1. If I were designing my research study in the field of psychology, I would most likely use a t-test as the method of data analysis. A t-test is best used when the researcher wants to determine deviations from a normal distribution curve. If there are one or more normal distributions (bell curves), t he researcher can compare the two groups using the t-test method. The variances do not need to be known to perform a t-test either, which is another reason why I would select this method of data analysis in my particular research question. Furthermore, the researcher can use the t-test to measure the differences between two populations regardless of how small or large the sample size. The t-test, also known as the student t-test, is one of the best methods applied to studies using a small sample size. If my research were to progress to where I needed to use a larger sample size, then I would need to move onto other types of data analysis like a z-test or chi-square.
The t-test ultimately uses simple mathematics such as ratios to calculate the differences between the experimental and control groups, or between two comparison groups. For example, if I were studying the difference between men and women on a certain psychological construct or on the basis of their reactions to come stimuli, then I could use a t-test. The t-test results would be able to tell me if it was indeed gender that was accountable for the differences in their reactions to the stimuli, as opposed to some...
Likewise, I could compare different age groups or anything that would allow for me to determine if there was an effect caused by the independent variable. After applying the t-test to my initial research design, I could then design a more robust study with even greater reliability and validity.
2. Multiple regressions are commonly used in statistical analysis. The reason why multiple regressions are so commonly used is that they perform one of the most fundamental functions in experimental research: which is to determine the relationship between an independent and a dependent variable. A multiple regression can be used for different types of research design including correlational and true experimental designs. Moreover, the researcher can use multiple regressions when the research design includes many different variables in a complex study. In the social sciences especially, the number of confounding variables can be quite profound and often impact the validity and reliability of the research. For instance, age, gender, geography, and educational background can all impact the outcomes in a study even when the researcher only wants to study something like the effect of some medication on behavioral outcomes.
For example, I might use multiple regressions to study the impact of social media usage on academic performance among high school students. The independent variable would be number of hours spent on any social media application on…
