The importance of random assignment

The importance of random assignment

Introduction

One of the most paramount aspects of a scientific experiment within the psychology realm is randomization. According to Bryant and Pace (2008), randomization refers to a sampling approach in which the researcher assigns the participants by chance, and not a choice, to either a control or experiment group. This paper highlights the essence of randomization, the methods of randomization, and how an inquirer might draw conclusions in the absence of randomization.

Importance of randomization in a psychology experiment

One of the critical outcomes realized due to randomization is that the researcher is allowed to eliminate the effect of potentially lurking variables. A lurking variable is a measure not part of an empirical study but has the potential to affect the relationship between the variables investigated. The problem with a lurking variable is that it can identify, albeit falsely, a strong relationship between measures. Alternatively, the variable might conceal the real relationship. Since randomization calls on the researcher to assign the subjects to treatment groups at random, likely differences between groups other than the explanatory measure or variable are alleviated.

Randomization is also fundamental because it reduces the chances of bias in the experiment (Suresh, 2011). Randomization ensures that the assignment of the sample members is purely by chance. Therefore, the researcher is not afforded the chance to choose who becomes part of the sample and who does not. Consequently, the selection bias is curbed. Additionally, randomization eliminates the chances that the researcher will interpret the outcomes of the experiment incorrectly, resulting from the tendency to look for information that conforms to the hypothesis and ignores data arguing against. Thus, as Suresh (2011) puts it, randomization prevents the researcher from overestimating the effects of treatment.

While including a large sample is an exceptional plan of action in a psychology experiment, such a cause does not always or necessarily mean that the inquirer will come up with valid results. According to Kaplan, Chambers, and Glasgow (2014), a large sample usually brings rise to multiple comparisons bias. Large sample size means that the researcher will have to conduct more and more tests. However, the multiple comparison bias denotes that more tests imply a higher chance that the investigator will decipher or attain false positives. Kaplan, Chambers, and Glasgow (2014) add that large sample size, though it can help a researcher to identify significant effects, increases the possibility of inconsequential effects.

Methods of randomization

A researcher may actualize randomization through numerous approaches. One of these is simple randomization, which is randomization founded on a single sequence of random assignments (Suresh, 2011). An excellent example of a simple randomization method is coin flip. Where there are two treatment groups, that is, the treatment and control, the researcher determines the assignment of a subject using the side of the coin (Suresh, 2011). For instance, if the coin lands such that the head is facing up, a subject is assigned to the control group. However, if the tails face up, then a subject is assigned to the treatment group.

Another type of randomization is block randomization. This approach is majorly used to randomize the participants into groups that result in equal sample sizes. The researcher determines the block size (Suresh, 2011). Besides, the block should be a multiple of the number of groups. For example, if the experimenter is considering two sample groups, the block size is of either 4, 6, or 8. Once the investigator has determined the block size, he or she calculates all the balanced combinations of assignments within the block (Suresh, 2011). Consequently, the researcher selects the blocks randomly to determine the assignment of a participant into groups.

The other type of randomization is stratified randomization. This approach is mostly used to address the researcher’s need for controlling and balancing the influence of the covariates (Suresh, 2011). The researcher facilitates this randomization method by generating a separate block for every set or combination of covariates. The investigator then assigns the participants to the appropriate covariate block (Suresh, 2011). Once the inquirer has identified every subject and assigned them to blocks, he or she performs simple randomization within each block, hence assigning the participants to one of the groups.

Drawing a conclusion without randomization

In a psychology experiment, a researcher can indeed draw conclusions without randomization. Nonetheless, it is essential to note that in such a case, a researcher can only draw an association as opposed to causal conclusions. The non-use of randomization in an experiment occurs only where the investigator determines that every participant generalize the entire population outright. If this is the case, then the researcher only requires one group to draw conclusions concerning the target population. Thus, the experimenter does not have to assign the subjects, which means that he or she can circumvent randomization. To assess the association between the sample, in this case, the researcher can use sampling distribution. According to LeBlanc (2004), sampling distribution represents a probability distribution of a statistic often attained through a large number of samples derived from a specified population.