Choosing The Appropriate Statistical Analysis: Independent Samples T-Test vs. Between Subjects ANOVA
Choosing the most appropriate statistical analysis for your research question can be a difficult job. In this blog, we will discuss when it would be appropriate to use the Independent samples t-test vs a between subjects ANOVA. It can be hard to know just which test fits in with your analysis goals and your data. Therefore, the first, and most important questions to ask yourself are: what are you trying to achieve by running this analysis? Is your goal to compare groups or to predict outcomes? Or do you simply want to better understand the relationship between two variables? These are the questions you should be starting off with. If your goal is to better understand relationships between variables, then a correlation may be right for you. However, if you want to determine if a particular variable predicts a certain outcome, then you should investigate the different kinds of regression analyses. If your aim is to look at differences, t-tests, ANOVAs, or MANOVAs among other analyses may be appropriate for you. Each statistical analysis listed has different parameters and assumptions that make a particular analysis the appropriate analysis. This blog will focus specifically on group differences in one continuous, dependent variable.
Both an independent sample t-test and a between subjects Analysis of Variance (ANOVA) test a hypothesis by comparing the means of different groups. While both types of analyses require that the dependent variable be continuous level in nature, the reason for selecting one analysis over the other is the number of groups. While an independent sample t-test compares the means of two groups, an ANOVA compares the means between two or more groups. Meaning once you’ve determined you have a single, continuous, dependent variable and you want to assess differences on that dependent variable among groups, the next step to determining the appropriate test is to determine how many levels or groups your independent variable is comprised of. For example, let’s say you are interested in the differences in spiritual well-being based on different stress groups. If you were looking at differences between one control group and one experimental group (a stress condition), then an independent samples t-test would be appropriate. However, if instead you had a control group and two experimental groups (a high stress condition and a low stress condition), now a between subjects ANOVA would be a more appropriate analysis given that you have more than two groups. In this situation, while it would also be possible to run three different independent sample t-tests (control v. low stress, control v. high stress, and low stress v. high stress) to determine the differences between the three groups, the more t-tests that are run, the greater the likelihood of a type one error, or finding a significant result when one does not exist. Running an ANOVA instead reduces this risk of this type of false positive result.
To recap, an independent samples t test is the appropriate analysis when the goal of research is to determine if there are statistically significant differences in a single, continuous, dependent variable between two groups. And a between subjects ANOVA is the most appropriate analysis if the goal of research is to determine if there are statistically significant differences in a single, dependent variable between two or more groups. Next week learn more about the dependent samples t-test and the repeated measures ANOVA.
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