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Limesurvey: How to Randomly Assign Participants to Different Conditions (i.e., Experimental Designs)

I’ve used LimeSurvey for a long time and really like the software. It’s powerful, yet very easy to use. But one thing I couldn’t figure out with the software was how to assign participants to one of several conditions within a single survey. For example, if you want to randomly assign participants in your survey to one of three groups and one of those groups will see some intervention, another group will see a different intervention, and the third group will see a control condition, as in an experimental design, I couldn’t figure out how to do that before. Turns out, it is possible. I found this tutorial that explained it, but it wasn’t all that clear, so I’m creating a tutorial here to make it very clear how this works.

What I wanted to do is like the hypothetical scenario I described above. I am conducting a survey and have two experimental conditions; participants are going to read one of two vignettes that are designed to influence them in specific ways and one control condition in which the vignette is designed not to influence participants in any way. I want to randomly assign every person who takes the survey to one of these three conditions so they only see one of the three vignettes. Here’s how you do this.

First, you have to create a variable in LimeSurvey that randomly assigns each participant to one of the groups. This should basically be the first variable in your survey. But, the key is that you don’t let participants see the variable. So, you add a new question to your first Question group:

You can name that variable whatever you want (this goes in the Code: box), but I named mine “random.”

Then, you need to choose the type of question. Go down to where it says “Question type:” and select “Equation”:

The equation itself goes in the “Question:” box, like this:

The equation you use is the following:

{rand(1,3)}

What this equation tells LimeSurvey to do is to select a random number between 1 and 3 (the two values in the parentheses) and assign that to this particular participant (if you have more conditions, you can increase the number of groups by increasing the second integer – i.e., change 3 to 4, 5, 6, etc.).

You have to do one more thing before you’re done creating the question. Go down to “Show advanced settings” and click on it. It will open the advanced settings options. Scroll down to where it says, “Always hide this question” and select “Yes” from the drop down menu. This tells LimeSurvey that you don’t want participants to see this question. Once you’ve done that, click on “Save” and you’ve now created the variable that randomly assigns participants to one of the three conditions.

You’ve finished the hard part. Now you have to tell LimeSurvey which variables go with which randomly assigned numbers and their corresponding conditions. You do this while creating the questions. So, start creating the questions you want the participants in each of the groups or conditions to see (these should obviously be in a later question group than the above question, or the branching won’t work). While creating the question, scroll down to where it says, “Relevance equation”. In that box is where you’re going to add the necessary code to assign each question to a randomly assigned condition. The code you’ll use is the following:

for those assigned to condition 1, add to that box: ((random==1))

for those assigned to condition 2, add to that box: ((random==2))
for those assigned to condition 3, add to that box: ((random==3))

It should look like this:

Once you’ve added the Relevance equations and hit “Save”, now you can test the survey. You should be randomly assigned to a different condition each time and see only the questions that meet the relevance equation criteria – meaning, if you were randomly assigned to condition 1, you’ll only see the questions assigned to that condition, ditto with condition 2 and condition 3.

And there you have it – you have created a survey that includes random assignment to different conditions.

29 responses to “Limesurvey: How to Randomly Assign Participants to Different Conditions (i.e., Experimental Designs)”

Thank you for the tutorial. I want to suggest the following correction: when assigning the conditions in ‘Relevance equation’, the code used should be:

The condition number should not be surrounded by quotation marks, it gave me an error.

M.A. Claassen,

Thanks for the information. The quotations worked for me, but if they aren’t necessary, that’s even simpler. I’ll remove them from the tutorial. Thanks for the suggestion.

your explanations are perfect, but, why it doesen’t work, when I do IT? I can’t understand whats wrong, i follow exactly every passage.

My hidden question is in a group. And Just after there are the 4 groups i want to randomize. Every group have is code, written like u tell.

i’m kind of lost, if u can help me, i’ll be gratefull

Thank you for your great explanation. I want to randomly assigned participants to two different conditions (A and B). As you explained, I already have done the procedure indicated by you. However, now I need to randomly assign the participants of the condition A to two different conditions (A1 and A2). Do you think that this is possible? I will be grateful if you can give some help. Thank you

This doesn’t work for me … I think I did everything you said. The “random” from ((random==1)) is surrounded by a red square (meaning this isn’t working). I started creating a new group of questions for the random. Then an other group for my conditions (I have 4). Was that wrong?

I also had some issues that the random variable was regenerated on each page.

In the forum ( https://www.limesurvey.org/forum/design-issues/82187-problems-randomising-survey-using-rand ) I found the following solution:

{if(random>0,random,rand(1,3))}

Now the value of random stays the same over the different groups and pages.

Hi there Ryan, thankyou SO MUCH for this – it was really helpful and provided a key part of the structure of my survey!

I’m just wondering if you might also know how to enable a save and return function when questions are randomly allocated in this way?

My understanding is that when using the ‘resume later’ / ‘save and return’ feature this undoes any randomisation because the randomisation is generated afresh each time you log on. However, I am wondering if tokens can overcome this? ie when a participant is allocated a token, does that ‘freeze’ the particular survey generated for them so when they log in at a later time they will still be allocated to the same randomisation stream and be able to pick up with the same set of questions at the same point where they logged out earlier?

Thanks again so much,

PS Sorry I don’t know why my name generated an angry looking cartoon icon! Complete accident – I’m not angry at all!! 🙂 🙂 🙂

Thanks for this post, it’s really helpful. I’m struggling to get my randomly assigned conditions to appear – at the moment my study skips straight to the second part (which is for all participants). I followed the above instructions, adding a new question group at the beginning for my ‘random’ questions, then adding the appropriate equations ( ((random==1)) etc.) to all my condition questions. I don’t think it’s an issue with the ‘random’ question itself, as that shows up fine on the test when I don’t automatically hide it. The only thing I can think is that I should have created different question groups for each of the conditions – would this make a difference? Any help much appreciated! Thanks, Kari

Guys its pretty simpel:

if u go with the normal equation : {rand(1,2)} u will get 1 or 2 and that is for the Question with the code:random (FOR EXAMPLE)

so in the question for 1 u fill in Relevance equations: random==1 (IT MUST BE THE NAME OF THE CODE THAT U GAVE so its “random” here )

if u go the other way with the equation: {if(random>0,random,rand(1,2))} “random” is the name of the variablecode so if its different for you it will get a red mark around (maybe u named it random1 or randnumber or butterfly i dont know 😀 ) just replace it then in the equation and later in the Relevance equations…

code:butterfly {if(butterfly>0,butterfly,rand(1,2))}

for Q1: Relevance equations:butterfly==1

if u go with the normal equation : {rand(1,2)} u will get 1 or 2 and that is for the question with the Equation in it —>here with the variablecodename code:random (FOR EXAMPLE) like did in the example above on the page !!

THIS actually helped!

Hello, will it be possible to see in the statistics which participant was assigned to which group? Thanks

Florian, yes you will see which participant was assigned to which group. They are assigned a number by the software, which makes it easy to tell who has been assigned to which group.

Thanks a lot!

Okay this took me forever to figure out but it might be helpful for others: the program is sensitive to capital letters. So I had the codename “Random” and then used the equation ((random==1)) etc. and it kept giving me errors. I then changed it to “random” (no capitals so the same as in the equation) and then it magically worked!

Hello, thank you for this great post.

I would like to know how to make it work in my situation, the Group A and B i want them to show always first in this specific order then i have a third group (C) that evritime one respondent gets here i want to present him one of nine possible set of questions.

This because initially i was thinking to do nine different version of my questionaire just to randomize Group C set of questions. But if i can do that based in the logic u present here would be great for me, while divulgating the survey (since instead 9 links i could just show one link).

I may not be understanding the situation exactly, but here’s my best guess. Rather than nine different versions of your survey, I’d make three. One survey for Group A. One survey for Group B. And one survey for Group C. For the Group C survey, use the random assignment procedure detailed in this post. That should do what you need.

If you wanted to get creative, you could try doing it all in one survey. I don’t know if it is possible to nest random assignment within a survey as I haven’t tried it (i.e., have one random assignment variable at the beginning and then another later). Theoretically, it would be possible to do all of this in one survey, but it would require some more complex survey logic. You could try it, but I think the easiest approach would be to create three separate surveys.

The newer Limesurvey versions (>= 3.x) support various randomization features. We have outlined them at https://survey-consulting.com/how-to-randomize-items-at-limesurvey/

For me it doesn’t work!

I do have a “random dummy group” with the code “random1” prior to the experimental part. It is an “equation” question with the code {rand(1,6)}. Afterwards there’s the experimental group with six stimuli (4 experimental groups, 2 without stimuli as control groups). They all have the code ((random1==3)) (or 4, 5, 6 etc) in the Relevance equation. If I preview the group the randomization works like a charm, though if I want to preview the entire survey this experimental part does never appear. the survey just skips the part and shows the first questions after the experimental question group. What am I missing?

I did everything and it worked like a charm right until I went through the survey in preview. Instead of showing me one of the 6 randomized stimuli, it skipped the experimental part all together and showed the next question after the randomized items… What happened? Do I have to put something into the “relevanze equation” of the initial “random”-dummy question?

Thank you for your help

Hi! Many thanks for this post.

I am creating a survey and I have 2 conditions. I only want to split the participants into one of two conditions for one part of the survey (I have created one question group with the questions I want group A to answer and another for group B).

I understand that I must include the equation ((random==1)) and ((random==2)) in the ‘relevance equation’ box for the question groups specific to group A and B respectively.

However, for the other question groups which I want all participants to answer (irrespective of the randomly assigned group), must I put an equation in the ‘relevance equation’ stating that I want both random 1 and random 2 to answer?

Hope my question makes sense! Thanks in advance!

If you don’t put anything in the relevance equation box, the questions should be shown to everyone.

Hi everyone, I have created the hidden first question in order to then randomly assign participants to one of two conditions ({rand(1,2)}. However, as I have already set a condition (for all questions) to ensure that they can only respond if they have given their consent at the beginning of the questionnaire, it tells me that I cannot edit the relevance equation as I have already set out conditions. This is where I would have to type in ((random==1)) or ((random==2)). Is there a way of adding this relevance equation elsewhere? In the “set conditions” menu for example? Many thanks, Sarah

An easier approach would be to:

(1) Remove the condition you have on all the questions about consent. (2) Instead, set up branching logic that terminates the survey if they do not consent. That will do the same thing as what you had previously done with the conditions on all the questions since they will not be able to answer any questions if they do not consent. (3) Then set up the condition I described in the post.

Hi I still do not understand how it works. I have 2 question groups and would like to show 1 question group randomly for people to complete. How should I make it work?

Thanks a lot.

Follow the directions above, but change “rand(1,3)” to “rand(1,2)” for just two groups.

Hi rcragun, Thanks for the very clear explanation. I’m using LimeSurvey Community Edition Version 6.2.1+230807, and I can’t see “Relevance equation” within any question or group. Is “Relevance equation” had a name change to “Condition” (under question ‘General Settings’) or “Randomization group name” (under question ‘Logic’)? Thanks again.

Unfortunately, I no longer use Limesurvey. Hopefully, someone else can answer your question.

Random Assignment in Psychology: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul McLeod, PhD

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BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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In psychology, random assignment refers to the practice of allocating participants to different experimental groups in a study in a completely unbiased way, ensuring each participant has an equal chance of being assigned to any group.

In experimental research, random assignment, or random placement, organizes participants from your sample into different groups using randomization.

Random assignment uses chance procedures to ensure that each participant has an equal opportunity of being assigned to either a control or experimental group.

The control group does not receive the treatment in question, whereas the experimental group does receive the treatment.

When using random assignment, neither the researcher nor the participant can choose the group to which the participant is assigned. This ensures that any differences between and within the groups are not systematic at the onset of the study.

In a study to test the success of a weight-loss program, investigators randomly assigned a pool of participants to one of two groups.

Group A participants participated in the weight-loss program for 10 weeks and took a class where they learned about the benefits of healthy eating and exercise.

Group B participants read a 200-page book that explains the benefits of weight loss. The investigator randomly assigned participants to one of the two groups.

The researchers found that those who participated in the program and took the class were more likely to lose weight than those in the other group that received only the book.

Importance

Random assignment ensures that each group in the experiment is identical before applying the independent variable.

In experiments , researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. Random assignment increases the likelihood that the treatment groups are the same at the onset of a study.

Thus, any changes that result from the independent variable can be assumed to be a result of the treatment of interest. This is particularly important for eliminating sources of bias and strengthening the internal validity of an experiment.

Random assignment is the best method for inferring a causal relationship between a treatment and an outcome.

Random Selection vs. Random Assignment

Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study.

On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups.

Random selection ensures that everyone in the population has an equal chance of being selected for the study. Once the pool of participants has been chosen, experimenters use random assignment to assign participants into groups.

Random assignment is only used in between-subjects experimental designs, while random selection can be used in a variety of study designs.

Random Assignment vs Random Sampling

Random sampling refers to selecting participants from a population so that each individual has an equal chance of being chosen. This method enhances the representativeness of the sample.

Random assignment, on the other hand, is used in experimental designs once participants are selected. It involves allocating these participants to different experimental groups or conditions randomly.

This helps ensure that any differences in results across groups are due to manipulating the independent variable, not preexisting differences among participants.

When to Use Random Assignment

Random assignment is used in experiments with a between-groups or independent measures design.

In these research designs, researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables.

There is usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable at the onset of the study.

How to Use Random Assignment

There are a variety of ways to assign participants into study groups randomly. Here are a handful of popular methods:

Random Number Generator : Give each member of the sample a unique number; use a computer program to randomly generate a number from the list for each group.
Lottery : Give each member of the sample a unique number. Place all numbers in a hat or bucket and draw numbers at random for each group.
Flipping a Coin : Flip a coin for each participant to decide if they will be in the control group or experimental group (this method can only be used when you have just two groups)
Roll a Die : For each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1, 2, or 3 places them in a control group and rolling 3, 4, 5 lands them in an experimental group.

When is Random Assignment not used?

When it is not ethically permissible: Randomization is only ethical if the researcher has no evidence that one treatment is superior to the other or that one treatment might have harmful side effects.
When answering non-causal questions : If the researcher is just interested in predicting the probability of an event, the causal relationship between the variables is not important and observational designs would be more suitable than random assignment.
When studying the effect of variables that cannot be manipulated: Some risk factors cannot be manipulated and so it would not make any sense to study them in a randomized trial. For example, we cannot randomly assign participants into categories based on age, gender, or genetic factors.

Drawbacks of Random Assignment

While randomization assures an unbiased assignment of participants to groups, it does not guarantee the equality of these groups. There could still be extraneous variables that differ between groups or group differences that arise from chance. Additionally, there is still an element of luck with random assignments.

Thus, researchers can not produce perfectly equal groups for each specific study. Differences between the treatment group and control group might still exist, and the results of a randomized trial may sometimes be wrong, but this is absolutely okay.

Scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when data is aggregated in a meta-analysis.

Additionally, external validity (i.e., the extent to which the researcher can use the results of the study to generalize to the larger population) is compromised with random assignment.

Random assignment is challenging to implement outside of controlled laboratory conditions and might not represent what would happen in the real world at the population level.

Random assignment can also be more costly than simple observational studies, where an investigator is just observing events without intervening with the population.

Randomization also can be time-consuming and challenging, especially when participants refuse to receive the assigned treatment or do not adhere to recommendations.

What is the difference between random sampling and random assignment?

Random sampling refers to randomly selecting a sample of participants from a population. Random assignment refers to randomly assigning participants to treatment groups from the selected sample.

Does random assignment increase internal validity?

Yes, random assignment ensures that there are no systematic differences between the participants in each group, enhancing the study’s internal validity .

Does random assignment reduce sampling error?

Yes, with random assignment, participants have an equal chance of being assigned to either a control group or an experimental group, resulting in a sample that is, in theory, representative of the population.

Random assignment does not completely eliminate sampling error because a sample only approximates the population from which it is drawn. However, random sampling is a way to minimize sampling errors.

When is random assignment not possible?

Random assignment is not possible when the experimenters cannot control the treatment or independent variable.

For example, if you want to compare how men and women perform on a test, you cannot randomly assign subjects to these groups.

Participants are not randomly assigned to different groups in this study, but instead assigned based on their characteristics.

Does random assignment eliminate confounding variables?

Yes, random assignment eliminates the influence of any confounding variables on the treatment because it distributes them at random among the study groups. Randomization invalidates any relationship between a confounding variable and the treatment.

Why is random assignment of participants to treatment conditions in an experiment used?

Random assignment is used to ensure that all groups are comparable at the start of a study. This allows researchers to conclude that the outcomes of the study can be attributed to the intervention at hand and to rule out alternative explanations for study results.

The Definition of Random Assignment According to Psychology

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Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the treatment group versus the control group. In clinical research, randomized clinical trials are known as the gold standard for meaningful results.

Simple random assignment techniques might involve tactics such as flipping a coin, drawing names out of a hat, rolling dice, or assigning random numbers to a list of participants. It is important to note that random assignment differs from random selection .

While random selection refers to how participants are randomly chosen from a target population as representatives of that population, random assignment refers to how those chosen participants are then assigned to experimental groups.

Random Assignment In Research

To determine if changes in one variable will cause changes in another variable, psychologists must perform an experiment. Random assignment is a critical part of the experimental design that helps ensure the reliability of the study outcomes.

Researchers often begin by forming a testable hypothesis predicting that one variable of interest will have some predictable impact on another variable.

The variable that the experimenters will manipulate in the experiment is known as the independent variable , while the variable that they will then measure for different outcomes is known as the dependent variable. While there are different ways to look at relationships between variables, an experiment is the best way to get a clear idea if there is a cause-and-effect relationship between two or more variables.

Once researchers have formulated a hypothesis, conducted background research, and chosen an experimental design, it is time to find participants for their experiment. How exactly do researchers decide who will be part of an experiment? As mentioned previously, this is often accomplished through something known as random selection.

Random Selection

In order to generalize the results of an experiment to a larger group, it is important to choose a sample that is representative of the qualities found in that population. For example, if the total population is 60% female and 40% male, then the sample should reflect those same percentages.

Choosing a representative sample is often accomplished by randomly picking people from the population to be participants in a study. Random selection means that everyone in the group stands an equal chance of being chosen to minimize any bias. Once a pool of participants has been selected, it is time to assign them to groups.

By randomly assigning the participants into groups, the experimenters can be fairly sure that each group will have the same characteristics before the independent variable is applied.

Participants might be randomly assigned to the control group , which does not receive the treatment in question. The control group may receive a placebo or receive the standard treatment. Participants may also be randomly assigned to the experimental group , which receives the treatment of interest. In larger studies, there can be multiple treatment groups for comparison.

There are simple methods of random assignment, like rolling the die. However, there are more complex techniques that involve random number generators to remove any human error.

There can also be random assignment to groups with pre-established rules or parameters. For example, if you want to have an equal number of men and women in each of your study groups, you might separate your sample into two groups (by sex) before randomly assigning each of those groups into the treatment group and control group.

Random assignment is essential because it increases the likelihood that the groups are the same at the outset. With all characteristics being equal between groups, other than the application of the independent variable, any differences found between group outcomes can be more confidently attributed to the effect of the intervention.

Example of Random Assignment

Imagine that a researcher is interested in learning whether or not drinking caffeinated beverages prior to an exam will improve test performance. After randomly selecting a pool of participants, each person is randomly assigned to either the control group or the experimental group.

The participants in the control group consume a placebo drink prior to the exam that does not contain any caffeine. Those in the experimental group, on the other hand, consume a caffeinated beverage before taking the test.

Participants in both groups then take the test, and the researcher compares the results to determine if the caffeinated beverage had any impact on test performance.

A Word From Verywell

Random assignment plays an important role in the psychology research process. Not only does this process help eliminate possible sources of bias, but it also makes it easier to generalize the results of a tested sample of participants to a larger population.

Random assignment helps ensure that members of each group in the experiment are the same, which means that the groups are also likely more representative of what is present in the larger population of interest. Through the use of this technique, psychology researchers are able to study complex phenomena and contribute to our understanding of the human mind and behavior.

Lin Y, Zhu M, Su Z. The pursuit of balance: An overview of covariate-adaptive randomization techniques in clinical trials . Contemp Clin Trials. 2015;45(Pt A):21-25. doi:10.1016/j.cct.2015.07.011

Sullivan L. Random assignment versus random selection . In: The SAGE Glossary of the Social and Behavioral Sciences. SAGE Publications, Inc.; 2009. doi:10.4135/9781412972024.n2108

Alferes VR. Methods of Randomization in Experimental Design . SAGE Publications, Inc.; 2012. doi:10.4135/9781452270012

Nestor PG, Schutt RK. Research Methods in Psychology: Investigating Human Behavior. (2nd Ed.). SAGE Publications, Inc.; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Defining Experimental Designs
Factorial Designs
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Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study.

It is possible to have both random selection and assignment in a study. Let’s say you drew a random sample of 100 clients from a population list of 1000 current clients of your organization. That is random sampling. Now, let’s say you randomly assign 50 of these clients to get some new additional treatment and the other 50 to be controls. That’s random assignment.

It is also possible to have only one of these (random selection or random assignment) but not the other in a study. For instance, if you do not randomly draw the 100 cases from your list of 1000 but instead just take the first 100 on the list, you do not have random selection. But you could still randomly assign this nonrandom sample to treatment versus control. Or, you could randomly select 100 from your list of 1000 and then nonrandomly (haphazardly) assign them to treatment or control.

And, it’s possible to have neither random selection nor random assignment. In a typical nonequivalent groups design in education you might nonrandomly choose two 5th grade classes to be in your study. This is nonrandom selection. Then, you could arbitrarily assign one to get the new educational program and the other to be the control. This is nonrandom (or nonequivalent) assignment.

Random selection is related to sampling . Therefore it is most related to the external validity (or generalizability) of your results. After all, we would randomly sample so that our research participants better represent the larger group from which they’re drawn. Random assignment is most related to design . In fact, when we randomly assign participants to treatments we have, by definition, an experimental design . Therefore, random assignment is most related to internal validity . After all, we randomly assign in order to help assure that our treatment groups are similar to each other (i.e. equivalent) prior to the treatment.

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Random Sampling vs. Random Assignment

Random sampling and random assignment are fundamental concepts in the realm of research methods and statistics. However, many students struggle to differentiate between these two concepts, and very often use these terms interchangeably. Here we will explain the distinction between random sampling and random assignment.

Random sampling refers to the method you use to select individuals from the population to participate in your study. In other words, random sampling means that you are randomly selecting individuals from the population to participate in your study. This type of sampling is typically done to help ensure the representativeness of the sample (i.e., external validity). It is worth noting that a sample is only truly random if all individuals in the population have an equal probability of being selected to participate in the study. In practice, very few research studies use “true” random sampling because it is usually not feasible to ensure that all individuals in the population have an equal chance of being selected. For this reason, it is especially important to avoid using the term “random sample” if your study uses a nonprobability sampling method (such as convenience sampling).

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Random assignment refers to the method you use to place participants into groups in an experimental study. For example, say you are conducting a study comparing the blood pressure of patients after taking aspirin or a placebo. You have two groups of patients to compare: patients who will take aspirin (the experimental group) and patients who will take the placebo (the control group). Ideally, you would want to randomly assign the participants to be in the experimental group or the control group, meaning that each participant has an equal probability of being placed in the experimental or control group. This helps ensure that there are no systematic differences between the groups before the treatment (e.g., the aspirin or placebo) is given to the participants. Random assignment is a fundamental part of a “true” experiment because it helps ensure that any differences found between the groups are attributable to the treatment, rather than a confounding variable.

So, to summarize, random sampling refers to how you select individuals from the population to participate in your study. Random assignment refers to how you place those participants into groups (such as experimental vs. control). Knowing this distinction will help you clearly and accurately describe the methods you use to collect your data and conduct your study.

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How to prepare and submit an IRB application successfully
How to prepare raw data for analyses
Dealing with departmental prescreening samples: Avoiding bad data
Authenticity in Academia: The personal challenge
Expertise and risk-taking in grad school
My folk theory of fiction
My Research

Yesterday I watched a friend suffer the consequences of not knowing the best way to get Qualtrics to spit out data in the easiest form. Instead of having a single outcome variable (what he was measuring to gauge the effects of a manipulation) and a single between-groups variable (one that reflected the random assignment), he had many variables, some of which were coded differently, that he had to combine.

That’s a headache, in any statistical software. So here I will walk you through how to set up a simple experiment on Qualtrics in a way that will give you a single outcome variable and a single predictor variable (factor or between-groups variable). My blog post on using logic can help with more complex designs. At a later date I might cover how to make sure all similar variables are coded the same way in Qualtrics.

Our example research question:

What kind of media makes people more anxious, the news or reality TV? We will have people self-report anxiety for the outcome variable. Participants will be randomly assigned to one of two conditions, News or Reality TV. Participants will watch a 3 minute clip (news or reality TV) and then complete the anxiety scale. In the real world, we’d also be collecting demographics and possibly some other information. These will be included in our first block.

Basic set up for random assignment in Qualtrics:

You will need four blocks on Qualtrics

Consent and demographics.
News stimulus block
Reality TV stimulus block.
Outcome variable block.

First step: set up your blocks in the initial screen you get after creating a new survey: Once you have your four blocks, select Survey Flow from the top tabs: You will then be able to set up your random assignment on this screen: Blocks are presented to participants in order.

Select the quantity of blocks you wish each participant to view.

As it is set up, participants will view (and complete) block one (consent and demographics), and then be randomly assigned to one of the two conditions. They will then all be presented with the Anxiety block (and complete the Anxiety Scale). When you download the data, you will get separates variable for the people assigned to News and TV (e.g., the variable that indicates the stimulus has been presented, and anything else, such as a timing variable that is on the separate blocks). You will get single variables for the Anxiety Scale (e.g., everyone will have A1, A2, A3, A4… completed in the same columns)

In real life, I have a customized end of survey message, but above it is just the Qualtrics standard.

End of Survey block

If you put an “End of Survey” block below a randomizer or any other sub-block, participants will not be presented with any further blocks (higher up in tree, but below the End block).

There are plenty of reasons to do this, but I won’t go into it now.

If you want to add a control condition, there are several ways to do it. You can set up a control block, with an innocuous message (matched in experimental blocks), that will be part of the random assignment below our randomizing block. Or you can set up a series of if-then statements, but that’s a bit more work.

To get a variable that specifies what condition each participant was assigned to, you need to select “export viewing order data for randomized surveys” when you download the data (export function on the Data and Analyses tab). You can then either use this variable (it will state the block each participant was assigned to) or recode it into a numerical variable, depending on the needs of your analyses and statistical software.

I’ll stop here for now, because this is the basic format. If you have a question, feel free to comment and I’ll try to answer it.

For questions concerning logic (conditional display of questions, etc) see my new post: Qualtrics tips: Using Logic .

To learn how to efficiently export data from randomization, see my blog post on Downloading Data from Qualtrics .

26 thoughts on “Random assignment in Qualtrics”

Hi Jessica, thank you so much for your explanation! I just exported my data from Qualtrics into SPSS, but it is not very clear to me how I analyse the data in regards to the randomisation now. Do I have to make a new item in SPSS which summarizes my 6 different advertisements (I have a between-subject experimental design)?

Do you have 6 conditions, or fewer conditions (2-3) with random assignment within condition? If you have 6, then the easiest thing to do is an automatic recode (under transform). That will need to be your first step in any case! This is assuming that you exported randomized order information from Qualtrics… then you just recode that variable. If you have fewer conditions, you will need to then make a new variable with the specific conditions (recode into different variable)

Hello, I’m working on a research project where we have created 2 questionnaires. One of them has condition x and the other one has condition y. I was wondering how I can make sure participants will get randomly assigned to one of the two questionnaires. Could you help me? Thanks in advance,

Put each questionnaire on a separate block, and then use survey flow to randomize blocks as described in the blog.

Hi Jessica, I understand that it is possible to create different cells in qualtrics for an experimental study and randomly assign our sample to these cells. Is it also possible to add a condition which helps us to move our sample to cells according to their gender, age or income randomly? Because in some experimental studies it is important to have equal number of demographic factors of sample in each cell ( like for 50 people in each cell, 25 man, 25 woman). Can we do this in qualtrics as well ? Thanks alot.

Yes. You have to use skip logic (on the individual question level) or branch logic (at the level of flow). Basically, if-then statements. I will probably do a blog on that eventually!!!

Blog would be really nice. Thanks for your answer.

Hi again Jessica, For conducting an experimental study and randomly assign our sample to cells (for example we have 8 condition in factorial design), and also randomly and in equall numbers assingning that sample to cells according to their gender (like for 50 people in each cell, 25 man, 25 woman); do I need to use randomization or block randomization?? Thanks.

I am working on a blog post that will address this… coming in the next few day!

see new blog 🙂

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I will. Thanks.

Hello Jessica, Thanks for your help. I have a question regarding this. I have a survey covering 5 products (so five parts). Each product /part has 8 blocks of questions. I have followed a similar instruction to what you have given here and have randomized how respondents will interact with the survey. As it is now, respondent A who is randomnly assigned question 1 of product 1, is randomnly assigned to question 3 of product 2, then to question 8 of product 3, ect… What i really want is for respondent A who assigned to question 2 of product 1, to be assigned to question 2 of product 2, then to question 2 of product 3 until the end. I want the order of assignment to product 1, to continue in the following blocks. Any idea how i can do that ? I tried branching but this is pulling individual question but not an entire block. Thank you.

If I understand correctly, each participant should answer the same question for each product (and only that one question per products), correct? And you only want ONE random assignment, to the first product/question combo? If so, you will need to set up a block for each question and product. Randomly assign to product/question (amongst all 40 options, if I understand correctly), and then set up if-then branching statements such that if participant originally is assigned Product 1, question 2 s/he then is presented with P2-Q2, P3-Q2, and so forth (this is all under the same branch, and you will need 40 branches… well, depending on how you do it). There are many ways to do this, with more or fewer instances of randomization. You could more simply randomly assign to question number, and then within that, present product blocks in random order. Let me know if this isn’t quite what you want to do!

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Hello Jessica, Thanks for your help. I have a question regarding this . For my research I employed a 2 (influencer-product congruence: congruent vs. incongruent) x 2 (influencer-consumer: congruent vs. incongruent) between-subjects experimental My question is the follow, I don’t know how to fulfill the experience conditions in Qualtrics

You can randomly assign to 1/4 cells (con1-con2, con1-inc2, con2-inc1, inc1-inc2). Or you can randomly assign for factor 1 first and then use logic to randomly assign to factor 2 within each condition of factor 2.

Hello Jessica I’m back once again, in the case of my search For the 4 conditions it makes 4 block in Qualtrics ,now my question is the follow since the 4 conditions have the same questions, how should I do to fulfill the 4 conditions with the same questions thanks again

Just show the questin block after the randomizer… If you need more help, maybe we can schedule a consultation!

Ok thank you and yes I would like to have this consultation

Library: Random Stimulus Assignment (within a survey)

Introduction.

Random Stimulus Assignment enables a researcher to randomly presents 1 of X stimuli, within a survey, while automatically recording which stimuli was presented.

This feature makes true experimental designs possible and can be combined with other advanced features such as Question Logic and Random Assignment (between surveys) to create complex online survey designs.

Sample Uses: Randomly present...

Text such as descriptions, paragraphs of varying instructions, vignettes, etc.
Images of any kind.
Links to media such as an audio or video clip.
Insert this element in your survey at the point you want it to appear.
Select how many stimuli you have (up to 15).
Enter each of your stimuli. (We recommend Microsoft IE 5.5+ for advanced formatting features such as images, formatted text, or links).
Question Logic (i.e., If stimuli #2 is presented, skip to question X) may be applied to each of your stimuli after this survey element has been created.

Library: Articles and Videos

Testimonials

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—Jon Elhai, Ph.D., Assistant Professor of Psychology, The University of South Dakota

RESEARCH RANDOMIZER

Random sampling and random assignment made easy.

Research Randomizer is a free resource for researchers and students in need of a quick way to generate random numbers or assign participants to experimental conditions. This site can be used for a variety of purposes, including psychology experiments, medical trials, and survey research.

GENERATE NUMBERS

In some cases, you may wish to generate more than one set of numbers at a time (e.g., when randomly assigning people to experimental conditions in a "blocked" research design). If you wish to generate multiple sets of random numbers, simply enter the number of sets you want, and Research Randomizer will display all sets in the results.

Specify how many numbers you want Research Randomizer to generate in each set. For example, a request for 5 numbers might yield the following set of random numbers: 2, 17, 23, 42, 50.

Specify the lowest and highest value of the numbers you want to generate. For example, a range of 1 up to 50 would only generate random numbers between 1 and 50 (e.g., 2, 17, 23, 42, 50). Enter the lowest number you want in the "From" field and the highest number you want in the "To" field.

Selecting "Yes" means that any particular number will appear only once in a given set (e.g., 2, 17, 23, 42, 50). Selecting "No" means that numbers may repeat within a given set (e.g., 2, 17, 17, 42, 50). Please note: Numbers will remain unique only within a single set, not across multiple sets. If you request multiple sets, any particular number in Set 1 may still show up again in Set 2.

Sorting your numbers can be helpful if you are performing random sampling, but it is not desirable if you are performing random assignment. To learn more about the difference between random sampling and random assignment, please see the Research Randomizer Quick Tutorial.

Place Markers let you know where in the sequence a particular random number falls (by marking it with a small number immediately to the left). Examples: With Place Markers Off, your results will look something like this: Set #1: 2, 17, 23, 42, 50 Set #2: 5, 3, 42, 18, 20 This is the default layout Research Randomizer uses. With Place Markers Within, your results will look something like this: Set #1: p1=2, p2=17, p3=23, p4=42, p5=50 Set #2: p1=5, p2=3, p3=42, p4=18, p5=20 This layout allows you to know instantly that the number 23 is the third number in Set #1, whereas the number 18 is the fourth number in Set #2. Notice that with this option, the Place Markers begin again at p1 in each set. With Place Markers Across, your results will look something like this: Set #1: p1=2, p2=17, p3=23, p4=42, p5=50 Set #2: p6=5, p7=3, p8=42, p9=18, p10=20 This layout allows you to know that 23 is the third number in the sequence, and 18 is the ninth number over both sets. As discussed in the Quick Tutorial, this option is especially helpful for doing random assignment by blocks.

Please note: By using this service, you agree to abide by the SPN User Policy and to hold Research Randomizer and its staff harmless in the event that you experience a problem with the program or its results. Although every effort has been made to develop a useful means of generating random numbers, Research Randomizer and its staff do not guarantee the quality or randomness of numbers generated. Any use to which these numbers are put remains the sole responsibility of the user who generated them.

Note: By using Research Randomizer, you agree to its Terms of Service .

Random Assignment in Psychology (Definition + 40 Examples)

Have you ever wondered how researchers discover new ways to help people learn, make decisions, or overcome challenges? A hidden hero in this adventure of discovery is a method called random assignment, a cornerstone in psychological research that helps scientists uncover the truths about the human mind and behavior.

Random Assignment is a process used in research where each participant has an equal chance of being placed in any group within the study. This technique is essential in experiments as it helps to eliminate biases, ensuring that the different groups being compared are similar in all important aspects.

By doing so, researchers can be confident that any differences observed are likely due to the variable being tested, rather than other factors.

In this article, we’ll explore the intriguing world of random assignment, diving into its history, principles, real-world examples, and the impact it has had on the field of psychology.

History of Random Assignment

Stepping back in time, we delve into the origins of random assignment, which finds its roots in the early 20th century.

The pioneering mind behind this innovative technique was Sir Ronald A. Fisher , a British statistician and biologist. Fisher introduced the concept of random assignment in the 1920s, aiming to improve the quality and reliability of experimental research .

His contributions laid the groundwork for the method's evolution and its widespread adoption in various fields, particularly in psychology.

Fisher’s groundbreaking work on random assignment was motivated by his desire to control for confounding variables – those pesky factors that could muddy the waters of research findings.

By assigning participants to different groups purely by chance, he realized that the influence of these confounding variables could be minimized, paving the way for more accurate and trustworthy results.

Early Studies Utilizing Random Assignment

Following Fisher's initial development, random assignment started to gain traction in the research community. Early studies adopting this methodology focused on a variety of topics, from agriculture (which was Fisher’s primary field of interest) to medicine and psychology.

The approach allowed researchers to draw stronger conclusions from their experiments, bolstering the development of new theories and practices.

One notable early study utilizing random assignment was conducted in the field of educational psychology. Researchers were keen to understand the impact of different teaching methods on student outcomes.

By randomly assigning students to various instructional approaches, they were able to isolate the effects of the teaching methods, leading to valuable insights and recommendations for educators.

Evolution of the Methodology

As the decades rolled on, random assignment continued to evolve and adapt to the changing landscape of research.

Advances in technology introduced new tools and techniques for implementing randomization, such as computerized random number generators, which offered greater precision and ease of use.

The application of random assignment expanded beyond the confines of the laboratory, finding its way into field studies and large-scale surveys.

Researchers across diverse disciplines embraced the methodology, recognizing its potential to enhance the validity of their findings and contribute to the advancement of knowledge.

From its humble beginnings in the early 20th century to its widespread use today, random assignment has proven to be a cornerstone of scientific inquiry.

Its development and evolution have played a pivotal role in shaping the landscape of psychological research, driving discoveries that have improved lives and deepened our understanding of the human experience.

Principles of Random Assignment

Delving into the heart of random assignment, we uncover the theories and principles that form its foundation.

The method is steeped in the basics of probability theory and statistical inference, ensuring that each participant has an equal chance of being placed in any group, thus fostering fair and unbiased results.

Basic Principles of Random Assignment

Understanding the core principles of random assignment is key to grasping its significance in research. There are three principles: equal probability of selection, reduction of bias, and ensuring representativeness.

The first principle, equal probability of selection , ensures that every participant has an identical chance of being assigned to any group in the study. This randomness is crucial as it mitigates the risk of bias and establishes a level playing field.

The second principle focuses on the reduction of bias . Random assignment acts as a safeguard, ensuring that the groups being compared are alike in all essential aspects before the experiment begins.

This similarity between groups allows researchers to attribute any differences observed in the outcomes directly to the independent variable being studied.

Lastly, ensuring representativeness is a vital principle. When participants are assigned randomly, the resulting groups are more likely to be representative of the larger population.

This characteristic is crucial for the generalizability of the study’s findings, allowing researchers to apply their insights broadly.

Theoretical Foundation

The theoretical foundation of random assignment lies in probability theory and statistical inference .

Probability theory deals with the likelihood of different outcomes, providing a mathematical framework for analyzing random phenomena. In the context of random assignment, it helps in ensuring that each participant has an equal chance of being placed in any group.

Statistical inference, on the other hand, allows researchers to draw conclusions about a population based on a sample of data drawn from that population. It is the mechanism through which the results of a study can be generalized to a broader context.

Random assignment enhances the reliability of statistical inferences by reducing biases and ensuring that the sample is representative.

Differentiating Random Assignment from Random Selection

It’s essential to distinguish between random assignment and random selection, as the two terms, while related, have distinct meanings in the realm of research.

Random assignment refers to how participants are placed into different groups in an experiment, aiming to control for confounding variables and help determine causes.

In contrast, random selection pertains to how individuals are chosen to participate in a study. This method is used to ensure that the sample of participants is representative of the larger population, which is vital for the external validity of the research.

While both methods are rooted in randomness and probability, they serve different purposes in the research process.

Understanding the theories, principles, and distinctions of random assignment illuminates its pivotal role in psychological research.

This method, anchored in probability theory and statistical inference, serves as a beacon of reliability, guiding researchers in their quest for knowledge and ensuring that their findings stand the test of validity and applicability.

Methodology of Random Assignment

Implementing random assignment in a study is a meticulous process that involves several crucial steps.

The initial step is participant selection, where individuals are chosen to partake in the study. This stage is critical to ensure that the pool of participants is diverse and representative of the population the study aims to generalize to.

Once the pool of participants has been established, the actual assignment process begins. In this step, each participant is allocated randomly to one of the groups in the study.

Researchers use various tools, such as random number generators or computerized methods, to ensure that this assignment is genuinely random and free from biases.

Monitoring and adjusting form the final step in the implementation of random assignment. Researchers need to continuously observe the groups to ensure that they remain comparable in all essential aspects throughout the study.

If any significant discrepancies arise, adjustments might be necessary to maintain the study’s integrity and validity.

Tools and Techniques Used

The evolution of technology has introduced a variety of tools and techniques to facilitate random assignment.

Random number generators, both manual and computerized, are commonly used to assign participants to different groups. These generators ensure that each individual has an equal chance of being placed in any group, upholding the principle of equal probability of selection.

In addition to random number generators, researchers often use specialized computer software designed for statistical analysis and experimental design.

These software programs offer advanced features that allow for precise and efficient random assignment, minimizing the risk of human error and enhancing the study’s reliability.

Ethical Considerations

The implementation of random assignment is not devoid of ethical considerations. Informed consent is a fundamental ethical principle that researchers must uphold.

Informed consent means that every participant should be fully informed about the nature of the study, the procedures involved, and any potential risks or benefits, ensuring that they voluntarily agree to participate.

Beyond informed consent, researchers must conduct a thorough risk and benefit analysis. The potential benefits of the study should outweigh any risks or harms to the participants.

Safeguarding the well-being of participants is paramount, and any study employing random assignment must adhere to established ethical guidelines and standards.

Conclusion of Methodology

The methodology of random assignment, while seemingly straightforward, is a multifaceted process that demands precision, fairness, and ethical integrity. From participant selection to assignment and monitoring, each step is crucial to ensure the validity of the study’s findings.

The tools and techniques employed, coupled with a steadfast commitment to ethical principles, underscore the significance of random assignment as a cornerstone of robust psychological research.

Benefits of Random Assignment in Psychological Research

The impact and importance of random assignment in psychological research cannot be overstated. It is fundamental for ensuring the study is accurate, allowing the researchers to determine if their study actually caused the results they saw, and making sure the findings can be applied to the real world.

Facilitating Causal Inferences

When participants are randomly assigned to different groups, researchers can be more confident that the observed effects are due to the independent variable being changed, and not other factors.

This ability to determine the cause is called causal inference .

This confidence allows for the drawing of causal relationships, which are foundational for theory development and application in psychology.

Ensuring Internal Validity

One of the foremost impacts of random assignment is its ability to enhance the internal validity of an experiment.

Internal validity refers to the extent to which a researcher can assert that changes in the dependent variable are solely due to manipulations of the independent variable , and not due to confounding variables.

By ensuring that each participant has an equal chance of being in any condition of the experiment, random assignment helps control for participant characteristics that could otherwise complicate the results.

Enhancing Generalizability

Beyond internal validity, random assignment also plays a crucial role in enhancing the generalizability of research findings.

When done correctly, it ensures that the sample groups are representative of the larger population, so can allow researchers to apply their findings more broadly.

This representative nature is essential for the practical application of research, impacting policy, interventions, and psychological therapies.

Limitations of Random Assignment

Potential for implementation issues.

While the principles of random assignment are robust, the method can face implementation issues.

One of the most common problems is logistical constraints. Some studies, due to their nature or the specific population being studied, find it challenging to implement random assignment effectively.

For instance, in educational settings, logistical issues such as class schedules and school policies might stop the random allocation of students to different teaching methods .

Ethical Dilemmas

Random assignment, while methodologically sound, can also present ethical dilemmas.

In some cases, withholding a potentially beneficial treatment from one of the groups of participants can raise serious ethical questions, especially in medical or clinical research where participants' well-being might be directly affected.

Researchers must navigate these ethical waters carefully, balancing the pursuit of knowledge with the well-being of participants.

Generalizability Concerns

Even when implemented correctly, random assignment does not always guarantee generalizable results.

The types of people in the participant pool, the specific context of the study, and the nature of the variables being studied can all influence the extent to which the findings can be applied to the broader population.

Researchers must be cautious in making broad generalizations from studies, even those employing strict random assignment.

Practical and Real-World Limitations

In the real world, many variables cannot be manipulated for ethical or practical reasons, limiting the applicability of random assignment.

For instance, researchers cannot randomly assign individuals to different levels of intelligence, socioeconomic status, or cultural backgrounds.

This limitation necessitates the use of other research designs, such as correlational or observational studies , when exploring relationships involving such variables.

Response to Critiques

In response to these critiques, people in favor of random assignment argue that the method, despite its limitations, remains one of the most reliable ways to establish cause and effect in experimental research.

They acknowledge the challenges and ethical considerations but emphasize the rigorous frameworks in place to address them.

The ongoing discussion around the limitations and critiques of random assignment contributes to the evolution of the method, making sure it is continuously relevant and applicable in psychological research.

While random assignment is a powerful tool in experimental research, it is not without its critiques and limitations. Implementation issues, ethical dilemmas, generalizability concerns, and real-world limitations can pose significant challenges.

However, the continued discourse and refinement around these issues underline the method's enduring significance in the pursuit of knowledge in psychology.

By being careful with how we do things and doing what's right, random assignment stays a really important part of studying how people act and think.

Real-World Applications and Examples

Random assignment has been employed in many studies across various fields of psychology, leading to significant discoveries and advancements.

Here are some real-world applications and examples illustrating the diversity and impact of this method:

Medicine and Health Psychology: Randomized Controlled Trials (RCTs) are the gold standard in medical research. In these studies, participants are randomly assigned to either the treatment or control group to test the efficacy of new medications or interventions.
Educational Psychology: Studies in this field have used random assignment to explore the effects of different teaching methods, classroom environments, and educational technologies on student learning and outcomes.
Cognitive Psychology: Researchers have employed random assignment to investigate various aspects of human cognition, including memory, attention, and problem-solving, leading to a deeper understanding of how the mind works.
Social Psychology: Random assignment has been instrumental in studying social phenomena, such as conformity, aggression, and prosocial behavior, shedding light on the intricate dynamics of human interaction.

Let's get into some specific examples. You'll need to know one term though, and that is "control group." A control group is a set of participants in a study who do not receive the treatment or intervention being tested , serving as a baseline to compare with the group that does, in order to assess the effectiveness of the treatment.

Smoking Cessation Study: Researchers used random assignment to put participants into two groups. One group received a new anti-smoking program, while the other did not. This helped determine if the program was effective in helping people quit smoking.
Math Tutoring Program: A study on students used random assignment to place them into two groups. One group received additional math tutoring, while the other continued with regular classes, to see if the extra help improved their grades.
Exercise and Mental Health: Adults were randomly assigned to either an exercise group or a control group to study the impact of physical activity on mental health and mood.
Diet and Weight Loss: A study randomly assigned participants to different diet plans to compare their effectiveness in promoting weight loss and improving health markers.
Sleep and Learning: Researchers randomly assigned students to either a sleep extension group or a regular sleep group to study the impact of sleep on learning and memory.
Classroom Seating Arrangement: Teachers used random assignment to place students in different seating arrangements to examine the effect on focus and academic performance.
Music and Productivity: Employees were randomly assigned to listen to music or work in silence to investigate the effect of music on workplace productivity.
Medication for ADHD: Children with ADHD were randomly assigned to receive either medication, behavioral therapy, or a placebo to compare treatment effectiveness.
Mindfulness Meditation for Stress: Adults were randomly assigned to a mindfulness meditation group or a waitlist control group to study the impact on stress levels.
Video Games and Aggression: A study randomly assigned participants to play either violent or non-violent video games and then measured their aggression levels.
Online Learning Platforms: Students were randomly assigned to use different online learning platforms to evaluate their effectiveness in enhancing learning outcomes.
Hand Sanitizers in Schools: Schools were randomly assigned to use hand sanitizers or not to study the impact on student illness and absenteeism.
Caffeine and Alertness: Participants were randomly assigned to consume caffeinated or decaffeinated beverages to measure the effects on alertness and cognitive performance.
Green Spaces and Well-being: Neighborhoods were randomly assigned to receive green space interventions to study the impact on residents’ well-being and community connections.
Pet Therapy for Hospital Patients: Patients were randomly assigned to receive pet therapy or standard care to assess the impact on recovery and mood.
Yoga for Chronic Pain: Individuals with chronic pain were randomly assigned to a yoga intervention group or a control group to study the effect on pain levels and quality of life.
Flu Vaccines Effectiveness: Different groups of people were randomly assigned to receive either the flu vaccine or a placebo to determine the vaccine’s effectiveness.
Reading Strategies for Dyslexia: Children with dyslexia were randomly assigned to different reading intervention strategies to compare their effectiveness.
Physical Environment and Creativity: Participants were randomly assigned to different room setups to study the impact of physical environment on creative thinking.
Laughter Therapy for Depression: Individuals with depression were randomly assigned to laughter therapy sessions or control groups to assess the impact on mood.
Financial Incentives for Exercise: Participants were randomly assigned to receive financial incentives for exercising to study the impact on physical activity levels.
Art Therapy for Anxiety: Individuals with anxiety were randomly assigned to art therapy sessions or a waitlist control group to measure the effect on anxiety levels.
Natural Light in Offices: Employees were randomly assigned to workspaces with natural or artificial light to study the impact on productivity and job satisfaction.
School Start Times and Academic Performance: Schools were randomly assigned different start times to study the effect on student academic performance and well-being.
Horticulture Therapy for Seniors: Older adults were randomly assigned to participate in horticulture therapy or traditional activities to study the impact on cognitive function and life satisfaction.
Hydration and Cognitive Function: Participants were randomly assigned to different hydration levels to measure the impact on cognitive function and alertness.
Intergenerational Programs: Seniors and young people were randomly assigned to intergenerational programs to study the effects on well-being and cross-generational understanding.
Therapeutic Horseback Riding for Autism: Children with autism were randomly assigned to therapeutic horseback riding or traditional therapy to study the impact on social communication skills.
Active Commuting and Health: Employees were randomly assigned to active commuting (cycling, walking) or passive commuting to study the effect on physical health.
Mindful Eating for Weight Management: Individuals were randomly assigned to mindful eating workshops or control groups to study the impact on weight management and eating habits.
Noise Levels and Learning: Students were randomly assigned to classrooms with different noise levels to study the effect on learning and concentration.
Bilingual Education Methods: Schools were randomly assigned different bilingual education methods to compare their effectiveness in language acquisition.
Outdoor Play and Child Development: Children were randomly assigned to different amounts of outdoor playtime to study the impact on physical and cognitive development.
Social Media Detox: Participants were randomly assigned to a social media detox or regular usage to study the impact on mental health and well-being.
Therapeutic Writing for Trauma Survivors: Individuals who experienced trauma were randomly assigned to therapeutic writing sessions or control groups to study the impact on psychological well-being.
Mentoring Programs for At-risk Youth: At-risk youth were randomly assigned to mentoring programs or control groups to assess the impact on academic achievement and behavior.
Dance Therapy for Parkinson’s Disease: Individuals with Parkinson’s disease were randomly assigned to dance therapy or traditional exercise to study the effect on motor function and quality of life.
Aquaponics in Schools: Schools were randomly assigned to implement aquaponics programs to study the impact on student engagement and environmental awareness.
Virtual Reality for Phobia Treatment: Individuals with phobias were randomly assigned to virtual reality exposure therapy or traditional therapy to compare effectiveness.
Gardening and Mental Health: Participants were randomly assigned to engage in gardening or other leisure activities to study the impact on mental health and stress reduction.

Each of these studies exemplifies how random assignment is utilized in various fields and settings, shedding light on the multitude of ways it can be applied to glean valuable insights and knowledge.

Real-world Impact of Random Assignment

Random assignment is like a key tool in the world of learning about people's minds and behaviors. It’s super important and helps in many different areas of our everyday lives. It helps make better rules, creates new ways to help people, and is used in lots of different fields.

Health and Medicine

In health and medicine, random assignment has helped doctors and scientists make lots of discoveries. It’s a big part of tests that help create new medicines and treatments.

By putting people into different groups by chance, scientists can really see if a medicine works.

This has led to new ways to help people with all sorts of health problems, like diabetes, heart disease, and mental health issues like depression and anxiety.

Schools and education have also learned a lot from random assignment. Researchers have used it to look at different ways of teaching, what kind of classrooms are best, and how technology can help learning.

This knowledge has helped make better school rules, develop what we learn in school, and find the best ways to teach students of all ages and backgrounds.

Workplace and Organizational Behavior

Random assignment helps us understand how people act at work and what makes a workplace good or bad.

Studies have looked at different kinds of workplaces, how bosses should act, and how teams should be put together. This has helped companies make better rules and create places to work that are helpful and make people happy.

Environmental and Social Changes

Random assignment is also used to see how changes in the community and environment affect people. Studies have looked at community projects, changes to the environment, and social programs to see how they help or hurt people’s well-being.

This has led to better community projects, efforts to protect the environment, and programs to help people in society.

Technology and Human Interaction

In our world where technology is always changing, studies with random assignment help us see how tech like social media, virtual reality, and online stuff affect how we act and feel.

This has helped make better and safer technology and rules about using it so that everyone can benefit.

The effects of random assignment go far and wide, way beyond just a science lab. It helps us understand lots of different things, leads to new and improved ways to do things, and really makes a difference in the world around us.

From making healthcare and schools better to creating positive changes in communities and the environment, the real-world impact of random assignment shows just how important it is in helping us learn and make the world a better place.

So, what have we learned? Random assignment is like a super tool in learning about how people think and act. It's like a detective helping us find clues and solve mysteries in many parts of our lives.

From creating new medicines to helping kids learn better in school, and from making workplaces happier to protecting the environment, it’s got a big job!

This method isn’t just something scientists use in labs; it reaches out and touches our everyday lives. It helps make positive changes and teaches us valuable lessons.

Whether we are talking about technology, health, education, or the environment, random assignment is there, working behind the scenes, making things better and safer for all of us.

In the end, the simple act of putting people into groups by chance helps us make big discoveries and improvements. It’s like throwing a small stone into a pond and watching the ripples spread out far and wide.

Thanks to random assignment, we are always learning, growing, and finding new ways to make our world a happier and healthier place for everyone!

19+ Experimental Design Examples (Methods + Types)
Cluster Sampling vs Stratified Sampling
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Simple Random Sampling | Definition, Steps & Examples

Simple Random Sampling | Definition, Steps & Examples

Published on August 28, 2020 by Lauren Thomas . Revised on December 18, 2023.

A simple random sample is a randomly selected subset of a population. In this sampling method, each member of the population has an exactly equal chance of being selected.

This method is the most straightforward of all the probability sampling methods , since it only involves a single random selection and requires little advance knowledge about the population. Because it uses randomization, any research performed on this sample should have high internal and external validity, and be at a lower risk for research biases like sampling bias and selection bias .

When to use simple random sampling, how to perform simple random sampling, other interesting articles, frequently asked questions about simple random sampling.

Simple random sampling is used to make statistical inferences about a population. It helps ensure high internal validity : randomization is the best method to reduce the impact of potential confounding variables .

In addition, with a large enough sample size, a simple random sample has high external validity : it represents the characteristics of the larger population.

However, simple random sampling can be challenging to implement in practice. To use this method, there are some prerequisites:

You have a complete list of every member of the population .
You can contact or access each member of the population if they are selected.
You have the time and resources to collect data from the necessary sample size.

Simple random sampling works best if you have a lot of time and resources to conduct your study, or if you are studying a limited population that can easily be sampled.

In some cases, it might be more appropriate to use a different type of probability sampling:

Systematic sampling involves choosing your sample based on a regular interval, rather than a fully random selection. It can also be used when you don’t have a complete list of the population.
Stratified sampling is appropriate when you want to ensure that specific characteristics are proportionally represented in the sample. You split your population into strata (for example, divided by gender or race), and then randomly select from each of these subgroups.
Cluster sampling is appropriate when you are unable to sample from the entire population. You divide the sample into clusters that approximately reflect the whole population, and then choose your sample from a random selection of these clusters.

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There are 4 key steps to select a simple random sample.

Step 1: Define the population

Start by deciding on the population that you want to study.

It’s important to ensure that you have access to every individual member of the population, so that you can collect data from all those who are selected for the sample.

Step 2: Decide on the sample size

Next, you need to decide how large your sample size will be. Although larger samples provide more statistical certainty, they also cost more and require far more work.

There are several potential ways to decide upon the size of your sample, but one of the simplest involves using a formula with your desired confidence interval and confidence level , estimated size of the population you are working with, and the standard deviation of whatever you want to measure in your population.

The most common confidence interval and levels used are 0.05 and 0.95, respectively. Since you may not know the standard deviation of the population you are studying, you should choose a number high enough to account for a variety of possibilities (such as 0.5).

You can then use a sample size calculator to estimate the necessary sample size.

Step 3: Randomly select your sample

This can be done in one of two ways: the lottery or random number method.

In the lottery method , you choose the sample at random by “drawing from a hat” or by using a computer program that will simulate the same action.

In the random number method , you assign every individual a number. By using a random number generator or random number tables, you then randomly pick a subset of the population. You can also use the random number function (RAND) in Microsoft Excel to generate random numbers.

Step 4: Collect data from your sample

Finally, you should collect data from your sample.

To ensure the validity of your findings, you need to make sure every individual selected actually participates in your study. If some drop out or do not participate for reasons associated with the question that you’re studying, this could bias your findings.

For example, if young participants are systematically less likely to participate in your study, your findings might not be valid due to the underrepresentation of this group.

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
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Statistics By Jim

Making statistics intuitive

Random Assignment in Experiments

By Jim Frost 4 Comments

Random assignment uses chance to assign subjects to the control and treatment groups in an experiment. This process helps ensure that the groups are equivalent at the beginning of the study, which makes it safer to assume the treatments caused any differences between groups that the experimenters observe at the end of the study.

photogram of tumbling dice to illustrate a process for random assignment.

Huh? That might be a big surprise! At this point, you might be wondering about all of those studies that use statistics to assess the effects of different treatments. There’s a critical separation between significance and causality:

Statistical procedures determine whether an effect is significant.
Experimental designs determine how confidently you can assume that a treatment causes the effect.

In this post, learn how using random assignment in experiments can help you identify causal relationships.

Correlation, Causation, and Confounding Variables

Random assignment helps you separate causation from correlation and rule out confounding variables. As a critical component of the scientific method , experiments typically set up contrasts between a control group and one or more treatment groups. The idea is to determine whether the effect, which is the difference between a treatment group and the control group, is statistically significant. If the effect is significant, group assignment correlates with different outcomes.

However, as you have no doubt heard, correlation does not necessarily imply causation. In other words, the experimental groups can have different mean outcomes, but the treatment might not be causing those differences even though the differences are statistically significant.

The difficulty in definitively stating that a treatment caused the difference is due to potential confounding variables or confounders. Confounders are alternative explanations for differences between the experimental groups. Confounding variables correlate with both the experimental groups and the outcome variable. In this situation, confounding variables can be the actual cause for the outcome differences rather than the treatments themselves. As you’ll see, if an experiment does not account for confounding variables, they can bias the results and make them untrustworthy.

Related posts : Understanding Correlation in Statistics , Causation versus Correlation , and Hill’s Criteria for Causation .

Example of Confounding in an Experiment

A photograph of vitamin capsules to represent our experiment.

Control group: Does not consume vitamin supplements
Treatment group: Regularly consumes vitamin supplements.

Imagine we measure a specific health outcome. After the experiment is complete, we perform a 2-sample t-test to determine whether the mean outcomes for these two groups are different. Assume the test results indicate that the mean health outcome in the treatment group is significantly better than the control group.

Why can’t we assume that the vitamins improved the health outcomes? After all, only the treatment group took the vitamins.

Related post : Confounding Variables in Regression Analysis

Alternative Explanations for Differences in Outcomes

The answer to that question depends on how we assigned the subjects to the experimental groups. If we let the subjects decide which group to join based on their existing vitamin habits, it opens the door to confounding variables. It’s reasonable to assume that people who take vitamins regularly also tend to have other healthy habits. These habits are confounders because they correlate with both vitamin consumption (experimental group) and the health outcome measure.

Random assignment prevents this self sorting of participants and reduces the likelihood that the groups start with systematic differences.

In fact, studies have found that supplement users are more physically active, have healthier diets, have lower blood pressure, and so on compared to those who don’t take supplements. If subjects who already take vitamins regularly join the treatment group voluntarily, they bring these healthy habits disproportionately to the treatment group. Consequently, these habits will be much more prevalent in the treatment group than the control group.

The healthy habits are the confounding variables—the potential alternative explanations for the difference in our study’s health outcome. It’s entirely possible that these systematic differences between groups at the start of the study might cause the difference in the health outcome at the end of the study—and not the vitamin consumption itself!

If our experiment doesn’t account for these confounding variables, we can’t trust the results. While we obtained statistically significant results with the 2-sample t-test for health outcomes, we don’t know for sure whether the vitamins, the systematic difference in habits, or some combination of the two caused the improvements.

Learn why many randomized clinical experiments use a placebo to control for the Placebo Effect .

Experiments Must Account for Confounding Variables

Your experimental design must account for confounding variables to avoid their problems. Scientific studies commonly use the following methods to handle confounders:

Use control variables to keep them constant throughout an experiment.
Statistically control for them in an observational study.
Use random assignment to reduce the likelihood that systematic differences exist between experimental groups when the study begins.

Let’s take a look at how random assignment works in an experimental design.

Random Assignment Can Reduce the Impact of Confounding Variables

Note that random assignment is different than random sampling. Random sampling is a process for obtaining a sample that accurately represents a population .

Photo of a coin toss to represent how we can incorporate random assignment in our experiment.

Random assignment uses a chance process to assign subjects to experimental groups. Using random assignment requires that the experimenters can control the group assignment for all study subjects. For our study, we must be able to assign our participants to either the control group or the supplement group. Clearly, if we don’t have the ability to assign subjects to the groups, we can’t use random assignment!

Additionally, the process must have an equal probability of assigning a subject to any of the groups. For example, in our vitamin supplement study, we can use a coin toss to assign each subject to either the control group or supplement group. For more complex experimental designs, we can use a random number generator or even draw names out of a hat.

Random Assignment Distributes Confounders Equally

The random assignment process distributes confounding properties amongst your experimental groups equally. In other words, randomness helps eliminate systematic differences between groups. For our study, flipping the coin tends to equalize the distribution of subjects with healthier habits between the control and treatment group. Consequently, these two groups should start roughly equal for all confounding variables, including healthy habits!

Random assignment is a simple, elegant solution to a complex problem. For any given study area, there can be a long list of confounding variables that you could worry about. However, using random assignment, you don’t need to know what they are, how to detect them, or even measure them. Instead, use random assignment to equalize them across your experimental groups so they’re not a problem.

Because random assignment helps ensure that the groups are comparable when the experiment begins, you can be more confident that the treatments caused the post-study differences. Random assignment helps increase the internal validity of your study.

Comparing the Vitamin Study With and Without Random Assignment

Let’s compare two scenarios involving our hypothetical vitamin study. We’ll assume that the study obtains statistically significant results in both cases.

Scenario 1: We don’t use random assignment and, unbeknownst to us, subjects with healthier habits disproportionately end up in the supplement treatment group. The experimental groups differ by both healthy habits and vitamin consumption. Consequently, we can’t determine whether it was the habits or vitamins that improved the outcomes.

Scenario 2: We use random assignment and, consequently, the treatment and control groups start with roughly equal levels of healthy habits. The intentional introduction of vitamin supplements in the treatment group is the primary difference between the groups. Consequently, we can more confidently assert that the supplements caused an improvement in health outcomes.

For both scenarios, the statistical results could be identical. However, the methodology behind the second scenario makes a stronger case for a causal relationship between vitamin supplement consumption and health outcomes.

How important is it to use the correct methodology? Well, if the relationship between vitamins and health outcomes is not causal, then consuming vitamins won’t cause your health outcomes to improve regardless of what the study indicates. Instead, it’s probably all the other healthy habits!

Learn more about Randomized Controlled Trials (RCTs) that are the gold standard for identifying causal relationships because they use random assignment.

Drawbacks of Random Assignment

Random assignment helps reduce the chances of systematic differences between the groups at the start of an experiment and, thereby, mitigates the threats of confounding variables and alternative explanations. However, the process does not always equalize all of the confounding variables. Its random nature tends to eliminate systematic differences, but it doesn’t always succeed.

Sometimes random assignment is impossible because the experimenters cannot control the treatment or independent variable. For example, if you want to determine how individuals with and without depression perform on a test, you cannot randomly assign subjects to these groups. The same difficulty occurs when you’re studying differences between genders.

In other cases, there might be ethical issues. For example, in a randomized experiment, the researchers would want to withhold treatment for the control group. However, if the treatments are vaccinations, it might be unethical to withhold the vaccinations.

Other times, random assignment might be possible, but it is very challenging. For example, with vitamin consumption, it’s generally thought that if vitamin supplements cause health improvements, it’s only after very long-term use. It’s hard to enforce random assignment with a strict regimen for usage in one group and non-usage in the other group over the long-run. Or imagine a study about smoking. The researchers would find it difficult to assign subjects to the smoking and non-smoking groups randomly!

Fortunately, if you can’t use random assignment to help reduce the problem of confounding variables, there are different methods available. The other primary approach is to perform an observational study and incorporate the confounders into the statistical model itself. For more information, read my post Observational Studies Explained .

Read About Real Experiments that Used Random Assignment

I’ve written several blog posts about studies that have used random assignment to make causal inferences. Read studies about the following:

Flu Vaccinations
COVID-19 Vaccinations

Sullivan L. Random assignment versus random selection . SAGE Glossary of the Social and Behavioral Sciences, SAGE Publications, Inc.; 2009.

Reader Interactions

November 13, 2019 at 4:59 am

Hi Jim, I have a question of randomly assigning participants to one of two conditions when it is an ongoing study and you are not sure of how many participants there will be. I am using this random assignment tool for factorial experiments. http://methodologymedia.psu.edu/most/rannumgenerator It asks you for the total number of participants but at this point, I am not sure how many there will be. Thanks for any advice you can give me, Floyd

May 28, 2019 at 11:34 am

Jim, can you comment on the validity of using the following approach when we can’t use random assignments. I’m in education, we have an ACT prep course that we offer. We can’t force students to take it and we can’t keep them from taking it either. But we want to know if it’s working. Let’s say that by senior year all students who are going to take the ACT have taken it. Let’s also say that I’m only including students who have taking it twice (so I can show growth between first and second time taking it). What I’ve done to address confounders is to go back to say 8th or 9th grade (prior to anyone taking the ACT or the ACT prep course) and run an analysis showing the two groups are not significantly different to start with. Is this valid? If the ACT prep students were higher achievers in 8th or 9th grade, I could not assume my prep course is effecting greater growth, but if they were not significantly different in 8th or 9th grade, I can assume the significant difference in ACT growth (from first to second testing) is due to the prep course. Yes or no?

May 26, 2019 at 5:37 pm

Nice post! I think the key to understanding scientific research is to understand randomization. And most people don’t get it.

May 27, 2019 at 9:48 pm

Thank you, Anoop!

I think randomness in an experiment is a funny thing. The issue of confounding factors is a serious problem. You might not even know what they are! But, use random assignment and, voila, the problem usually goes away! If you can’t use random assignment, suddenly you have a whole host of issues to worry about, which I’ll be writing about in more detail in my upcoming post about observational experiments!

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About Randomizers

With a randomizer in your survey flow , you can randomly present question blocks , embedded data , and other survey flow elements to your respondents.

Adding a Randomizer

Any items in your survey flow can be randomized. This randomization can happen at any point in the flow.

To add a Randomizer

Survey Flow button in the Survey tab. On block Add Below button, beneath blocks Add a New Element Here button. What do you want to add? menu with Randomizer selected

Click Add Below or Add a New Element Here .
Select Randomizer . Qtip: If the randomizer isn’t created exactly where you want it in the survey flow, don’t worry. You can move elements in the survey flow by selecting the item and pressing ↑ or ↓ on your keyboard. You can also click and hold Move to drag and drop the element in your survey flow.

Enter the number of elements you want to present to your respondents.
If desired, click the checkbox for Evenly Present Elements to present each element a roughly equal number of times across all respondents.
Click Apply .

Evenly Present Elements

When selected, “Evenly Present Elements” makes sure that even as elements are randomized, they are randomized evenly. This setting keeps careful watch on the counts to ensure every element is presented an equal number of times.

When deselected, items are randomly presented with no attention paid to whether the number of times they’ve been presented is equal or not.

Evenly Present Elements selected on the lower-right of the randomizer elements

Editing Counts

When Evenly Present Elements is selected, you can click Edit Count to view how many times each element has been displayed.

Edit Count on lower-right of the Randomizer element

These numbers should be roughly the same, but you can override the counts if you need to temporarily give preference to a specific element. In the below example, if you set question block Comp & Benefits to “5” and the others are set at “15”, the Comp & Benefits block will be displayed 10 times in a row to “catch up” with the other blocks and ensure even presentation.

window with each block indicated in gray with a yellow box where you type the count for each block

You also have the option to reset all counts back to 0 or to restore all counts to what they were before you changed them.

Restore Original Counts : This option will restore the counts back to their original number before you edited them. Warning: This option only restores counts for the timeframe in which you have the Edit Counts window open. If you edit your counts, save the changes, then return to edit the counts, the Restore Original Counts option will not revert your counts to before your edits.
Reset All Counts : This option will set the count for all elements to 0.

Using the Randomizer with Other Elements

In addition to blocks, you can use the randomizer to randomize other elements in the survey flow. For example, you can use a randomizer to randomly assign specific embedded data labels to each respondent or to randomly send your respondents to different survey endings with end of survey elements.

You can also randomize branches to show elements conditionally or to group multiple elements together (like blocks, end of survey elements, and embedded data). Even if you don’t want to conditionally present items, you’ll need to use a branch to group them together.

Branches under randomizer organize blocks to be presented together

To group multiple elements

If you are not planning on setting different conditions with branches , you can use the group element to keep elements grouped together in the survey flow.

Survey Flow button in the Survey tab. On the Randomizer, Add Below on the lower-right or Add a New Element indented below. Group element selected

Click Add Below or Add a New Element Here under the randomizer.
Select Group .

Items indented under a group. Adjusting randomizer count to 1

Repeat steps as needed until all groups and sets are in place.
Adjust your randomizer count accordingly.

Export Randomized Viewing Order

Downloading randomization data allows you to view the order in which your survey elements were displayed to respondents.

Each survey flow element has a unique Flow ID, which can be viewed when you open the survey flow and select Show Flow IDs .

Show flow IDs selected in upper-right of the survey flow. Upper-right of every element, a new ID has appeared, where it says FL underscore and then a number.

These IDs are used in the exported data to identify which elements within the randomizer were shown to the respondent. This column of data usually appears towards the end of the exported data file.

Types of Projects this Feature Is Available In

Randomizers are compatible with every type of project that lets you customize a survey. This includes:

Survey projects
Ad Hoc Employee Research
XM Solutions

Time Between Ticket Statuses

The longer a call center representative ignores a ticket, the longer the customer goes without a solution, and the more likely that the customers will be (justifiably) upset. Therefore, it can be incredibly important to a close-the-loop program that there are report-outs on the time taken to resolve a ticket. In Qualtrics, you can report on the time between various ticket statuses, gaining insight into the time it takes your team to take action on or resolve issues.

Viewing Your Support History

After logging into your Customer Success Hub, you can view your past support ticket history, including transcripts of your interactions with support and any connected escalations, and reply directly to email tickets you’ve submitted. If you are a Brand Administrator, you can also view the support tickets for your entire brand from this page.

Getting Technical Support

You can get in contact with the Qualtrics Support Team through your Customer Success Hub. This option is ideal for troubleshooting, technical issues, and specific XM platform questions.

Survey Flow (EX)

The survey flow is a block-level view of your survey. From here, you can customize where participants go in your survey and what they see. You can perform simple tasks like rearranging blocks, or you can add elements like branch logic that direct and personalize your participants’ survey experience. Participants will start at the top of the flow and work their way through until they reach an end point and finish the survey.

COVID-19 Brand Trust Pulse

In this time of intense uncertainty and uncharted territories, what makes a brand trusted today has shifted. Brands need to understand how consumers have reprioritized the drivers of trust, and how their actions during this crisis can positively (or negatively) impact consumers’ trust.

Block Options

Blocks are sets of questions within your survey. Typically, questions are separated into blocks for the purpose of conditionally displaying an entire block of questions, or for randomly presenting entire blocks of questions. These branching and randomization options are found in the survey flow.

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Random Assignment

Randomizer: (in a survey flow) you can randomly present question blocks, Embedded Data, and other Survey Flow elements to your respondents.

Example: You could use the Randomizer element to assign participants to either a control block of questions or an experimental block of questions.

Create the desired blocks in your survey (consider naming them to make it easier to locate in the “Survey Flow”).
When you have completed all of your blocks, open up “Survey Flow.”

Click “Evenly Present Elements” to ensure that each of the nested blocks is roughly presented a equal number of times across all participants.
Remember to “Save Flow” when you’re done.

You can use the “Randomizer” with other elements within Survey Flow as well. For example, you may want to randomize your independent variables (control and experimental) using “Embedded Data” as shown below.

Using this feature, each participant will be randomly assigned to a Group or Variable which can be used as criteria in a “Branch logic,” “Display logic,” or other logic, which address in the actual survey on the specific block(s). *See “Branch logic” for more help*

[You can even randomize Branches!]

For your information, you can randomize nearly anything in Qualtrics’ “Survey Flow” to show elements conditionally or to group multiple elements together (like blocks, End of Survey Elements, and Embedded Data).

COMMENTS

New advanced logic feature: Random Assignment
Our new feature, Random Assignment, lets you randomly assign each survey respondent to a group which is shown a particular version of a question, an image, or descriptive text . For example, you might want half of your survey takers to see one image, and the other half to see a different image, to see how the two different images affect responses.
Random Assignment in Experiments
Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups. While random sampling is used in many types of studies, random assignment is only used ...
A/B Tests (Random Assignment)
Click an existing question to edit it. Or, add a new question to your survey. Click the Options tab. Click Enable Question Text A/B Test (Random Assignment). Go back to the Edit tab. In the A field, enter the question text for the first variable. Click + to add another variable if needed.
What's the difference between random assignment and random ...
Random selection, or random sampling, is a way of selecting members of a population for your study's sample. In contrast, random assignment is a way of sorting the sample into control and experimental groups. Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal ...
Random assignment of Question A/B
Therefore I created a random number as {rand(1,2)} and set it up in the previous group as "equation" question type. The next question group then has the questions/texts A and B with the relevance as ((randnumber=="1")) or "2" respectively. So that, if the hidden random number question generates "1", text A should be shown.
Limesurvey: How to Randomly Assign Participants to Different Conditions
For the Group C survey, use the random assignment procedure detailed in this post. That should do what you need. If you wanted to get creative, you could try doing it all in one survey. I don't know if it is possible to nest random assignment within a survey as I haven't tried it (i.e., have one random assignment variable at the beginning ...
Random Assignment in Psychology: Definition & Examples
Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study. On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. Random selection ensures that everyone in the population has an equal ...
The Definition of Random Assignment In Psychology
Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the treatment group versus the control group.
Random Selection vs. Random Assignment
Random selection and random assignment are two techniques in statistics that are commonly used, but are commonly confused.. Random selection refers to the process of randomly selecting individuals from a population to be involved in a study.. Random assignment refers to the process of randomly assigning the individuals in a study to either a treatment group or a control group.
Random Selection & Assignment
Random Selection & Assignment. Random selection is how you draw the sample of people for your study from a population.Random assignment is how you assign the sample that you draw to different groups or treatments in your study.. It is possible to have both random selection and assignment in a study. Let's say you drew a random sample of 100 clients from a population list of 1000 current ...
Random Sampling vs. Random Assignment
Random assignment is a fundamental part of a "true" experiment because it helps ensure that any differences found between the groups are attributable to the treatment, rather than a confounding variable. So, to summarize, random sampling refers to how you select individuals from the population to participate in your study. Random assignment ...
Random assignment in Qualtrics : Wild World of Research
Basic set up for random assignment in Qualtrics: You will need four blocks on Qualtrics. Consent and demographics. News stimulus block. Reality TV stimulus block. Outcome variable block. First step: set up your blocks in the initial screen you get after creating a new survey: Once you have your four blocks, select Survey Flow from the top tabs:
Library: Random Stimulus Assignment (within a survey)
Introduction. Random Stimulus Assignment enables a researcher to randomly presents 1 of X stimuli, within a survey, while automatically recording which stimuli was presented. This feature makes true experimental designs possible and can be combined with other advanced features such as Question Logic and Random Assignment (between surveys) to ...
Research Randomizer
RANDOM SAMPLING AND. RANDOM ASSIGNMENT MADE EASY! Research Randomizer is a free resource for researchers and students in need of a quick way to generate random numbers or assign participants to experimental conditions. This site can be used for a variety of purposes, including psychology experiments, medical trials, and survey research.
Random Assignment in Psychology (Definition + 40 Examples)
The application of random assignment expanded beyond the confines of the laboratory, finding its way into field studies and large-scale surveys. Researchers across diverse disciplines embraced the methodology, recognizing its potential to enhance the validity of their findings and contribute to the advancement of knowledge.
Simple Random Sampling
Revised on December 18, 2023. A simple random sample is a randomly selected subset of a population. In this sampling method, each member of the population has an exactly equal chance of being selected. This method is the most straightforward of all the probability sampling methods, since it only involves a single random selection and requires ...
Randomization: Sample, Assignment and Sequence
This random assignment of participants to experimental (and control) groups is useful in controlling for unknown, unobservable and random variables. ... When designing an experiment or survey, considerations can be made on whether certain questions, stimuli or blocks can be randomized without detracting from the user experience.
Random Assignment in Experiments
Correlation, Causation, and Confounding Variables. Random assignment helps you separate causation from correlation and rule out confounding variables. As a critical component of the scientific method, experiments typically set up contrasts between a control group and one or more treatment groups. The idea is to determine whether the effect, which is the difference between a treatment group and ...
Microsoft Forms: Random Assignment of Survey Participants
Here's how you can do it: 1. Create a question in your survey that asks participants to enter a random number between 1 and 100. 2. Use the branching feature in Microsoft Forms to create two different paths for participants based on their response to the random number question. 3. For one path, set the branching condition to "If the response is ...
Randomizer
Step 1: Preparing for Your Employee Engagement Survey; Step 2: Building Your Engagement Survey; Step 3: Configuring Project Participants & Distributing Your Project; Step 4: Reporting on Your Employee Engagement Project Results; Step 5: Closing Your Project & Preparing for Next Year's Project; New Dashboards Experience
Random Assignment
Random Assignment. Randomizer: (in a survey flow) you can randomly present question blocks, Embedded Data, and other Survey Flow elements to your respondents. Example: You could use the Randomizer element to assign participants to either a control block of questions or an experimental block of questions. Create the desired blocks in your survey ...
PDF Random assignment: It's all in the cards
2. Explain HOW you (the researcher) will conduct random assignment. 3. Argue WHY you (the researcher) will conduct random assignment. In your answer, be sure to discuss at least one confounding variable that is equally distributed between the control and experimental groups. Underline the confounding variable.
Randomization
Randomization is a statistical process in which a random mechanism is employed to select a sample from a population or assign subjects to different groups. [1] [2] [3] The process is crucial in ensuring the random allocation of experimental units or treatment protocols, thereby minimizing selection bias and enhancing the statistical validity. [4]It facilitates the objective comparison of ...

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Random Assignment in Psychology: Definition & Examples

Importance

Random Selection vs. Random Assignment

Random Assignment vs Random Sampling

When to Use Random Assignment

How to Use Random Assignment

When is Random Assignment not used?

Drawbacks of Random Assignment

What is the difference between random sampling and random assignment?

Does random assignment increase internal validity?

Does random assignment reduce sampling error?

When is random assignment not possible?

Does random assignment eliminate confounding variables?

Why is random assignment of participants to treatment conditions in an experiment used?

Further Reading

The Definition of Random Assignment According to Psychology

Random Assignment In Research

Random Selection

Example of Random Assignment

A Word From Verywell

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Basic set up for random assignment in Qualtrics:

First step: set up your blocks in the initial screen you get after creating a new survey: Once you have your four blocks, select Survey Flow from the top tabs: You will then be able to set up your random assignment on this screen: Blocks are presented to participants in order.

Select the quantity of blocks you wish each participant to view.

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Library: Random Stimulus Assignment (within a survey)

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Random Assignment in Psychology (Definition + 40 Examples)

History of Random Assignment

Early Studies Utilizing Random Assignment

Evolution of the Methodology

Principles of Random Assignment

Basic Principles of Random Assignment

Theoretical Foundation

Differentiating Random Assignment from Random Selection

Methodology of Random Assignment

Tools and Techniques Used

Ethical Considerations

Conclusion of Methodology

Benefits of Random Assignment in Psychological Research

Facilitating Causal Inferences

Ensuring Internal Validity

Enhancing Generalizability

Limitations of Random Assignment

Ethical Dilemmas

Generalizability Concerns

Practical and Real-World Limitations

Response to Critiques

Real-World Applications and Examples

Real-world Impact of Random Assignment

Health and Medicine

Workplace and Organizational Behavior

Environmental and Social Changes

Technology and Human Interaction

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