Research Method: Randomization of Participants

Introduction

I believe that you are a first-time researcher. It is important to note that research is essential as it involves explaining how information is factual. Therefore, the activities involved in such exercises should be able to prove the validity of the data that is being studied.

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One critical aspect that determines the factualness of a study is participants who take charge of experimental tasks. However, most researchers usually face the challenge of organizing the participants regarding selection and assigning as you are experiencing. As a result, they opt to use all the samples in experiments to avoid the hurdles that come with the process of selecting and assigning such time loss. Nevertheless, you would most likely prefer option as it probably produces accurate results. However, experts have confirmed that the most effective way of approaching this matter is through randomization as it results in more logical outcomes.

What is Randomization Participation Assignment?

To understand the effectiveness of randomization and how it is achieved in this aspect, you should first comprehend its concept. According to Suresh, it refers to the use of chance selection that ensures that each member of the exercise has an equal opportunity to participate (2011). Here, as an experimenter, you will not set a systematic criterion to use while assigning participants experimental roles in the study. Instead, you will utilize an informal approach that involves the dependency of luck and uncertainty (Suresh, 2011).

How Random Assignment Works

In most cases, random assignment is usually confused with random selection. In a random assignment, the researcher primarily aims at determining the relationship between two variables (Rajagopalan, Deodurg & Srikanth, 2013). For instance, "The impact of Communication in Competitive Advantage," in which communication is the independent variable and competitive advantage is the dependent one. According to Rajagopalan, Deodurg, and Srikanth, the experimenter divides the sample into two groups which include the "control group" and the "experimental group" (2013). The control group does not go through the prescribed test whereas the experimental one receives treatment to show how one variable affects the other (Rajagopalan, Deodurg & Srikanth, 2013). Importantly, the division of samples to control and experimental is critical in explaining how the independent variable manipulates the dependent one to form a convincing explanation (Rajagopalan, Deodurg & Srikanth, 2013).

Methods of Randomly Assigning Participants

As mentioned above, you will use chance techniques in assigning participants to their roles. For instance, you can achieve this process by flipping coins (Suresh, Yasaswin, & Reshma, 2013). In this technique, you can divide the sample into two groups in which you will be choosing two individuals at each flip. Then, you can decide that those who will get a "head" will be used as the control experiments whereas those who get a "tail" will fall under the experimental group.

Alternatively, you can use the tactic of the Random Number Generator. In the Random Number Generator, the researcher practically generates the number that will be used in the classification of the sample into control and experimental (Suresh, Yasaswin & Reshma, 2016). To achieve randomization in this technique, the experimenter writes the numbers on small pieces of paper after which he mixes them and asks the individuals in the sample to pick (Suresh, Yasaswin & Reshma, 2016). For example, you can decide to use odd and even numbers while generating them; then, you can determine what category will be control and experimental.

Importance of Randomization

First, randomization yields more accurate results than using the whole sample (Verdugo et al., 2009). By randomly assigning the subjects into control and experimental groups, the researcher observes the differences as a result of the test (Verdugo et al., 2009). Here, the experimenter can conclude as he/she notices the changes that are seen from the experimental group in which the test was applied (Verdugo et al., 2009). Notably, the researcher rarely notices the changes when the entire sample is used without dividing it into testable divisions; thus, making, the process interferes with the correctness of the outcome.

Chiefly, randomization is the most unbiased method of assigning participants into experimental teams. As noted by Suresh, this technique involves the use of chance or rather lack (2011). Importantly, there is no systematic strategy, such as personality selection, in which the analyzer uses in assigning participants. This option facilitates a fair exercise where no one will feel more favored or less important (Suresh, 2011).

Consequently, the randomization prevents the predictability of the study (Verdugo et al., 2009). The subjects in the samples may exhibit similar traits that enhance the predictability of the outcome (Verdugo et al., 2009). Notably assigning participants randomly mixes the characters; thus it becomes difficult for the researcher to pre-tell the results of the test.

How to Make Conclusions in Situations Where Randomization Is Not Possible

It is important to note that not all cases that randomization is applicable. For instance, in situations where there are more than two variables that are the primary concern of the research. In such cases, the researcher is advised to construct comparison groups (Cook & Steiner, 2009). Here, the experimenter assembles another sample that obtains different characteristics from the existing one (Cook & Steiner, 2009).

References

Cook, T. D., & Steiner, P. M. (2009). Some empirically viable alternatives to random assignment. Journal of Policy Analysis and Management: The Journal of the Association for Public Policy Analysis and Management, 28(1), 165-166.

Rajagopalan, R., DEODURG, D. P. M., & SRIKANTH, D. (2013). Overview of randomized controlled trials. Asian J Pharm Clin Res, 6(3), 32-38.

Suresh, K. P. (2011). An overview of randomization techniques: an unbiased assessment of outcome in clinical research. Journal of human reproductive sciences, 4(1), 8.

Suresh, K. P., Yasaswini, S., & Reshma, K. (2016). Application of randomization techniques for an unbiased assessment of outcome in clinical studies.

Verdugo, R. A., Deschepper, C. F., Munoz, G., Pomp, D., & Churchill, G. A. (2009). Importance of randomization in microarray experimental designs with Illumina platforms. Nucleic acids research, 37(17), 5610-5618.