Essay on the Importance of Random Assignment

Published: 2021/12/06
Number of words: 2002

Psychology experiments involve making a comparison of scores derived under distinct conditions. The approaches used in assigning participants to conditions may control various extraneous variables. These approaches fall under three classifications. The first classification entails the creation of groups through random assignment. This approach creates what is commonly referred to as independent samples and it is the best approach to creating groups’ equality on all unknown and known attributes (Festing, 2020).

Random Assignment

Creating equivalent groups

Random assignment directly relates to internal validity and is interested in the manner in which participants are assigned to experimental conditions. It is an important attribute of experimentation. The aim of random assignment entails avoiding bias in the constitution of the distinct groups (Onghena, 2020). A psychological researcher interested in conducting a true scientific experiment should concentrate on creating groups that are equal so that any differences in the participants under the distinct conditions derived can be confidently attributed to the impacts of the treatments themselves, presuming that all other things are held constant (Gueron, 2021). The researcher would like to be rationally certain that the independent variable and not the approach of assigning participants to groups triggered the differences obtained. Random assignment is the best approach to attaining this. Additionally, randomly assigning participants to experimental conditions is a fundamental presumption of various statistical tactics that we utilize in making inferences from populations to samples (Festing, 2020). Satisfying this presumption is important for utilizing these statistical processes.

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Therefore, random assignment helps in creating randomly indistinguishable study samples. This shows that random assignment is very important. Through the random assignment of participants (or groups of respondents) to either the control or experimental group, every respondent (or group of respondents) has a probability of being assigned to the control or the experimental group (Jamison, 2017). To put it in another way, by giving every respondent an equal chance of being a member of every group, random assignment manages to control all factors in the participants in both groups except for the independent variable being tested (Hilgers et al., 2017). By doing this, the researcher ensures that the experiment generates approximates of the mean treatment impact. However, for clarity, the unbiased approximate concept defines the fact that any observed effect distinctions between the survey outcomes and the true population are because of chance.

This assertion on equality in groups is grounded on the establishment of an infinite number of random assignments of respondents (or groups of respondents) to treatment groups in the survey and not to a single random assignment of one survey. However, scholars are not expected to take part in an infinite number of random assignments in a limitless number of surveys for this presumption to hold (Goldberg, 2019). The groups’ assignment equality is bolstered in surveys with large samples but not in those with small sample sizes because the extraneous variables in the large sample sizes are evened out. Therefore, even when using random samples to reduce the effect of extraneous variables in determining the cause and effect of a situation, the researcher must pick a large sample but there is still no specification on how large the sample should be (Goldberg, 2019).

It is the only way to be certain about cause and effect

For instance, if psychological scholars are interested in determining whether a psychological intervention effectively helps to manage psychological symptoms of a certain condition, they will engage in a random assignment survey whereby the experimental group receives the psychological intervention and the control does not. The patients are picked randomly and assigned randomly into the experimental conditions to determine who fall in the control and the experimental group. Ascertaining that the two groups are equivalent on all attributes except that one group fails to receive the psychological intervention helps the doctors in making a comparison of the results for the experimental and control groups. Psychologists can conclude whether or not the psychological intervention led to a cure. This significant benefit has resulted in scholars describing random assignment studies as the standard approach to true scientific experiments (Allen, 2017). Therefore, how are samples randomly assigned to both experimental and control groups?


Many methods have been proposed for participants’ random assignment in clinical trials. In this exploration, the common randomization approaches analyzed include block randomization, simple randomization, and stratified randomization. Every approach is defined together with its benefits and disadvantages. It is very important to pick an approach that will generate valid and interpretable outcomes for the survey.

Simple random allocation

Random allocation on grounds of one sequence of random assignments is referred to as simple randomization (McCall, 2019). This approach maintains the complete randomness of a subject to a certain group (McCall, 2019). The most fundamental and common approach of simple randomization entails flipping a coin. For instance, with two study groups (experimental and control), the side that coin lands on determines the assignment of every participant. For instance, the head can mean the control group while the tail can mean the experimental group. Other approaches that can be utilized with simple random allocation encompass the use of a shuffled deck of cards (like odd – control, even – experimental) or throwing a dice (like over – experimental and below and even – control). A random number table that can be found in statistics books or random numbers that are generated by the computer can equally be utilized for the simple allocation of participants.

This approach is simple and can be easily implemented in a psychological study. Moreover, in large clinical surveys, simple random allocation can be trusted to bring about simple numbers of participants among groups. Nonetheless, randomization outcomes could be an issue in relatively small sample size clinical studies, leading to an unequal number of respondents in every group (McCall, 2019).

Block random allocation

The method of block random allocation is modeled to randomly allocate participants into groups that lead to equal sample sizes (Festing, 2020). This approach is utilized to ascertain a sample size balance across groups over time. Blocks are balanced and small with fixed group assignments that mains the participants’ numbers in every group similar at all instances (McCall, 2019). The size of the block is determined by the scholar and ought to be a multiple of the sum of groups (that is, with two treatment groups, the size of the block is 4, 6, or 8). Blocks are best utilized in smaller increases as scholars can control the balance in the groups with ease.

After determining the size of the block, all likely balanced combinations of assignments within the block (that is equal sum for all groups in the block) have to be computed (Festing, 2020). Blocks are later selected randomly to determine the assignment of participants into either of the two groups (McCall, 2019). However, despite being able to make the two groups’ sample size equal, groups can be generated that are rarely comparable on grounds of specific covariates. For instance, one group may possess more respondents with extraneous variables (like suffering from other psychological disorders like substance abuse and anxiety while the participants in the other group do not have such variables) that may confound the information and may negatively affect the outcomes of the clinical experiment. Scholars insist that controlling these covariates is important because of the severe outcomes of the interpretations of the findings. An imbalance of this nature could introduce a statistical analysis bias and lower the strength of the survey (McCall, 2019). Therefore, covariates and sample sizes have to be balanced in clinical surveys.

Stratified random allocation

A stratified random allocation is an approach that responds to the need to balance and control the impact of covariates (McCall, 2019). This approach can be utilized in attaining a balance among groups on grounds of the covariates of the participants. Certain covariates have to be determined by the scholar who comprehends the probable impact that every covariate has on the dependent variables (McCall, 2019). Stratified random allocation is attained by generating a distinct block for every combination of base characteristics and the respondents are assigned to the appropriate block of base characteristics. After the identification of all the subjects and they are assigned to blocks, simple random allocation is performed with all the blocks to assign the participants to the two groups.

The stratified random allocation approach controls for the likely impact of the covariates that may jeopardize the clinical study conclusions (McCall, 2019). For instance, a clinical study of distinct psychological intervention methods after the administration of medication will have several base characteristics. There is a common frame of knowledge that the participant’s age influences the prognosis rate. Therefore, age may be a confounding variable and impact the result of the clinical study. The stratified random allocation may balance the control and psychological intervention for age or additional confounding base characteristics (McCall, 2019). Therefore, despite this approach being significantly simple and useful, particularly for smaller clinical studies, it gets complicated to implement if several basic characteristics have to be controlled. Moreover, a stratified random assignment has a second limitation; it only works when all the participants are identified before group assignments. Nonetheless, this approach is rarely applicable since clinical study subjects are frequently enrolled one at a time continuingly. When the covariates of all the participants are not available before allocation to groups, it is hard to use stratified random allocation (McCall, 2019).

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How are results drawn in groups that are not randomly assigned?

When respondents are not assigned randomly to conditions, the groups that result have a high probability of being dissimilar in several ways. Therefore, scholars perceive them as not being equivalent and this significantly affects the study’s internal validity (Price et al., 2017). Take, for instance, a scholar who would like to test a new method of teaching multiplication to students. One approach would be to survey with the experimental group encompassing one class of two graders and another class of two graders. This design results in a non-equivalent groups design since the learners are not assigned randomly to conditions by scholars, which means there could be significant distinctions between them. Therefore, at the end of the study, if differences between the groups are arrived at, it might be because of the different teaching approaches – which was the independent variable being tested or might have been the result of other confounding variables (Price et al., 2017). Therefore, random assignment is the only approach to ascertain that indeed there is a cause-effect relationship between the independent and dependent variable.


Random assignment is very important in psychological clinical trials in determining the cause-effect between the dependent and the independent variables. The approach increases the internal validity of the study by ensuring that both groups are equal. It also helps to significantly control the confounding variables that may influence the results generated. There are various approaches for randomization like simple, stratified, and blocking randomization. Hence, to increase the reliability of the findings generate, random assignment is encouraged.


Allen, M. (2017). Random Assignment of Participants. The Sage Encyclopedia of Communications Research Methods.

Festing, M. F. (2020). The “completely randomized” and the “randomized block” are the only experimental designs suitable for widespread use in pre-clinical research. Scientific Reports.

Goldberg, M. H. (2019). How often do random assignments fail? Estimates and recommendations. Journal of Environmental Psychology, 1 – 40.

Gueron, J. M. (2021). The Politics of Random Assignment: Implementing Studies and Impacting Policy. Manpower Demonstration Research Corporation, 1 – 31.

Hilgers, R.-D., Uschner, D., Rosenberger, W. F., & Heussen, N. (2017). ERDO – a framework to select an appropriate randomization procedure for clinical trials. BMC Medical Research Methodology, 1 – 12.

Jamison, J. C. (2017). The Entry of Randomized Assignment into the Social Sciences. Policy Research Working Paper, 1 – 27.

McCall, W. (2019). Random Assignment. Sage Publications, 173 – 209.

Onghena, P. (2020). Randomization. Sage Research Methods Foundation, 1 – 13.

Price, P. C., Jhangiani, R., & Chiang, I. -C. (2017). Quasi-Experimental Research. Research Methods in Psychology, 1- 20.

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