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  • Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on March 8, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomized designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors, not research biases like sampling bias or selection bias .

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, other interesting articles, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment and avoid biases.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

  • a control group that’s given a placebo (no dosage, to control for a placebo effect ),
  • an experimental group that’s given a low dosage,
  • a second experimental group that’s given a high dosage.

Random assignment to helps you make sure that the treatment groups don’t differ in systematic ways at the start of the experiment, as this can seriously affect (and even invalidate) your work.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

  • participants recruited from cafes are placed in the control group ,
  • participants recruited from local community centers are placed in the low dosage experimental group,
  • participants recruited from gyms are placed in the high dosage group.

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym-users may tend to engage in more healthy behaviors than people who frequent cafes or community centers, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

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Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

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 in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sample vs random assignment

Random sampling enhances the external validity or generalizability of your results, because it helps ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

  • a control group that receives no intervention.
  • an experimental group that has a remote team-building intervention every week for a month.

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

  • Random number generator: Use a computer program to generate random numbers from the list for each group.
  • Lottery method: Place all numbers individually in a hat or a bucket, and draw numbers at random for each group.
  • Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
  • Use a dice: When you have three groups, 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 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomized block design involves placing participants into blocks based on a shared characteristic (e.g., college students versus graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing men and women or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women, etc.). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviors, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers). These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

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.

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  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
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Research bias

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

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

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 validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

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  • J Athl Train
  • v.43(2); Mar-Apr 2008

Issues in Outcomes Research: An Overview of Randomization Techniques for Clinical Trials

Minsoo kang.

1 Middle Tennessee State University, Murfreesboro, TN

Brian G Ragan

2 University of Northern Iowa, Cedar Falls, IA

Jae-Hyeon Park

3 Korea National Sport University, Seoul, Korea

To review and describe randomization techniques used in clinical trials, including simple, block, stratified, and covariate adaptive techniques.

Background:

Clinical trials are required to establish treatment efficacy of many athletic training procedures. In the past, we have relied on evidence of questionable scientific merit to aid the determination of treatment choices. Interest in evidence-based practice is growing rapidly within the athletic training profession, placing greater emphasis on the importance of well-conducted clinical trials. One critical component of clinical trials that strengthens results is random assignment of participants to control and treatment groups. Although randomization appears to be a simple concept, issues of balancing sample sizes and controlling the influence of covariates a priori are important. Various techniques have been developed to account for these issues, including block, stratified randomization, and covariate adaptive techniques.

Advantages:

Athletic training researchers and scholarly clinicians can use the information presented in this article to better conduct and interpret the results of clinical trials. Implementing these techniques will increase the power and validity of findings of athletic medicine clinical trials, which will ultimately improve the quality of care provided.

Outcomes research is critical in the evidence-based health care environment because it addresses scientific questions concerning the efficacy of treatments. Clinical trials are considered the “gold standard” for outcomes in biomedical research. In athletic training, calls for more evidence-based medical research, specifically clinical trials, have been issued. 1 , 2

The strength of clinical trials is their superior ability to measure change over time from a treatment. Treatment differences identified from cross-sectional observational designs rather than experimental clinical trials have methodologic weaknesses, including confounding, cohort effects, and selection bias. 3 For example, using a nonrandomized trial to examine the effectiveness of prophylactic knee bracing to prevent medial collateral ligament injuries may suffer from confounders and jeopardize the results. One possible confounder is a history of knee injuries. Participants with a history of knee injuries may be more likely to wear braces than those with no such history. Participants with a history of injury are more likely to suffer additional knee injuries, unbalancing the groups and influencing the results of the study.

The primary goal of comparative clinical trials is to provide comparisons of treatments with maximum precision and validity. 4 One critical component of clinical trials is random assignment of participants into groups. Randomizing participants helps remove the effect of extraneous variables (eg, age, injury history) and minimizes bias associated with treatment assignment. Randomization is considered by most researchers to be the optimal approach for participant assignment in clinical trials because it strengthens the results and data interpretation. 4 – , 9

One potential problem with small clinical trials (n < 100) 7 is that conventional simple randomization methods, such as flipping a coin, may result in imbalanced sample size and baseline characteristics (ie, covariates) among treatment and control groups. 9 , 10 This imbalance of baseline characteristics can influence the comparison between treatment and control groups and introduce potential confounding factors. Many procedures have been proposed for random group assignment of participants in clinical trials. 11 Simple, block, stratified, and covariate adaptive randomizations are some examples. Each technique has advantages and disadvantages, which must be carefully considered before a method is selected. Our purpose is to introduce the concept and significance of randomization and to review several conventional and relatively new randomization techniques to aid in the design and implementation of valid clinical trials.

What Is Randomization?

Randomization is the process of assigning participants to treatment and control groups, assuming that each participant has an equal chance of being assigned to any group. 12 Randomization has evolved into a fundamental aspect of scientific research methodology. Demands have increased for more randomized clinical trials in many areas of biomedical research, such as athletic training. 2 , 13 In fact, in the last 2 decades, internationally recognized major medical journals, such as the Journal of the American Medical Association and the BMJ , have been increasingly interested in publishing studies reporting results from randomized controlled trials. 5

Since Fisher 14 first introduced the idea of randomization in a 1926 agricultural study, the academic community has deemed randomization an essential tool for unbiased comparisons of treatment groups. Five years after Fisher's introductory paper, the first randomized clinical trial involving tuberculosis was conducted. 15 A total of 24 participants were paired (ie, 12 comparable pairs), and by a flip of a coin, each participant within the pair was assigned to either the control or treatment group. By employing randomization, researchers offer each participant an equal chance of being assigned to groups, which makes the groups comparable on the dependent variable by eliminating potential bias. Indeed, randomization of treatments in clinical trials is the only means of avoiding systematic characteristic bias of participants assigned to different treatments. Although randomization may be accomplished with a simple coin toss, more appropriate and better methods are often needed, especially in small clinical trials. These other methods will be discussed in this review.

Why Randomize?

Researchers demand randomization for several reasons. First, participants in various groups should not differ in any systematic way. In a clinical trial, if treatment groups are systematically different, trial results will be biased. Suppose that participants are assigned to control and treatment groups in a study examining the efficacy of a walking intervention. If a greater proportion of older adults is assigned to the treatment group, then the outcome of the walking intervention may be influenced by this imbalance. The effects of the treatment would be indistinguishable from the influence of the imbalance of covariates, thereby requiring the researcher to control for the covariates in the analysis to obtain an unbiased result. 16

Second, proper randomization ensures no a priori knowledge of group assignment (ie, allocation concealment). That is, researchers, participants, and others should not know to which group the participant will be assigned. Knowledge of group assignment creates a layer of potential selection bias that may taint the data. Schulz and Grimes 17 stated that trials with inadequate or unclear randomization tended to overestimate treatment effects up to 40% compared with those that used proper randomization. The outcome of the trial can be negatively influenced by this inadequate randomization.

Statistical techniques such as analysis of covariance (ANCOVA), multivariate ANCOVA, or both, are often used to adjust for covariate imbalance in the analysis stage of the clinical trial. However, the interpretation of this postadjustment approach is often difficult because imbalance of covariates frequently leads to unanticipated interaction effects, such as unequal slopes among subgroups of covariates. 18 , 19 One of the critical assumptions in ANCOVA is that the slopes of regression lines are the same for each group of covariates (ie, homogeneity of regression slopes). The adjustment needed for each covariate group may vary, which is problematic because ANCOVA uses the average slope across the groups to adjust the outcome variable. Thus, the ideal way of balancing covariates among groups is to apply sound randomization in the design stage of a clinical trial (before the adjustment procedure) instead of after data collection. In such instances, random assignment is necessary and guarantees validity for statistical tests of significance that are used to compare treatments.

How To Randomize?

Many procedures have been proposed for the random assignment of participants to treatment groups in clinical trials. In this article, common randomization techniques, including simple randomization, block randomization, stratified randomization, and covariate adaptive randomization, are reviewed. Each method is described along with its advantages and disadvantages. It is very important to select a method that will produce interpretable, valid results for your study.

Simple Randomization

Randomization based on a single sequence of random assignments is known as simple randomization. 10 This technique maintains complete randomness of the assignment of a person to a particular group. The most common and basic method of simple randomization is flipping a coin. For example, with 2 treatment groups (control versus treatment), the side of the coin (ie, heads  =  control, tails  =  treatment) determines the assignment of each participant. Other methods include using a shuffled deck of cards (eg, even  =  control, odd  =  treatment) or throwing a die (eg, below and equal to 3  =  control, over 3  =  treatment). A random number table found in a statistics book or computer-generated random numbers can also be used for simple randomization of participants.

This randomization approach is simple and easy to implement in a clinical trial. In large trials (n > 200), simple randomization can be trusted to generate similar numbers of participants among groups. However, randomization results could be problematic in relatively small sample size clinical trials (n < 100), resulting in an unequal number of participants among groups. For example, using a coin toss with a small sample size (n  =  10) may result in an imbalance such that 7 participants are assigned to the control group and 3 to the treatment group ( Figure 1 ).

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Block Randomization

The block randomization method is designed to randomize participants into groups that result in equal sample sizes. This method is used to ensure a balance in sample size across groups over time. Blocks are small and balanced with predetermined group assignments, which keeps the numbers of participants in each group similar at all times. According to Altman and Bland, 10 the block size is determined by the researcher and should be a multiple of the number of groups (ie, with 2 treatment groups, block size of either 4 or 6). Blocks are best used in smaller increments as researchers can more easily control balance. 7 After block size has been determined, all possible balanced combinations of assignment within the block (ie, equal number for all groups within the block) must be calculated. Blocks are then randomly chosen to determine the participants' assignment into the groups.

For a clinical trial with control and treatment groups involving 40 participants, a randomized block procedure would be as follows: (1) a block size of 4 is chosen, (2) possible balanced combinations with 2 C (control) and 2 T (treatment) subjects are calculated as 6 (TTCC, TCTC, TCCT, CTTC, CTCT, CCTT), and (3) blocks are randomly chosen to determine the assignment of all 40 participants (eg, one random sequence would be [TTCC / TCCT / CTTC / CTTC / TCCT / CCTT / TTCC / TCTC / CTCT / TCTC]). This procedure results in 20 participants in both the control and treatment groups ( Figure 2 ).

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Although balance in sample size may be achieved with this method, groups may be generated that are rarely comparable in terms of certain covariates. 6 For example, one group may have more participants with secondary diseases (eg, diabetes, multiple sclerosis, cancer) that could confound the data and may negatively influence the results of the clinical trial. Pocock and Simon 11 stressed the importance of controlling for these covariates because of serious consequences to the interpretation of the results. Such an imbalance could introduce bias in the statistical analysis and reduce the power of the study. 4 , 6 , 8 Hence, sample size and covariates must be balanced in small clinical trials.

Stratified Randomization

The stratified randomization method addresses the need to control and balance the influence of covariates. This method can be used to achieve balance among groups in terms of participants' baseline characteristics (covariates). Specific covariates must be identified by the researcher who understands the potential influence each covariate has on the dependent variable. Stratified randomization is achieved by generating a separate block for each combination of covariates, and participants are assigned to the appropriate block of covariates. After all participants have been identified and assigned into blocks, simple randomization occurs within each block to assign participants to one of the groups.

The stratified randomization method controls for the possible influence of covariates that would jeopardize the conclusions of the clinical trial. For example, a clinical trial of different rehabilitation techniques after a surgical procedure will have a number of covariates. It is well known that the age of the patient affects the rate of healing. Thus, age could be a confounding variable and influence the outcome of the clinical trial. Stratified randomization can balance the control and treatment groups for age or other identified covariates.

For example, with 2 groups involving 40 participants, the stratified randomization method might be used to control the covariates of sex (2 levels: male, female) and body mass index (3 levels: underweight, normal, overweight) between study arms. With these 2 covariates, possible block combinations total 6 (eg, male, underweight). A simple randomization procedure, such as flipping a coin, is used to assign the participants within each block to one of the treatment groups ( Figure 3 ).

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Although stratified randomization is a relatively simple and useful technique, especially for smaller clinical trials, it becomes complicated to implement if many covariates must be controlled. 20 For example, too many block combinations may lead to imbalances in overall treatment allocations because a large number of blocks can generate small participant numbers within the block. Therneau 21 purported that a balance in covariates begins to fail when the number of blocks approaches half the sample size. If another 4-level covariate was added to the example, the number of block combinations would increase from 6 to 24 (2 × 3 × 4), for an average of fewer than 2 (40 / 24  =  1.7) participants per block, reducing the usefulness of the procedure to balance the covariates and jeopardizing the validity of the clinical trial. In small studies, it may not be feasible to stratify more than 1 or 2 covariates because the number of blocks can quickly approach the number of participants. 10

Stratified randomization has another limitation: it works only when all participants have been identified before group assignment. This method is rarely applicable, however, because clinical trial participants are often enrolled one at a time on a continuous basis. When baseline characteristics of all participants are not available before assignment, using stratified randomization is difficult. 7

Covariate Adaptive Randomization

Covariate adaptive randomization has been recommended by many researchers as a valid alternative randomization method for clinical trials. 9 , 22 In covariate adaptive randomization, a new participant is sequentially assigned to a particular treatment group by taking into account the specific covariates and previous assignments of participants. 9 , 12 , 18 , 23 , 24 Covariate adaptive randomization uses the method of minimization by assessing the imbalance of sample size among several covariates. This covariate adaptive approach was first described by Taves. 23

The Taves covariate adaptive randomization method allows for the examination of previous participant group assignments to make a case-by-case decision on group assignment for each individual who enrolls in the study. Consider again the example of 2 groups involving 40 participants, with sex (2 levels: male, female) and body mass index (3 levels: underweight, normal, overweight) as covariates. Assume the first 9 participants have already been randomly assigned to groups by flipping a coin. The 9 participants' group assignments are broken down by covariate level in Figure 4 . Now the 10th participant, who is male and underweight, needs to be assigned to a group (ie, control versus treatment). Based on the characteristics of the 10th participant, the Taves method adds marginal totals of the corresponding covariate categories for each group and compares the totals. The participant is assigned to the group with the lower covariate total to minimize imbalance. In this example, the appropriate categories are male and underweight, which results in the total of 3 (2 for male category + 1 for underweight category) for the control group and a total of 5 (3 for male category + 2 for underweight category) for the treatment group. Because the sum of marginal totals is lower for the control group (3 < 5), the 10th participant is assigned to the control group ( Figure 5 ).

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The Pocock and Simon method 11 of covariate adaptive randomization is similar to the method Taves 23 described. The difference in this approach is the temporary assignment of participants to both groups. This method uses the absolute difference between groups to determine group assignment. To minimize imbalance, the participant is assigned to the group determined by the lowest sum of the absolute differences among the covariates between the groups. For example, using the previous situation in assigning the 10th participant to a group, the Pocock and Simon method would (1) assign the 10th participant temporarily to the control group, resulting in marginal totals of 3 for male category and 2 for underweight category; (2) calculate the absolute difference between control and treatment group (males: 3 control – 3 treatment  =  0; underweight: 2 control – 2 treatment  =  0) and sum (0 + 0  =  0); (3) temporarily assign the 10th participant to the treatment group, resulting in marginal totals of 4 for male category and 3 for underweight category; (4) calculate the absolute difference between control and treatment group (males: 2 control – 4 treatment  =  2; underweight: 1 control – 3 treatment  =  2) and sum (2 + 2  =  4); and (5) assign the 10th participant to the control group because of the lowest sum of absolute differences (0 < 4).

Pocock and Simon 11 also suggested using a variance approach. Instead of calculating absolute difference among groups, this approach calculates the variance among treatment groups. Although the variance method performs similarly to the absolute difference method, both approaches suffer from the limitation of handling only categorical covariates. 25

Frane 18 introduced a covariate adaptive randomization for both continuous and categorical types. Frane used P values to identify imbalance among treatment groups: a smaller P value represents more imbalance among treatment groups.

The Frane method for assigning participants to either the control or treatment group would include (1) temporarily assigning the participant to both the control and treatment groups; (2) calculating P values for each of the covariates using a t test and analysis of variance (ANOVA) for continuous variables and goodness-of-fit χ 2 test for categorical variables; (3) determining the minimum P value for each control or treatment group, which indicates more imbalance among treatment groups; and (4) assigning the participant to the group with the larger minimum P value (ie, try to avoid more imbalance in groups).

Going back to the previous example of assigning the 10th participant (male and underweight) to a group, the Frane method would result in the assignment to the control group. The steps used to make this decision were calculating P values for each of the covariates using the χ 2 goodness-of-fit test represented in the Table . The t tests and ANOVAs were not used because the covariates in this example were categorical. Based on the Table , the lowest minimum P values were 1.0 for the control group and 0.317 for the treatment group. The 10th participant was assigned to the control group because of the higher minimum P value, which indicates better balance in the control group (1.0 > 0.317).

Probabilities From χ 2 Goodness-of-Fit Tests for the Example Shown in Figure 5 (Frane 18 Method)

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Covariate adaptive randomization produces less imbalance than other conventional randomization methods and can be used successfully to balance important covariates among control and treatment groups. 6 Although the balance of covariates among groups using the stratified randomization method begins to fail when the number of blocks approaches half the sample size, covariate adaptive randomization can better handle the problem of increasing numbers of covariates (ie, increased block combinations). 9

One concern of these covariate adaptive randomization methods is that treatment assignments sometimes become highly predictable. Investigators using covariate adaptive randomization sometimes come to believe that group assignment for the next participant can be readily predicted, going against the basic concept of randomization. 12 , 26 , 27 This predictability stems from the ongoing assignment of participants to groups wherein the current allocation of participants may suggest future participant group assignment. In their review, Scott et al 9 argued that this predictability is also true of other methods, including stratified randomization, and it should not be overly penalized. Zielhuis et al 28 and Frane 18 suggested a practical approach to prevent predictability: a small number of participants should be randomly assigned into the groups before the covariate adaptive randomization technique being applied.

The complicated computation process of covariate adaptive randomization increases the administrative burden, thereby limiting its use in practice. A user-friendly computer program for covariate adaptive randomization is available (free of charge) upon request from the authors (M.K., B.G.R., or J.H.P.). 29

Conclusions

Our purpose was to introduce randomization, including its concept and significance, and to review several randomization techniques to guide athletic training researchers and practitioners to better design their randomized clinical trials. Many factors can affect the results of clinical research, but randomization is considered the gold standard in most clinical trials. It eliminates selection bias, ensures balance of sample size and baseline characteristics, and is an important step in guaranteeing the validity of statistical tests of significance used to compare treatment groups.

Before choosing a randomization method, several factors need to be considered, including the size of the clinical trial; the need for balance in sample size, covariates, or both; and participant enrollment. 16 Figure 6 depicts a flowchart designed to help select an appropriate randomization technique. For example, a power analysis for a clinical trial of different rehabilitation techniques after a surgical procedure indicated a sample size of 80. A well-known covariate for this study is age, which must be balanced among groups. Because of the nature of the study with postsurgical patients, participant recruitment and enrollment will be continuous. Using the flowchart, the appropriate randomization technique is covariate adaptive randomization technique.

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Simple randomization works well for a large trial (eg, n > 200) but not for a small trial (n < 100). 7 To achieve balance in sample size, block randomization is desirable. To achieve balance in baseline characteristics, stratified randomization is widely used. Covariate adaptive randomization, however, can achieve better balance than other randomization methods and can be successfully used for clinical trials in an effective manner.

Acknowledgments

This study was partially supported by a Faculty Grant (FRCAC) from the College of Graduate Studies, at Middle Tennessee State University, Murfreesboro, TN.

Minsoo Kang, PhD; Brian G. Ragan, PhD, ATC; and Jae-Hyeon Park, PhD, contributed to conception and design; acquisition and analysis and interpretation of the data; and drafting, critical revision, and final approval of the article.

Elena Llaudet

Associate Professor of Political Science Suffolk University

Elena Llaudet

RANDOM TREATMENT ASSIGNMENT

Random Treatment Assignment Makes Treatment and Control Groups Comparable When Sample Size is Large Enough By Elena Llaudet, co-author of Data Analysis for Social Science: A Friendly and Practical Introduction (Princeton University Press, 2022)

To simplify matters, imagine there were only five types of individuals in the world: orange, blue, pink, green, and purple. If we were to assign individuals to two different groups--the treatment group and the control group--at random (that is, through a random process such as the flip of a coin), the two groups would contain similar proportions of each type of individual as long as the sample size is large enough. Let's examine this further:

Random Assignment with n=20

(Note about the graph: From left to right, the histrograms show the number of individuals from each type that are (a) in the full sample, (b) in the treatment group, and (c) in the control group. While n stands for the total number of individuals in the sample, n_treatment stands for the total number of individuals in the treatment group, and n_control stands for the total number of individuals in the control group.)

(Looking for more animated GIFs and/or interactive graphs to help explain statistical concepts? Check my  website .)

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The Definition of Random Assignment According to Psychology

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

random assignment treatment and control group

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

random assignment treatment and control group

Materio / Getty Images

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."

What is a Randomized Control Trial (RCT)?

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

Editor-in-Chief for Simply Psychology

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

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working 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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A randomized control trial (RCT) is a type of study design that involves randomly assigning participants to either an experimental group or a control group to measure the effectiveness of an intervention or treatment.

Randomized Controlled Trials (RCTs) are considered the “gold standard” in medical and health research due to their rigorous design.

Randomized Controlled Trial RCT

Control Group

A control group consists of participants who do not receive any treatment or intervention but a placebo or reference treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

Because the participants are randomly assigned, the characteristics between the two groups should be balanced, enabling researchers to attribute any differences in outcome to the study intervention.

Since researchers can be confident that any differences between the control and treatment groups are due solely to the effects of the treatments, scientists view RCTs as the gold standard for clinical trials.

Random Allocation

Random allocation and random assignment are terms used interchangeably in the context of a randomized controlled trial (RCT).

Both refer to assigning participants to different groups in a study (such as a treatment group or a control group) in a way that is completely determined by chance.

The process of random assignment controls for confounding variables , ensuring differences between groups are due to chance alone.

Without randomization, researchers might consciously or subconsciously assign patients to a particular group for various reasons.

Several methods can be used for randomization in a Randomized Control Trial (RCT). Here are a few examples:

  • Simple Randomization: This is the simplest method, like flipping a coin. Each participant has an equal chance of being assigned to any group. This can be achieved using random number tables, computerized random number generators, or drawing lots or envelopes.
  • Block Randomization: In this method, participants are randomized within blocks, ensuring that each block has an equal number of participants in each group. This helps to balance the number of participants in each group at any given time during the study.
  • Stratified Randomization: This method is used when researchers want to ensure that certain subgroups of participants are equally represented in each group. Participants are divided into strata, or subgroups, based on characteristics like age or disease severity, and then randomized within these strata.
  • Cluster Randomization: In this method, groups of participants (like families or entire communities), rather than individuals, are randomized.
  • Adaptive Randomization: In this method, the probability of being assigned to each group changes based on the participants already assigned to each group. For example, if more participants have been assigned to the control group, new participants will have a higher probability of being assigned to the experimental group.

Computer software can generate random numbers or sequences that can be used to assign participants to groups in a simple randomization process.

For more complex methods like block, stratified, or adaptive randomization, computer algorithms can be used to consider the additional parameters and ensure that participants are assigned to groups appropriately.

Using a computerized system can also help to maintain the integrity of the randomization process by preventing researchers from knowing in advance which group a participant will be assigned to (a principle known as allocation concealment). This can help to prevent selection bias and ensure the validity of the study results .

Allocation Concealment

Allocation concealment is a technique to ensure the random allocation process is truly random and unbiased.

RCTs use allocation concealment to decide which patients get the real medicine and which get a placebo (a fake medicine)

It involves keeping the sequence of group assignments (i.e., who gets assigned to the treatment group and who gets assigned to the control group next) hidden from the researchers before a participant has enrolled in the study.

This helps to prevent the researchers from consciously or unconsciously selecting certain participants for one group or the other based on their knowledge of which group is next in the sequence.

Allocation concealment ensures that the investigator does not know in advance which treatment the next person will get, thus maintaining the integrity of the randomization process.

Blinding (Masking)

Binding, or masking, refers to withholding information regarding the group assignments (who is in the treatment group and who is in the control group) from the participants, the researchers, or both during the study .

A blinded study prevents the participants from knowing about their treatment to avoid bias in the research. Any information that can influence the subjects is withheld until the completion of the research.

Blinding can be imposed on any participant in an experiment, including researchers, data collectors, evaluators, technicians, and data analysts.

Good blinding can eliminate experimental biases arising from the subjects’ expectations, observer bias, confirmation bias, researcher bias, observer’s effect on the participants, and other biases that may occur in a research test.

In a double-blind study , neither the participants nor the researchers know who is receiving the drug or the placebo. When a participant is enrolled, they are randomly assigned to one of the two groups. The medication they receive looks identical whether it’s the drug or the placebo.

Evidence-based medicine pyramid.

Figure 1 . Evidence-based medicine pyramid. The levels of evidence are appropriately represented by a pyramid as each level, from bottom to top, reflects the quality of research designs (increasing) and quantity (decreasing) of each study design in the body of published literature. For example, randomized control trials are higher quality and more labor intensive to conduct, so there is a lower quantity published.

Prevents bias

In randomized control trials, participants must be randomly assigned to either the intervention group or the control group, such that each individual has an equal chance of being placed in either group.

This is meant to prevent selection bias and allocation bias and achieve control over any confounding variables to provide an accurate comparison of the treatment being studied.

Because the distribution of characteristics of patients that could influence the outcome is randomly assigned between groups, any differences in outcome can be explained only by the treatment.

High statistical power

Because the participants are randomized and the characteristics between the two groups are balanced, researchers can assume that if there are significant differences in the primary outcome between the two groups, the differences are likely to be due to the intervention.

This warrants researchers to be confident that randomized control trials will have high statistical power compared to other types of study designs.

Since the focus of conducting a randomized control trial is eliminating bias, blinded RCTs can help minimize any unconscious information bias.

In a blinded RCT, the participants do not know which group they are assigned to or which intervention is received. This blinding procedure should also apply to researchers, health care professionals, assessors, and investigators when possible.

“Single-blind” refers to an RCT where participants do not know the details of the treatment, but the researchers do.

“ Double-blind ” refers to an RCT where both participants and data collectors are masked of the assigned treatment.

Limitations

Costly and timely.

Some interventions require years or even decades to evaluate, rendering them expensive and time-consuming.

It might take an extended period of time before researchers can identify a drug’s effects or discover significant results.

Requires large sample size

There must be enough participants in each group of a randomized control trial so researchers can detect any true differences or effects in outcomes between the groups.

Researchers cannot detect clinically important results if the sample size is too small.

Change in population over time

Because randomized control trials are longitudinal in nature, it is almost inevitable that some participants will not complete the study, whether due to death, migration, non-compliance, or loss of interest in the study.

This tendency is known as selective attrition and can threaten the statistical power of an experiment.

Randomized control trials are not always practical or ethical, and such limitations can prevent researchers from conducting their studies.

For example, a treatment could be too invasive, or administering a placebo instead of an actual drug during a trial for treating a serious illness could deny a participant’s normal course of treatment. Without ethical approval, a randomized control trial cannot proceed.

Fictitious Example

An example of an RCT would be a clinical trial comparing a drug’s effect or a new treatment on a select population.

The researchers would randomly assign participants to either the experimental group or the control group and compare the differences in outcomes between those who receive the drug or treatment and those who do not.

Real-life Examples

  • Preventing illicit drug use in adolescents: Long-term follow-up data from a randomized control trial of a school population (Botvin et al., 2000).
  • A prospective randomized control trial comparing medical and surgical treatment for early pregnancy failure (Demetroulis et al., 2001).
  • A randomized control trial to evaluate a paging system for people with traumatic brain injury (Wilson et al., 2009).
  • Prehabilitation versus Rehabilitation: A Randomized Control Trial in Patients Undergoing Colorectal Resection for Cancer (Gillis et al., 2014).
  • A Randomized Control Trial of Right-Heart Catheterization in Critically Ill Patients (Guyatt, 1991).
  • Berry, R. B., Kryger, M. H., & Massie, C. A. (2011). A novel nasal excitatory positive airway pressure (EPAP) device for the treatment of obstructive sleep apnea: A randomized controlled trial. Sleep , 34, 479–485.
  • Gloy, V. L., Briel, M., Bhatt, D. L., Kashyap, S. R., Schauer, P. R., Mingrone, G., . . . Nordmann, A. J. (2013, October 22). Bariatric surgery versus non-surgical treatment for obesity: A systematic review and meta-analysis of randomized controlled trials. BMJ , 347.
  • Streeton, C., & Whelan, G. (2001). Naltrexone, a relapse prevention maintenance treatment of alcohol dependence: A meta-analysis of randomized controlled trials. Alcohol and Alcoholism, 36 (6), 544–552.

How Should an RCT be Reported?

Reporting of a Randomized Controlled Trial (RCT) should be done in a clear, transparent, and comprehensive manner to allow readers to understand the design, conduct, analysis, and interpretation of the trial.

The Consolidated Standards of Reporting Trials ( CONSORT ) statement is a widely accepted guideline for reporting RCTs.

Further Information

  • Cocks, K., & Torgerson, D. J. (2013). Sample size calculations for pilot randomized trials: a confidence interval approach. Journal of clinical epidemiology, 66(2), 197-201.
  • Kendall, J. (2003). Designing a research project: randomised controlled trials and their principles. Emergency medicine journal: EMJ, 20(2), 164.

Akobeng, A.K., Understanding randomized controlled trials. Archives of Disease in Childhood , 2005; 90: 840-844.

Bell, C. C., Gibbons, R., & McKay, M. M. (2008). Building protective factors to offset sexually risky behaviors among black youths: a randomized control trial. Journal of the National Medical Association, 100 (8), 936-944.

Bhide, A., Shah, P. S., & Acharya, G. (2018). A simplified guide to randomized controlled trials. Acta obstetricia et gynecologica Scandinavica, 97 (4), 380-387.

Botvin, G. J., Griffin, K. W., Diaz, T., Scheier, L. M., Williams, C., & Epstein, J. A. (2000). Preventing illicit drug use in adolescents: Long-term follow-up data from a randomized control trial of a school population. Addictive Behaviors, 25 (5), 769-774.

Demetroulis, C., Saridogan, E., Kunde, D., & Naftalin, A. A. (2001). A prospective randomized control trial comparing medical and surgical treatment for early pregnancy failure. Human Reproduction, 16 (2), 365-369.

Gillis, C., Li, C., Lee, L., Awasthi, R., Augustin, B., Gamsa, A., … & Carli, F. (2014). Prehabilitation versus rehabilitation: a randomized control trial in patients undergoing colorectal resection for cancer. Anesthesiology, 121 (5), 937-947.

Globas, C., Becker, C., Cerny, J., Lam, J. M., Lindemann, U., Forrester, L. W., … & Luft, A. R. (2012). Chronic stroke survivors benefit from high-intensity aerobic treadmill exercise: a randomized control trial. Neurorehabilitation and Neural Repair, 26 (1), 85-95.

Guyatt, G. (1991). A randomized control trial of right-heart catheterization in critically ill patients. Journal of Intensive Care Medicine, 6 (2), 91-95.

MediLexicon International. (n.d.). Randomized controlled trials: Overview, benefits, and limitations. Medical News Today. Retrieved from https://www.medicalnewstoday.com/articles/280574#what-is-a-randomized-controlled-trial

Wilson, B. A., Emslie, H., Quirk, K., Evans, J., & Watson, P. (2005). A randomized control trial to evaluate a paging system for people with traumatic brain injury. Brain Injury, 19 (11), 891-894.

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8.1 Experimental design: What is it and when should it be used?

Learning objectives.

  • Define experiment
  • Identify the core features of true experimental designs
  • Describe the difference between an experimental group and a control group
  • Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

random assignment treatment and control group

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

  • random assignment of participants into experimental and control groups
  • a “treatment” (or intervention) provided to the experimental group
  • measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

Steps in classic experimental design: Sampling to Assignment to Pretest to intervention to Posttest

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

  • True experimental designs require random assignment.
  • Control groups do not receive an intervention, and experimental groups receive an intervention.
  • The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
  • Testing effects may cause researchers to use variations on the classic experimental design.
  • Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
  • Control group- the group in an experiment that does not receive the intervention
  • Experiment- a method of data collection designed to test hypotheses under controlled conditions
  • Experimental group- the group in an experiment that receives the intervention
  • Posttest- a measurement taken after the intervention
  • Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
  • Pretest- a measurement taken prior to the intervention
  • Random assignment-using a random process to assign people into experimental and control groups
  • Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
  • Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
  • True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

Image attributions

exam scientific experiment by mohamed_hassan CC-0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Pretest-Posttest Control Group Design: An Introduction

The pretest-posttest control group design , also called the pretest-posttest randomized experimental design , is a type of experiment where participants get randomly assigned to either receive an intervention (the treatment group) or not (the control group). The outcome of interest is measured 2 times, once before the treatment group gets the intervention — the pretest — and once after it — the posttest.

The objective is to measure the effect of the intervention which can be:

  • A medical treatment
  • An education program
  • A policy change, etc.

pretest-posttest control group design representation

The pretest-posttest control group design has 3 major characteristics:

  • The study participants are randomly assigned to either the treatment or the control group (this random assignment can occur either before of after the pretest).
  • Both groups are exposed to the same conditions except for the intervention: the treatment group receives the intervention, whereas the control group does not.
  • The outcome is measured simultaneously for both groups at 2 points in time — the pretest and the posttest.

The pretest-posttest control group is the most commonly used design in randomized controlled trials.

Advantages of the pretest-posttest control group design

By using a pretest, a control group, and random assignment, this design controls all internal threats to validity.

Advantage of having a pretest measurement

This design is better than the posttest-only control group design because it adds a pretest.

Adding a pretest:

  • Increases the power of the design to detect an effect .
  • Allows studying the effect of the intervention at different sublevels of the pretest .
  • Helps analyzing initial differences between groups (and therefore quantifying their effect on the study outcome).
  • Helps controlling attrition bias i.e. the unequal loss to follow-up of participants between the treatment and the control group which can affect the outcome measured at the posttest.

Advantage of using random assignment and having a control group

Random assignment and the control group will both limit the effects of:

  • Selection bias : Which happens when participants themselves get to choose if they receive the intervention or not. This may create unequal and incomparable study groups. Randomization allows unbiased assignment of participants to treatment options, and therefore makes the study groups comparable.
  • Maturation : Which is the effect of time (between the pretest and the posttest) on study participants (e.g. participants growing older, or getting tired over time) which might influence the outcome, thus becoming a rival explanation for the intervention regarding the study outcome. Participants are subject to maturation both in the treatment and the control group, therefore, any difference between the outcome of these groups will be due to the effect of the treatment alone and will not be affected by maturation.
  • History : Which is any event that might co-occur with the intervention and has the potential to influence the outcome. Co-occurring events affect both the treatment and the control group, and therefore any difference between the outcome of these groups will be due to the effect of the treatment alone and will not be affected by history.
  • Testing : Which is the effect of taking a pretest on the result of a posttest. For instance, if the pretest sensitizes participants and compels them to behave in a certain way that affects the outcome of the posttest. The presence of a control group protects against testing effects, as these will affect both groups and therefore any difference between the outcome of these groups will be due to the effect of the treatment alone and will not be affected by testing.
  • Regression to the mean : When pretest scores are exceptionally good by chance, the posttest scores will naturally regress toward the mean. This happens because an exceptionally good performance is hard to maintain. Regression toward the mean can be mistaken for the effect of the treatment, and therefore is a source of bias. Since participants from both groups are subject to regression, therefore, comparing the outcome of the treatment group with that of the control group will take care of this regression effect.

Limitations of the pretest-posttest control group design

Participants included in any randomized study might not be typical people in the population i.e. they may not represent well the population of interest, this is because:

  • Not everyone in the population of interest is eligible for the experiment,
  • and not everyone who is eligible can be recruited ,
  • and not everyone who is recruited will give us their consent to be included in the study,
  • and not everyone who consented will be randomized .

So the outcome of a randomized study may not generalize well to the population .

More specifically, this design:

  • Does not allow us to study how the effect of the treatment changes over time: To do so, we need to add more posttest measures.
  • Is susceptible for interactions between the intervention and other factors (such as the pretest, history, instrumentation, etc.): One solution for this problem is to use the Solomon four-group design.

Example of a study that used the pretest-posttest control group design

Koenig et al. used a pretest-posttest control group design to study the effect of a yoga program on the classroom behavior of autistic children. These children were randomly assigned to either receive the yoga program or their standard morning routine.

The study concluded that yoga can significantly improve the classroom behavior of autistic children.

But because the researchers used a convenience sample from a particular school and the classrooms that were allowed to participate were hand-picked by administrators, the study outcome may not generalize well to all children with autism.

  • Campbell DT, Stanley JC.  Experimental and Quasi-Experimental Designs for Research . Wadsworth; 1963.
  • Reichardt CS.  Quasi-Experimentation: A Guide to Design and Analysis . The Guilford Press; 2019.
  • Shadish WR, Cook TD, Campbell DT.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference . 2nd edition. Cengage Learning; 2001.

Further reading

  • Purpose and Limitations of Random Assignment
  • Posttest-Only Control Group Design
  • Solomon Four-Group Design
  • Matched Pairs Design
  • Randomized Block Design

Lesson 8: Treatment Allocation and Randomization

Treatment allocation in a clinical trial can be randomized or nonrandomized. Nonrandomized schemes, such as investigator-selected treatment assignments, are susceptible to large biases. Even nonrandomized schemes that are systematic, such as alternating treatments, are susceptible to discovery and could lead to bias. Obviously, to reduce biases, we prefer randomized schemes. Credibility requires that the allocation process be non-discoverable . The investigator should not know what the treatment will be assigned until the patient has been determined as eligible. Even using envelopes with the treatment assignment sealed inside is prone to discovery.

Randomized schemes for treatment allocation are preferable in most circumstances. When choosing an allocation scheme for a clinical trial, there are three technical considerations:

  • reducing bias;
  • producing a balanced comparison;
  • quantifying errors attributable to chance.

Randomization procedures provide the best opportunity for achieving these objectives.

  • Identify three benefits of randomization
  • Distinguish simple randomization from constrained randomization.
  • State the purpose of randomization in permuted blocks.
  • State the objective of stratified randomization.
  • Contrast the benefits of permuted blocks to those of adaptive randomization schemes.
  • Use a SAS program to produce a permuted blocks randomization plan.
  • Use an allocation ratio that will maximize statistical power in the situation where greater variability is expected in one treatment group than the other.
  • Provide the rationale against randomizing prior to informed consent.

8.1 - Randomization

In some early clinical trials, randomization was performed by constructing two balanced groups of patients and then randomly assigning the two groups to the two treatment groups. This is not always practical as most trials do not have all the patients recruited on day one of the studies. Most clinical trials today invoke a procedure in which individual patients, upon entering the study, are randomized to treatment.

Randomization is effective in reducing bias because it guarantees that treatment assignment will not be based on the patient's prognostic factors. Thus, investigators cannot favor one treatment group over another by assigning patients with better prognoses to it, either knowingly or unknowingly. Procedure selection bias has been documented to have a very strong effect on outcome variables.

Another benefit of randomization which might not be as obvious is that it typically prevents confounding of the treatment effects with other prognostic variables. Some of these factors may or may not be known. The investigator usually does not have a complete picture of all the potential prognostic variables, but randomization tends to balance the treatment groups with respect to the prognostic variables.

Some researchers argue against randomization because it is possible to conduct statistical analysis, e.g., analysis of covariance (ANCOVA), that adjusts for the prognostic variables. It always is best, however, to prevent a problem rather than adjust for it later. In addition, ANCOVA does not necessarily resolve the problem satisfactorily because the investigator may be unaware of certain prognostic variables and because it assumes a specific statistical model that may not be correct.

Although randomization provides great benefit in clinical trials, there are certain methodological problems and biases that it cannot prevent. One example where randomization has little, if any, the impact is external validity in a trial that has imposed very restrictive eligibility criteria. Another example occurs with respect to assessment bias, which treatment masking and other design features can minimize. For instance, when a patient is asked "how do you feel?" or "how bad is your pain?" to describe their condition the measurement bias is introduced.

Simple Randomization

The most popular form of randomization is simple randomization. In this situation, a patient is assigned a treatment without any regard for previous assignments. This is similar to flipping a coin - the same chance regardless of what happened in the previous coin flip.

One problem with simple randomization is the small probability of assigning the same number of subjects to each treatment group. Severe imbalance in the numbers assigned to each treatment is a critical issue with small sample sizes.

Another disadvantage of simple randomization is that it can lead to an imbalance among the treatment groups with respect to prognostic variables that affect the outcome variables.

For example, suppose disease severity in a trial is designated as mild, moderate, and severe. Suppose that simple randomization to treatment groups A and B is applied. The following table illustrates what possibly could occur.

The moderate is fairly well balanced, the mild and severe groups are much more imbalanced. This results in Group A getting more of the severe cases and Group B more of the mild cases.

8.2 - Constrained Randomization

Randomization in permuted blocks is one approach to achieve balance across treatment groups. The randomization scheme consists of a sequence of blocks such that each block contains a pre-specified number of treatment assignments in random order. The purpose of this is so that the randomization scheme is balanced at the completion of each block. For example, suppose equal allocation is planned in a two-armed trial (groups A and B) using a randomization scheme of permuted blocks. The target sample size is 120 patients (60 in A and 60 in B) and the investigator plans to enroll 12 subjects per week. In this situation, blocks of size 12 are natural, so the randomization plan looks like

Each week the patients are assigned a treatment based on a randomly assigned option specified for the week. Notice that there are exactly six As and six Bs within each block, so that at the end of each week there is balance between the two treatment arms. If the trial is terminated, say after 64 patients have been enrolled, there may not be exact balance but it will be close .

Ordinarily, a natural block size is not evident, so logistical procedures may suggest a block size. A variation of blocked randomization is to use block sizes of unequal length. This might be helpful for a trial where the investigator is unmasked. For example, if the investigator knows that the block size is six, and within a particular block treatment A already has been assigned to three patients, then it is obvious that the remaining patients in the block will be assigned treatment B. If the investigator knows the treatment assignment prior to evaluating the eligibility criteria, then this could lead to procedure selection bias. It is not good to use a discoverable assignment of treatments. A next step to take would be to vary the block size in order to keep the investigator's procedure selection bias minimized.

To illustrate that randomization with permuted blocks is a form of constrained randomization, let \(N_A\) and \(N_B\) denote the number of As and Bs, respectively, to be contained within each block. Suppose that when an eligible patient is ready to be randomized there are \(n_A\) and \(n_B\) patients already randomized to groups A and B, respectively. Then the probability that the patient is randomized to treatment A is:

\(Pr[A]=\begin{cases} 0 & \text{ if } n_A=N_A \\ \dfrac{N_A-n_A}{N_A+N_B-n_A-n_B}& \text{ if } 0<n_A<N_A \\ 1 & \text{ if } n_B=N_B \end{cases}\)

This probability rule is based on the model of \(N_A\) "A" balls and \(N_B\)   "B" balls in an urn or jar which are sampled without replacement. The probability of being assigned treatment A changes according to how many patients already have been assigned treatment A and treatment B within the block.

As an example, suppose each block is supposed to have \(N_A = N_B = 6\) and \(n_A = 3\) and \(n_B = 2\) already have been assigned. Thus, there are \(N_A - n_A = 3\) A balls left in the urn and \(N_B - n_B = 4\) B balls left in the urn, so the probability of the next eligible patient being assigned treatment A is 3/7.

8.3 - Stratified Randomization

Another type of constrained randomization is called stratified randomization. Stratified randomization refers to the situation in which strata are constructed based on values of prognostic variables and a randomization scheme is performed separately within each stratum. For example, suppose that there are two prognostic variables, age and gender, such that four strata are constructed:

The strata size usually vary (maybe there are relatively fewer young males and young females with the disease of interest). The objective of stratified randomization is to ensure balance of the treatment groups with respect to the various combinations of the prognostic variables. Simple randomization will not ensure that these groups are balanced within these strata so permuted blocks are used within each stratum are used to achieve balance.

If there are too many strata in relation to the target sample size, then some of the strata will be empty or sparse. This can be taken to the extreme such that each stratum consists of only one patient each, which in effect would yield a similar result as simple randomization. Keep the number of strata used to a minimum for good effect.

8.4 - Adaptive Randomization

Adaptive randomization refers to any scheme in which the probability of treatment assignment changes according to assigned treatments of patients already in the trial. Although permuted blocks can be considered as such a scheme, adaptive randomization is a more general concept in which treatment assignment probabilities are adjusted.

One advantage of permuted blocks over adaptive randomization is that the entire randomization scheme can be determined prior to the onset of the study, whereas many adaptive randomization schemes require recalculation of treatment assignment probabilities for each new patient.

Urn models provide some approaches for adaptive randomization. Here is an exercise that will help to explain this type of scheme. Suppose that there is one "A" ball and one "B" ball in an urn and the objective of the trial is the equal allocation between treatments A and B. Suppose that an "A" ball is blindly selected so that the first patient is assigned treatment A. Then the original "A" ball and another "B" ball are placed in the urn so that the second patient has a 1/3 chance of receiving treatment A and a 2/3 chance of receiving treatment B. At any point in time with \(n_A\)"A" balls and \(n_B\)"B" balls in the urn, the probability of being assigned treatment A is \(\dfrac{n_A}{(n_A+ n_B)}\). The scheme changes based on what treatments have already been assigned to patients.

This type of urn model for adaptive randomization yields tight control of balance in the early phase of a trial. As \(n_A\) and \(n_B\) get larger, the scheme tends to approach simple randomization, so the advantage of such an approach occurs when the trial has a small target sample size.

8.5 - Minimization

Minimization is another, rather complicated type of adaptive randomization. Minimization schemes construct measures of imbalance for each treatment when an eligible patient is ready for randomization. The patient is assigned to the treatment which yields the lowest imbalance score. If the imbalance scores are all equal, then that patient is randomly assigned a treatment. This type of adaptive randomization imposes tight control of balance, but it is more labor-intensive to implement because the imbalance scores must be calculated with each new patient. Some researchers have developed web-based applications and automated 24-hour telephone services that solicit information about the stratifiers and a computer algorithm uses the data to determine the randomization

One popular minimization scheme is based on marginal totals of the stratifying variables. As an example, consider a three-armed clinical trial (treatments A, B, C). Suppose there are four stratifying variables, whereby each stratifier has three levels (low, medium, high), yielding \(3^4 = 81\) strata in this trial. When 200 patients have been randomized and patient #201 is ready for randomization. The observations of the stratifying variables are recorded as follows.

Suppose that patient #201 is ready for randomization and that this patient is observed to have the low level of stratifier #1, the medium level of stratifier #2, the high level of stratifier #3, and the high level of stratifier #4. Based on the 200 patients already in the trial, the number of patients with each of these levels is totaled for each treatment group. (Notice that patients may be double counted in this table.)

Patient #201 would be assigned to treatment A because it has the lowest marginal total. If two or more treatment arms are tied for the smallest marginal total, then the patient is randomly assigned to one of the tied treatment arms. This is not a perfect scheme but it is a strategy for making sure that the assignments are as balanced within each treatment group with respect to each of the four variables.

8.6 - "Play the Winner" Rule

Another type of adaptive randomization scheme is called the "play the winner" rule. Suppose there is a two-armed clinical trial and the urn contains one "A" ball and one "B" ball for the first patient. Suppose that the patient randomly is assigned treatment A. Now you need to know if the treatment was successful with the patient that received this treatment. If the patient does well on treatment A, then the original "A" ball and another "A" ball are placed in the urn. If the patient fails on treatment A, then the original "A" ball and a "B" ball are placed in the urn. Thus, the second patient has probability of 1/3 or 2/3 of receiving treatment A depending on whether treatment A was a success or failure for the first patient. This process continues. If one treatment is more successful than the other, the odds are stacked in favor of that treatment.

The advantage of the "play the winner" rule is that a higher proportion of patients will be assigned to the more successful treatment. This seems to be an ethical approach.

The disadvantages of the "play the winner" rule are that:

  • sample size calculations are difficult, and
  • the outcome on each patient must be determined prior to the entry of the next patient.

Thus, the "play the winner" rule is not practical for most trials. The procedure can be modified, however, to be performed in stages. For example, if the target sample size is 200 patients, then the trial can be put on hold after each set of 50 patients to assess outcome and redefine the probability of treatment assignment for the patients yet to be recruited, i.e., "play the winner" after every 50 patients instead of every patient.

8.7 - Administration of the Randomization Process

Sas® example, providing permuted blocks randomization scheme for equal allocation to treatments a and b.

The RANUNI function in SAS yields random numbers from the Uniform(0,1) distribution (randomly selected a decimal between 0 and 1). These random numbers can be used to generate a randomization scheme. For example, suppose that the probability of assignment to treatments A, B, and C are to be 0.25, 0.25, and 0.5, respectively. Let U denote the random number generated and assign treatment as follows:

  • if \(0.00 < U \leq 0.25\)
  • if \(0.25 < U \leq 0.50\)
  • if \(0.50 < U \leq 1.00\)

This can be adapted for whatever your scheme requires.

Here is a SAS program that provides a permuted blocks randomization scheme for equal allocation to treatments A and B. In the example, the block size is 6 and the total sample size is 48.

Did you get something like this?

Remember, your output is not likely to be identical to what we got above, but the number assigned to each treatment should be the same in each group after every set of 4 patients.

Future treatment assignments in a randomization scheme should not be discoverable by the investigator. Otherwise, the minimization of selection bias offered by randomization is lost. The administration of the randomization scheme should not be physically available to the investigator. This usually is not the case in multi-center trials, but the problem usually arises in small single-center trials. Logistical problems can arise in trials with hospitalized patients in which 24-hour access to randomization is necessary. Sometimes, sealed envelopes are used as a means of keeping the randomized treatment assignments confidential until a patient is eligible for entry. However, it is relatively easy for investigators to tamper with the envelope system.

Example randomization plan

Many clinical trials rely on pharmacies to package the drugs so that they are masked to investigators and patients. For example, consider a two-armed trial with a target sample size of 96 randomized subjects (48 within each treatment group). The pharmacist constructs 96 drug packets and randomly assigns numeric codes from 01 to 96 which are printed on the drug packet labels. The pharmacist gives the investigator the masked drug packets (with their numeric codes). When a subject is eligible for randomization, the investigator selects the next drug packet (in numeric order). In this way the investigator is kept from knowing which treatment is assigned to which patient.

Here is a SAS program that provides ...

8.8 - Unequal Treatment Allocation

To maximize the efficiency (statistical power) of treatment comparisons, investigators typically employ equal allocation of patients to treatment groups (this assumes that the variability in the outcome measure is the same for each treatment).

Unequal allocation may be preferable in some situations. An unequal allocation that favors an experimental therapy over placebo could help recruitment and it would increase the experience with the experimental therapy. This also provides the opportunity to perform some subset analyses of interest, e.g., if more elderly patients are assigned to the experimental therapy, then the unequal allocation would yield more elderly patients on the experimental therapy.

Another example where unequal allocation may be desirable occurs when one therapy is extremely expensive in comparison to the other therapies in the trial. For budget reasons, you may not be able to assign as many to the expensive therapy.

If it is known that one treatment is more variable (less precise) in the outcome response than the other treatments, then the statistical power for treatment comparisons is maximized with unequal allocation. The allocation ratio should be

\(r = n_1/n_2 = \sigma^1 / \sigma^2\)

which is a ratio of the known standard deviations. Thus, the treatment that yields less precision (larger standard deviation) should receive more patients, an unequal allocation. Because there is more 'noise', more patients, larger sample size will help to cut through this noise.

8.9 - Randomization Prior to Informed Consent

Randomization prior to informed consent can increase the number of trial participants, but it causes some difficulties. This is not recommended practice. Here's why...

One particular scheme with experimental and standard treatments that has received some attention is as follows. Eligible patients are randomized prior to providing consent. If the patient is assigned to the standard therapy, then it is offered to the patient without the need for consent. If the patient is randomized to the experimental therapy, then the patient is asked for consent. If this patient refuses, however, then he/she is offered the standard therapy. An "intent-to-treat" analysis is performed based on the randomized assignment.

This approach can increase trial participation, but patients who are randomized to the experimental treatment and refuse will dilute the treatment difference at the time of data analysis. In addition, the "intent-to-treat" analysis will introduce bias.

There are ethical problems as well because:

  • subjects are randomized to treatment without having been properly informed and without providing their consent, and
  • subjects randomized to standard therapy have been denied the chance of receiving the experimental therapy.

For all of these reasons, randomization prior to informed consent is not recommended.

8.10 - Summary

In this lesson, among other things, we learned:

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AP Statistics : How to do random assignments in an experiment

Study concepts, example questions & explanations for ap statistics, all ap statistics resources, example questions, example question #1 : data collection.

A researcher wants to randomly assign participants to a treatment and control group. Which of the following approaches ensures that the treatment assignment is random?

Assigning the treatment based on who needs it the most 

Obtaining nationally representative samples for both

Assigning the treatment by gender

Flipping a coin

The only random procedure here is the coin flip. In expectation, the coin flip ensures that no background variables influence treatment assignment whereas the other examples either have nothing to do with random assignment (e.g. nationally representative sample) or completely contradict the purpose of random assignment (e.g. assigning the treatment based on who needs it the most). 

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  • Knowledge Base
  • Methodology
  • Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 13 February 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomised designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

  • A control group that’s given a placebo (no dosage)
  • An experimental group that’s given a low dosage
  • A second experimental group that’s given a high dosage

Random assignment to helps you make sure that the treatment groups don’t differ in systematic or biased ways at the start of the experiment.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

  • Participants recruited from pubs are placed in the control group
  • Participants recruited from local community centres are placed in the low-dosage experimental group
  • Participants recruited from gyms are placed in the high-dosage group

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym users may tend to engage in more healthy behaviours than people who frequent pubs or community centres, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

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Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

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 in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sample vs random assignment

Random sampling enhances the external validity or generalisability of your results, because it helps to ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8,000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

  • A control group that receives no intervention
  • An experimental group that has a remote team-building intervention every week for a month

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

  • Random number generator: Use a computer program to generate random numbers from the list for each group.
  • Lottery method: Place all numbers individually into a hat or a bucket, and draw numbers at random for each group.
  • Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
  • Use a dice: When you have three groups, for each number on the list, roll a die to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomised block design involves placing participants into blocks based on a shared characteristic (e.g., college students vs graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing children and adults or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviours, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers).

These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

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 generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

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6.2 Experimental Design

Learning objectives.

  • Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
  • Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it.
  • Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.
  • Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 college students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Table 6.2 “Block Randomization Sequence for Assigning Nine Participants to Three Conditions” shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Table 6.2 Block Randomization Sequence for Assigning Nine Participants to Three Conditions

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right (see Note 6.28 “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Figure 6.2 Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions

Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This is what is shown by a comparison of the two outer bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?”

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999). There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002). The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Doctors treating a patient in Surgery

Research has shown that patients with osteoarthritis of the knee who receive a “sham surgery” experience reductions in pain and improvement in knee function similar to those of patients who receive a real surgery.

Army Medicine – Surgery – CC BY 2.0.

Within-Subjects Experiments

In a within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in carryover effects. A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This is called a context effect . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is counterbalancing , which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this, he asked one group of participants to rate how large the number 9 was on a 1-to-10 rating scale and another group to rate how large the number 221 was on the same 1-to-10 rating scale (Birnbaum, 1999). Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often do exactly this.

Key Takeaways

  • Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
  • Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
  • Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

Discussion: For each of the following topics, list the pros and cons of a between-subjects and within-subjects design and decide which would be better.

  • You want to test the relative effectiveness of two training programs for running a marathon.
  • Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
  • In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
  • You want to see if concrete nouns (e.g., dog ) are recalled better than abstract nouns (e.g., truth ).
  • Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.

Birnbaum, M. H. (1999). How to show that 9 > 221: Collect judgments in a between-subjects design. Psychological Methods, 4 , 243–249.

Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88.

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590.

Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

  • Open access
  • Published: 24 February 2024

Urokinase in the treatment of tuberculous pleurisy: a systematic review and meta-analysis

  • Wenyao Jing 1 ,
  • Ruolan Weng 1 ,
  • Ping Lin 1 &
  • Miao Luo 1  

BMC Infectious Diseases volume  24 , Article number:  258 ( 2024 ) Cite this article

Metrics details

To evaluate the efficacy of urokinase (UK) treatment for tuberculous pleural effusion (TPE).

We searched Chinese biomedical literature database, WanFang data, CNKI, PubMed, EMbase, Web of Science and The Cochrane Library for the randomized controlled trials (RCTs) of urokinase treatment for tuberculous pleurisy from January 2000 to February 2023. Pleural tuberculosis, urokinase and randomized controlled trial were used as keywords. The eligible studies were meta-analyzed by using Revman 5.4.1: risk of bias was assessed, mean difference (MD) and 95% CI were used for continuous variables, pooled studies were conducted using random-effects or fixed-effects models, forest plots were drawn to analyze efficacy, and funnel plots were drawn to discuss publication bias.

Twenty-nine RCTs were included. The meta-analyzed results showed that, on the basis of routine anti-tuberculosis, comparison between the treatment group treated with urokinase and the control group treated with antituberculosis alone, the time of pleural effusion absorption [MD-5.82, 95%CI (− 7.77, − 3.87); P <0.00001] and the residual pleural thickness [MD-1.31, 95%CI (− 1.70, − 0.91); P<0.00001], pleural effusion drainage volume [MD 822.81, 95%CI (666.46,977.96); P <0.00001], FVC%pred [MD 7.95, 95%CI (4.51,11.40); P <0.00001], FEV1%pred [MD 12.67, 95%CI (10.09,15.24); P <0.00001] were significantly different.

The clinical effect of urokinase is better than that of antituberculous therapy alone: it can increase total pleural effusion, decrease residual pleural thickness, improve the pulmonary function, and shorten the time of pleural effusion absorption.

Peer Review reports

Introduction

Tuberculous pleural effusion is the most common infectious pleural disease and one of the major respiratory diseases in China [ 1 ]. The global tuberculosis report 2022 shows that, an estimated 10.6 million people became ill with tuberculosis in 2021, and 1.6 million people died from tuberculosis in 2021, among which about 64,000 died in China [ 2 ]. Tuberculous pleurisy is more prevalent in those countries with high prevalence of tuberculosis, and in China, tuberculous pleurisy accounts for about 50% of pleural effusion cases [ 3 ]. The traditional treatment for TPE is systemic anti-tuberculosis therapy combined with local fluid extraction, but many patients may easily develop pleural hypertrophy, adhesions, and encapsulated effusion due to delayed treatment [ 4 , 5 ]. In addition, the residual pleural hypertrophy (RPT) after treatment is quite common, affecting up to 50% of the total patients. In clinical practice, there are often TPE patients with pleural hypertrophy who suffer from chest collapse on the affected side, resulting in pulmonary restrictive ventilation disorders. Therefore, the prevention and early treatment of RPT is of great significance for the long-term recovery of the patient’s quality of life and work ability. In recent years, research on the treatment of RPT with urokinase injection has been drawing increasing attention. In such a context, this study aimed to conduct a meta-analysis on the efficacy of UK in the treatment of TPE, in order to clarify the therapeutic effect of UK on TPE patients. The studies included in this meta-analysis were randomized controlled trials that were identified from a comprehensive literature search across multiple databases according to the inclusion criteria established based on the TPE Diagnosis and Treatment Guidelines of China [ 6 ].

Materials and methods

Search strategy.

The PubMed, CBM, EMbase, CNKI, Wanfang, Web of Science, and Cochrane Library databases were searched for RCTs related to the UK treatment for TPE that were publicly published from 2000 to 2023. The literature search was carried out by combining subject words and keywords. Specifically, the English search terms include “Tuberculous Pleurisies”, “Tuberculous Pleural Effusion”, “Urokinase”, and “RCT”. We have used corresponding keywords in the Chinese database. Taking CBM as an example, the detailed search strategy is shown in Table  1 .

Inclusion and exclusion criteria

Inclusion criteria.

Participant:Patients with clinical symptoms and imaging diagnosis who meet the diagnostic criteria for tuberculosis pleuritis in the Guidelines for Primary Diagnosis and Treatment of Tuberculosis (2018) or in Internal Medicine [ 7 , 8 ].

Intervention:Routine anti-tuberculosis therapy + thoracic puncture drainage or thoracic tube drainage + intrapleural injection of UK;

Comparison:Routine anti-tuberculosis therapy + thoracic puncture drainage or thoracic tube drainage ± intrapleural injection of an equal amount of 0.9% sodium chloride

Outcome:Absorption time of pleural effusion, residual pleural thickness, pleural drainage volume, FEV1% pred, and FVC% pred.

Study Design:RCT

All subjects in the experimental had no contraindications for the use of UK, such as abnormal coagulation function, hypersensitivity to UK, or history of hemorrhagic diseases within the past month.

Exclusion criteria

Non-cross-sectional studies, etc.;

Abstracts, lectures, reviews, repetitive reports, studies with incomplete clinical information, studies with incomplete data, studies in languages other than Chinese and English;

Non-tuberculous pleural effusion (e.g.hemothorax, non-tuberculous empyema, pleural effusion caused by other reasons);

Studies involving combined intrathoracic injection of drugs that may affect the efficacy evaluation of UK, such as heparin and hormones;

Studies whose data could not be utilized due to the fact that the data did not match the efficacy indicators in the inclusion criteria, and studies that did not clearly describe the experimental group and the control group.

Outcome indicators

Absorption time of pleural effusion, residual pleural thickness, pleural drainage volume, FEV1% pred, and FVC% pred.

Literature screening and data extraction

The titles and abstracts of the preliminarily-retrieved studies from literature search were independently reviewed by two researchers. After excluding studies that were obviously irrelevant, the full texts of the remaining studies were examined and cross-checked by these two researchers for further screening. Disagreements, if any, were resolved by discussing with a third researcher. The study quality was evaluated by the Jadad scale method, where a score of 1-3 indicates low-quality and a score of 3-5 indicates high-quality [ 9 ] . The data of interest were extracted using a self-developed table, mainly including the basic study information, the baseline characteristics of study subjects, intervention measures, and outcome indicators. The bias risk ratio chart and the quality evaluation summary of the 29 included studies are shown respectively in Figs.  1 and 2 [ 4 , 5 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] .

figure 1

Risk of bias graph. The vertical axis of the figure is the risk assessment entry, and the horizontal axis is the percentage of “yes”, “no”, and “unclear” in the evaluation entry

figure 2

Risk of bias summary

Statistical methods

The Review Manager 5.4.1 software was used for data processing and analysis. Continuous variables were represented by mean difference (MD) and the corresponding 95% CI [ 37 ]. When P  > 0.05, it indicated no statistically significant heterogeneity between studies, and a fixed effects model was used for meta-analysis. When P  < 0.05, heterogeneity between studies was confirmed. Accordingly, the sources of heterogeneity were analyzed. If there was no significant clinical heterogeneity between studies, a random effects model was used for combined analysis, and the results were explained and discussed. After combined analysis, P  < 0.05 indicated a statistically significant difference [ 38 ]. When there was significant clinical and statistical heterogeneity in the results of the included studies, only descriptive analysis was performed. The funnel plot was used to analyze possible publication bias. If the plot was symmetrical, it indicated no publication bias; if the plot was asymmetrical, it indicated the possible existence of publication bias.

Literature search results

A total of 1087 Chinese studies and 13 English studies were retrieved from the preliminary literature search. After screening the titles and abstracts and excluding reviews and non-clinical studies, 317 articles were identified for full-text review and 105 articles met our inclusion and exclusion criteria. Further, through quality evaluation screening, 29 RCTs were finally included in our meta-analysis, covering a total of 2903 TPE patients (1459 in the UK treatment group and 1444 in the control group). The literature screening process and the search results are shown in Fig. 3 . Basic Characteristics (Table 2 ) and Bias Risk Evaluation Results (Table  3 ).

figure 3

Study Flow Diagram

Efficacy analysis

Absorption time of pleural effusion.

For the analysis of absorption time of pleural effusion, 18 RCTs were included. The heterogeneity test indicated the existence of heterogeneity among included studies (x 2  = 1581.44, I 2  = 99%, P  < 0.00001), so a random effects model was used for combined analysis. It was found that there was a statistically significant difference between the treatment group and the control group [MD-5.82, 95%CI (−7.77, −3.87); P  < 0.00001], suggesting that the treatment group was superior to the control group in reducing the absorption time of pleural effusion (Fig.  4 ).

figure 4

Meta-analysis forest plot of pleural effusion absorption time

Residual pleural thickness after treatment

A total of 16 RCTs reported the effect of UK on the pleural thickness [ 4 , 5 , 11 , 13 , 14 , 15 , 17 , 19 , 22 , 23 , 27 , 29 , 30 , 33 , 35 , 36 ]. The heterogeneity test indicated the existence of heterogeneity among included studies (x 2  = 1476.75, I 2  = 99%, P  < 0.00001)], so a random effects model was used for combined analysis. It was found that there was a statistically significant difference between the treatment group and the control group [MD-1.31, 95%CI (−1.70, −0.91); P <0.00001], suggesting that the treatment group was superior to the control group in reducing pleural thickness (Fig.  5 ).

figure 5

Meta-analysis forest plot of residual pleural thickness

Pleural effusion drainage volume

A total of 22 RCTs reported the effect of UK on the pleural effusion drainage volume [ 4 , 5 , 10 , 12 , 13 , 14 , 15 , 16 , 17 , 20 , 21 , 23 , 24 , 26 , 27 , 28 , 30 , 31 , 32 , 34 , 35 , 36 ]. The heterogeneity test indicated the existence of heterogeneity among included studies (x 2  = 429.96, I 2  = 95%, P  < 0.00001), so a random effects model was used for combined analysis. Compared with the control group, the pleural effusion drainage volume was obviously increased in the treatment group and the difference was statistically significant [MD 822.81, 95%CI (666.46, 977.96); P <0.00001] (Fig.  6 ).

figure 6

Forest plot of meta-analysis for comparison of drainage volume of pleural effusion

FVC% pred after treatment and FEV1% pred after treatment

A total of 4 RCTs reported the effect of UK on the FVC% pred [ 10 , 15 , 18 , 34 ]. The heterogeneity test indicated the existence of heterogeneity among included studies (x 2  = 17.29, I 2  = 83%, P  = 0.0006), so a random effects model was used for combined analysis. It was found that there was a statistically significant difference between the treatment group and the control group [MD 7.95, 95%CI (4.51, 11.40); P <0.00001], suggesting that UK was able to significantly improve. A total of 5 RCTs reported the effect of UK on the FEV1% pred [ 4 , 10 , 13 , 15 , 34 ]. The heterogeneity test indicated the existence of heterogeneity among included studies (x 2  = 11.26, I 2  = 64%, P  = 0.02), so a random effects model was used for combined analysis. It was found that there was a statistically significant difference between the treatment group and the control group [MD 12.67, 95%CI (10.09, 15.24); P<0.00001], suggesting that UK was able to significantly improve lung function (Fig.  7 ).

figure 7

Forest plot of meta-analysis comparing FVC% pred and Forest plot of meta-analysis comparing Fev1% pred

Subgroup analysis

Subgroup analyses were conducted in terms of UK dosage among the 29 included RCTs. Specifically, there were 2 articles with UK dosage< 100,000 IU [ 16 , 18 ] 22 articles with UK dosage = 100,000 IU [ 4 , 5 , 11 , 12 , 13 , 14 , 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 30 , 32 , 33 , 34 , 36 ], 1 article with UK dosage = 125,000 IU [ 31 ], 3 articles with UK dosage = 200,000 IU [ 10 , 15 , 29 ], and 1 article with UK dosage = 250,000 IU [ 35 ].

UK on FEV1% pred

For the subgroup analysis of UK on the FEV1% pred, there were 2 articles with UK dosage of 200,000 IU [ 10 , 15 ], and 3 articles with UK dosage of 100,000 IU [ 5 , 13 , 34 ]. Both subgroups were analyzed using a random effects model. The differences in the 200,000 IU subgroup [MD 12.14, 95%CI (7.21, 17.07); P  < 0.0001] and in the 100,000 IU subgroup [MD 13.41, 95%CI (10.73, 16.10); P  < 0.00001] were both statistically significant (Fig.  8 ).

figure 8

Forest plot for meta-analysis of Fev1% pred in UK subgroups compared with controls

UK on pleural thickness

For the subgroup analysis of UK on the pleural thickness, there were 13 articles with UK dosage of 100,000 IU [ 4 , 5 , 11 , 13 , 14 , 17 , 22 , 23 , 27 , 30 , 32 , 33 , 36 ], and 2 articles with UK dosage of 200,000 IU [ 15 , 29 ]. Both subgroups were analyzed using a random effects model. Similarly, the differences in the 200,000 IU subgroup [MD-0.73 (− 1.05, − 0.42), P  < 0.0001] and in the 100,000 IU subgroup [MD-1.28 (− 1.76, − 0.80), P  < 0.0001] were both statistically significant (Fig. 9 ).

figure 9

Effects of UK subgroup and control group on pleural thickness

UK on pleural effusion drainage volume

For the subgroup analysis of UK on the pleural effusion drainage volume, there were 17 articles with UK dosage of 100,000 IU [ 4 , 5 , 12 , 13 , 14 , 17 , 20 , 21 , 23 , 24 , 26 , 27 , 28 , 30 , 32 , 34 , 36 ]. The heterogeneity test indicated the existence of heterogeneity among these studies (χ 2  = 413.68, P  < 0.0001, I 2  = 96%). Accordingly, a random effects model was used for combined analysis, and the difference was found to be statistically significant [MD 852.58 (658.051047.10), P  < 0.0001]. In addition, there were 2 articles with UK dosage of 200,000 IU [ 10 , 15 ], including 102 subjects in the UK treatment group and 98 subjects in the control group. Th heterogeneity test indicated no heterogeneity among these two studies (χ 2  = 0.05, P  = 0.83, I 2  = 0%). A random effects model was used for combined analysis, and the difference was also statistically significant [MD 762.47 (673.48851.46), P  < 0.0001] (Fig.  10 ).

figure 10

Effect of UK subgroup and control group on drainage volume of pleural effusion

Publication Bias

Funnel plots were drawn with the sample size as the vertical axis and the effect size as the horizontal axis. It was found that the funnel plots for the complete absorption time of pleural effusion (Fig.  11 ), the residual pleural thickness (Fig.  12 ) and the pleural thickness (Fig.  13 ) all appeared to be asymmetric, indicating the presence of publication bias [ 38 ]. As only a small number of studies were included in the subgroup analyses for FVC% pred and FVE1% pred, funnel plot analysis was not conducted.

figure 11

Pleural effusion time to complete absorption, funnel plot

figure 12

Residual pleural thickness, funnel plot

figure 13

Volume of pleural effusion drainage, funnel plot

Our meta-analysis of 29 RCTs showed that the UK treatment group had a significant increase in the pleural effusion drainage volume and lung function (FEV1% pred, FVC% pred), and a significant decrease in the pleural thickness and absorption time of pleural effusion. All these differences were statistically significant ( P  < 0.05), suggesting that the combined UK therapy could significantly increase the pleural effusion drainage volume, shorten the absorption time of pleural effusion, reduce pleural thickness, and improve lung function (FEV1% pred, FVC% pred). However, obvious heterogeneity was observed in the results of these 5 indicators, which may be related to the length of the patient’s disease course, the UK dosage, and the injection method. To reduce the possibility of analysis bias, we conducted subgroup analyses for various indicators in terms of the UK dosage. It was found that the results for the decrease in pleural thickness, increase in pleural effusion drainage volume, and improvement in FEV1% pred were similar to those of the overall analysis, and the differences between the treatment group and control group were statistically significant.

Tuberculous pleurisy is the extrapulmonary tuberculosis caused by the first invasion of tuberculous bacteria into the pleural cavity of human body. There are three ways for tuberculous bacteria to reach the pleural cavity, namely direct spread of lesions, lymphatic dissemination, and hematogenous dissemination [ 39 ]. At present, the main methods for treating TPE include routine anti-tuberculosis therapy, the use of adrenal cortical hormones, puncturing for drainage, thoracic intervention treatment, thoracoscopic local treatment, and surgical treatment [ 40 ]. After formal and comprehensive anti-tuberculosis treatment, the vast majority of TPE patients could recover. However, due to the high response of the pleura to tuberculosis toxin, it can easily cause exudation. Consequently, some patients may develop pleural effusion in a short period of time due to fibrin cell fragments and cellulose covering the surface of the pleura in the pleural fluid [ 41 ]. Meanwhile, the continuous production and excessive accumulation of pleural effusion can further lead to pleural adhesiveness thickening and increased compression on the lungs [ 42 ] thereby affecting the patient’s lung function and quality of life [ 43 ]. In clinical practice, the intrathoracic injection of hormones and anti-tuberculosis drugs can only reduce inflammatory exudation but not treat the already exuded fluid. An earlier study showed that plasminogen activator inhibitors (PAI) played a decisive role in the fibrinolytic level of pleural effusion, especially PAI-1, which might be related to tissue regeneration, repair and fibrosis development after pleural injury [ 44 ] .Pollack reported that UK could exert a good therapeutic effect when the formation of pleural fluid had not exceeded 6 weeks and the fibrins had not yet been widely deposited, adhered or separated [ 45 ]. Huang found that the intrathoracic injection of UK could effectively prevent and treat pleural hypertrophy and adhesion in clinical practice [ 46 ]. Zhang pointed out that the large amount of fibrin contained in TPE would lead to effusion thickening and generation of protein clots, which might induce the occurrence of multiple pathological processes such as multiloculated and pleural fibrosis [ 47 ]. In this regard, the plasmin activated by UK can crack the fibrin loculated in the pleural effusion, eliminate the blockage of the fiberloculated to the puncture needle or drainage tube, thus facilitating the drainage of pleural effusion [ 48 ]. The research by Lin showed that [ 49 ], after injection of UK, the pleura was significantly thinned and the cellulose deposition and loculated were significantly reduced compared with the situation after simple conventional anti-tuberculosis treatment. According to the above research results, UK has an obvious effect in the treatment of TPE.

The results of our meta-analysis suggest that the intrathoracic injection of UK is able to promote the absorption of pleural fluid and increase the pleural drainage volume for TPE patients, so as to exert a positive effect in reducing pleural thickness and improving lung function. This is consistent with the related reports at home and abroad [ 31 , 32 , 49 , 50 , 51 ], providing additional evidence for the therapeutic effect of UK on TPE. Compared with the meta-analysis conducted by Xia [ 52 ],we performed a comprehensive screening and quality evaluation on the retrieved studies from literature search. It was found that two included studies in Xia’s meta-analysis were questionable: the study by Li Shiying grouped the patients according to the sequence of hospitalization time; the study by Gao Chunrong grouped the patients according to the sequence of admission in terms of odd or even numbers, and the results were incomplete without any explanation on the reasons of missing data.

In summary, UK is more effective in treating TPE compared with the conventional anti-tuberculosis therapy alone. Specifically, it can increase the pleural drainage volume, reduce the residual pleural thickness, shorten the absorption time of pleural effusion, and improve lung function (FEV1% pred, FVC% pred). Our study supports that UK has good efficacy in the treatment of TPE and provides a useful reference for clinical practice.

However, some limitations should be highlighted: ① Our meta-analysis only included Chinese and English articles without searching studies in other languages; ② There were differences in terms of the conventional anti-tuberculosis treatment plan, the pleural effusion drainage method, the UK dosage, and the injection method among different studies, so the experimental results were subjected to bias to some extent; ③ The data provided by the included studies were limited, and the course of disease was not investigated; ④ Most of the included studies did not provide a specific description of the double blind methods implemented to the subjects, experimenters, and evaluators, resulting in an increased risk of implementation bias and a generally low Jadad score; ⑤ Most of the included studies had a small sample size, and there might be deviations between the results and the actual situation. Given the limitations of this study, our findings need to be further verified by more high-quality, large-scale clinical studies both domestically and internationally.

Availability of data and materials

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

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Acknowledgments

This work was supported by grants from Guangxi Natural Science Foundation (2015GXNSFAA139116), from Guangxi Appropriate Health Technology Research and Development Project (Grant No. S201407), and Basic Ability Improvement Project for Young and Middle aged Teachers in Guangxi Zhuang Autonomous Region (Grant No. KY2016YB311). The sponsor had no role in the design or conduct of this research.

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Jing, W., Weng, R., Lin, P. et al. Urokinase in the treatment of tuberculous pleurisy: a systematic review and meta-analysis. BMC Infect Dis 24 , 258 (2024). https://doi.org/10.1186/s12879-024-08975-0

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COMMENTS

  1. Random Assignment in Experiments

    In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization. With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

  2. Random Assignment in Experiments

    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.

  3. Random Assignment in Psychology: Definition & Examples

    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.

  4. How to Do Random Allocation (Randomization)

    Random allocation is a technique that chooses individuals for treatment groups and control groups entirely by chance with no regard to the will of researchers or patients' condition and preference. This allows researchers to control all known and unknown factors that may affect results in treatment groups and control groups.

  5. Issues in Outcomes Research: An Overview of Randomization Techniques

    One critical component of clinical trials that strengthens results is random assignment of participants to control and treatment groups. Although randomization appears to be a simple concept, issues of balancing sample sizes and controlling the influence of covariates a priori are important.

  6. RANDOM TREATMENT ASSIGNMENT

    Random Treatment Assignment Makes Treatment and Control Groups Comparable When Sample Size is Large Enough By Elena Llaudet, co-author of Data Analysis for Social Science: A Friendly and Practical Introduction (Princeton University Press, 2022)

  7. 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.

  8. Randomized Control Trial (RCT)

    A randomized control trial (RCT) is a type of study design that involves randomly assigning participants to either an experimental group or a control group to measure the effectiveness of an intervention or treatment. Randomized Controlled Trials (RCTs) are considered the "gold standard" in medical and health research due to their rigorous design.

  9. Randomized Controlled Trial (RCT) Overview

    A randomized controlled trial (RCT) is a prospective experimental design that randomly assigns participants to an experimental or control group. RCTs are the gold standard for establishing causal relationships and ruling out confounding variables and selection bias.

  10. 8.1 Experimental design: What is it and when should it be used?

    Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups ...

  11. Treatment and control groups

    In such cases, a third, non-treatment control group can be used to measure the placebo effect directly, as the difference between the responses of placebo subjects and untreated subjects, ... it is statistically efficient to do this random assignment separately for each pair of twins, so that one is in the treatment group and one in the ...

  12. Pretest-Posttest Control Group Design: An Introduction

    The pretest-posttest control group design, also called the pretest-posttest randomized experimental design, is a type of experiment where participants get randomly assigned to either receive an intervention (the treatment group) or not (the control group).

  13. Control Groups & Treatment Groups

    When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups: The treatment group (also called the experimental group) receives the treatment whose effect the researcher is interested in.

  14. Lesson 8: Treatment Allocation and Randomization

    Randomization is effective in reducing bias because it guarantees that treatment assignment will not be based on the patient's prognostic factors. Thus, investigators cannot favor one treatment group over another by assigning patients with better prognoses to it, either knowingly or unknowingly.

  15. Control Group in an Experiment

    Control group: Does not consume vitamin supplements; Treatment group: Regularly consumes vitamin supplements.; In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome.

  16. How to do random assignments in an experiment

    Explanation: . The only random procedure here is the coin flip. In expectation, the coin flip ensures that no background variables influence treatment assignment whereas the other examples either have nothing to do with random assignment (e.g. nationally representative sample) or completely contradict the purpose of random assignment (e.g. assigning the treatment based on who needs it the most).

  17. Random Assignment in Experiments

    In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation. With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

  18. Random assignment

    Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment (e.g., a treatment group versus a control group) using randomization, such as by a chance procedure (e.g., flipping a coin) or a random number generator. [1]

  19. Treatment Assignment Strategy in A/B Test

    One of the requirements of a valid randomized controlled experiment is random treatment assignment. Despite its importance, this is unfortunately also the part that most people seem to not care too much about. ... More users in the treatment group in Exp 1 are assigned control group in Exp 2, which implies that Exp 2 control group has more paid ...

  20. 6.2 Experimental Design

    Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too. In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition ...

  21. Urokinase in the treatment of tuberculous pleurisy: a systematic review

    It was found that there was a statistically significant difference between the treatment group and the control group [MD-5.82, 95%CI (−7.77, −3.87); P < 0.00001], suggesting that the treatment group was superior to the control group in reducing the absorption time of pleural effusion (Fig. 4).