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Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

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• Knowledge Base

Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Statistical Design and Analysis of Biological Experiments

Chapter 1 principles of experimental design, 1.1 introduction.

The validity of conclusions drawn from a statistical analysis crucially hinges on the manner in which the data are acquired, and even the most sophisticated analysis will not rescue a flawed experiment. Planning an experiment and thinking about the details of data acquisition is so important for a successful analysis that R. A. Fisher—who single-handedly invented many of the experimental design techniques we are about to discuss—famously wrote

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ( Fisher 1938 )

(Statistical) design of experiments provides the principles and methods for planning experiments and tailoring the data acquisition to an intended analysis. Design and analysis of an experiment are best considered as two aspects of the same enterprise: the goals of the analysis strongly inform an appropriate design, and the implemented design determines the possible analyses.

The primary aim of designing experiments is to ensure that valid statistical and scientific conclusions can be drawn that withstand the scrutiny of a determined skeptic. Good experimental design also considers that resources are used efficiently, and that estimates are sufficiently precise and hypothesis tests adequately powered. It protects our conclusions by excluding alternative interpretations or rendering them implausible. Three main pillars of experimental design are randomization , replication , and blocking , and we will flesh out their effects on the subsequent analysis as well as their implementation in an experimental design.

An experimental design is always tailored towards predefined (primary) analyses and an efficient analysis and unambiguous interpretation of the experimental data is often straightforward from a good design. This does not prevent us from doing additional analyses of interesting observations after the data are acquired, but these analyses can be subjected to more severe criticisms and conclusions are more tentative.

In this chapter, we provide the wider context for using experiments in a larger research enterprise and informally introduce the main statistical ideas of experimental design. We use a comparison of two samples as our main example to study how design choices affect an analysis, but postpone a formal quantitative analysis to the next chapters.

1.2 A Cautionary Tale

For illustrating some of the issues arising in the interplay of experimental design and analysis, we consider a simple example. We are interested in comparing the enzyme levels measured in processed blood samples from laboratory mice, when the sample processing is done either with a kit from a vendor A, or a kit from a competitor B. For this, we take 20 mice and randomly select 10 of them for sample preparation with kit A, while the blood samples of the remaining 10 mice are prepared with kit B. The experiment is illustrated in Figure 1.1 A and the resulting data are given in Table 1.1 .

One option for comparing the two kits is to look at the difference in average enzyme levels, and we find an average level of 10.32 for vendor A and 10.66 for vendor B. We would like to interpret their difference of -0.34 as the difference due to the two preparation kits and conclude whether the two kits give equal results or if measurements based on one kit are systematically different from those based on the other kit.

Such interpretation, however, is only valid if the two groups of mice and their measurements are identical in all aspects except the sample preparation kit. If we use one strain of mice for kit A and another strain for kit B, any difference might also be attributed to inherent differences between the strains. Similarly, if the measurements using kit B were conducted much later than those using kit A, any observed difference might be attributed to changes in, e.g., mice selected, batches of chemicals used, device calibration, or any number of other influences. None of these competing explanations for an observed difference can be excluded from the given data alone, but good experimental design allows us to render them (almost) arbitrarily implausible.

A second aspect for our analysis is the inherent uncertainty in our calculated difference: if we repeat the experiment, the observed difference will change each time, and this will be more pronounced for a smaller number of mice, among others. If we do not use a sufficient number of mice in our experiment, the uncertainty associated with the observed difference might be too large, such that random fluctuations become a plausible explanation for the observed difference. Systematic differences between the two kits, of practically relevant magnitude in either direction, might then be compatible with the data, and we can draw no reliable conclusions from our experiment.

In each case, the statistical analysis—no matter how clever—was doomed before the experiment was even started, while simple ideas from statistical design of experiments would have provided correct and robust results with interpretable conclusions.

1.3 The Language of Experimental Design

By an experiment we understand an investigation where the researcher has full control over selecting and altering the experimental conditions of interest, and we only consider investigations of this type. The selected experimental conditions are called treatments . An experiment is comparative if the responses to several treatments are to be compared or contrasted. The experimental units are the smallest subdivision of the experimental material to which a treatment can be assigned. All experimental units given the same treatment constitute a treatment group . Especially in biology, we often compare treatments to a control group to which some standard experimental conditions are applied; a typical example is using a placebo for the control group, and different drugs for the other treatment groups.

The values observed are called responses and are measured on the response units ; these are often identical to the experimental units but need not be. Multiple experimental units are sometimes combined into groupings or blocks , such as mice grouped by litter, or samples grouped by batches of chemicals used for their preparation. More generally, we call any grouping of the experimental material (even with group size one) a unit .

In our example, we selected the mice, used a single sample per mouse, deliberately chose the two specific vendors, and had full control over which kit to assign to which mouse. In other words, the two kits are the treatments and the mice are the experimental units. We took the measured enzyme level of a single sample from a mouse as our response, and samples are therefore the response units. The resulting experiment is comparative, because we contrast the enzyme levels between the two treatment groups.

Figure 1.1: Three designs to determine the difference between two preparation kits A and B based on four mice. A: One sample per mouse. Comparison between averages of samples with same kit. B: Two samples per mouse treated with the same kit. Comparison between averages of mice with same kit requires averaging responses for each mouse first. C: Two samples per mouse each treated with different kit. Comparison between two samples of each mouse, with differences averaged.

In this example, we can coalesce experimental and response units, because we have a single response per mouse and cannot distinguish a sample from a mouse in the analysis, as illustrated in Figure 1.1 A for four mice. Responses from mice with the same kit are averaged, and the kit difference is the difference between these two averages.

By contrast, if we take two samples per mouse and use the same kit for both samples, then the mice are still the experimental units, but each mouse now groups the two response units associated with it. Now, responses from the same mouse are first averaged, and these averages are used to calculate the difference between kits; even though eight measurements are available, this difference is still based on only four mice (Figure 1.1 B).

If we take two samples per mouse, but apply each kit to one of the two samples, then the samples are both the experimental and response units, while the mice are blocks that group the samples. Now, we calculate the difference between kits for each mouse, and then average these differences (Figure 1.1 C).

If we only use one kit and determine the average enzyme level, then this investigation is still an experiment, but is not comparative.

To summarize, the design of an experiment determines the logical structure of the experiment ; it consists of (i) a set of treatments (the two kits); (ii) a specification of the experimental units (animals, cell lines, samples) (the mice in Figure 1.1 A,B and the samples in Figure 1.1 C); (iii) a procedure for assigning treatments to units; and (iv) a specification of the response units and the quantity to be measured as a response (the samples and associated enzyme levels).

1.4 Experiment Validity

Before we embark on the more technical aspects of experimental design, we discuss three components for evaluating an experiment’s validity: construct validity , internal validity , and external validity . These criteria are well-established in areas such as educational and psychological research, and have more recently been discussed for animal research ( Würbel 2017 ) where experiments are increasingly scrutinized for their scientific rationale and their design and intended analyses.

1.4.1 Construct Validity

Construct validity concerns the choice of the experimental system for answering our research question. Is the system even capable of providing a relevant answer to the question?

Studying the mechanisms of a particular disease, for example, might require careful choice of an appropriate animal model that shows a disease phenotype and is accessible to experimental interventions. If the animal model is a proxy for drug development for humans, biological mechanisms must be sufficiently similar between animal and human physiologies.

Another important aspect of the construct is the quantity that we intend to measure (the measurand ), and its relation to the quantity or property we are interested in. For example, we might measure the concentration of the same chemical compound once in a blood sample and once in a highly purified sample, and these constitute two different measurands, whose values might not be comparable. Often, the quantity of interest (e.g., liver function) is not directly measurable (or even quantifiable) and we measure a biomarker instead. For example, pre-clinical and clinical investigations may use concentrations of proteins or counts of specific cell types from blood samples, such as the CD4+ cell count used as a biomarker for immune system function.

1.4.2 Internal Validity

The internal validity of an experiment concerns the soundness of the scientific rationale, statistical properties such as precision of estimates, and the measures taken against risk of bias. It refers to the validity of claims within the context of the experiment. Statistical design of experiments plays a prominent role in ensuring internal validity, and we briefly discuss the main ideas before providing the technical details and an application to our example in the subsequent sections.

Scientific Rationale and Research Question

The scientific rationale of a study is (usually) not immediately a statistical question. Translating a scientific question into a quantitative comparison amenable to statistical analysis is no small task and often requires careful consideration. It is a substantial, if non-statistical, benefit of using experimental design that we are forced to formulate a precise-enough research question and decide on the main analyses required for answering it before we conduct the experiment. For example, the question: is there a difference between placebo and drug? is insufficiently precise for planning a statistical analysis and determine an adequate experimental design. What exactly is the drug treatment? What should the drug’s concentration be and how is it administered? How do we make sure that the placebo group is comparable to the drug group in all other aspects? What do we measure and what do we mean by “difference?” A shift in average response, a fold-change, change in response before and after treatment?

The scientific rationale also enters the choice of a potential control group to which we compare responses. The quote

The deep, fundamental question in statistical analysis is ‘Compared to what?’ ( Tufte 1997 )

highlights the importance of this choice.

There are almost never enough resources to answer all relevant scientific questions. We therefore define a few questions of highest interest, and the main purpose of the experiment is answering these questions in the primary analysis . This intended analysis drives the experimental design to ensure relevant estimates can be calculated and have sufficient precision, and tests are adequately powered. This does not preclude us from conducting additional secondary analyses and exploratory analyses , but we are not willing to enlarge the experiment to ensure that strong conclusions can also be drawn from these analyses.

Risk of Bias

Experimental bias is a systematic difference in response between experimental units in addition to the difference caused by the treatments. The experimental units in the different groups are then not equal in all aspects other than the treatment applied to them. We saw several examples in Section 1.2 .

Minimizing the risk of bias is crucial for internal validity and we look at some common measures to eliminate or reduce different types of bias in Section 1.5 .

Precision and Effect Size

Another aspect of internal validity is the precision of estimates and the expected effect sizes. Is the experimental setup, in principle, able to detect a difference of relevant magnitude? Experimental design offers several methods for answering this question based on the expected heterogeneity of samples, the measurement error, and other sources of variation: power analysis is a technique for determining the number of samples required to reliably detect a relevant effect size and provide estimates of sufficient precision. More samples yield more precision and more power, but we have to be careful that replication is done at the right level: simply measuring a biological sample multiple times as in Figure 1.1 B yields more measured values, but is pseudo-replication for analyses. Replication should also ensure that the statistical uncertainties of estimates can be gauged from the data of the experiment itself, without additional untestable assumptions. Finally, the technique of blocking , shown in Figure 1.1 C, can remove a substantial proportion of the variation and thereby increase power and precision if we find a way to apply it.

1.4.3 External Validity

The external validity of an experiment concerns its replicability and the generalizability of inferences. An experiment is replicable if its results can be confirmed by an independent new experiment, preferably by a different lab and researcher. Experimental conditions in the replicate experiment usually differ from the original experiment, which provides evidence that the observed effects are robust to such changes. A much weaker condition on an experiment is reproducibility , the property that an independent researcher draws equivalent conclusions based on the data from this particular experiment, using the same analysis techniques. Reproducibility requires publishing the raw data, details on the experimental protocol, and a description of the statistical analyses, preferably with accompanying source code. Many scientific journals subscribe to reporting guidelines to ensure reproducibility and these are also helpful for planning an experiment.

A main threat to replicability and generalizability are too tightly controlled experimental conditions, when inferences only hold for a specific lab under the very specific conditions of the original experiment. Introducing systematic heterogeneity and using multi-center studies effectively broadens the experimental conditions and therefore the inferences for which internal validity is available.

For systematic heterogeneity , experimental conditions are systematically altered in addition to the treatments, and treatment differences estimated for each condition. For example, we might split the experimental material into several batches and use a different day of analysis, sample preparation, batch of buffer, measurement device, and lab technician for each batch. A more general inference is then possible if effect size, effect direction, and precision are comparable between the batches, indicating that the treatment differences are stable over the different conditions.

In multi-center experiments , the same experiment is conducted in several different labs and the results compared and merged. Multi-center approaches are very common in clinical trials and often necessary to reach the required number of patient enrollments.

Generalizability of randomized controlled trials in medicine and animal studies can suffer from overly restrictive eligibility criteria. In clinical trials, patients are often included or excluded based on co-medications and co-morbidities, and the resulting sample of eligible patients might no longer be representative of the patient population. For example, Travers et al. ( 2007 ) used the eligibility criteria of 17 random controlled trials of asthma treatments and found that out of 749 patients, only a median of 6% (45 patients) would be eligible for an asthma-related randomized controlled trial. This puts a question mark on the relevance of the trials’ findings for asthma patients in general.

1.5 Reducing the Risk of Bias

1.5.1 randomization of treatment allocation.

If systematic differences other than the treatment exist between our treatment groups, then the effect of the treatment is confounded with these other differences and our estimates of treatment effects might be biased.

We remove such unwanted systematic differences from our treatment comparisons by randomizing the allocation of treatments to experimental units. In a completely randomized design , each experimental unit has the same chance of being subjected to any of the treatments, and any differences between the experimental units other than the treatments are distributed over the treatment groups. Importantly, randomization is the only method that also protects our experiment against unknown sources of bias: we do not need to know all or even any of the potential differences and yet their impact is eliminated from the treatment comparisons by random treatment allocation.

Randomization has two effects: (i) differences unrelated to treatment become part of the ‘statistical noise’ rendering the treatment groups more similar; and (ii) the systematic differences are thereby eliminated as sources of bias from the treatment comparison.

Randomization transforms systematic variation into random variation.

In our example, a proper randomization would select 10 out of our 20 mice fully at random, such that the probability of any one mouse being picked is 1/20. These ten mice are then assigned to kit A, and the remaining mice to kit B. This allocation is entirely independent of the treatments and of any properties of the mice.

To ensure random treatment allocation, some kind of random process needs to be employed. This can be as simple as shuffling a pack of 10 red and 10 black cards or using a software-based random number generator. Randomization is slightly more difficult if the number of experimental units is not known at the start of the experiment, such as when patients are recruited for an ongoing clinical trial (sometimes called rolling recruitment ), and we want to have reasonable balance between the treatment groups at each stage of the trial.

Seemingly random assignments “by hand” are usually no less complicated than fully random assignments, but are always inferior. If surprising results ensue from the experiment, such assignments are subject to unanswerable criticism and suspicion of unwanted bias. Even worse are systematic allocations; they can only remove bias from known causes, and immediately raise red flags under the slightest scrutiny.

The Problem of Undesired Assignments

Even with a fully random treatment allocation procedure, we might end up with an undesirable allocation. For our example, the treatment group of kit A might—just by chance—contain mice that are all bigger or more active than those in the other treatment group. Statistical orthodoxy recommends using the design nevertheless, because only full randomization guarantees valid estimates of residual variance and unbiased estimates of effects. This argument, however, concerns the long-run properties of the procedure and seems of little help in this specific situation. Why should we care if the randomization yields correct estimates under replication of the experiment, if the particular experiment is jeopardized?

Another solution is to create a list of all possible allocations that we would accept and randomly choose one of these allocations for our experiment. The analysis should then reflect this restriction in the possible randomizations, which often renders this approach difficult to implement.

The most pragmatic method is to reject highly undesirable designs and compute a new randomization ( Cox 1958 ) . Undesirable allocations are unlikely to arise for large sample sizes, and we might accept a small bias in estimation for small sample sizes, when uncertainty in the estimated treatment effect is already high. In this approach, whenever we reject a particular outcome, we must also be willing to reject the outcome if we permute the treatment level labels. If we reject eight big and two small mice for kit A, then we must also reject two big and eight small mice. We must also be transparent and report a rejected allocation, so that critics may come to their own conclusions about potential biases and their remedies.

1.5.2 Blinding

Bias in treatment comparisons is also introduced if treatment allocation is random, but responses cannot be measured entirely objectively, or if knowledge of the assigned treatment affects the response. In clinical trials, for example, patients might react differently when they know to be on a placebo treatment, an effect known as cognitive bias . In animal experiments, caretakers might report more abnormal behavior for animals on a more severe treatment. Cognitive bias can be eliminated by concealing the treatment allocation from technicians or participants of a clinical trial, a technique called single-blinding .

If response measures are partially based on professional judgement (such as a clinical scale), patient or physician might unconsciously report lower scores for a placebo treatment, a phenomenon known as observer bias . Its removal requires double blinding , where treatment allocations are additionally concealed from the experimentalist.

Blinding requires randomized treatment allocation to begin with and substantial effort might be needed to implement it. Drug companies, for example, have to go to great lengths to ensure that a placebo looks, tastes, and feels similar enough to the actual drug. Additionally, blinding is often done by coding the treatment conditions and samples, and effect sizes and statistical significance are calculated before the code is revealed.

In clinical trials, double-blinding creates a conflict of interest. The attending physicians do not know which patient received which treatment, and thus accumulation of side-effects cannot be linked to any treatment. For this reason, clinical trials have a data monitoring committee not involved in the final analysis, that performs intermediate analyses of efficacy and safety at predefined intervals. If severe problems are detected, the committee might recommend altering or aborting the trial. The same might happen if one treatment already shows overwhelming evidence of superiority, such that it becomes unethical to withhold this treatment from the other patients.

1.5.3 Analysis Plan and Registration

An often overlooked source of bias has been termed the researcher degrees of freedom or garden of forking paths in the data analysis. For any set of data, there are many different options for its analysis: some results might be considered outliers and discarded, assumptions are made on error distributions and appropriate test statistics, different covariates might be included into a regression model. Often, multiple hypotheses are investigated and tested, and analyses are done separately on various (overlapping) subgroups. Hypotheses formed after looking at the data require additional care in their interpretation; almost never will \(p\) -values for these ad hoc or post hoc hypotheses be statistically justifiable. Many different measured response variables invite fishing expeditions , where patterns in the data are sought without an underlying hypothesis. Only reporting those sub-analyses that gave ‘interesting’ findings invariably leads to biased conclusions and is called cherry-picking or \(p\) -hacking (or much less flattering names).

The statistical analysis is always part of a larger scientific argument and we should consider the necessary computations in relation to building our scientific argument about the interpretation of the data. In addition to the statistical calculations, this interpretation requires substantial subject-matter knowledge and includes (many) non-statistical arguments. Two quotes highlight that experiment and analysis are a means to an end and not the end in itself.

There is a boundary in data interpretation beyond which formulas and quantitative decision procedures do not go, where judgment and style enter. ( Abelson 1995 )

Often, perfectly reasonable people come to perfectly reasonable decisions or conclusions based on nonstatistical evidence. Statistical analysis is a tool with which we support reasoning. It is not a goal in itself. ( Bailar III 1981 )

There is often a grey area between exploiting researcher degrees of freedom to arrive at a desired conclusion, and creative yet informed analyses of data. One way to navigate this area is to distinguish between exploratory studies and confirmatory studies . The former have no clearly stated scientific question, but are used to generate interesting hypotheses by identifying potential associations or effects that are then further investigated. Conclusions from these studies are very tentative and must be reported honestly as such. In contrast, standards are much higher for confirmatory studies, which investigate a specific predefined scientific question. Analysis plans and pre-registration of an experiment are accepted means for demonstrating lack of bias due to researcher degrees of freedom, and separating primary from secondary analyses allows emphasizing the main goals of the study.

Analysis Plan

The analysis plan is written before conducting the experiment and details the measurands and estimands, the hypotheses to be tested together with a power and sample size calculation, a discussion of relevant effect sizes, detection and handling of outliers and missing data, as well as steps for data normalization such as transformations and baseline corrections. If a regression model is required, its factors and covariates are outlined. Particularly in biology, handling measurements below the limit of quantification and saturation effects require careful consideration.

In the context of clinical trials, the problem of estimands has become a recent focus of attention. An estimand is the target of a statistical estimation procedure, for example the true average difference in enzyme levels between the two preparation kits. A main problem in many studies are post-randomization events that can change the estimand, even if the estimation procedure remains the same. For example, if kit B fails to produce usable samples for measurement in five out of ten cases because the enzyme level was too low, while kit A could handle these enzyme levels perfectly fine, then this might severely exaggerate the observed difference between the two kits. Similar problems arise in drug trials, when some patients stop taking one of the drugs due to side-effects or other complications.

Registration

Registration of experiments is an even more severe measure used in conjunction with an analysis plan and is becoming standard in clinical trials. Here, information about the trial, including the analysis plan, procedure to recruit patients, and stopping criteria, are registered in a public database. Publications based on the trial then refer to this registration, such that reviewers and readers can compare what the researchers intended to do and what they actually did. Similar portals for pre-clinical and translational research are also available.

1.6 Notes and Summary

The problem of measurements and measurands is further discussed for statistics in Hand ( 1996 ) and specifically for biological experiments in Coxon, Longstaff, and Burns ( 2019 ) . A general review of methods for handling missing data is Dong and Peng ( 2013 ) . The different roles of randomization are emphasized in Cox ( 2009 ) .

Two well-known reporting guidelines are the ARRIVE guidelines for animal research ( Kilkenny et al. 2010 ) and the CONSORT guidelines for clinical trials ( Moher et al. 2010 ) . Guidelines describing the minimal information required for reproducing experimental results have been developed for many types of experimental techniques, including microarrays (MIAME), RNA sequencing (MINSEQE), metabolomics (MSI) and proteomics (MIAPE) experiments; the FAIRSHARE initiative provides a more comprehensive collection ( Sansone et al. 2019 ) .

The problems of experimental design in animal experiments and particularly translation research are discussed in Couzin-Frankel ( 2013 ) . Multi-center studies are now considered for these investigations, and using a second laboratory already increases reproducibility substantially ( Richter et al. 2010 ; Richter 2017 ; Voelkl et al. 2018 ; Karp 2018 ) and allows standardizing the treatment effects ( Kafkafi et al. 2017 ) . First attempts are reported of using designs similar to clinical trials ( Llovera and Liesz 2016 ) . Exploratory-confirmatory research and external validity for animal studies is discussed in Kimmelman, Mogil, and Dirnagl ( 2014 ) and Pound and Ritskes-Hoitinga ( 2018 ) . Further information on pilot studies is found in Moore et al. ( 2011 ) , Sim ( 2019 ) , and Thabane et al. ( 2010 ) .

The deliberate use of statistical analyses and their interpretation for supporting a larger argument was called statistics as principled argument ( Abelson 1995 ) . Employing useless statistical analysis without reference to the actual scientific question is surrogate science ( Gigerenzer and Marewski 2014 ) and adaptive thinking is integral to meaningful statistical analysis ( Gigerenzer 2002 ) .

In an experiment, the investigator has full control over the experimental conditions applied to the experiment material. The experimental design gives the logical structure of an experiment: the units describing the organization of the experimental material, the treatments and their allocation to units, and the response. Statistical design of experiments includes techniques to ensure internal validity of an experiment, and methods to make inference from experimental data efficient.

13. Study design and choosing a statistical test

Sample size.

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

13 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

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Experimental design

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Data for statistical studies are obtained by conducting either experiments or surveys. Experimental design is the branch of statistics that deals with the design and analysis of experiments. The methods of experimental design are widely used in the fields of agriculture, medicine , biology , marketing research, and industrial production.

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In an experimental study, variables of interest are identified. One or more of these variables, referred to as the factors of the study , are controlled so that data may be obtained about how the factors influence another variable referred to as the response variable , or simply the response. As a case in point, consider an experiment designed to determine the effect of three different exercise programs on the cholesterol level of patients with elevated cholesterol. Each patient is referred to as an experimental unit , the response variable is the cholesterol level of the patient at the completion of the program, and the exercise program is the factor whose effect on cholesterol level is being investigated. Each of the three exercise programs is referred to as a treatment .

Three of the more widely used experimental designs are the completely randomized design, the randomized block design, and the factorial design. In a completely randomized experimental design, the treatments are randomly assigned to the experimental units. For instance, applying this design method to the cholesterol-level study, the three types of exercise program (treatment) would be randomly assigned to the experimental units (patients).

The use of a completely randomized design will yield less precise results when factors not accounted for by the experimenter affect the response variable. Consider, for example, an experiment designed to study the effect of two different gasoline additives on the fuel efficiency , measured in miles per gallon (mpg), of full-size automobiles produced by three manufacturers. Suppose that 30 automobiles, 10 from each manufacturer, were available for the experiment. In a completely randomized design the two gasoline additives (treatments) would be randomly assigned to the 30 automobiles, with each additive being assigned to 15 different cars. Suppose that manufacturer 1 has developed an engine that gives its full-size cars a higher fuel efficiency than those produced by manufacturers 2 and 3. A completely randomized design could, by chance , assign gasoline additive 1 to a larger proportion of cars from manufacturer 1. In such a case, gasoline additive 1 might be judged to be more fuel efficient when in fact the difference observed is actually due to the better engine design of automobiles produced by manufacturer 1. To prevent this from occurring, a statistician could design an experiment in which both gasoline additives are tested using five cars produced by each manufacturer; in this way, any effects due to the manufacturer would not affect the test for significant differences due to gasoline additive. In this revised experiment, each of the manufacturers is referred to as a block, and the experiment is called a randomized block design. In general, blocking is used in order to enable comparisons among the treatments to be made within blocks of homogeneous experimental units.

Factorial experiments are designed to draw conclusions about more than one factor, or variable. The term factorial is used to indicate that all possible combinations of the factors are considered. For instance, if there are two factors with a levels for factor 1 and b levels for factor 2, the experiment will involve collecting data on a b treatment combinations. The factorial design can be extended to experiments involving more than two factors and experiments involving partial factorial designs.

A computational procedure frequently used to analyze the data from an experimental study employs a statistical procedure known as the analysis of variance. For a single-factor experiment, this procedure uses a hypothesis test concerning equality of treatment means to determine if the factor has a statistically significant effect on the response variable. For experimental designs involving multiple factors, a test for the significance of each individual factor as well as interaction effects caused by one or more factors acting jointly can be made. Further discussion of the analysis of variance procedure is contained in the subsequent section.

Regression and correlation analysis

Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables . A model of the relationship is hypothesized, and estimates of the parameter values are used to develop an estimated regression equation . Various tests are then employed to determine if the model is satisfactory. If the model is deemed satisfactory, the estimated regression equation can be used to predict the value of the dependent variable given values for the independent variables.

In simple linear regression , the model used to describe the relationship between a single dependent variable y and a single independent variable x is y = β 0 + β 1 x + ε. β 0 and β 1 are referred to as the model parameters, and ε is a probabilistic error term that accounts for the variability in y that cannot be explained by the linear relationship with x . If the error term were not present, the model would be deterministic; in that case, knowledge of the value of x would be sufficient to determine the value of y .

In multiple regression analysis , the model for simple linear regression is extended to account for the relationship between the dependent variable y and p independent variables x 1 , x 2 , . . ., x p . The general form of the multiple regression model is y = β 0 + β 1 x 1 + β 2 x 2 + . . . + β p x p + ε. The parameters of the model are the β 0 , β 1 , . . ., β p , and ε is the error term.

Either a simple or multiple regression model is initially posed as a hypothesis concerning the relationship among the dependent and independent variables. The least squares method is the most widely used procedure for developing estimates of the model parameters. For simple linear regression, the least squares estimates of the model parameters β 0 and β 1 are denoted b 0 and b 1 . Using these estimates, an estimated regression equation is constructed: ŷ = b 0 + b 1 x . The graph of the estimated regression equation for simple linear regression is a straight line approximation to the relationship between y and x .

As an illustration of regression analysis and the least squares method, suppose a university medical centre is investigating the relationship between stress and blood pressure . Assume that both a stress test score and a blood pressure reading have been recorded for a sample of 20 patients. The data are shown graphically in Figure 4 , called a scatter diagram . Values of the independent variable, stress test score, are given on the horizontal axis, and values of the dependent variable, blood pressure, are shown on the vertical axis. The line passing through the data points is the graph of the estimated regression equation: ŷ = 42.3 + 0.49 x . The parameter estimates, b 0 = 42.3 and b 1 = 0.49, were obtained using the least squares method.

A primary use of the estimated regression equation is to predict the value of the dependent variable when values for the independent variables are given. For instance, given a patient with a stress test score of 60, the predicted blood pressure is 42.3 + 0.49(60) = 71.7. The values predicted by the estimated regression equation are the points on the line in Figure 4 , and the actual blood pressure readings are represented by the points scattered about the line. The difference between the observed value of y and the value of y predicted by the estimated regression equation is called a residual . The least squares method chooses the parameter estimates such that the sum of the squared residuals is minimized.

A commonly used measure of the goodness of fit provided by the estimated regression equation is the coefficient of determination . Computation of this coefficient is based on the analysis of variance procedure that partitions the total variation in the dependent variable, denoted SST, into two parts: the part explained by the estimated regression equation, denoted SSR, and the part that remains unexplained, denoted SSE.

The measure of total variation, SST, is the sum of the squared deviations of the dependent variable about its mean: Σ( y − ȳ ) 2 . This quantity is known as the total sum of squares. The measure of unexplained variation, SSE, is referred to as the residual sum of squares. For the data in Figure 4 , SSE is the sum of the squared distances from each point in the scatter diagram (see Figure 4 ) to the estimated regression line: Σ( y − ŷ ) 2 . SSE is also commonly referred to as the error sum of squares. A key result in the analysis of variance is that SSR + SSE = SST.

The ratio r 2 = SSR/SST is called the coefficient of determination. If the data points are clustered closely about the estimated regression line, the value of SSE will be small and SSR/SST will be close to 1. Using r 2 , whose values lie between 0 and 1, provides a measure of goodness of fit; values closer to 1 imply a better fit. A value of r 2 = 0 implies that there is no linear relationship between the dependent and independent variables.

When expressed as a percentage , the coefficient of determination can be interpreted as the percentage of the total sum of squares that can be explained using the estimated regression equation. For the stress-level research study, the value of r 2 is 0.583; thus, 58.3% of the total sum of squares can be explained by the estimated regression equation ŷ = 42.3 + 0.49 x . For typical data found in the social sciences, values of r 2 as low as 0.25 are often considered useful. For data in the physical sciences, r 2 values of 0.60 or greater are frequently found.

In a regression study, hypothesis tests are usually conducted to assess the statistical significance of the overall relationship represented by the regression model and to test for the statistical significance of the individual parameters. The statistical tests used are based on the following assumptions concerning the error term: (1) ε is a random variable with an expected value of 0, (2) the variance of ε is the same for all values of x , (3) the values of ε are independent, and (4) ε is a normally distributed random variable.

The mean square due to regression, denoted MSR, is computed by dividing SSR by a number referred to as its degrees of freedom ; in a similar manner, the mean square due to error, MSE , is computed by dividing SSE by its degrees of freedom. An F-test based on the ratio MSR/MSE can be used to test the statistical significance of the overall relationship between the dependent variable and the set of independent variables. In general, large values of F = MSR/MSE support the conclusion that the overall relationship is statistically significant. If the overall model is deemed statistically significant, statisticians will usually conduct hypothesis tests on the individual parameters to determine if each independent variable makes a significant contribution to the model.

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• v.34(22); 2015 Nov 12

Focus: Study Design & Statistical Analysis

Statistical relevance—relevant statistics, part i, bernd klaus.

1 European Molecular Biology Laboratory, Heidelberg, Germany

As part of a new EMBO Journal statistics series, this commentary introduces key concepts in statistical analysis and discusses best practices in study design.

Statistical analysis is an important tool in experimental research and is essential for the reliable interpretation of experimental results. It is essential that statistical design should be considered at the very beginning of a research project, not merely as an afterthought. For example if the sample size for an experiment only allows for an underpowered statistical analysis, then the interpretation of the experiment will have to be limited. An experiment cannot be reverse engineered to become more statically significant, although experiments can of course be repeated independently to account for biological variation (see section on technical versus biological replicates below). Statistical methods are tools applied to situations in which we encounter variability, noise and uncertainty. They help make more definitive scientific conclusions, and to make better use of available resources.

In this new EMBO Journal statistics series, I will introduce key concepts and best practices. The text will be short and conceptual in style, while the supplement will provide examples demonstrating the introduced concepts. I will use the free statistic software R (R Core Team, 2015 ) to illustrate examples, and readers can try the code on their own data.

In this first part, I will give some guidelines for initial study design and analysis of experiments. Subsequent columns will discuss specific statistical topics in more detail. Most of the issues touched upon in this first column are further discussed in the book of Ruxton and Colegrave (Ruxton & Colegrave, 2010 ), which includes many examples relevant to the analysis of experiments for biological researchers.

Guidelines and terms for the design and analysis of experiments

Experiment versus study.

The terms experiment and study are sometimes used interchangeably, but they represent different concepts. In an experiment , one uses highly controlled conditions to look at a (model) system, performs specific well‐designed interventions at controlled times and intensities, and has an efficient assay to measure the effect of interest. You control the “experimental units” (such as cells, mice, and genotypes) and plan which experiments to perform and when. This allows for a stringent control over experimental variables and to draw very specific conclusions. However, this comes with the inherent risk of exerting too tight control—for example, the model system may not be relevant and therefore not support the hypothesis you are testing, or the controlled conditions might not be exactly the right ones.

On the other hand, the observations in a study are made “in the wild”—for example, on human subjects recruited to a study according to certain inclusion and exclusion criteria, but still taking into account their individual history, genetic makeup, and lifestyle. Likewise, an ecologist studying animals or plants encountered in the field does not have full control over their environment or other potentially important variables. Generally, a study requires much bigger sample size than an experiment and is more complicated to analyze, usually requiring involvement of a specially trained expert at some point. In this series, I will mainly focus on the analysis of experiments .

Hypothesis‐driven research

Although there are various “hypothesis‐free” exploratory experiments, such as the sequencing of a genome or the genome‐wide binding site mapping of a transcription factor, it is important to remember that most biological experiments are hypothesis‐driven. This means that an experiment should be based on a scientific question or hypothesis—although this may sound obvious, it is a point that is sometimes neglected.

As a general rule, do not plan your experiments as an accumulation of conditions (e.g. “Do cells treated with drug A for 20 or 40 min express protein X but not Y?”)—instead start with clear, single research questions, one at a time like:

• Is drug A better than drug B in inducing a given effect?
• Is there a genetic interaction between gene X and gene Y?
• Are transfected cells behaving differently than control cells?

Only then should one consider important choices such as which model, which conditions, which intervention, or which readout to use?

Controls & replicates

Imagine you want to use proteomics to study the effect of different doses of a cytokine on the phosphorylation of cellular downstream targets over time. Further assume that the cells used are inexpensive and easy to culture, but the proteomic analysis is expensive and time‐consuming. In this scenario, there is a tradeoff between the number of conditions and the temporal resolution you can achieve. Importantly, the expected effect size should guide the design of the experiment: The higher the expected effect, the lower the number of biological replicates that are needed—in this example, to reliably detect protein phosphorylation. If the cytokine is known to affect its target proteins fairly quickly, then only a few time points and few replicates per time point are needed. If, on the other hand, the expected differences between the conditions are more subtle, then more replicates per condition might be required. In cases where a high temporal resolution is achievable, this can serve as a legitimate internal control: A higher number of time points can make up for fewer replicates, since the measurements are related due to their temporal proximity.

Experimental units and control categories

The choice of experimental units is a subtle point. Very often, experimental units will simply be the biological units used, such as mice, yeast strains or cultured cells. However, experimental units can also be time periods, for example, if animals receive a specific treatment for defined periods of time—not the animal but rather the treatment time would be the experimental unit here.

Another important aspect when deciding on experimental units is the choice of appropriate controls . The two major categories are positive and negative controls: Positive controls show that an experimental system works in principle, while negative controls represent a baseline (e.g. wild type) condition.

For an example, let us assume we want to knock down, using short‐interfering (si) RNAs, the expression of certain genes to study their influence on intracellular protein transport. Here, a negative control could be sequence‐scrambled siRNA applied to the cells, while a positive control for the working of the assay system could be a siRNA against a gene with an already known role in intracellular transport. It is furthermore advisable to establish an “experimentalist control” by “blinding” the experimenter to ensure that s/he does not know which condition the readout belongs to.

For a thorough discussion of various different types of controls, see Glass ( 2014 ) (Section 3 therein).

Blocks/batches

We aim to perform experiments within a homogeneous group of experimental units. These homogeneous groups, referred to as blocks , help to reduce the variability between the units and increase the meaning of differences between conditions (as well as the power of statistics to detect them).

For example, it is beneficial to take measurements for many (ideally all) experimental conditions at the same time. If the measurements are done over a more extended period of time, then day‐to‐day variability between the measurements needs to be estimated and eliminated. If all control conditions are measured on one day and all treatment conditions on another day, then it is not possible to disentangle the day effect from the treatment effect and, in the worst case, the data become inconclusive. As a general rule, at least some “common conditions” are essential to assess potential block effects.

As an example, assume there are six treatment conditions you want to apply to mice (the experimental units ), but you can fit only five mice per cage (i.e. block ). In this case, not all treatments can be applied simultaneously in each cage/block. You can, however, apply four identical treatments to each of the cages and only alternate the fifth condition each time (see Fig  1 ). Now, the “cage effect” can be estimated by computing the mean of the differences between the four treatments that are identical, as given by the formula in Fig  1 . A priori the conditions E and F are not directly comparable since they were measured on mice from two different cages. However, the replicated treatments allow a computation of a “cage effect” that corresponds to the average difference between the identical conditions measured in the two cages. Then, the difference between E and F can be computed as E − F − “cage effect”.

Illustration of batches and how to correct for them. All but two treatments have been applied to mice in two different cages (= batches). The batch/cage effect can now be computed based on the treatments that are shared between the cages.

Undiscovered block effects that strongly influence the result of the experiments are commonly called batch effects and they may cloud scientific conclusions. The severe influence of batch effects has been revealed in high‐throughput experiments (Leek et al , 2010 ); Importantly, batch effects also exist in small‐scale experiments, but are harder to detect, and while they may affect the scientific conclusions, they often remain hidden.

In practice, drafting a plan detailing which measurements to perform is very helpful in order to maximize the number of measurements within one batch, or to try to balance the conditions of interest within the batch. For data tables, it is a good idea to add as much useful metadata (e.g. date, time, and experimenter) as possible. As an example, see Table  1 .

An example of a comprehensively annotated data table

Randomization

Even after careful identification of blocks, other factors may still influence experimental outcome, such as mouse age and sex differences, and different genetic backgrounds. In order to balance out these factors, randomization techniques are used. Randomization reduces confounding effects by equalizing variables that influence experimental units and that have not been accounted for in the experimental design. This requires randomly allocating the experimental units to the experimental conditions. Thus, ideally, the allocation of units to conditions should not be predictable.

For example, if an experiment compares the effect of a genetic modification on tomato growth, many potentially complex factors apart from the genetic modification itself could influence growth: For example, the growth chamber could be slightly warmer on one end than the other, the quality of the compost variable, or different irrigation techniques used. In this case, it will be necessary to randomize the positioning of the plants.

Replication

Replication of measurements is very important. Without replication, it is impossible to judge whether there is an actual difference between conditions, or whether an observed difference is merely due to chance.

For example, if you would like to compare the height of two plant varieties by only taking one plant height measurement and observing a difference of 10 cm, it is impossible to say whether this difference is meaningful or due to natural variation. On the other hand, if multiple plants of each variety are measured, and the height differences always turn out somewhere around 10 cm, the observed difference is less likely due to chance, as illustrated in Fig  2 . The difference is strong relative to the variability between the measurements.

Comparison of two groups. The difference is strong relative to the variability between the measurements within each group.

Technical versus biological replicates

When referring to replicates, it is important to distinguish between biological and technical replicates (see Fig  3 ). Technical replicates refer to experimental samples isolated from one biological sample, for example preparing three sequencing libraries from RNA extracted from the cells of a single mouse; in contrast, biological replication would mean extracting RNA from three different mice for the comparisons of interest. In other words, it is not sufficient to merely “re‐pipet” an experiment from the same sample, as this does not constitute biological, but merely technical replication. In general, technical replicates tend to show less variability than biological replicates, thus potentially leading to false‐positive results. Technical replicates can be useful when a new technique is reported, but, in general, biological replicates should be reported. Either way, this has to be clearly labeled in a paper and technical and biological replicates should not be integrated into a single statistic.

Illustration of the difference between technical and biological replicates.

After the experimental data have been obtained, a next step is to look at the data via exploratory graphics. Appropriate graphics are also very important for the final presentation of the work. In the next column, best practices for the display of both numerical and categorical data will be introduced and suitable estimators for the mean and the variance of the data will be discussed.

Conflict of interest

The author declares that he has no conflict of interest.

• Glass DJ (2014) Experimental Design for Biologists , 2 nd edn Cold Spring Harbour, NY, USA: Cold Spring Harbor Laboratory Press; [ Google Scholar ]
• Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K , Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in high‐throughput data . Nat Rev Genet 11 : 733–739 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• R Core Team (2015) R: A Language and Environment for Statistical Computing . Vienna, Austria: R Foundation for Statistical Computing; [ Google Scholar ]
• Ruxton GD, Colegrave N (2010) Experimental Design for the Life Sciences , 3 rd edn Oxford: Oxford University Press; [ Google Scholar ]

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Research Design and Statistical Analysis

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Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.  The book’s goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations.  Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions.  Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations, emphasis is also placed on data exploration, effect size measures, confidence intervals, and using power analyses to determine sample size. "Real-world" data sets are used to illustrate data exploration, analysis, and interpretation. The book offers a rare blend of the underlying statistical assumptions, the consequences of their violations, and practical advice on dealing with them.

Changes in the New Edition:

• Each section of the book concludes with a chapter that provides an integrated example of how to apply the concepts and procedures covered in the chapters of the section. In addition, the advantages and disadvantages of alternative designs are discussed.
• A new chapter (1) reviews the major steps in planning and executing a study, and the implications of those decisions for subsequent analyses and interpretations.
• A new chapter (13) compares experimental designs to reinforce the connection between design and analysis and to help readers achieve the most efficient research study.
• A new chapter (27) on common errors in data analysis and interpretation.
• Increased emphasis on power analyses to determine sample size using the G*Power 3 program.
• Many new data sets and problems.
• More examples of the use of SPSS (PASW) Version 17, although the analyses exemplified are readily carried out by any of the major statistical software packages.
• A companion website with the data used in the text and the exercises in SPSS and Excel formats; SPSS syntax files for performing analyses; extra material on logistic and multiple regression; technical notes that develop some of the formulas; and a solutions manual and the text figures and tables for instructors only.

Part 1 reviews research planning, data exploration, and basic concepts in statistics including sampling, hypothesis testing, measures of effect size, estimators, and confidence intervals.  Part 2 presents between-subject designs. The statistical models underlying the analysis of variance for these designs are emphasized, along with the role of expected mean squares in estimating effects of variables, the interpretation of nteractions, and procedures for testing contrasts and controlling error rates. Part 3 focuses on repeated-measures designs and considers the advantages and disadvantages of different mixed designs. Part 4 presents detailed coverage of correlation and bivariate and multiple regression with emphasis on interpretation and common errors, and discusses the usefulness and limitations of these procedures as tools for prediction and for developing theory.

This is one of the few books with coverage sufficient for a 2-semester course sequence in experimental design and statistics as taught in psychology, education, and other behavioral, social, and health sciences.  Incorporating the analyses of both experimental and observational data provides continuity of concepts and notation. Prerequisites include courses on basic research methods and statistics. The book is also an excellent resource for practicing researchers.

TABLE OF CONTENTS

Part 1 | 165  pages, foundations of research design and data analysis, chapter chapter 1 | 16  pages, planning the research, chapter chapter 2 | 28  pages, exploring the data, chapter chapter 3 | 18  pages, basic concepts in probability, chapter chapter 4 | 26  pages, developing the fundamentals of hypothesis testing using the binomial distribution, chapter chapter 5 | 33  pages, further development of the foundations of statistical inference, chapter chapter 6 | 30  pages, the t distribution and its applications, chapter chapter 7 | 12  pages, integrated analysis i, part 2 | 140  pages, between-subjects designs, chapter chapter 8 | 31  pages, between-subjects designs: one factor, chapter chapter 9 | 38  pages, multi-factor between-subjects designs, chapter chapter 10 | 33  pages, contrasting means in between-subjects designs, chapter chapter 11 | 24  pages, trend analysis in between-subjects designs, chapter chapter 12 | 12  pages, integrated analysis ii, part 3 | 126  pages, repeated-measures designs, chapter chapter 13 | 23  pages, comparing experimental designs and analyses, chapter chapter 14 | 30  pages, one-factor repeated-measures designs, chapter chapter 15 | 35  pages, multi-factor repeated-measures and mixed designs, chapter chapter 16 | 22  pages, nested and counterbalanced variables in repeated-measures designs, chapter chapter 17 | 14  pages, integrated analysis iii, part 4 | 225  pages, correlation and regression, chapter chapter 18 | 32  pages, an introduction to correlation and regression, chapter chapter 19 | 26  pages, more about correlation, chapter chapter 20 | 35  pages, more about bivariate regression, chapter chapter 21 | 23  pages, introduction to multiple regression, chapter chapter 22 | 22  pages, inference, assumptions, and power in multiple regression, chapter chapter 23 | 29  pages, additional topics in multiple regression, chapter chapter 24 | 25  pages, regression with qualitative and quantitative variables, chapter chapter 25 | 20  pages, ancova as a special case of multiple regression, chapter chapter 26 | 11  pages, integrated analysis iv, part 5 | 10  pages, chapter chapter 27 | 8  pages, some final thoughts: twenty suggestions and cautions.

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organisations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organise and summarise the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalise your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarise your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, frequently asked questions about statistics.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalise your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalisable findings, you should use a probability sampling method. Random selection reduces sampling bias and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to be biased, they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalising your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalise your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialised, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalised in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardised indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarise them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organising data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualising the relationship between two variables using a scatter plot .

By visualising your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimise the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasises null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

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• Central Limit Theorem | Formula, Definition & Examples
• Central Tendency | Understanding the Mean, Median & Mode
• Correlation Coefficient | Types, Formulas & Examples
• Descriptive Statistics | Definitions, Types, Examples
• How to Calculate Standard Deviation (Guide) | Calculator & Examples
• How to Calculate Variance | Calculator, Analysis & Examples
• How to Find Degrees of Freedom | Definition & Formula
• How to Find Interquartile Range (IQR) | Calculator & Examples
• How to Find Outliers | Meaning, Formula & Examples
• How to Find the Geometric Mean | Calculator & Formula
• How to Find the Mean | Definition, Examples & Calculator
• How to Find the Median | Definition, Examples & Calculator
• How to Find the Range of a Data Set | Calculator & Formula
• Inferential Statistics | An Easy Introduction & Examples
• Levels of measurement: Nominal, ordinal, interval, ratio
• Missing Data | Types, Explanation, & Imputation
• Normal Distribution | Examples, Formulas, & Uses
• Null and Alternative Hypotheses | Definitions & Examples
• Poisson Distributions | Definition, Formula & Examples
• Skewness | Definition, Examples & Formula
• T-Distribution | What It Is and How To Use It (With Examples)
• The Standard Normal Distribution | Calculator, Examples & Uses
• Type I & Type II Errors | Differences, Examples, Visualizations
• Understanding Confidence Intervals | Easy Examples & Formulas
• Variability | Calculating Range, IQR, Variance, Standard Deviation
• What is Effect Size and Why Does It Matter? (Examples)
• What Is Interval Data? | Examples & Definition
• What Is Nominal Data? | Examples & Definition
• What Is Ordinal Data? | Examples & Definition
• What Is Ratio Data? | Examples & Definition
• What Is the Mode in Statistics? | Definition, Examples & Calculator

3rd Edition

Research Design and Statistical Analysis Third Edition

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Description

Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.  The book’s goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations.  Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions.  Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations, emphasis is also placed on data exploration, effect size measures, confidence intervals, and using power analyses to determine sample size. "Real-world" data sets are used to illustrate data exploration, analysis, and interpretation. The book offers a rare blend of the underlying statistical assumptions, the consequences of their violations, and practical advice on dealing with them. Changes in the New Edition: Each section of the book concludes with a chapter that provides an integrated example of how to apply the concepts and procedures covered in the chapters of the section. In addition, the advantages and disadvantages of alternative designs are discussed. A new chapter (1) reviews the major steps in planning and executing a study, and the implications of those decisions for subsequent analyses and interpretations. A new chapter (13) compares experimental designs to reinforce the connection between design and analysis and to help readers achieve the most efficient research study. A new chapter (27) on common errors in data analysis and interpretation. Increased emphasis on power analyses to determine sample size using the G*Power 3 program. Many new data sets and problems. More examples of the use of SPSS (PASW) Version 17, although the analyses exemplified are readily carried out by any of the major statistical software packages. A companion website with the data used in the text and the exercises in SPSS and Excel formats; SPSS syntax files for performing analyses; extra material on logistic and multiple regression; technical notes that develop some of the formulas; and a solutions manual and the text figures and tables for instructors only. Part 1 reviews research planning, data exploration, and basic concepts in statistics including sampling, hypothesis testing, measures of effect size, estimators, and confidence intervals.  Part 2 presents between-subject designs. The statistical models underlying the analysis of variance for these designs are emphasized, along with the role of expected mean squares in estimating effects of variables, the interpretation of nteractions, and procedures for testing contrasts and controlling error rates. Part 3 focuses on repeated-measures designs and considers the advantages and disadvantages of different mixed designs. Part 4 presents detailed coverage of correlation and bivariate and multiple regression with emphasis on interpretation and common errors, and discusses the usefulness and limitations of these procedures as tools for prediction and for developing theory. This is one of the few books with coverage sufficient for a 2-semester course sequence in experimental design and statistics as taught in psychology, education, and other behavioral, social, and health sciences.  Incorporating the analyses of both experimental and observational data provides continuity of concepts and notation. Prerequisites include courses on basic research methods and statistics. The book is also an excellent resource for practicing researchers.

Table of Contents

Jerome L Myers is Professor Emeritus at the University of Massachusetts at Amherst. He received his Ph.D. in Psychology from the University of Wisconsin. Arnold Well is a Professor Emeritus at the University of Massachusetts at Amherst. He received his Ph.D. in Experimental Psychology from the University of Oregon. Robert F. Lorch, Jr. is a Professor of Psychology at the University of Kentucky. He received his Ph.D. in Psychology from the University of Massachusetts at Amherst.

Critics' Reviews

"This book is written in a clear and comprehensible way. Chapter by chapter the reader gets to know statistics from basic to more advanced level. ... There are descriptions and interpretations of statistical concepts and examples of experiments where they can be used. The authors also give the hint on the analysis of experiment. The book is very good lecture about statistics and I read it with a great interest." - Anna Szczepa´nska, Pozna´n University of Life Sciences, Poland, in International Statistical Review "The authors do an exceptional job in covering important topics in a manner that is sophisticated, rigorous, and yet readily accessible. As in previous editions, the authors lay a solid foundation that allows the reader to easily generalize to situations beyond what is covered.　 The integrative chapters use real data to show how concepts interrelate – what a wonderful idea. The book continues to be a terrific text book for graduate students as well as a valuable resource book for more experienced researchers." – Edward J. O’Brien, University of New Hampshire, USA "I love the "integrated analysis" chapters. They will allow students to practice their new skills, to think critically about data sets, and to learn to write results and discussion sections for papers. " - Celia M. Klin, Binghamton University, USA 　 "The Myers & Well book is the best available book for a one-year graduate statistics sequence…I currently use the 2nd edition…I use it because it provides the best fit for the material I think needs to be covered … and it is an outstanding reference that students should have." - William Levine, University of Arkansas, USA

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Research Design + Statistics Tests

Aligning research design and statistical analyses.

Kamil Mysiak

Towards Data Science

F rom the first day I sat in my undergraduate “Research Methods” course staring at SPSS output, I knew I found my calling. I can still recall my first research paper. Watching my the completed surveys come in, diligently cleaning the data and crossing my fingers in the hopes of significant results. Despite my results coming back not significant I knew I found my passion.

In spite of engrossing myself in the topic, I found it particularly difficult aligning research design to statistical analysis. As the terminology began to roll-in (ie. t-tests, ANOVA, effect size, IV, MANOVA, ANCOVA, regression, R², etc.) I grew more confused and frustrated. The sheer amount of terminology used in designing experiments, analyzing, and interpreting results can be a daunting reality.

Although to fully describe the in-depth nature of each research design along with the appropriate statistical models we would require a lengthy textbook, I hope to provide a condensed summary. Below is a high-level summary of research design principals and appropriate statistical models used to analyze the data.

For an experiment to be considered a true experiment there needs to be some manipulation. In other words, we need…

Written by Kamil Mysiak

Data Scientist | I/O Psychologist | Motorcycle Enthusiast | On a Search for my Personal Legend/ https://www.linkedin.com/in/kamil-mysiak-b789a614/

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Comprehensive guidelines for appropriate statistical analysis methods in research

Affiliations.

• 1 Department of Anesthesiology and Pain Medicine, Daegu Catholic University School of Medicine, Daegu, Korea.
• 2 Department of Medical Statistics, Daegu Catholic University School of Medicine, Daegu, Korea.
• PMID: 39210669
• DOI: 10.4097/kja.24016

Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes.

Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed.

Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making.

Conclusions: This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies.

Keywords: Algorithms; Biostatistics; Data analysis; Guideline; Statistical data interpretation; Statistical model..

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Workplace gossip erodes proactive work behavior: anxiety and neuroticism as underlying mechanisms

• Chengyin Gao 1 , 2 ,
• Sadia Shaheen 3 &
• Muhammad Waseem Bari 3  

BMC Psychology volume  12 , Article number:  464 ( 2024 ) Cite this article

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Services organizations highly value proactive employees. Managers are interested in promoting frontline employees’ proactive behavior because proactivity is crucial for organizational success. The mechanism of negative workplace gossip on workplace prosocial behavior is unclear. This research investigates the factors hindering this valuable behavior, specifically focusing on negative workplace gossip and employee anxiety, through the lens of the conservation of resources theory.

Design/methodology/approach

Data were collected from a sample of 352 female frontline employees across diverse service organizations using a two-wave design. Statistical analyses were conducted using appropriate software (e.g., SPSS, AMOS) to test the hypothesized relationships.

The study’s findings reveal that negative workplace gossip reduces employees’ proactive work behavior, and anxiety mediates the relationship between NWGS and proactive work behavior. Further, Neuroticism strengthens the relationship between NWGS and anxiety. These results offer a novel perspective on the detrimental consequences of gossip in services sector.

Practical implications

Originality/value While research on negative gossip exists, this study specifically examines its impact on frontline service employees, a crucial but under-studied group in service organizations.

Peer Review reports

Introduction

Today’s business environment is constantly evolving, requiring employees to take initiative and drive positive changes in their work. By being proactive, employees can better manage their growing workload and seize new opportunities as they arise [ 1 , 2 ]. Proactive work behavior means taking initiative and challenging the current situation in an anticipatory manner rather than a passive manner [ 3 , 4 , 5 ]. Essentially, it’s a self-centered behavior that employees exhibit without the instructions of the supervisor in order to change the status quo [ 4 ]. Proactive work behavior enhances individual as well as organizational performance [ 6 ]. Thus, it is essential to investigate the predictors of proactive work behavior in order to enhance organizational performance (such as growth, image, and profitability as well as employee outcomes such as (satisfaction, engagement, and career growth) [ 6 , 7 ]. However, available research has depicted that negative workplace events reduce proactive work behavior [ 8 , 9 ].

Multiple antecedents of PWB have been examined in the current literature such as individual factors (e.g. high negativity effect, personality types (McCormick et al., 2019; Parker et al., 2019), and organizational factors (e.g., role stressors (He et al., 2022), time pressure (Sonnentag & Spychala, 2012) organizational climate (Caniëls & Baaten, 2019), abusive supervision (Ouyang et al., 2015)), and contextual factors includes leadership styles, job design, and autonomy (Nurjaman et al., 2019; Permata & Mangundjaya, 2021).

Despite the fact that gossip is a widespread issue in workplaces, the negative side of gossip, particularly among frontline service employees, remains under-investigated. In services sectors employee proactive work behavior helps in improving customers’ views about the quality of the services, satisfying customers’ needs, and increasing organizational performance [ 2 , 9 ]. Employees’ proactive work behavior is not only necessary for production organizations but is equally important for service organizations. A supportive working environment boosts employee energy and they come up with the motivation to perform their duties in an efficient manner [ 7 ]. But workplace stressors such as negative workplace gossip reduce employees’ energies and enhance negative emotions at the workplace such as emotional exhaustion and anxiety [ 10 ].

Gossip is considered as ever-present in the workplace because 90% of the dialogues consisted of gossip [ 11 ]. Gossips defined as colloquial and judgmental conservation about someone who is not present [ 5 ]. Gossip damages the mutual relationship between all the colleagues who are involved in spreading gossip (Liu et al. 2020). Additionally, it can also harm employee attachment to the organization [ 12 ]. Several studies have highlighted its adverse consequences in the workplace. For instance, workplace gossip can have a negative impact on knowledge sharing [ 13 ], employee satisfaction [ 14 ], commitment to the organization [ 15 ] and employee creativity [ 13 , 16 ]. However, little is known in the service sector context, particularly where frontline employees continuously have to serve and maintain harmonious relationships with customers or clients. For instance, in the nursing profession, nurses have to serve patients in a timely manner when they need assistance, treatment, and other help. The same is true for other frontline employees such as female frontline workers working in salons, and serving as bus hostesses referred as pink color jobs). In such types of professions, frontline employees have to maintain a high level of interaction and collaboration with the customers as well as coworkers. Due to this reason, we particularly focus on the services sector to investigate the impact of workplace gossip on the proactive behavior of frontline employees. Gossip can be categorized as positive workplace gossips and negative workplace gossip [ 16 ]. We are particularly focusing on the negative workplace gossip that is receiving considerable attention from academic researchers and practitioners. Negative gossip often spreads faster and has a stronger influence on others than positive rumors [ 17 ]. Despite its prevalence, the impact of negative workplace gossip (NGW) on frontline employees’ proactive work behavior within the service sector remains under investigation. NWG can have an adverse impact on employee’s emotions, perceptions, and behavior [ 18 ]. When employees find themselves as a victim of NGW it can cause them to go through distress and psychological unrest [ 19 ]. Consequently, NWG hinders the ability of employees to focus on core responsibilities due to psychological unrest and stress.

Employee personality traits, like neuroticism, might influence how negative workplace gossip (NWG) indirectly affects proactive work behavior through anxiety. Studies have shown a positive connection between exposure to negative workplace gossip (NWG) and destructive behaviors among employees high in neuroticism [ 20 ]. Employees with high neurotic personalities react more toward negative events in contrast to those employees who score low in neuroticism [ 21 ]. Researchers agree that neuroticism aggravates the connection between NWG and negative emotional outcomes such as frustration and envy [ 21 ]. Neuroticism also results in depressive symptoms in employees. In a similar vein, the connection between unpleasant events and negative outcomes is stronger for employees who are high in neuroticism [ 20 ]. Therefore, we propose, that neuroticism stronger the impact of NWG on anxiety, which results in decreasing their proactive work behavior.

To explain how NWG and proactive work behavior are linked, we rely on the conservation of resource theory [ 22 ]. Negative workplace gossip can be viewed as a resource threat [ 23 ]. It can damage one’s reputation, social standing, and psychological well-being, thereby depleting personal resources [ 24 ]. When exposed to negative gossip, individuals may experience increased anxiety. This emotional response can further deplete personal resources, making it difficult to engage in proactive work behaviors. Thus, we tried to contribute to the current literature in several ways. First, this research aims to expand the current understanding of negative workplace gossip (NWG) by examining its impact on employee proactiveness. We propose that NWG not only fosters negative employee behaviors like deviance but also has the potential to deplete positive behaviors like proactive work behaviors. Second, while prior research has explored the link between negative workplace gossip (NWG) and employee proactiveness through emotional responses, this study sheds light on employee anxiety as a potential, yet unexplored, mediating factor in this relationship Third, this research delves specifically into how neuroticism might amplify the effect of negative workplace gossip on employee anxiety. This clarifies how negative workplace gossip (NWG) is particularly problematic for employees who are more sensitive to stressors due to their personality traits characterized by higher levels of neuroticism. We prioritize in-depth exploration of how these factors (neuroticism and anxiety) influence the behavioral consequences of negative gossip, rather than simply examining a wider range of potential effects.

The manuscript follows an academic structure. It begins with a literature review, followed by a methods section. The results of the study are then presented, followed by a discussion of their implications section. The paper concludes with limitations and suggestions for future research.

Theory and hypothesis

Impact of workplace negative gossip and proactive work behavior.

The aim of this study is to explore negative workplace gossip from the perspective of the gossipers. This viewpoint is closely linked to workplace victimization [ 25 ], where the target perceives themselves as a victim. Negative workplace gossip influences workplace attitudes and behaviors in various ways. Employees can often sense when they are the subject of gossip due to noticeable changes in the environment and the suspicious behavior of others [ 26 ]. For example, colleagues may stop talking when the target approaches or avoid making eye contact [ 27 ]. Conversely, some individuals may inform the target about the negative evaluations made by others [ 8 ]. Negative workplace gossip often involves hostile assessments of the target and is considered an informal conversation that can damage the target’s image and reputation (Fay & Urbach, 2023).

The most common topics which can be discussed about the victim contain affairs, divorce, job titles, etc. [ 8 ]. These types the topics are commonly discussed about the frontline females who regularly interact with the customers. The nature of negative workplace gossip depends upon the situation and nature of the relationship with the victim. Research suggests that negative workplace gossip (NWG) can have detrimental effects on employees. It can erode their confidence, weaken their motivation to work, decrease their overall engagement, and hinder their proactiveness [ 7 ]. Employees tend to involve in proactive work behavior when they found support from the work environment [ 28 ]. Workplace events and situational factors are essential components of employees’ proactive work behavior [ 7 ]. On the contrary workplace stressor and unpleasant situations hinder employees’ proactive work behavior [ 29 ]. NWG acts as a stressor and influences employees’ positive work behavior. Thus, to cope with such stressors the victim needs to utilize his essential psychological resources. According to the COR theory, the depletion of employee psychological resources leads to lower performance and difficulties in handling workplace situations [ 30 , 31 ]. Therefore, employees safeguard their resources by not utilizing them at the workplace. Proactive work behavior is not a mandatory behavior of employees and it is out of the punishment and rewards parameters. Thus, employees who deplete their resources due to workplace stressors (such as NWG) are less likely involved in proactive work behavior.

We therefore hypothesized.

NWG is negatively linked with employee proactive work behavior.

The mediating role of anxiety in the relationship between workplace negative gossip and proactive work behavior

Research indicates that workplace stressors, such as negative workplace gossip (NWGS), can drain employees’ psychological and social resources, increasing the likelihood of undesirable workplace behaviors. These behaviors may include deviant actions (e.g., sabotage, theft), withdrawal behaviors (e.g., absenteeism, reduced communication), and diminished work engagement [ 17 , 32 ]. Numerous researchers have found that stressful situations lead to tension, frustration, and exhaustion, which impair employees’ ability to perform their tasks proactively [ 33 ]. NWGS, as a workplace stressor, causes the victim to feel depressed and experience negative emotions. According to Conservation of Resources (COR) theory, negative evaluations by others, such as negative gossip, can result in frustration, stress, and anxiety, weakening employees’ competence to perform their daily tasks proactively (Hobfoll, 2011a; Malik, 2023). This study suggests that NWGS may deplete employees’ emotional resources, leading to feelings of frustration and anxiety [ 34 ]. These negative emotions can, in turn, hinder job performance by reducing concentration and increasing the likelihood of errors.

Particularly, when the victim is unable to respond back to the gossiper he became a victim of anxiety. Under high stress, employees may struggle to manage their energy and resources, potentially leading to performance decline. Proactive work behavior is defined as anticipatory, self-started, persistent, and future-oriented behavior that beats the mandatory requirements of one’s job [ 35 ]. Due to the frequent nature of problems faced by frontline employees, a proactive approach is crucial. By anticipating and addressing potential issues, they can prevent them from recurring in the future. The researcher described proactive behavior at the organizational level, team level, and individual level [ 36 ]. But the focus of this study is individual frontline employees’ proactive work behavior. Employees need a great amount of energy and support from the work environment in order to exhibit proactive work behavior [ 37 ]. Effective proactive work behavior requires a future-focused mindset. By analyzing the current situation and anticipating potential needs, employees can plan and take action to ensure successful task completion [ 38 ]. Therefore, employees who exhibit proactive work behavior need energy, support, and a compassionate work environment. Thus, a proactive employee needs extra physical as well as psychological resources at the workplace so that he can perform in a proactive manner [ 39 ]. In a situation where employees suffer from any type of stress such as the workplace gossip employees suffer from anxiety which depletes their valuable resources [ 32 ]. Thus, the employees who became victims of gossip remained less interested in exhibiting proactive work behavior. But they tried to restore their resources by avoiding any exceptional work such as proactive work behavior. Proactivity occurs only in a situation when an employee is fully motivated, enthusiastic, and energetic [ 40 ]. Therefore, employees who are suffering from stressful situations protect their resources by not engaging in proactive work behavior.

Anxiety mediates the relationship between NWG and Proactive work behavior.

The moderating role of neuroticism in the connection between workplace negative gossip and anxiety and proactive work behavior

Neuroticism is characterized as a negative personality trait in employees, leading to emotions such as frustration, mood swings, envy, and jealousy, which hinder their ability to cope with stressful situations like negative workplace gossip (NWGS) (Roelofs et al., 2024; Zellars et al., 2002). Studies indicate that neurotic employees are more reactive to stress compared to those with lower levels of neuroticism (Wang et al., 2015). Employees with high neuroticism exhibit less emotional stability, making them more susceptible to stressful events such as NWGS (Bowling et al., 2005; Tian et al., 2019). These employees, prone to experiencing negative emotions and anxiety, often show lower levels of positive organizational behaviors during stressful situations. This tendency is due to their focus on conserving resources as a coping mechanism, prioritizing the protection of existing resources over proactive work behaviors or exceeding expectations. Research shows a positive correlation between negative workplace events and neuroticism, with highly neurotic employees being more vulnerable to stress and less capable of performing tasks proactively. Drawing on the conservation of resource theory, employees who score high in neuroticism react to stressful situations more aggressively and exhibit negative emotions such as anxiety in a contrast to employees who score low in neuroticism. The employees who are emotionally stable have plentiful psychological resources thus they react less toward negative situations such as NWGS, and they perform their tasks in an above-average manner.

Neuroticism moderates the relationship between NWG and proactive work behavior such that the relationship will be stronger in the presence of high neuroticism in contrast to low neuroticism.

Neuroticism moderates the mediated relationship between NWG and proactive work behavior such that the relationship will be weaken in the presence of high neuroticism in contrast to low neuroticism. Figure 1 explains the study framework and hypotheses relationships. 

Research framework

Methodology

We choose a quantitative design and a time lag data collection method. A quantitative study design is best suited for data collection from a larger population and enriches the generalizability of the findings. Furthermore, a time lag data collection method is best suitable to study the temporal effects of variables (e.g., negative workplace gossip, proactive work behavior, and anxiety), it also benefits to investigate the causal relationships and helps to minimize the common method bias.

Due to the informal nature of pink-collar employees in Pakistan and the difficulties associated with the approachability of the respondents we preferred to choose the convenience sampling technique. The duty schedule of these workers usually may not be fixed, such as nurses’ childcare workers and bus hostesses. Additionally, we do not have a complete list of the population, therefore, we used non-probability sampling techniques. Convenience sampling techniques benefit us to collect the data from those employees who are available at the time of the data collection, as well as it also supports to coordinate with the participants.

We recruited participants in various ways. First, we targeted those service organizations where the majority of pink-collar workers are serving such as salons, bus hostesses, nurses, and childcare organizations. Then we contacted the managers/owners of those organizations through emails and personal contacts. We also used the available references such as references of the students, friends, and family members. First of all, the objective of this study was elaborated to the managers and owners of the organizations. Then after getting permission from the management of the services organizations, the participants were approached and contacted through emails, WhatsApp, and by physically distributing the questionnaire. Before data collection written informed consent was taken from all the participants and it was assured to them that there is no right and wrong answer of the given questions. We only want to record your valuable opinion regarding this study. It was also assured to them that they are fully free to quit this study at any point of time without bearing any penalty.

We collected data from female employees working at various service organizations such as beauty salons, bus hostesses, nurses, and child care centers also known as pink collar workers. We selected the above-mentioned organization believing that most of the female in Pakistan works in these organizations. All the protocols of the research were applied before data collection. Ethical approval was obtained from the Ethics Committee of Lyallpur Business School during their 6th meeting of board of studies. The board is affiliated with Government College University in Faisalabad. A written informed consent was taken from the participant before data collection. It was elaborated to all of them there is no right and wrong answer and they are totally free to leave the study whenever they want.

Data were collected by personal visits and with the reference of friends, students, and colleagues. Additionally, to alleviate the issue of common method bias data were collected in two times lags. In lag 1, data were collected on independent variable (negative workplace gossip), moderator (neuroticism), and mediator anxiety. After four weeks’ interval data were collected on the dependent variable (proactive work behavior). The objective of this study is to particularly focus on the pink-collar workers in a developing country. The experiences of females regarding negative work place gossips may differ significantly as compared to male workers due to the collectivist and masculine nature of culture. Female workers particularly those doing lower-level jobs are more sensitive to negative workplace gossips as contrast to males. However, a robust study can be done by doing a comparison between male and female experiences regarding negative workplace gossip, anxiety, neuroticism, and proactive work behavior. therefore, we have highlighted this point in the future research directions.

All the variables were measured on five-point Likert scale ranging from 1 = strongly disagree and 5 = strongly agree.

Workplace negative gossips

We adapted a three-item scale to measure Workplace negative gossips from Chandra and Robinson (2010). The sample items include “At work others (e.g., coworkers/supervisor) made false allegations about me (α = 0.87).

6-items anxiety scale was adapted from [ 41 ]. The sample items include “tense”, “uneasy”, and “worried” (α = 0.94).

• Neuroticism

Neuroticism 8 items scale was adapted from [ 42 ]. The sample items of the scale include “Do you tend to say what is in your mind?” “Do you sometimes feel lonely?” (α = 0.95).

• Proactive work behavior

Proactive work behavior 3 items scale was adapted form [ 43 ] further validated by [ 44 ]. The sample items include “Initiated better ways of doing your core tasks” “Come up with ideas to improve the way in which your core tasks are done”. (α = 0.92)

Data has been analyzed by using AMOS.21 and SPSS. First, we conduct the confirmatory factor analysis by using AMOS 21. Then we checked the hypothesized model by using PROCESS macro by Hayes. We used PROCESS model 4 for mediation and model-7 for moderated mediation analysis.

Measurement model

We used confirmatory factor analysis to test the measurement model. There were four latent variables in the measurement model such as negative workplace gossip, anxiety, proactive work behavior, and neuroticism. According to the results of the measurement model, all the fit indices are within the acceptable range such as (χ2 = 362.376, df = 154, p  < .001, CFI = 0.97, TLI = 0.96, IFI = 0.97 and RMSEA = 0.06) all the yielded results depict a good fit. (Please see Table 1 )

Table  2 represents the mean, standard deviation, CR, α, AVE, and the square root of AVE. We test the convergent and discriminant validity of the proposed model. The statistical results of AVE prove the convergent validity of the model because all the values are greater than the cutoff point which is 0.5 (see Table  2 ). The discriminant validity of the model is also established according to the statistical evidence because the square root of AVE is greater than their correlations (see Table  2 , the square root of AVE is shown in diagonal). Thus both convergent and discriminant validity is proved. Additionally, the CR and α values of negative workplace gossip, neuroticism, anxiety, and proactive work behavior are meeting the threshold criteria which is 0.6 (see Table  2 ).

We also presented a correlation analysis of the proposed model. The correlation analysis shows negative workplace gossip is positively related to neuroticism ( r  = .73, P  < .01), employee anxiety ( r  = .769, P  < .01), and negatively relayed to proactive work behavior ( r − .752 =, P  < .01). Neuroticism is positively related to employee anxiety (r 0.789=, P  < .01) and negatively related to proactive work behavior (r-0.737 =, P  < .01). Employee anxiety is negatively related to employee proactive work behavior (r-0.780 =, P  < .01) (see Table  2 ).

Hypothesis analysis

We test the proposed model by using the PROCESS macro by Hayes. We applied model 7 to test the moderated mediation and previously a number of researchers used this model to test the same type of model such as [ 45 , 46 , 47 ]. Therefore, we strongly believe that model 7 is perfectly suitable to test the hypothesized relationships of our proposed model. For clarity first, we present the result of the mediation analysis in Table  3 . According to the proposed model negative workplace gossip is negatively related to employee proactive work behavior. The obtained results support this expectation (β = −0.139, p  < .05) therefore, hypothesis 1 is accepted. Hypothesis 2 states, employee anxiety mediates the relationship between negative workplace gossip and proactive work behavior which has been proved with the help of statistical data as depicted by the 95% Bootstrapped confidence interval which has no zero [-0.513; − 0.268]. The direction of the UL and LL support that there is a mediation effect of employee anxiety in the connection between negative workplace gossip and employee proactive work behavior.

According to hypothesis 3, neuroticism moderates the relationship between workplace negative gossip and anxiety proved by the statistical results (β = − 0.057, p  < .05). According to the expectation, the connection between proactive work behavior and anxiety is stronger when a person is high in neuroticism (see Table  4 ).

In the current study, we test the impact of negative workplace gossips on proactive work behavior through employee anxiety. Additionally, the moderating role of neuroticism in the relationship between negative workplace gossips and anxiety is also tested. Data were collected from only female workers, working in different service sectors such as nursing, hotels, working as bus hostesses, and working in salons.

Females who are high in neuroticism deplete their psychological, emotional, and physical resources in stressful situations (e.g., NWG) more frequently in contrast to those who are low in neuroticism. High neurotic employees need more energy to manage negative workplace gossip when they experience negative gossip from coworkers and society. Consequently, the drain of energy in managing negative gossip, they remained less involved in proactive work behavior. For instance, preparing themselves for challenging goals, thinking of new ideas for improvement, and being vigorous and responsive at the workplace. The results of the study are verified by [ 6 , 8 , 20 ] as well as COR theory [ 48 ].

Our findings provide strong support for all hypothesized relationships. Notably, negative workplace gossip was found to significantly elevate employee anxiety in a collectivist cultural context. This heightened anxiety, in turn, appears to be associated with decreased proactive work behavior.

This study contributes valuable insights into the dynamics of negative workplace gossip within collectivist societies. Furthermore, by focusing on female employees, the research highlights a potential vulnerability specific to this demographic. In collectivist cultures, women may be disproportionately targeted by negative gossip, particularly when their work roles are traditionally considered less prestigious compared to their male counterparts.

The findings of the moderated mediation analysis shed light on the underlying mechanisms that contribute to lower levels of attentiveness, energy, and passion among female employees in these service sector jobs. This study contributes to the literature on the service sector in Pakistan by providing a deeper understanding of the root causes associated with reduced proactive behavior among blue-collar female workers.

Although this study particularly deals with negative workplace gossip, however, any type of personal mistreatment enhances employee anxiety and consequently reduces proactive work behavior. Based on recent research different types of personal mistreatment such as bullying, abusive supervision, ostracism, and undermining have resource depletion effects and reduce proactive work behavior [ 20 ].

Theoretical implications

This study has numerous contributions. First, this study enhances our knowledge regarding negative workplace gossips by investigating proactive work behavior in services sector. Existing research on job performance [ 49 ] and organizational citizenship behavior [ 50 ] provides us a theoretical support to understand the effect of negative workplace gossips on proactive work behavior of employees. Existing studies on negative workplace gossips has not focused on the blue-collar worker’s job outcomes (e.g., proactive work behavior). Leaving promising research gap in the current literature.

Second, this paper breaks new ground in the study of workplace gossip by exploring its impact on employee proactivity through the lens of anxiety. This nuanced approach deepens our understanding of how negative rumors can hinder employee’s proactive work behavior. While prior research has centered on how workplace gossip shapes employee psychology, emotions (Spoelma & Hetrick, 2021; Guo et al., 2022; Sun et al., 2023), and attitudes (Brady et al., 2017; Chen, 2018; Zhou et al., 2021), this study takes a different angle, exploring how these internal shifts ultimately influence employee behavior. Taking the research on negative gossip one step further, we explored how they make people less willing to be proactive at work.

Third, while understanding individual emotional responses to gossip is valuable, a crucial next step is exploring how it shapes workplace behavior, particularly for women in collectivist societies. This study pioneers this investigation, specifically delving into how female workers navigate the implications of negative gossip in such cultural contexts.

Forth, building on previous research by Nhu et al. (2021) who called for more studies on what influences employees proactive work behavior, this study examines how negative gossip at work can discourage employees in the service sector from going the extra mile. To get a complete understanding of how people behave at work, we need to consider all the factors that influence them, and that definitely includes negative work place gossips.

This study offers several practical implications for managers as well as for organizations in services sectors. In the services sector, employees’ proactive behavior is very essential to serving customers in an adequate and timely manner. In services sector employees need to be attentive, energetic and prepared to deal every type of customer. But negative workplace gossips can drain their energies which push them towards anxiety particularly for high neurotic employees. Therefore, they invest their energies to manage negative gossips and anxiety which reduces their attentiveness and proactivity at the workplace.

This study contributes to the understanding of fostering service employee proactivity by proposing several interventions for managers in the service sector. Firstly, implementing recognition programs, such as appreciation ceremonies, could acknowledge the value of blue-collar employees and contribute to a more positive work environment. Secondly, offering targeted counseling sessions could help blue-collar employees understand the significance of their role and how their contributions impact the organization’s success. More importantly, proactive measures are necessary to address negative workplace gossip. Managers can implement educational programs to equip employees with the skills to identify and effectively deal with such behaviors. Negative workplace gossip represents a critical and detrimental phenomenon that can significantly hinder employee performance [ 51 ]. These training initiatives should raise awareness about the importance of eradicating such detrimental behaviors. Training programs can range from formal, off-site workshops to informal, on-the-job training sessions.

Thirdly, our result stated that negative work place gossips influence more to high neurotic employees, thus it is necessary to find out the employees who are high in neuroticism and managers should find out the ways to mitigate the effect of negative workplace gossips for neurotic employees. Managers need to do personality tests before hiring a blue-collar employee and should avoid those employees who are high in neuroticism. The managers should also arrange training sessions for high neurotic employee and train them how they can deal with uneven situations. Hence, organizations should pay more attention to those employees who are high in neuroticism. The organization should create a culture of social support and friendly environment. So, employees can restore their energies by sharing their problems with each other.

Limitations and future research directions

This study is not without limitations. First, a potential limitation of this study is its focus solely on the influence of negative workplace gossip on proactive work behavior. Future research could explore the potentially contrasting role of positive workplace gossip in promoting employee proactivity. Examining the impact of both positive and negative gossip on employee behavior would provide a more comprehensive understanding of this dynamic.

Second, this study’s generalizability may be limited due to the inclusion of only female service sector workers. Future research should aim to compare the reactions of male and female employees to negative workplace gossip to explore potential gender differences in this response. Third, we collect data from collectivist society the study can be replicate on individualistic cultures for better generalizability. Furthermore, this study employed a single moderator variable. However, it is important to acknowledge that other personality traits or dispositions, such as extraversion, trait emotional exhaustion, and attribution style, could also potentially moderate the relationship between negative workplace gossip and employee proactive work behavior.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Gao, C., Shaheen, S. & Bari, M.W. Workplace gossip erodes proactive work behavior: anxiety and neuroticism as underlying mechanisms. BMC Psychol 12 , 464 (2024). https://doi.org/10.1186/s40359-024-01966-5

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People-environment relations following COVID-19 pandemic lifestyle restrictions: a multinational, explorative analysis of intended biophilic design changes

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• Kalterina Shulla 1 ,
• Bernd-Friedrich Voigt 2 ,
• Salim Lardjane 3 ,
• Kerstin Fischer 4 ,
• Piotr Kędzierski 5 ,
• Giuseppe Scandone 6 &
• Thomas Süße 7  

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The study analyzes the consequences of the COVID-19 pandemic restrictions for the human–environment relations through the lenses of biophilic design. The mixed-method quantitative and qualitative explanatory research combines contextual and personal variables, such as, among others, country, age group, gender, overcrowding, time spent outside, access to nature/food and the exposure to biophilic elements, during and after the lockdown. The results indicate that psychological pressure on individuals caused by pandemic restrictions imposed early 2020, triggered changes in human-environmental relation. More precisely, our comparative analysis of six European countries (Italy, Germany, Poland, Spain, Denmark and Sweden) indicates that people-environment relations do not depend on the objective severity of country-wise restrictions, but rather on the individual perceptions of these restrictions. The results complement the lack of the research for the role of biophilic design in understanding and enhancing human–environment relations during the COVID-19 pandemic restrictions and thereafter.

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1 Introduction

The power of nature for physical and mental well-being and the healing role in stressful conditions is undeniable, as human aesthetic, intellectual, cognitive and spiritual cravings are fulfilled [ 1 ]. Exposure to real or simulated natural views can quickly trigger restorative activity in the brain and reduce stress levels [ 2 ]. Contemplating a nature-integrated urban environment can enhance positive emotions [ 3 , 4 ] as deprivation from it can worsen negative states [ 5 ]. In early 2020, the COVID-19 pandemic imposed worldwide restrictions that were unique because of their variety and differing severity which ultimately resulted in graduated states of psychological pressure on individuals [ 6 , 7 ]. However, when people are forced to cope with crises in unusual circumstances, changes are triggered in their patterns of living and working toward a more sustainable and resilient lifestyle [ 8 ]. “Worry” can divert life priorities, but a strong self-concept of nature can serve as a buffer for a moderate impact on environmental values [ 9 ], enabling “salutogenic” experiences out of stress and psychological states even in extreme environments [ 10 ]. The restrictions of the COVID-19 global pandemic highlighted an attention towards Biophilic Design, as it embraces elements of direct and indirect experience of nature.

As personal growth is attributed to societal dynamics and changes in norms [ 11 ], the way people use and perceive the environment can be encouraged or limited by such norms and cultural contexts [ 12 ]. Ironically, despite its far-reaching negative effects, the pandemic also created a new environment for self-reflection and changes in personal perceptions and actions toward its natural surroundings [ 13 ] and the individuals’ lifestyle choices, such as choice of food, housing, mobility, etc.; factors which are mainly not beyond individual control [ 14 ]. While the ecological footprint and environmental impact of the crisis have been widely considered in research of sustainable design practices [ 15 ], biophilic design (BD) encompasses the mutual benefits of connecting with nature to both humans (physiological and psychological benefits) and the environment [ 16 ]. Therefore, this study aims to understand the effects of the COVID-19 pandemic on changes of people-environment relations considering objective as well as perceived severity of lifestyle restrictions. Beyond this, the research aims to depict a pattern of predictive variables regarding the likelihood of integrating BD in future life. We do so in combining a country-comparative research approach, explorative quantitative and qualitative analysis of intended BD changes.

2 Conceptual background

The term ‘biophilia’ (love of life), composed of the ancient Greek words for “life” (bio) and “love” (philia), describes harmonious relationships between humans and the biosphere [ 17 ]. The term was first used by Erich Fromm in “The Heart of Man” (1964) and later by Edward Wilson in “Biophilia” [ 18 ]. The individual’s physiological and psychological response to nature enables the effects of BD elements such as the direct experience of nature in the built environment (natural light, air, plants, animals, water, landscapes), the indirect experience of nature (contact with the representation or image of nature, natural materials, etc.) and the experience of space and place (spatial features of prospect refuge, etc.) [ 19 , 20 ].

The human response to design stimuli allows BD elements to improve quality and sustainability by enhancing health and well-being, productivity, biodiversity, circularity, and resilience [ 21 ]. The green building movement in the early 1990s enforced the link between improved environmental quality and worker productivity [ 22 ] through the use of BD to connect with the indoor environment. Additional benefits include addressing workplace stress, student performance, patient recovery, and community cohesiveness [ 23 ] and improving well-being in prisons [ 24 ]. The BD elements in the landscape (even those not perceived as such) enable the incorporation of diverse strategies into the built environment [ 25 ]. In urban settings, these elements, the new “Hanging Gardens of Babylon”, are indicators of sustainability and resilience. When there is freedom to choose for home or workplace environment relations, the choice is often dominated by a viewpoint with a generous prospect, elevated position, open, savanna-like terrain, proximity to a body of water, etc. [ 26 ].

BD research is mainly related to two theoretical concepts from environmental psychology: The first is Stress Reduction Theory (SRT) [ 27 , 28 ] which explains the extent to which contemplating nature can trigger restorative activity in the brain, which in turn is responsible for reduced stress levels and positive emotions. The second explanatory concept comes from Attention Restoration Theory (ART) [ 29 ], which states that a lack of concentration as well as mental fatigue, which can be attributed to a prolonged direct attention span, can be positively influenced by a visual or physical stay in nature and the increase in concentration can be achieved through restorative processes with less energy-draining attention [ 30 ]. When a person is facing an unpleasant and stressful change in its person-environment relation because of a perceived external behavioral control, patterns of BD (see Annex 2) and biophilia values [ 31 ] can trigger individual restorative responses [ 10 ]. These responses might ultimately result in an adjustment of the environmental surroundings, or at least enhance a person`s motivation to do so by effecting the likelihood of using BD. Mindsponge Theory [ 32 ] conceptualizes this relation of perceived behavioral control in nature and models it with intentions towards behavior change [ 33 ]. A crucial part of this systemic conceptualization of the person-environment relation is the element of “perception of external information by the sensory systems, such as visual or auditory information” [ 34 ].

Literature shows that pandemic restrictions have divergent but socioeconomically moderate psychological effects (either positively or negatively related to states of stress) and that enforced restrictions can be perceived differently (i.e., at the individual level). In addition, deprivation from one of the domains can have such great importance that it can dominate the totality of the measures and, as a consequence, can result in a perceived stronger severity of the measures despite the moderate or weak objective status of the country [ 35 ]. The severe restrictions imposed in Europe and all over the world (although differing across countries), especially during the first wave of the pandemic (March–June 2020), limited life choices [ 36 , 37 ]. These restrictions were accompanied by psychological distress and a decrease in psychological well-being in the general public [ 38 ], among others, due to limited access to physical activity, lack of blue/green landscapes, views of nature from home [ 39 ] and remote interactions, which caused loneliness, especially for women and younger adults [ 40 ]. During this period in Italy, the lack of adequate space, terraces and gardens resulted in increased stress and aggressiveness [ 41 ], where the correlation with the “home satisfaction” factor in those conditions was related to spatial features of adequacy, flexibility, and crowding [ 42 ]. As human risk perception can lead to immediate action, in France, hours and days before the lockdown, people moved from their homes to other places, closer to family, or with better living conditions in terms of size, crowding, landscape, etc. [ 43 ]. Additional challenges in the living environment were also due to the necessity of adapting to working from home [ 44 , 45 ]. During the first wave, for instance, more than 60% of the workers in Germany were obligated to work from home, confronting the lack of a separable home-office working space and triggering a large-scale invasion of work into the private sphere [ 46 ].

The related post pandemic research has analyzed the role of biophilic features for recovery from COVID-19. Afacan (2021) explores the role of biophilic design in enhancing psychological resilience during the pandemic, related to recovery tension mood, depression and anger [ 47 ]. Furthermore, integrating natural elements into both residential and public spaces, especially in times of crisis, can significantly improve mental and physical health and foster a sense of community and connection [ 48 ]. BD principles are vital for enhancing post-pandemic living spaces, through maximizing natural light and ventilation, incorporating plants and green spaces, using natural materials, and designing flexible, multi-functional spaces. These approaches not only create aesthetically pleasing environments but also support well-being and sustainability, making living spaces more adaptable and resilient to future crises [ 49 ]. Incorporating natural elements into architectural design not only create aesthetically pleasing environments but also encourage deeper connections with nature, leading to healthier, more resilient living spaces, and better mental and physical health [ 50 ]. Furthermore, investigations on the relevance of various influential factors for the efficiency and effectiveness of working from home, for physical and mental well-being have been conducted [ 51 , 52 ]. There is a need for coordinated cross-disciplinary research to address COVID-19's mental health impacts and understanding the pandemic's psychological effects during and after the pandemic [ 53 ]. However, the role of BD as an indicator of enhancing connection between nature and humans triggered by lived experiences during the pandemic is under-researched. This study aims to fill this gap, by analyzing people-environment relations following COVID-19 pandemic lifestyle restrictions, through a multinational, explorative analysis of intended BD changes.

3 Systematization of restrictive measures during the pandemic in Italy, Germany, Poland, Spain, Denmark and Sweden

The restrictive measures taken during the COVID-19 pandemic for most of the countries, consists on establishing lockdowns, declaring state of emergency, ban on outside activities, border and travel/international flights, and events. The Oxford COVID-19 Government Response Tracker (OxCGRT) defines the stringency of the measures in eight domains: school and workplace closings, canceling public events, restrictions on gathering size, close public transport, stay-at-home requirements, restrictions on internal movement and international travel [ 54 , 55 ]. These restrictions were considered as basis for defining the comparative groups, contrasting case/country selections: (1) countries that experienced strong/moderate restrictions (Italy, Spain, Germany and Poland) and ‘(2) countries with relatively weak restrictions (Denmark and Sweden). Countries are used as proxies, not considering internal differences (i.e., Italy, “in November 2020, was divided into three zones (red, orange, and yellow)) depending on the severity of the outbreak, with different restrictions applied in each zone.

The six selected countries were affected differently by the pandemic, as reflected by the varying severity of the measures taken. During the first wave of the pandemic, the state of emergency was declared in Italy, Spain, and Denmark. The lockdown was implemented in Italy, Spain and Poland, partially in Germany, while Denmark and Sweden had no national lockdowns (see Table  1 , below) for an overview of the restrictions considering the above domains, plus the lockdown status and the state of the emergency in the six countries). Sections  3.1 . and 3.2 . display detailed illustrations of the restrictive measures in the groups.

3.1 Countries with strong/moderate restrictions, Italy and Spain

Italy, one of the first countries in Europe to be heavily impacted by the COVID-19 pandemic, took a series of strict measures to curb the spread of the virus, with a significant impact on citizens, among the positioned in the group of countries with more self-protective measures [ 56 ]. The Italian government declared a state of emergency on January 31st, 2020 (which lasted until 1st of April 2022), and a nationwide lockdown on March 9th, 2020, closing all nonessential businesses and allowing people to leave their homes for essential reasons, such as buying groceries or going to the doctor [ 57 ]. Face masks were mandatory in all public spaces, and social distancing was enforced. In the summer of 2020, restrictions were gradually lifted, and people were allowed to travel within the country for tourism purposes. However, new restrictions were imposed in the fall due to a rise in cases; nevertheless, these restrictions were less severe, although social distancing was still enforced. Vaccination campaigns were underway, and people who were fully vaccinated had more freedom, such as attending events and travelling abroad. The pandemic resulted in an increase in remote work, with many companies allowing their employees to work from home [ 58 ]. This consequently led people to move out of cities toward smaller towns and villages with more affordable housing and space. The pandemic has accelerated the trend of suburbanization in Italy, with more people looking for larger homes with outdoor spaces [ 59 ]. According to the Digital Innovation Observatories of the School of Management of the Politecnico di Milano, in 2022, there were approximately 3.6 million remote workers, almost 500 thousand fewer than in 2021, with a decrease in particular in the Public Administration (PA) and Small Medium Enterprises (SMEs); however, there is slight but constant growth in large companies, which, with 1.84 million workers, accounted for approximately half of the total smart workers. Despite this, there is increased awareness and action in organizations to create workspace environments that motivate and give meaning to work in the office. Approximately 52% of large companies, 30% of SMEs and 25% of PAs have already carried out interventions to modify the environment or are doing so in recent months. In the future, these initiatives are planned or under evaluation for 26% of large companies, 21% of public administrations and 14% of SMEs [ 60 ]. Furthermore, during the pandemic parks and public gardens have become more important as places for people to exercise and relax while maintaining social distance. A study conducted in Italy during the first COVID-19 pandemic wave (April–May 2020) highlighted the fact that restrictions influenced citizens’ perceptions of urban green spaces, with a consequent increase in general interest in parks and public garden [ 61 ].

Spain was one of the European countries with the highest incidence during the first wave [ 62 ], and in the global context, Spain experienced one of the worst situations [ 63 ]. The government imposed a nationwide lockdown by mid-March 2020, which included the prohibition of nonessential transit and blanket recommendations for WFH. Easing measures started later in May through several phases. Having high heterogeneity across the territory, the lifting of limitations would progress through the phases as rearranged during the process, making the progressive lifting of the restrictions challenging due to the highly decentralized system [ 64 ]. First, outside exercise was allowed, but borders remained closed, and no travel between different territorial units was permitted. Face masks were highly recommended both on public transport and outside. Afterward, shops, food markets and restaurants reopened with social distancing and reduced capacity, while public transit reopened with full service but reduced passenger numbers. The country entered a ‘new normality’ in late June, where travel between provinces was also allowed again. These restrictions were difficult to implement because of the conflicted political environment, which resulted in some territorial administrations taking preliminary measures at the subnational level in an uncoordinated manner [ 65 ]. Apart from the economic impact, the lockdown measures also had a significant social impact in Spain. Official data indicate an increase in gender-based violence (a 48% increase in calls for gender violence helplines during the first weeks of April 2020 compared to before the lockdown) [ 66 ]. Regarding WFH in Spain, nearly 83% of professionals were not granted the opportunity to WFH in 2020, whereas only 9.8% were telecommuting. In 2021, however, there was an increase in home office use to 25.2%. During the first month of the pandemic, there was a 38% reduction in physical activity [ 67 ].

Germany has taken a medium amount of protective measures during the pandemic [ 56 ]. The first contact restrictions were already announced in March 2020, followed by restrictions on travel and the closing of small shops and schools. In April, the obligation to remain in quarantine for 2 weeks when returning from another country and the recommendation to wear a face mask were lifted. Soon after, face masks were required for public transportation. In May 2020, schools and small shops opened slowly again. Contact restrictions depended on the number of cases in the district. In October, there were increasing lockdowns and contact restrictions introduced across the country, which lasted until January 2021. In August 2021, shops, restaurants, etc., are increasingly being opened for people who have gone through an infection, have been vaccinated twice or have a recent negative test. In December, these opportunities held wide only for people who were fully vaccinated or had recovered. The pandemic resulted in intensified suburbanization in Germany during 2020, although this process has steadily increased due to internal migration because of the lowest rates since the mid-1990s [ 68 ]. The residential green spaces attached to residential buildings, mostly designed with “semi-public access”, were appreciated by residents as creating refugia in challenging times and were more actively used than they were in prepandemic times, especially when sitting in parks was not allowed [ 69 ]. During the pandemic in Hamburg, a larger number of visitors were recorded in protected nature areas and local nature reserves, to an extent causing considerable problems for the wildlife there. For instance, in the nature reserve Duvenstedter Brook, many people nature parks more as urban recreational areas, chasing deer or playing badminton on protected stretches of heath.

Poland experienced a relatively mild first phase of the pandemic compared to other European countries, where the first case was identified a month later than in Germany and France. The government declared lockdown and enforced self-isolation measures (24 March 2020) and even applied measures that were not yet recommended by international institutions; for example, the first EU country to shut down its external borders, including those with other EU Member States. Factors slowing the progression of the initial phases of the pandemic include the relatively younger population compared to the most affected European countries, the larger population living in rural areas and the low rate of mobility domestically and internationally among the Polish people [ 70 ]. The impact of COVID-19 also resulted in changes in real estate and suburbanization in Poland, driving a wave of people to buy property (houses with land plots) to escape the dread of living in apartment buildings, either occasionally or permanently [ 71 ]. In addition, the pandemic fostered many measures by the Polish government related to the economy, taxes, employment and extraordinary changes in court proceedings and the system of justice.

3.2 Countries with weak restrictions, Denmark and Sweden

In Denmark, the anti-COVID-19 measures taken were comparatively brief [ 72 ]. Denmark was indeed in the group of countries in which there were fewer self-protective measures [ 56 ]. Starting in the middle of March 2020, schools, public institutions, hairdressers, restaurants, shopping malls, etc., were closed down, but on April 20th, 2020, these were opened again, with fitness centers and swimming pools being the last ones to open again on June 1st. Over the course of the summer, face masks were first recommended and then needed, first for public transport and later for restaurants, shops and public institutions. In northern Jutland, seven municipalities were completely isolated from their environment due to an outbreak of a new variant on some mink farms. Apart from those restrictions, no mobility restrictions were imposed within the country. This changed shortly before Christmas 2020, when schools, restaurants, public institutions, theatres, etc., were closed for almost two months; starting on February 28th, the country started opening everything again. On May 21st, almost all restrictions were lifted. Furthermore, many Danes have their own houses, gardens and/or summer houses. In 2020, 2.7 million Danes lived in detached houses, whereas 1.6 million lived in multiunit houses where they did not own themselves; this relationship has not changed between 2021 and 2022 [ 73 ]. Given the short period in which public life was restricted and given access to nature for a large proportion of the population, it can be expected that the impact of COVID-19 measures on the Danish population’s attitude towards the environment is not very pronounced.

Sweden chose a different strategy during the pandemic, mainly based on voluntary measures and citizen behaviors and recommendations rather than restrictions, and a complete lockdown was never implemented (Sweden country snapshot: public health agencies and services in the response to COVID-19, according to World Health Organization WHO. No state of emergency was declared because the Swedish Constitution does not provide for a state of emergency during a public health crisis. This less rigid approach focused more on mitigation measures for slowing, but not stopping, the pandemic and relied on existing high levels of institutional and interpersonal trust. The affected geographical regions or households were not under enforced quarantine, and facemasks were not recommended outside health care [ 74 ]. Recommendations consisted mainly of “staying at home even with the slightest symptom of an infection, physical distancing, enhanced hygiene measures, avoiding public transportation, and working from home if possible” [ 75 ]. Physical distancing was recommended in public spaces but mandatory in bars, restaurants and events. A maximum of 50 people was allowed to gather. In some opinions, this was considered to have caused less serious consequences than did the severe policies used in most countries [ 76 ]. WFH, which accounts for approximately 40% of the total workforce in Sweden and is independent of previous work experience, influences the establishment of new habits [ 77 ]. Studies suggest that workload, performance and well-being decreased during the pandemic [ 78 ].

Assuming positive effects on well-being through a stronger connection between individuals and the natural environment while also considering the unusual circumstances of the world pandemic, this study addresses individuals’ likelihood of using BD after the pandemic. More precisely, the research interest stretches out to identify contextual variables (country, overcrowding, time spent outside, and access to nature/food,) and personal variables (age group and sex) influencing the likelihood of using BD by focusing on the following:

Individuals’ exposure (during and after the lockdown) to several BD elements intentionally or unintentionally, including indoors (color, water, air, sunlight, plants, animals, natural materials, views and vistas, façade greening, geology and landscape, habitats and ecosystems) and outdoors (location, green neighborhood, wide prospect, proximity to natural resorts, etc.), as reported through a questionnaire to test whether the severity of the lockdown restriction of the COVID-19 pandemic fostered stronger people-environment relations, as valued by the likelihood of using BD elements.

The role of the context of (strong/weak) restrictions in several European countries (Italy, Germany, Poland, Spain, Denmark and Sweden) to test whether people-environment relations differ according to the objective and/or perceived severity of the measures in a country context.

The study design was exploratory, mixed-method, cross-sectoral and comparative. The data were collected through a survey directed to European countries via an online Google form conducted from 30 January to 28 February 2023 (see Annex 1). Following a random, uncontrolled sampling strategy, the survey was shared with learning networks such as the Bosch Alumni Network (an international network across 140 countries currently hosting more than 8000 members), the network of European RCEs (Regional Centers of Expertise on Education for Sustainable Development), and the COST (European Cooperation in Science and Technology) action networks of Indoor Air Pollution and Circular city, as well as with practitioners of several universities in Europe. The questions were closed and open (with the purpose of revealing the unexpected elements of change) and organized into four sections: (1) background questions containing variables such as age (different generations have different attitudes and approaches to restrictions), gender and countries (which are used as proxies); (2) questions about exposure to BD elements (color, water, air, sunlight, plants, animals, views and vistas, geology and landscape, habitats and ecosystems) indoors and outdoors, before and after the lockdown; based on the indoor and outdoor elements of the BD [ 80 , 81 , 82 ], from the framework of 14 Biophilic Patterns [ 31 ]; (3) questions about flexibility and adaptation of the living environment after the lockdown concerning the elements of BD; and (4) additional information on any major changes incentivized by the lockdown restrictions concerning lifestyle and wellbeing.

The data were processed through mixed methods, namely, descriptive statistics, statistical model building and testing and a thematic analysis [ 83 ] of the qualitative data using affinity diagramming (Lucero) [ 84 ]. Table 2 provides an overview of the methodological approach.

To facilitate quantitative correspondence analyses [ 88 , 89 ], numerical variables were recorded in three modalities (less, same, more) or (low, intermediate, high). Data visualizations were derived as two-dimensional planes using the FactoMiner package [ 85 ] of R statistical software [ 90 ]. Finally, a logistic model of the included variables was estimated to explain the likelihood of including BD using R statistical software.

The qualitative data were analyzed using affinity diagrams [ 84 ] to identify the categories that emerged. Affinity diagramming is a variant of thematic analysis [ 83 ]. In this process, all the comments were printed in different colors depending on the country and were clustered and labeled in several iterative steps so that the categories emerged bottom up in several different steps: (a) initial familiarization with the data, (b) creating initial codes, (c) collating codes with supporting quotes, (d) grouping codes into themes, (e) reviewing and revising themes, and (f) writing up the narrative based on the categories emerging. This thematic analysis process was carried out using the affinity mapping technique by creating large visual representations of the data points (chart making and ‘walking the wall’). Affinity diagrams allow identifying patterns in participants’ answers, illustrating what consequences of the restrictions on their lifestyles they were foregrounding themselves. For the quantification of the comments, the instances in each category per country were counted and divided by the total number of comments for each country, in line with the recommendations provided by Lucero [ 84 ]. For instance, seven Swedish participants made a comment that reported a change toward a healthier lifestyle, which corresponds to 16.7% of the total number of comments (42) made by the Swedish participants.

5.1 General descriptive analyses

The 403 participants in the survey were mainly from European Union countries and the United Kingdom (89%), such as Italy (17%), Germany (16%), Sweden (14%), Poland (11%), Denmark (9%), Spain (9%), the UK (3%), France (2%), and other EU countries (8% Czech Republic, Belgium, Greece, Romania, Bulgaria, The Netherlands, Portugal and Lithuania). The rest were from EU neighboring countries (9%, Albania, Serbia, Bosnia-Herzegovina, Belarus, Moldova and Turkey) and from other countries in the world (2% United States of America, Canada, Cameroon, Jordan, Kenya and Saudi Arabia). Ninety percent of the respondents had a level of education as a graduate/postgraduate from different fields. The majority of respondents belong to the 90-ties and 80-ties (37% and 27%, respectively). The rest were born on 70-ties (16.5%), 60-ties (9.5%), and 2000s (6%) in the 50-ties (2%). A total of 58% of the respondents were female, 41% were male, and 1% other.

The perceived severity of the restrictions from the respondents corresponds with the objective severity of restriction in Italy and Spain (strong) and in Germany and Poland (moderate) for Sweden (weak); for the Danish participants, the restrictive measures are perceived as moderate and strong by the majority of the respondents in contrast with the countries’ weak objective status. Table 3 displays objective restrictions (based on the criteria followed by the Oxford Covid-19 government response tracker; as also displayed in Table  1 ) and subjective restrictions as perceived by the respondents of the six countries. In total, 403 participants in this survey perceived the restrictive measures taken in their countries as strong and moderate.

The descriptive statistics revealed that the most influential variables were (1) overcrowding/limited space discomfort (58%), (2) difficulties to work (50%), and 3) difficulties accessing green spaces (33%). Although no significant limits were reported for the choice of food (only 10%), the respondents reported changes in their nutritional status after the pandemic related to: the use of regional products (52.7%), switching to organic products (48.7%) and growing their own vegetation through urban gardening or farming (43.3%). Approximately 80% of the respondents considered visual and nonvisual connections with nature after the pandemic to be very important.

A comparison of the “time spent outside in nature”, “during” and “after” the pandemic with that “before” the pandemic revealed that “during”, for 43% of the respondents is “less”, and “after” for, 60% of the participants is “more”. One-quarter of the participants had a steady attitude “same” for “before” and “after” the pandemic.

Figure  1 shows the “Likelihood of including Biophilic design” in relation to the “Time spent outside”, indicating that this is more likely for respondents who have spent “more” or “less” time outside. The graph was generated using the data from the six selected countries: Denmark, Germany, Italy, Poland, Spain, and Sweden.

Likelihood of including biophilic design in relation to “time spent outside” during the pandemic

Figure  2 shows the exposure to BD in the living environment before and after the pandemic, specifically to the following elements: balcony/terrace; private garden/common garden; green roof/façade; views and vistas from home, green or blue; plants/vegetation growing in home gardens/roofs/vases; glass surfaces, sunlight illumination (dynamic & diffuse light); orientation, ventilation, thermal and airflow variability; natural materials (natural wood grains; leather; stone, fossil textures; bamboo, rattan, dried grasses, cork, organic palette. There were no major changes in the specific elements of BD in indoor living environments despite slight increases in the amount of vegetation growing in home gardens/roofs/vases and in vistas from home and glass surfaces, sunlight and illumination. Nevertheless, the majority of respondents reported that they would like to include these BD elements in the future.

Exposure to BD elements before and after the pandemic: balcony/terrace; private garden/common garden; green roof/façade; views and vistas from home, green or blue; plants/vegetation growing in home gardens/roofs/vases; glass surfaces, sunlight illumination (dynamic & diffuse light); orientation, ventilation, thermal and airflow variability; natural materials (natural wood grains; leather; stone, fossil textures; bamboo, rattan, dried grasses, cork, organic palette. Axes x- represents the BD elements. Axes y-represent the % of survey participants

Furthermore, the likelihood of BD outdoors when changing habitation is considered important, especially linked to proximity to urban gardens/green areas (53.1%), proximity to a water body (sea, lake, river, etc.) 45.1%, proximity to rural areas/suburbs (natural terrain with trees and vegetation) 42.7%, proximity to city centers and services (39.1%), proximity to relatives or family (36.7%), and elevated position (i.e., looking downhill or a viewpoint with a wide prospect) by 22.7% of respondents. Other changes are related to working habits, where 65% of respondents preferred flexible virtual/office presence time, 44% fewer working hours, 21% preferred to switch to a full-time home office and only 8% preferred full-time presence. One-quarter of the respondents had changed jobs/occupations after the lockdown. As a result, 63% of participants reported having adopted their home to create space for home office and 39% for recreational activities.

Only a small percentage (11%) of respondents reported having created an indoor individual space for Prospect (an unimpeded view over a distance for surveillance and planning), Refuge (withdrawal from environmental conditions or the main flow of activity, in which the individual is protected from behind and overhead), Mystery (partially obscured views or other sensory devices that entice the individual to travel deeper into the environment) and Risk (an identifiable threat coupled with a reliable safeguard). Furthermore, these elements are considered very likely to be included in the future by the majority of the respondents. Fifty-three percent of the respondents reported other changes, especially related to activities in nature, meditation and self-reflection, work, nutrition towards a more vegetal diet and taking a pet.

5.2 Statistical analysis

A first logistic model was estimated using all the available data (all countries) (see Table  3 ) to evaluate the effect of variables on the high vs. low/moderate Likelihood of including BD elements in the environment”. Variables that have significant descriptive power can be described by three cluster types: age and sex; variables pertaining to the severity of the experience lived during lockdown (overcrowding, limited choice of food, and time spent outside during lockdown); and variables reflecting the need for interaction (pet). The most significant variable is undoubtedly the degree of “overcrowding” experienced during lockdown, which may, as the time spent outside during lockdown, act as a proxy for the experienced severity of the restrictions (as distinguished from the perceived severity and from the objective severity of the restrictions-) see Table  4 ).

Based on these overall results, we conducted narrower correspondence analyses to focus the dependence of the “likelihood” on the above identified variables with respect to the subsample of the six selected cluster countries. Figures 3 , 4 and 5 show the results for the variables “Age”, “Time spent outside during the pandemic” and “Overcrowding”.

Correspondence Analyses factors maps for the likelihood of using biophilic design related to the variable: age

Correspondence Analyses factors maps for the likelihood of using biophilic design related to the variable: time spent outside during the pandemic

Correspondence Analyses factors maps for the likelihood of using biophilic design related to the variable: overcrowding

Figure  3 indicates a moderate “Likelihood of including BD in the environment” for males, while females and individuals who chose the “other” option have more extreme points of view (either low or high likelihood). Figure  4 indicates that the “Likelihood of including BD elements in the environment” is high for respondents who went outside “less” during the pandemic but also for those who went outside “more”. The other dependencies are as follows: the more severe the effect of the lockdown is, the higher the “Likelihood of including BD elements in the environment” as shown in Fig.  5 , for the dependence on overcrowding. An analysis of the variable “Choice of food” did not reveal relevant additional information.

Based on these insights, we ran further analyses on the subsample of 6 countries, aggregated by country. We included objective variables, such as the severity of the restrictions and country. However, neither variable showed a significant influence. After recursively discarding the nonsignificant factors, the final logistic model obtained is displayed in Table  5 .

Two factors appear to correlate the most with the” Likelihood of including BD in the environment” (which was also found by the descriptive analysis):”Time spent outside during lockdown” and the “Will to change the environment”. In particular, it was found that (1) having spent “less” time outside or “more” time outside during lockdown was positively correlated with the likelihood of including BD in the environment, and (2) the higher the willingness to change the environment after experiencing COVID-19 lockdown restrictions was, the higher the likelihood of including BD in the environment. Those who spent “less” time outside during lockdown had approximately two times greater chances of including BD in their environment in the future than those who spent the “same” amount of time outside. Those who spent “more” time outside during lockdown had approximately four times greater chances of including BD in their environment in the future (than those who spent the “same” amount of time outside). Those for whom the “Will to change the environment” is “high” have approximately seven times more chances to include BD in their environment in the future (than those for which the “Will to change the environment is “low”). Those for whom the “Will to change the environment” is “moderate” have approximately the same chances of including BD in their environment in the future (as those for whom the “Will to change the environment” is “low”) (Figs. 6 , 7 and 8 ).

Causal structure diagram explaining the likelihood of including biophilic design in person-environment relation

Lifestyle changes as reported by the survey participants by country. Axes y represent the % of survey participants and Axes x represent the reporting lifestyle changes

The relative role of lifestyle changes as reported by the participants

The strongest combination of factors appears to be: “Will to change the environment”- “high” and “Time spent outside during lockdown”- “more”. For this combination, the probability of including BD in the environment in the future is estimated to be 91%. The weakest combination of factors appears to be: “Will to change the environment”- “low”, and”Time spent outside during lockdown”- “same”. For this combination, the probability of including BD in the environment in the future is estimated to be 24%. Finally, these two variables may act as proxies for the severity of the restrictions experienced (as a subjective indicator of severity rather than objective severity by lockdown measures, as indicated through indices of the Oxford COVID-19 government response tracker).

We conducted further analysis to explain the motivation to include BD elements in the future environment by running a Bayesian causal analysis [ 88 , 89 ] using the R package bnlearn [ 90 ]. The dataset included the six cluster countries. A first Bayesian network representation of the joint distribution of all variables coded at two levels (high vs. low or moderate, more vs. less or same) was obtained and gradually modified to account for causal dependencies while not degrading the fitness measure retained (BIC criterion). For the final model, two main causal factors explain the” Likelihood of including BD elements in the future environment”: “Importance given to BD” and” “Importance of spending time outside”. Moreover, following the causal path, the lockdown had a causal impact on these two factors, mainly through the experience of “Overcrowding during lockdown”, which may be considered a good proxy of the perceived severity of restrictions. The obtained causal structure can be summarized by the following diagram:

As indicated, individuals who experienced strong overcrowding during lockdown are more likely to attach high importance to BD elements in their environment and hence are more likely to change their environment through BD elements and to value spending time outside. Interestingly, people who are less willing to change their environment, give less importance to BD in general and are less likely to have experienced overcrowding during lockdown. Furthermore, people who experienced more overcrowding during lockdown may have had fewer opportunities to go outside during lockdown and may have had problems accessing food, and being “stuck at home”, which explains the relevance of these variables in our logistic regressions. We confirmed this insight by running the following contingency Tables 6 and 7 :

5.3 Qualitative analyses

Altogether and across countries, 32 comments concerned a healthier lifestyle, which amounted to 16.4% of the total number of comments (195). The qualitative analysis of a total of 210 qualitative comments from the Danish, German, Italian, Polish, Spanish and Swedish participants yielded 12 different thematic categories, while participants were free to write as much as they wanted; most participants made exactly one point; only three comments were assigned to two different categories. First, there is one group of 20 comments that seem to refer to the time during the pandemic, not afterwards. In the following, we disregard these comments, leaving us with 193 valid data points. In addition, 44 comments (mostly by the Swedish participants) suggested that there were no changes in their lifestyle due to the pandemic.

The next largest category comprises comments in which participants describe how they perform more outdoor activities. For instance, participants reported on longer walks, spending time in the garden and enjoying fresh air; for example, participants wrote “ I spend more time in my city’s park ”, that they “ try to integrate exercise and fresh air ” or that they “ take time to enjoy nature .” Relatedly, eighteen participants reported having resumed a healthier lifestyle, eating less meat, performing more physical activity and cooking more at home. One participant writes, “ The COVID-19 pandemic and lockdown were starting points for changing what I don’t like in my life. The years after the lockdown were full of news in my life and radically changed myself. ” An additional ten participants reported doing more sports, for instance, “ starting fitness at home ”, “ doing more sports ” or doing “ more physical activities outdoors ”. In addition, eleven participants reported more attention given to their mental health. Many have taken up regular meditation exercises, while others report more awareness and appreciation of the small things of life; for instance, some simply take “ more time for meditation ”, another wrote, “ The overwhelming anxiety forced me to become more meditative ” and again, others state that they prioritize time for themselves now and value time alone, outside or time for recreational purposes more.

Like those with greater appreciation of life and nature, ten participants also reported greater appreciation of social connections; one participant reported moving into the city because the countryside was lonely. More generally, participants described spending more time with friends and family; many stated that they go out more often and “ spend more time with family ” organize “ weekly potlucks with my friends to create a sense of community ” or “ think about friendship in a new way ”.

Eighteen participants also reported changes in their homes; several had obtained a pet or more plants, i.e., “ took a pet and changed home ”, but some also described creating a separate space for a home office; for instance, “ I created a home office with a good amount of daylight and a view outside ”. Some even bought a house or moved into the countryside—one even changed country. Eight participants reported on improvements to their homes: “ cleaning my room more often and having less things there, making it a productive space ” or “ general home and garden improvements ”. Furthermore, participants reported that they improved the efficiency of their work or achieved a better work-life balance, i.e., by means of “ smart working ” or the use of “ new technologies that allow me to rest better ”. Eight participants had found a new job or changed careers. Three reported new skills that they acquired during the pandemic. Finally, three participants state that they are still careful and attend to social distancing; for instance, they say that they are still “ careful ”, “ keep distance from people ” and “ pay more attention to hygiene in public spaces ”. A family in Poland sold their apartment to buy a house in the suburbs, which they had never considered before the pandemic, to obtain more living space and better air quality, with a sizable plot of land, a vegetable garden, and a small orchard.

Participants’ qualitative data thus align with the quantitative survey data, suggesting that the pandemic indeed had an effect on lifestyle choices such that they became more attentive to nature, value nature and outdoor activities in the sun and fresh air. For some, these new preferences have led to changes in habitation. Regarding the differences among the six countries under consideration, the Polish participants reported more career changes than did the participants from other countries and less than the effect of the pandemic on their outdoor activities and attention to mental health and physical activity; however, they seem to have used the pandemic widely to change their eating habits. Among the Swedish, Polish and Danish participants, many reported no changes; especially among the Swedish participants, 38.1% stated that there were no changes at all. These results are in line with the severity of the restrictions imposed in these countries during the pandemic. Furthermore, extended social distancing effects were mentioned only by German and Swedish participants. In all other respects, participants from all six countries reported similar changes in lifestyle; this is interesting in its own right since across countries, people seem to have used the pandemic to rethink their lifestyles, where many of the changes reported are towards a more biophilic and sustainable lifestyle. However, it needs to be stated that these conclusions do only apply for those participants who responded to the qualitative part of the questionnaire.

The qualitative analysis added further insights to the interpretation of our quantitative analysis because it indicated that whether people changed their lifestyles was related to the severity of the country lockdown measures, but how and what changed depended to some extent on the respondents’ personal circumstances. There are also other changes that are not related to BD concepts, such as new jobs, increased efficiency, and new education.

6 Conclusions

The study background reinforces the idea that the COVID-19 pandemic restrictions and lockdowns were accompanied by general discomfort due to alterations in livelihood, work, and activities in nature and in-person social interaction. Based on the findings of this study, it can be argued that the severity of restrictive measures (strong/moderate/weak) imposed by countries during the global COVID-19 pandemic influenced the likelihood of including BD for changes in people-environment relationships. However, this effect occurs mainly when restrictions are individually perceived or experienced as severe due to personal circumstances. Our findings suggest that pandemic restrictions triggered a motivation to include BD and by this to change the person-environment relation. In this regard, individual-level perceived severity of restrictions appears to be a stronger proxy of the intended environmental behavior change than country-level objective indictors. These findings underline the relevance of the element of individual environment perception within the systemic reasoning of mindsponge theory [ 22 ]. It can also be concluded, that, in times of perceived crisis of the person-environment relation, the restorative effects of BD proposed by SRT [] and ART [] may serve as an explanation of an individual`s intention to change the lifestyle towards more natural surroundings. The mixed method analyses conducted at different sample levels revealed that, despite drastic social distancing measures, the experienced discomfort created by “Overcrowding” was identified as the most influential variable in relation to the “Time spent outside nature”, independent of other variables such as country, gender, and age. Likewise, the comparative data evaluation between Italy, Spain, Germany, Poland, Sweden and Denmark does not reinforce the assumption that people-environment relations differ according to the severity of the measures in a country context but rather according to individual responses to crises.

Experienced restrictions (mainly defined by “Overcrowding” and “Time spent outside in nature”) influence the likelihood of including BD elements in the future. Individuals who experienced changes in their individual person-environment relations through pandemic restrictions by having “more” or “less” access to nature were more likely to change their behavior, as indicated by the use of BD elements, both indoors and outdoors, compared to individuals who experienced “same” (unaffected) nature access. However, “Overcrowding” here is also subjective to individual perceptions (unrelated, for example, to the official definition for person/sqm2) and may have created more discomfort for individuals who were less likely to have had the opportunity to go outside during lockdown and may have had problems accessing food, being “stuck at home” or “difficulties to work”, as also explained by the role of these variables in the logistic regressions.

The COVID-19 pandemic appears to have influenced the trend toward relocation in proximity to urban gardens/green areas, water (sea, lake, river, etc.) or to rural areas/suburbs and not so much in specific elements indoors (as supported by the suburbanization statistics in the 6 countries). Often, the desired changes contradict with possibilities, as the results indicate an appreciation of natural elements but not always being possible to change, linked with “whenever is freedom to choose”.

To conclude, there is a post pandemic tendency toward greater connection with nature and healthier habits regarding nutrition and lifestyle within the freedom of individual choice. The likelihood of changing the environment through BD elements is related to experienced and perceived changes during the pandemic. Individuals who were experiencing “Overcrowding” are more likely to place high importance on BD elements in their environment now and hence are more willing to change their person-environment relationships. Given these preconditions, respondents revealed higher likelihood of including BD elements in the future, while a relatively unchanged routine during the pandemic did not result in post pandemic changes or increased attention to BD elements, both indoors and outdoors.

7 Limitations and implications for theory and practice

This is a cross-sectoral and not longitudinal survey-based data collection study due to the impossibility of starting a survey during the pandemic. Thus, we referred to people recalling, considering the survey time in early 2023, when the pandemic had not yet officially ended. For the purpose of this study, the survey questions that relate to the perceived severity of the restrictions are limited to discomfort by overcrowding, difficulties working, access to green spaces, exposure to BD elements and choice of food, excluding other elements, for example, travelling (local and international), and access to social events and contacts.

Regarding the lifestyle changes reported by the participants, it must be considered that all those who did not complete the open question in the survey may not have actually changed anything (the reason for not providing information on lifestyle changes is not known). Furthermore, when reporting on their lifestyle changes, the participants had recently been primed to consider biophilic lifestyle changes since they were asked about those changes in the questions before. The order in which the questions were asked may thus have introduced a certain bias regarding the reporting of biophilic lifestyle changes, meaning that these changes may have occurred, but people may have been less inclined to report on other kinds of changes.

Our study relies on data of a specific demographic group, particularly educational background, which may not be representative of the general population. The survey was distributed among participants from particular organizations or institutions, and 90% of the respondents had a higher level of education which may not mirror a generalization for the broader population. Therefore, our evaluations must be interpreted in light of the preferences of this specific demographic group. Furthermore, our conclusions consider the data of only six countries. While the country selection was specifically motivated by a classification of objective severity of measures, we understand, that a different or extended set of countries may have resulted in different results and interpretations. Future research can extend the range and heterogeneity of the data based on country and further demographic variables to enhance generalizability of conclusions. At the same time, this may serve as an evaluation of our conclusion, that it is the perceived severity (at most over-crowding) rather than the objective severity of restrictions that has the stronger impact on the likelihood of including BD in future life. To this regard, a shift of level of analysis towards regional or local surroundings may provide further insights on the relevance of (perceived) overcrowding for the likelihood of BD in person-environment relations. This will ultimately help extending the scope of this research approach beyond effects related to COVID-19 pandemic.

The pandemic highlighted the value of nature in cities and the living environment for the health and well-being of citizens and it uncovered the unhealthy aspects of current urbanization and living-working styles. Our qualitative data underline the general finding that the pandemic has increased the trend of suburbanization, stressing that accessible urban nature is a key component of creating sustainable urban communities and human health and well-being. However, we need to point out, that the majority of respondents in our study wishes/plans for changes rather than does. As such, our results emphasize that proper societal structures and long-term measures are important for enabling larger-scale changes in people-environment relations. Future longitudinal studies will have to find out if such measures will be effective.

Data availability

The datasets generated during and/or analyses during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The study is the third part of the project “Publication series: Sustainability in post pandemic society”, funded by the International Alumni Centre Berlin (iac), a center of excellence funded by the Robert Bosch Stiftung for impact-oriented alumni work and networks in philanthropy.

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KSH and BFV contributed to the conceptualization and design, writing and revision and methodological framework, the SL contributed to the statistical analyses, the KF contributed to the qualitative analyses and country background, and the PL, GS and TS contributed to corresponding countries’ background and introduction. All the authors contributed to the data collection.

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1.1 Survey: changes in people-environment relations in a post-pandemic society

2.1 Three categories of the 14 biophilic patterns according to Browning, Ryan and Clancy [ 67 ]

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Shulla, K., Voigt, BF., Lardjane, S. et al. People-environment relations following COVID-19 pandemic lifestyle restrictions: a multinational, explorative analysis of intended biophilic design changes. Discov Sustain 5 , 229 (2024). https://doi.org/10.1007/s43621-024-00423-y

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DOI : https://doi.org/10.1007/s43621-024-00423-y

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