Need Help With – Comparing T-Tests and ANOVAs in Research Design Custom Paper Help

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?

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.

please ask a question with feedback and a reply about table. Thank You.

💬 Feedback with Question

You did a great job explaining the main difference between t-tests and ANOVAs—especially highlighting how t-tests compare only two means while ANOVAs can compare three or more. I also appreciate that you mentioned the difference between between-subjects and within-subjects designs, which is often a confusing area for students.

One thing that could make your comparison even clearer is using a summary table to show which test fits which kind of design. A table can quickly highlight which independent and dependent variable setups go with each test.

Question:
How might creating a table of these tests (showing number of groups, independent vs. dependent variables, and type of design) help you decide more confidently between a repeated measures ANOVA and a mixed factorial ANOVA when you are unsure which test fits your research question?

📝 Example Reply About the Table

Thank you for the suggestion about using a table! I think creating a table would really help me visually map out which tests go with which research designs. For example, I could include columns for:

• Number of groups being compared

• Between- or within-subjects design

• Number of independent variables

• Example research question

By filling in this table for t-tests, one-way ANOVA, repeated measures ANOVA, and mixed factorial ANOVA, I could see at a glance which test fits my study. It would make it easier to recognize that a repeated measures ANOVA is used when all conditions use the same participants, while a mixed factorial ANOVA is better when there’s at least one between-subjects and one within-subjects factor.

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