The Importance of Random Assignment

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The Importance of Random Assignment Edwina Osaghae PSY-510-X4235 Research Methods in Psych I Gregory Privitera May 20, 2023

Thank you for your email, let me explain how randomization works well for human trials/ experiments. First, let's explain what it is; In a good experiment or trial, randomization is there to automatically control for all variables that may confound the data and stand a chance against biases. The goal of researchers, in an experiment, is to give evidence-based explanations, for specific behaviors to the general public. To be able to make a generalized conclusion, the sample needs to be unbiased in the sampling of the population they are describing. To give an unbiased view of the population, when researchers are selecting participants, they randomly assign participants based on what the study needs and puts them blindly together in their sections. Why do they do this you ask, randomization helps minimize the variability and gives an unbiased sample. When dealing with people, there can be personal biases or accidental biases, but when you have randomized the selection the biases have a chance to be evenly divided and not be grouped together. For example, if a school wanted to test how a specific program works for studying they wouldn't just pick all one class, they would poll the whole school at random and select students evenly to give a generalized view of all their students. An experiment that is randomized is essential in testing the efficacy of the treatment as well so the researchers can alter the program, drug, etc. in future research to produce the desired results. Randomization also goes deeper than just picking a handful of students, the method described above is called simple random sampling. Simple random is a type of sampling where researchers will randomly select a subset of participants, based on the area/subject they are researching, from a specific population and each member of the population has an equal chance of being selected. Another commonly used type would be stratified random sampling (SRS). In SRS, the population is further divided into smaller subgroups known as strata. In stratified random sampling, the strata are formed based on

the participants' shared characteristics, meaning it makes the distribution even when the original sample population was not. For example, a company has 1,000 employees, 800 of whom are male and 200 are female, to ensure the sample you choose reflects the balance of gender in the company, you sort the population into two strata based on gender. Then you randomly sampled each group, selecting 80 men and 20 women, which would give you a which gives you a representative sample of 100 people for the population. Randomization is a good tool to help come to a generalized concussion, however, not all cases can use a randomized sample and pose an experiment, in those cases the researcher can make a casual reasoning based upon prospective data. An exam given in the book was there is an outbreak of a medical side-effect and they want to explain it to the public but fast and accurate. They track down the patients who were prescribed the new drug and then compared them with those patients who did not take the drug. If they could conclude that only the patients that were given the new drug have developed the symptoms, then the new drug would be the causal agent. They further state that as more confounding things came up they would look deeper into the differences and infer from there. Now for bigger populations, generalizing when the population is geographically and demographically diverse can prove detrimental to the data because it doesn't include variables that are deeper and socially in tune with specific subsets of the population. For these types, researchers use cluster sampling. In cluster sampling, researchers will divide the population they are studying into smaller groups known as clusters. From these clusters, they put the participants together in similar groups and then randomly select among these clusters to form a sample. An example of this would be if the Department of Education wanted to implement a new elementary school program, they would gather all several schools in different regions then put each

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