skills, including one or a combination of several art-based ER strategies (aber i ). The application of the therapeutic strategies is flexible: it depends on the patient’s cognitive functions, depressive symptoms, and severity of the symptoms. The time of inpatient discharge (t d ) affects each patient’s treatment duration

Integrative framework

While an individual framework comprises a single phase, an integrative framework includes stepwise sequential phases. In addition to skill development in the individual treatment, three phases of the integrative model are designed to foster adaptive motivational responses and cognitive-behavioral flexibility, which enables patients to achieve greater emotional clarity [ 106 ]. In the integrative treatment, all three phases are performed for 6–12 weeks.

The first phase of the integrative model begins with psychoeducation, in which the patient is taught the concept of ER and the importance of identifying his or her habitual reactions, such as in terms of rumination and dampening [ 91 ], that have characterized his or her life. This therapeutic process is important because ER is an automatic process requiring the consideration of motivation [ 107 ]. Psychoeducation regarding ER and monitoring patients’ responses to emotional experiences precede the skill development procedure. For instance, for patients’ self-monitoring, retrospective self-report questionnaires can capture data on ER skill use. While these methodologies are easy to use and cost-efficient [ 108 ], they are demanding tools for use in capturing natural fluctuating patterns in ER [ 109 ]. As an alternative, ecological momentary assessment can be used in treatment to capture situational context and adaptiveness of the skill use [ 108 ]. In addition to patients’ self-monitoring, a psychotherapist should monitor their emotional responses during and between therapy sessions: psychotherapists function as human raters. Because self-monitoring may not be feasible for all patients, assessing the typical patterns with which patients use maladaptive emotion regulatory strategies is important. Specifically, therapists need to assess a patient’s ER repertoire: the quantity of ER strategies, the frequency of strategy use, and how the patient’s strategy use changes.

The second phase entails adopting and implementing ER strategies with processes resembling those of the individual model. These processes entail the selection and repetition of adaptive strategies. They differ from the individual model in that the duration of Phase II can vary from one patient to another depending on the severity of depressive symptoms and the frequency of maladaptive strategies used. The ER practices delivered in Phase II are art-based tasks through which therapists and patients explore and try adaptive strategies. As shown in Fig. 5 , the intervention program includes four ABER strategies selected and translated in Step 3: art-based distraction, art-based SD, art-based positive rumination, and art-based acceptance. The patients work with therapists in 4–8 1.5-h sessions to engage in art-based practices.

Summary steps and components for the integrative intervention model

Finally, the integrative framework includes a third phase for evaluation. While the previous sessions in Phase II focus on skill development, the sessions in Phase III focus on assessing changes in patients. All individual progress in ER is tracked and monitored. In this task, therapists help patients assess changes in their emotion-regulatory skill use and their achievements in terms of self-perception, effectiveness, and adaptiveness. Patients are given opportunities to take a broad view of the changes in their artworks during all treatment phases. Furthermore, patients receive a few tasks as homework to briefly review their strategy use in daily life from the beginning of the treatment until the current moment. The review process helps them assess their progress and supports their strengthening. It takes 6–12 weeks to complete the integrative treatment course, depending on the clinical impression. For instance, the duration of Phase II is expected to take 4–8 weeks, according to the clinical impression. A therapist or clinician renders his or her impression regarding the degree of the patients’ severity of depressive symptoms, use of maladaptive ER strategies, willingness to participate in the intervention, and insight into their treatment.

Step 5: Adoption and implementation

Implementation is an essential aspect of program development. In Step 5 of IM, the focus is on planning the adoption and implementation of the proposed intervention. This process is required at the environmental level [ 21 ] and ensures successful adoption and sustainable use in collaborating organizations. Thus, pilot tests can be conducted to gain practical insights into implementation decisions and refine the intervention. Using a PAR framework, we pilot-tested the individual model to ensure that the intervention is appropriate and helpful for patients. This PAR pilot study was performed to inform future practices while connecting intervention research with actual action in a primary care setting.

The advisory group’s results, which indicated that the intervention needed to be sufficiently pliable to be used in a variety of primary care settings, informed and supported the step for pretesting. Implementation was prepared in a primary care setting, in which the program was pretested with a steering group of psychiatrists, nurses, and an art psychotherapist. Two clinicians were in charge of informing the intervention program and facilitating patient involvement. The therapist, who had received appropriate training and instruction, was responsible for delivering the intervention and supporting all practical aspects of patient engagement. With support from the therapist, the patients were in charge of applying one or a combination of two strategies in therapeutic sessions.

We performed this initial testing in a psychiatric ward in Seoul. Between February 2023 and February 2024, during the first two phases of the pilot testing, approximately 24 sessions were conducted, and 45 inpatients, including 16 patients with depressive disorders, voluntarily participated in the program. At the end of each session, the participants were asked to report their experiences through free narratives and complete a short questionnaire survey (quantitative and free-text comments) that provided additional information regarding their involvement. The mean time expenditure for the patients was 1.1 h (SD: 18.0; range: 0.5–2). Patients’ emotional experiences were reflected in their artworks, and Fig. 6 shows a short overview of their art products. The detailed findings from these pilot trials are outside the scope of the IM protocol and will be available in a future publication.

Examples of the art products of the participating patients with depression. Noted. Figure 6 briefly outlines patient engagement through their artworks made during the treatment sessions in the first pilot phase: a shows an artwork a patient made in a treatment session, which applied art-based acceptance; b shows an artwork showing a patient’s reflection on his experience, which applied art-based self-distancing and acceptance; c and d show artworks in which patients apply art-based positive rumination and distraction. Different art materials were provided in each session depending on the ER strategies used. The art-based practices of ER promoted relaxation and expression of the patient’s inner feelings and thoughts

Step 6. Evaluation plan

The sixth step of IM is the planning of evaluation strategies to assess the potential impacts of the proposed intervention [ 20 ]. For this purpose, we designed two phases based on a PAR framework: patient feedback and expert feedback. The rationale for this plan was that comprehensive evaluations could investigate the necessity of refinement and what is needed to produce a more feasible and effective intervention. In particular, we expected that the engagement of patients as well as health professionals in the evaluation process would integrate the organizational perspective into patient-oriented quality improvements. From these two phases, we developed questions and measures for evaluation, conducting preliminary PAR studies to determine the feasibility and efficacy of the complete program. Table 4 presents the evaluation strategies for gaining patient and expert feedback. Meanwhile, Table 5 presents an overview and timeline of PAR 2 and PAR 3.

First, we developed a set of patient-reported outcome measures to obtain patient feedback. Quantitative assessments of treatment satisfaction, perceived helpfulness of treatment, and perceived difficulty were conducted following the end of a therapeutic session. Patient evaluations must be carried out regularly during treatment to assess the efficacy of the integrative model. At the end of the program, unstructured or semi-structured interviews are recommended to explore patients’ experiences of the treatment process. In addition, we planned a two-phase mixed-methods study to obtain feedback from participating healthcare professionals using an online survey and focus group interviews. The assessments included process measures, such as perceived difficulty, program appropriateness, and recommendations for improvements to its implementation on a professional level. A web-based survey was disseminated among clinicians and nurses to assess the feasibility of the intervention. Together, this enabled us to increase the time efficiency and cost-effectiveness of the evaluation process.

Feasibility was assessed in five ways. First, the feasibility with which patients participated in the program was described. In our preliminary study, for instance, we calculated the percentage of patients approached for program participation relative to those who did not. Second, the feasibility of retaining patients in a treatment session was reported. To capture the feasibility of retention in treatment, we calculated the percentage of patients who failed to complete treatment compared with the percentage of those who completed it. Third, the feasibility of administering treatment was measured with a self-reported survey of patients’ perceived difficulty in participation and a survey of healthcare professionals’ perceived difficulty in implementation. To report the feasibility of administering treatment, we calculated the mean hours a patient spent in completing treatment. In addition to feasibility, acceptability was operationalized in three ways: a quantitative self-report survey of patient satisfaction, patient perceptions of helpfulness of treatment, and patient willingness to recommend program participation were used. In our preliminary study, we developed responses for the patient survey and calculated the means and standard deviations for each item.

We received patient feedback in the first two pilot phases (PAR 2), and the results showed that the intervention program was feasible and acceptable for implementation in the primary care setting (the mean scores were as follows: Treatment satisfaction = 4.82, Perceived helpfulness of treatment = 4.57, Perceived difficulty = 4.45). The patients provided further recommendations for improved intervention in free-text comments. In addition to this patient feedback, we began conducting PAR3 in February 2024. The feedback research is being conducted through an online questionnaire that includes multiple-choice questions and open-ended questions, with focus group interviews being conducted virtually through Zoom. The results for PAR 2 and PAR 3 will be reported in separate articles.

In this paper, we proposed conceptual frameworks for an intervention that targets emotion dysregulation in depression. IM was used as the conceptual protocol to develop the intervention. To the best of our knowledge, this is the first art-based ER intervention incorporating previous theories, research evidence, and review data in relation to affective science and intervention research, combining PAR components with IM. We developed the intervention following the rationale and stepwise process of IM, which identifies theory- and evidence-based strategies to address key barriers to ER. In addition, to evaluate the developed intervention, preliminary PAR studies were conducted, including the acceptability of the trials and the ABER intervention to patients; the rate of recruitment, attendance, and attrition; perceived difficulties in intervention implementation; and psychological outcomes. Consequently, the intervention is theoretically underpinned and supported by empirical evidence regarding ER and the results of our pilot studies.

The current study benefits from integrating the PAR approach into the IM framework in two ways. First, using PAR studies in the IM resulted in the cogeneration of knowledge among academic researchers, implementers, and the intended participants. PAR ensured experiential knowledge to deliver content that addressed difficulties in ER in collaborative partnerships. Another contribution was enhancing the feasibility and acceptability of the proposed intervention. In particular, preliminary PAR studies helped investigate whether modifications were needed before the intervention’s adoption. Even though IM is a time-consuming process, the use of PAR made it more cost-effective and time-efficient.

In addition to these strengths, it is crucial to acknowledge and affirm the study’s limitations. First, the current study offers only preliminary evidence for the given conceptual framework. Although the proposed intervention may precisely target emotional dysfunction in depression, such as in the restrictive use of adaptive ER skills with repetitive use of maladaptive strategies, the integrative and individual frameworks of ABER have not been evaluated through randomized clinical trials. As the current study pilot-tested the intervention in an inpatient setting that served an acute, transdiagnostic population, implementers could extend the use of these frameworks by performing a fine-grained analysis of treatment contexts (e.g., by adapting the model for depressed outpatients in primary care). As such, the intervention must be examined and refined on the basis of the results of empirical studies on multidisciplinary design. In addition, this article did not examine the therapists’ capability of delivering treatment, fidelity of implementation, and feasibility of measuring tools. Intervention researchers interested in these variables are encouraged to extend our models by testing the broad contextual variables that influence its process. Similarly, further research is required to investigate standardized forms of assessment in treatment (e.g., a measurable rating scale for patient monitoring) to increase the efficiency of the intervention.

Conclusions

This article proposes empirical and theoretical intervention frameworks that can improve ER in depression. This IM study is unique, as the development process incorporates PAR components. Moreover, the intervention consists of four art-based regulatory strategies that enrich the present literature on intervention research targeting dysfunctional ER in major depression. Our participatory action studies demonstrate that, in a primary care setting, the individual protocol is feasible and acceptable for implementation. This result represents a potential step forward toward filling a gap in current mental health treatments for patients with MDD. Despite the tiresome and time-consuming process of intervention development, the application of IM augmented by PAR is helpful in optimizing chances for an effective behavior change. Further testing is required to assess the impact of the therapeutic program proposed in this study.

Availability of data and materials

The author confirms that the data generated or analysed during this study are included in this published article: however, raw datasets are not publicly available due to local legal restrictions. Since the data being generated by PAR2 and PAR3 are outside the scope of the current intervention mapping study, they are available elsewhere.

Abbreviations

Art-based emotion regulation

Cognitive-behavioral therapy

Emotion regulation

Emotion Regulation Group Therapy

Generalized anxiety disorder

Intervention mapping

Mindfulness-based stress reduction

Major depressive disorder

Participatory action research

The Self-Assessment Manikin

Self-distancing

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The present researchers express their gratitude to the Kangdong Sacred Heart Hospital for its help and support in this research. Appreciation is also extended to all participating patients, clinicians, health care professionals, and the advisory board in all steps of the development. There are no individuals or funding organizations, other than the co-authors, who contributed directly or indirectly to this article.

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ML contributed to plan and design the study with support from the rest of the study team. YT registered the trial. ML collected, and analyzed participant data. ML drafted and edited the manuscript. All authors reviewed and/or approved the final manuscript for submission.

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Lee, M., Choi, H. & Jo, Y.T. Targeting emotion dysregulation in depression: an intervention mapping protocol augmented by participatory action research. BMC Psychiatry 24 , 595 (2024). https://doi.org/10.1186/s12888-024-06045-y

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Crafting taxonomies for understanding power consumption in industrial kitchens: a methodological framework and real-world application.

1. Introduction

Proposes a new methodological framework to develop taxonomies for IKs, leveraging the main findings from a comprehensive survey of the background and state of the art concerning the definition and development of taxonomies in different domains. The proposed framework leverages concepts from corporate and faceted taxonomies. The former offers multiple entry points and dimensions (facets) to be analyzed. In contrast, the latter provides a structured and standardized vocabulary specific to the organization’s business context, ensuring consistency and relevance of the facets.
Demonstrates the proposed methodology through a case study that uses data collected from 50 restaurants in Portugal. This case study evaluates the effectiveness of the proposed methodology. It provides some insights into the organizational (e.g., size, number of workers) and energy-related aspects (e.g., predominant appliances and costs with electricity) of the Portuguese industrial kitchen sector in light of the developed taxonomy.

2. Background and Related Works

2.1. types of taxonomies, 2.2. modeling taxonomies, 2.3. related works on taxonomy definition, 2.4. summary, 3. methodological framework, 3.1. stage 1: definition of knowledge domain, 3.2. stage 2: definition of terms and concepts, 3.3. stage 3: data collection, 3.4. stage 4: information analysis, 4. case study: a taxonomy of portuguese industrial kitchens, 4.1. knowledge domain, 4.1.1. purpose, 4.1.2. target audience, 4.2. terms and concepts, 4.3. data collection, preliminary data analysis, 4.4. information analysis, 4.4.1. data analysis methodology.

Group Formation: all the possible cluster arrangements were organized and displayed using dendrograms, and the clusters were formed by slicing the distance axis ( y -axis) at different values based on visual inspection.
Analysis of Aspects: Various aspects were analyzed based on the answers obtained from the questions asked to the restaurants interviewed. These questions included the type of gastronomy, the energy cost in euros, the number of employees in the kitchen, the size of the kitchen in square meters, the time of existence in years, and the equipment source of energy. The possible answers for each question are available in Appendix A . The percentage of responses for each alternative was then calculated in order to draw conclusions about each group formed. This made it possible to determine which type of gastronomy was predominant in a given group, as well as to identify trends in relation to energy expenditure, number of employees, and other aspects.
Concept Map Creation: The taxonomy structure is usually represented graphically through conceptual maps, making the hierarchical relationships between the taxonomy elements more visible. In this work, the software used to create the conceptual map was Cmap Tools [ 36 ]. As a tool for organizing the concepts, this software uses a hierarchical diagram, presenting the information in descending, with the most general information at the beginning of the hierarchical chain.

4.4.2. Results and Discussion

Iteration 1.

Energy Cost: The blue and green groups were classified as high because most restaurants spend from EUR 1000 to over EUR 1500 on energy (56.25%).
Number of employees: The blue group was classified as inconclusive because the answers were well distributed between each of the categories presented, so there was no predominance. On the other hand, the green group was classified as high, at 66.67%.
Kitchen Size: The blue and red groups were classified as small because most of the restaurants in these groups had a maximum of 60 m 2 in their kitchens (75% of the restaurants in the case of the blue group and 100% of the restaurants in the case of the green group). On the other hand, the green group was classified as inconclusive because there was no pattern in this aspect, as each restaurant in this group answered a different alternative for the size of its kitchen.
Time of existence: All the groups were classified as high because, in all the groups, most of the restaurants have been in existence for more than 12 years: 75% of the restaurants in the case of the blue group, 100% in the case of the green group, and 38.71% in the case of the red group.
Equipment operation: All the groups were considered inconclusive because, in all of them, the distribution between restaurants using gas and those using electricity was balanced, resulting in percentages close to 50% in all cases.

Iteration 2

5. conclusions, limitations and future work, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

AC	Air Conditioning
IK	Industrial Kitchen

Appendix A. Questionnaire Used in the Interviews with the 50 Restaurants

Content	Reference	Text	Type/Range
Question	[…]		Quantity
Variables	Registration		[9 to 306]
Values
Question	[…]		Single
Variables	P0		1 to 2
Values	1	Yes	1 to 1
	2	No	2 to 2
	1
Question	[…]		Open
Variables	Date	open-ended verbatim
Values
Question	[…]		Open
Variables	Time	open-ended verbatim
Values
Question	[…]		Open
Variables	Name_Cont	open-ended verbatim
Values
Question	[…]		Open
Variables	Location	open-ended verbatim
Values
Question	[…]		Single
Variables	P1		1 to 98
Values	1	Traditional Portuguese	1 to 1
	2	Fast-Food	2 to 2
	3	Indian	3 to 3
	4	Italian	4 to 4
	5	Chinese	5 to 5
	6	Burger restaurant	6 to 6
	7	Churrascaria	7 to 7
	8	Steakhouse	8 to 8
	9	Japanese	9 to 9
	10	Asian	10 to 10
	98	Other	98 to 98
Question	[…]		Open
Variables	P1_Out	open-ended verbatim
Values
Question
	[…]		Single
Variables	P2		1 to 99
Values	1	Less than 500 euros	1 to 1
	2	501 to 750 euros	2 to 2
	3	751 to 1000 euros	3 to 3
	4	1000 to 1500	4 to 4
	5	Maior que 1500 euros	5 to 5
	99	(Do Not Read) NS/NR (No Response/Not Reported)	99 to 99
Question	[…]		Single
Variables	P3		1 to 4
Values	1	1 to 3	1 to 1
	2	4 to 5	2 to 2
	3	6 to 10	3 to 3
	4	More than 10	4 to 4
Question	[…]		Single
Variables	P4		1 to 4
Values	1	0 to 30	1 to 1
	2	31 to 60	2 to 2
	3	61 to 100	3 to 3
	4	More than100	4 to 4
Question	[…]		Single
Variables	P5		1 to 5
Values	1	Less than 1 year	1 to 1
	2	1 to 4 years	2 to 2
	3	4 to 8 years	3 to 3
	4	8 to 12 years	4 to 4
	5	More than 12 years	5 to 5
Question	[…]		Matrix
Variables	P6A_1	Temperature controllers	1 to 4
	P6A_2	Plate warmer	1 to 4
	P6A_3	Air conditioning	1 to 4
	P6A_4	Refrigerator	1 to 4
	P6A_5	Chicken rotisseries	1 to 4
	P6A_6	Bain-maries	1 to 4
	P6A_7	Braziers	1 to 4
	P6A_8	Electric buffet	1 to 4
	P6A_9	Boiler	1 to 4
	P6A_10	Proofing chambers	1 to 4
	P6A_11	Plate warmer carts	1 to 4
	P6A_12	Electric kettles	1 to 4
	P6A_13	Cutters or choppers (of meat, chicken, etc.)	1 to 4
Values	1	Does not exist	1 to 1
	2	1	2 to 2
	3	2	3 to 3
	4	More than 2	4 to 4
Question	[…]		Matrix
Values	P6B_1	Crepe makers/Waffles	1 to 4
	P6B_2	Electric water heater	1 to 4
	P6B_3	Extractor	1 to 4
	P6B_4	Stove	1 to 4
	P6B_5	Oven	1 to 4
	P6B_6	Fryer	1 to 4
	P6B_7	Dishwasher	1 to 4
	P6B_8	Microwave	1 to 4
	P6B_9	Salamander/Grill	1 to 4
	P6B_10	Refrigerated display case	1 to 4
Values	1	Does not exist	1 to 1
	2	1	2 to 2
	3	2	3 to 3
	4	More than 2	4 to 4
Question	[…]		Single
Variables	P7		1 to 98
Values	1	Electricity	1 to 1
	2	Gas	2 to 2
	98	Other	98 to 98
Question	[…]		Open
Variables	P7Out	open-ended verbatim
Values
Question	[…]		Multiple
Variables	P8_1	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: High consumption, but I don’t know how to save	0 to 1
	P8_2	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: Difficult to promote the adoption of more efficient behaviors among the employees of the establishment	0 to 1
	P8_3	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: Difficult to promote the use of equipment in a more efficient way	0 to 1
	P8_4	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: I don’t know which equipment consumes the most energy	0 to 1
	P8_5	P.8) To conclude. What are the main challenges: The kitchen’s structure/organization does not allow for a layout that enables the intelligent use of equipment	0 to 1
	P8_6	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: Lack of information on how to manage equipment for more efficient consumption	0 to 1
	P8_7	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: The equipment in the establishments is not very efficient	0 to 1
	P8_8	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: Difficult to maintain equipment in a way that keeps them efficient	0 to 1
	P8_9	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: More efficient equipment has a high cost	0 to 1
	P8_10	P.8) To conclude. What are the main challenges related to the energy consumption of your establishment’s kitchen: I don’t encounter any difficulties	0 to 1
Values	0	No	0 to 0
Values	1	Yes	1 to 1
Question	[…]	P.9)	Single
Variables	P9		1 to 2
Values	1	Yes	1 to 1
Values	2	No	2 to 2
Question	[…]		Open
Variables	P9A	open-ended verbatim
Values
Question	[…]		Open
Variables	Email	open-ended verbatim
Values

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Title	Year	Summary
A Faceted Taxonomy for Requirement Elicitation Techniques [ ]	2003	Propose a taxonomy for the techniques used in the requirements elicitation phase through a faceted classification scheme.
The Proposition of a Taxonomy for the Analysis of Environmental Management in Brazil [ ]	2004	Propose a taxonomy to analyze Environmental Management in industrial organizations with a formalized environmental management system and analyze the profile of industrial organizations regarding product and process technologies in relation to the adopted environmental management standard.
Taxonomy: a fundamental element for Knowledge Management [ ]	2005	Introduce different types of taxonomy as well as their development. The study focuses on Knowledge Management, so numerous IT tools are presented.
Constitutive elements of the taxonomy concept [ ]	2010	Search in the literature, in different areas of knowledge, the semantic understanding of the term taxonomy, in addition to identifying and analyzing different definitions of taxonomy.
The taxonomy as classificatory structure: an application in the domains of interdisciplinary knowledge [ ]	2010	Present the method used in structuring the taxonomy of Environmental Geochemistry. Demonstrate the steps for modeling domains, based on the Theory of Faceted Classification and on the principles of the Theory of Integrative Levels, pointing to the conceptual map as a graphical form of representation.
Bloom’s taxonomy and its adequacy to define instructional objective in order to obtain excellence in teaching [ ]	2010	Present Bloom’s Taxonomy and the changes that have occurred in recent years, as well as clarify how it can be used within the context of engineering teaching.
Taxonomy for creative techniques applied to the design process [ ]	2011	Classify the creative techniques used in the process of product development through a faceted taxonomy.
Methodology for construction of faceted corporate taxonomies [ ]	2021	Systematize the procedures for building corporate taxonomies, to reframe and characterize them as faceted.
A taxonomy for Blockchain-based distributed storage technologies [ ]	2021	Propose a categorization and taxonomy of blockchain-based distributed storage technologies.
Lexicon annotation in sentiment analysis for dialectal Arabic: Systematic review of current trends and future directions [ ]	2023	Present a taxonomy of data annotation methods in sentiment analysis for dialectal Arabic research.

Criteria/Group	Blue	Green	Red
Type of gastronomy	Portuguese traditional	Portuguese traditional	Portuguese traditional
Energy costs (EUR)	High	High	Low
Number of employees in the kitchen	Inconclusive	High	Low
Kitchen size (m )	Small	Inconclusive	Small
Time of existence of the restaurant	High	High	High
Equipment operation	Inconclusive	Inconclusive	Inconclusive

Criteria / Group	B1	B2	B3	B4	R1	R2	R3
Energy costs (EUR)	High	High	Low	High	Low	Inconclusive	High
Number of employees in the kitchen	Low	Low	Low	Inconclusive	Low	Low	Low
Kitchen size (m )	Small	Small	Small	Small	Small	Small	Small
Time of existence of the restaurant	High	High	Low	High	Low	High	High
Equipment operation	Electricity	Gas	Inconclusive	Gas	Electricity	Gas	Electricity

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Ribeiro, M.; Morais, H.; Pereira, L. Crafting Taxonomies for Understanding Power Consumption in Industrial Kitchens: A Methodological Framework and Real-World Application. Sustainability 2024 , 16 , 7639. https://doi.org/10.3390/su16177639

Ribeiro M, Morais H, Pereira L. Crafting Taxonomies for Understanding Power Consumption in Industrial Kitchens: A Methodological Framework and Real-World Application. Sustainability . 2024; 16(17):7639. https://doi.org/10.3390/su16177639

Ribeiro, Miriam, Hugo Morais, and Lucas Pereira. 2024. "Crafting Taxonomies for Understanding Power Consumption in Industrial Kitchens: A Methodological Framework and Real-World Application" Sustainability 16, no. 17: 7639. https://doi.org/10.3390/su16177639

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Methodological Framework
Definition: Methodological framework is a set of procedures, methods, and tools that guide the research process in a systematic and structured manner. It provides a structure for conducting research, collecting and analyzing data, and drawing conclusions. The framework outlines the steps to be taken in a research project, including the research ...
How methodological frameworks are being developed: evidence from a
Phase 1 - identifying evidence to inform the methodological framework. This phase is split into two; the first is identifying previous frameworks or guidance which are used for the foundations of the new methodological framework, the second is identifying new data to help develop the methodological framework.
How methodological frameworks are being developed: evidence from a
There is no formal definition of a methodological framework amongst the academic community. There is, however, unspoken agreement that a methodological framework provides structured practical guidance or a tool to guide the user through a process, using stages or a step-by-step approach [1,2,3,4,5].Specific descriptions of a methodological framework include: 'a body of methods, rules and ...
The Ultimate Guide To Research Methodology
What Is Research Methodology? Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.
A tutorial on methodological studies: the what, when, how and why
The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies.
What Is a Conceptual Framework?
Developing a conceptual framework in research. Step 1: Choose your research question. Step 2: Select your independent and dependent variables. Step 3: Visualize your cause-and-effect relationship. Step 4: Identify other influencing variables. Frequently asked questions about conceptual models.
What is a framework? Understanding their purpose, value, development
Frameworks are important research tools across nearly all fields of science. They are critically important for structuring empirical inquiry and theoretical development in the environmental social sciences, governance research and practice, the sustainability sciences and fields of social-ecological systems research in tangent with the associated disciplines of those fields (Binder et al. 2013 ...
Research Frameworks: Critical Components for Reporting Qualitative
The Importance of Research Frameworks. Researchers may draw on several elements to frame their research. Generally, a framework is regarded as "a set of ideas that you use when you are forming your decisions and judgements" 13 or "a system of rules, ideas, or beliefs that is used to plan or decide something." 14 Research frameworks may consist of a single formal theory or part thereof ...
How methodological frameworks are being developed: evidence from a
We took a broad view and did not limit our study to methodological frameworks in research and academia. Methods: A scoping review was conducted, drawing on Arksey and O'Malley's methods and more recent guidance. We systematically searched two major electronic databases (MEDLINE and Web of Science), as well as grey literature sources and the ...
What Is a Research Methodology?
What Is a Research Methodology? | Steps & Tips. Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023. Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing ...
Research Methodology
Research methodology refers to the philosophical and theoretical frameworks that guide the research process. Research methods refer to the techniques and procedures used to collect and analyze data. It is concerned with the underlying principles and assumptions of research.
A tutorial on methodological studies: the what, when, how and why
Methodological studies - studies that evaluate the design, analysis or reporting of other research-related reports - play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste. We provide an overview of some of the key aspects of methodological studies such ...
Literature review as a research methodology: An overview and guidelines
This is generally referred to as the "literature review," "theoretical framework," or "research background." However, for a literature review to become a proper research methodology, as with any other research, follow proper steps need to be followed and action taken to ensure the review is accurate, precise, and trustworthy.
PDF Methodological Frameworks and Sampling in Qualitative Research
METHODOLOGICAL FRAMEWORKS IN QUALITATIVE RESEARCH Methodology, according to Dew (2007, p. 433), refers to 'the principles underlying particular research approaches, as distinct from "methods", which are ways of collecting data'. Methodology determines a method for researchers to produce data for analysis (Carter & Little 2008; Hesse ...
PDF CHAPTER CONCEPTUAL FRAMEWORKS IN RESEARCH distribute
hole come together and build on and into each other. Ideally, a conceptual framework helps you become more discerning and selective in terms of methods, grounding theories, and. pproaches to your research (Ravitch & Riggan, 2016).Collaboration is a horizontal value in qualitative research, and we strongly cri-tique.
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Your Step-by-Step Guide to Writing a Good Research Methodology
Provide the rationality behind your chosen approach. Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome. 3. Explain your mechanism.
PDF Frameworks for Qualitative Research
research methods. For many qualitative researchers it is their general frame-work or paradigm that is most important to them. If they are devoted to a particular research method, it is often because that method is an expression of their paradigm. If paradigms or frameworks are a central issue for qualitative researchers,
Conceptual Framework
Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between ...
PDF How methodological frameworks are being developed: evidence from a
framework is a tool to guide the developer through a se-quence of steps to complete a procedure. Methodology is defined as the group of methods used in a specified field, and framework is defined as a structure of rules or ideas. The primary research question posed in this review is 'what approaches are used in developing a
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This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks.
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To address this research gap, this paper proposes a methodological framework to develop taxonomies for understanding the electricity consumption in industrial kitchens. The proposed framework was developed following an extensive survey of the existing literature, and it is based on four main steps: identification of the knowledge domain ...

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The Ultimate Guide To Research Methodology

What Is Research Methodology?

Facilitating Rigorous Inquiry

Ensuring Reproducibility And Reliability

Guiding Decision-Making Processes

Contributing To Academic Excellence

Enhancing Problem-Solving Abilities

Understanding Research Methodology

Basics Of Research

Types of Research

Components Of Research Methodology

Research Design

Data Collection Methods

Data Analysis Techniques

The research paper we write have:

Choosing a Research Method

Quantitative Research Methods

Experimental Research

Survey Research

Descriptive Research

Qualitative Research Methods

Ethnography

Grounded Theory

Exploratory, Descriptive, And Explanatory Designs

Descriptive Design

Explanatory Design

Cross-Sectional Vs. Longitudinal Designs

Longitudinal Design

Experimental Vs Non-experimental Designs

Non-Experimental Design

Designing Effective Surveys

Conducting Interviews

Observation

2. Non-participant Observation

Archival Research

2. Challenges and Considerations

Common Challenges in Research Methodology

Sampling Issues

Sample Size Determination:

Data Quality And Validity

Construct Validity:

Time And Resource Constraints

Resource Availability:

Managing Bias in Research

Confirmation Bias:

Tips On How To Write A Research Methodology

Tip 1. Clear Research Objectives

Tip 2. Comprehensive Literature Review

Tip 3. Detailed Research Plan

Tip 4. Ethical Considerations

Tip 5. Stay Updated On Methodologies

Tip 6. Adaptability In Methods

Tip 7. Iterative Approach

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What is a framework? Understanding their purpose, value, development and use

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What is a framework?

Mechanisms of framework development and use: a meta-framework

The value of a meta-framework

Discussion and directions forward

Guiding points for framework engagement

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