RSM801 Week 2 Discussion Table Gordy.docx
Hi everyone,
This week, we’re completing the table of statistical techniques and comparing how they differ, and I’m looking forward to using this discussion to help clarify the differences.
Differences Between the Techniques
The key distinctions between these techniques are based on the number of groups or conditions being compared (two or more than two), the number of times the same participants are measured (once or multiple), and the number of independent variables under consideration. T-tests are used for hypothesis testing when there are only two means to compare. The independent samples t-test is used for "between-subjects" experiments, in which separate groups of participants are measured for each condition or group. The paired samples t-test is used for "within-subjects" or "repeated measures" designs in which the same group of participants is measured for each condition or group. ANOVAs represent a generalization of the t-test. A one-way ANOVA is used when there is one independent variable with three or more levels (i.e., groups or conditions), and a two-way ANOVA is used when there are two independent variables and all combinations of the variables are compared (the two-way ANOVA also allows testing for interactions between independent variables). A repeated measures ANOVA is also used when participants are measured for each condition or group, but it is often preferred over the repeated measures t-test because it controls for individual differences that might add error variance. Factorial ANOVAs include both between-subjects and within-subjects factors (i.e., independent variables), and also allow testing for more complex interactions between independent variables. In all of these analyses, the dependent variable(s) are assumed to be continuous and randomly sampled, while the independent variable(s) may be fixed or random.
References:
Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). Sage Publications Limited.
Gravetter, F. J., & Wallnau, L. B. (2013). Statistics for the behavioral sciences (10th ed.). Belmont, CA: Wadsworth, Cengage Learning.
Privitera, G. J. (2024). Research methods for the behavioral sciences (7th ed.). Sage Publications.