Assignment task:
When I compared the techniques, the main difference between t-tests and ANOVAs is the number of means being compared. T-tests are limited to two means, while ANOVAs can handle three or more means, and factorial ANOVAs allow for more than one independent variable (Gravetter & Wallnau, 2013). Another difference is in the design: independent samples tests use separate groups of participants (between-subjects), while paired samples and repeated measures tests use the same participants measured multiple times (within-subjects) (Field, 2024). Factorial ANOVAs are more flexible and can combine both between- and within-subjects factors. Across all these techniques, the independent variable(s) are fixed (defined by the researcher), while the dependent variable is random (scores that vary across participants).
Reflection and Question:
Seeing these techniques side by side helped me understand how they fit together as a system. For me, the biggest challenge is selecting the proper test for a specific research design. I consider how researchers consistently decide between a repeated measures ANOVA and a mixed factorial ANOVA when a design includes both repeated and independent elements. How do you reliably determine which test better answers the research question when the design could potentially be analyzed in more than one way? Need Assignment Help?
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.
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