The Importance of Random Assignment
Dear Student,
I have received your questions regarding randomization. In this regard, I will review the importance of randomization, how to randomly assign participants, and how to sample when randomization is impossible. The subsections below cover the identified areas of concern.
The Importance of Randomization
Randomization prevents biases because even though one might have a large population, the large population might be concentrated with only one group, leading to an unfair result. Also, randomization ensures that the groups selected for an experiment are as similar as possible so the results are as accurate as possible (Rosenthal & Rosnow, 2014). Randomization also helps control the remaining variables that can impact the results, causing them to be different from what they ought to be. A randomly selected sample fairly represents the population and does not need the researcher’s interface. Then, it is pretty set. Randomizing a sample gives an excellent cause-effect relationship between variables (Rosenthal & Rosnow, 2014). Randomizing a selection enables it to capture all genders, races, and groups. Also, randomizing, even when the sample size is significant, is still necessary because it allows the researcher to control the values of exploratory variables (Rosenthal & Rosnow, 2014). Hence, if there is a relationship between the experimental and response variables, one can conclude that it is causal. From all these reasons, one can see that randomization is the only approach that can be used to ensure the results are as fair and as inclusive as possible.
How to Randomly Assign Participants
The first randomization technique a researcher can use is simple random sampling. This method involves selecting the participants based on luck and probability. In this case, every participant has an equal chance of being a part of a sample. This technique is theoretically easy to comprehend and best applied to a sample population of 100 or more (Míguez, Martino, & Luengo, 2018). Every participant has a chance for equal treatment. Lottery and random numbers are suitable methods to use when applying this technique. Another way to do random sampling is using the permuted block randomization method. This method happens when the researcher randomly assigns participants to a treatment group. A block, in this case, is a randomly ordered treatment population. Every treatment block is given a fair treatment assignment throughout the experiment (Míguez, Martino, & Luengo, 2018).
Another method that a researcher can use is stratified random sampling. This method is best understood by knowing the meaning of “strata.” This word means characteristics. Every population used for a study has specific characteristics (Míguez, Martino, & Luengo, 2018). These include gender, age, cast, and background, among other features. Therefore, stratified random sampling helps the researcher to consider these strata when sampling a population. The stratum can be predefined, or the researcher can appropriately define the strata. For instance, if a researcher wants to randomize states, the researcher can choose to use literacy as such. The strata will be 1) Literate, 2) Intermediate, and 3) Illiterate (Míguez, Martino, & Luengo, 2018). The only inherent problem with randomization is those subconscious decisions can influence it. For instance, suppose a teacher randomly asks learners questions to see those who are good at mathematics. In that case, the teacher can subconsciously target the students they believe are mischievous.
How to Sample When Randomization is Impossible
When randomization is impossible, one can assign various sampled individuals to different blocks, and various analytic strategies can be used. The first method employed is standard regression, which allows the researcher to adjust variances in the baseline patient with the characteristics of different groups (Solomon, Cavanaugh & Draine, 2009). A severe limitation of this standard regression when used in a nonrandomized design is that they are restricted to the unknown measured variables for the data being used. Another advanced analytical approach is propensity score matching (Nabi, 2020). For instance, propensity score matching shows the probability of a sample receiving a treatment. The impacts of treatment on the sample can then be measured on a population with similar property scores through this method. This sample can theoretically control for a larger population; propensity score adjustments like standard regression cannot account for unmeasured or unknown factors. In the end, it contains cases of confounding.
Another approach that is also used is instrumental variable analysis. This method is used mainly because it can identify and exploit quasi-random events in their natural state, impacting the treatment process on the population but not affecting the outcomes (Solomon, Cavanaugh & Draine, 2009). In other words, it mimics the randomization of a targeted sample. The instrumental variable approach has its validity in the researcher’s ability of the chosen instrument to influence the outcomes through treatment (Nabi, 2020). Apart from the above methods used in the clinical environment, a researcher can also use quota sampling when they cannot use a randomization method. This method involves specifying only the required sub-samples.
Besides that, convenience sampling refers to approaches where simplicity is considered over the randomness selected in a sample. Volunteer sampling is another helpful way to sample without relying on randomization. This sampling approach involves asking people to volunteer to participate. The participant can do so through phone calls, text messages, radio stations, and television. Questionnaires can also be used in this case. Purposive sampling can also come in handy. This technique relies on obtaining a sample to increase the information obtained. Snowballing is another method that can be used. In this case, respondents are nominated by other people to participate in the study.
I hope this information was helpful. Please feel free to email me if you have additional questions.
Regards.
References
Míguez, J., Martino, L., & Luengo, D. (2018). Independent Random Sampling Methods. Germany: Springer International Publishing.
Nabi G. Randomised. (2020). Controlled Trials in Medical Research: Do we need alternatives? Scottish Medical Journal 65(1) p. 1-2. https://doi.org/10.1177/0036933019900569
Rosenthal, R., & Rosnow, R. L. (2014). Beginning Behavioral Research: A Conceptual Primer. Canada: W. Ross MacDonald School Resource Services Library.
Solomon, P., Cavanaugh, M. M., & Draine, J. (2009). Randomized Controlled Trials: Design and Implementation for Community-Based Psychosocial Interventions. New York: Oxford UP.
ORDER A PLAGIARISM-FREE PAPER HERE
We’ll write everything from scratch
Question 
Imagine that you are an online tutor for an undergraduate Research Methods course. One day, you get this email:
The Importance of Random Assignment
Dear Tutor, I am having some trouble understanding why it is so important to assign participants to experimental conditions randomly. If you have a large enough sample, the results would probably be valid even if you didn’t bother to randomize the participants. Why is it essential to go through the trouble of randomizing participants? Also, if you randomly assign participants, how should you do it?
Respond to this Student’s email, explaining the concepts at the appropriate level for an undergraduate. In your response, be sure that you explain why it is essential to randomize participants. Also, explain some of the ways that a researcher can randomly assign participants. Last, be sure to explain how a researcher can make valid conclusions even in situations where randomization is not possible.