Mixed Design ANOVA

This paragraph introduces the concept of a mixed design ANOVA (Analysis of Variance), also known as a repeated measures ANOVA with a between-subjects factor. The focus is on a study involving four groups of overweight individuals whose body fat percentage was measured at three time points: baseline, 6 months post-intervention, and at a 12-month follow-up. The aim is to determine if there's a reduction in body fat percentage over time and if this reduction varies across the groups, which include control, diet only, exercise only, and diet with exercise. The paragraph also explains the process of setting up such an analysis in SPSS, including defining within-subject factors (time) and between-subject factors (group), and how to create profile plots for interaction interpretation.

The second paragraph delves into the specifics of setting up a mixed design ANOVA in SPSS. It details the steps for defining factors, selecting the appropriate post-hoc tests for multiple comparisons, and adjusting for confidence intervals using the Bonferroni method. The paragraph also emphasizes the importance of checking for sphericity and equality of error variances to ensure the assumptions of the ANOVA are met. Descriptive statistics and the results of the Mauchly's test for sphericity are discussed, along with the interpretation of the test results, including the significance of the F values and P values in determining whether the variances are equivalent across groups and time points.

This paragraph focuses on interpreting the results of the mixed design ANOVA. It explains the significance of the main effects for time and group, as well as the interaction effect between time and group. The paragraph guides through the process of examining the estimated marginal means and conducting pairwise comparisons to determine which time points differ significantly. It also discusses the interpretation of post-hoc tests, specifically the Tukey HSD test, to understand the differences between the groups and the control group, and how to report these findings accurately.

The fourth paragraph continues the analysis by examining the changes in body fat percentage over time and the differences between the groups. It discusses the significance of the interaction effect and the need to focus on this when interpreting the results. The paragraph also addresses how to modify the SPSS syntax for further analysis of the interaction effect and how to interpret the plot of the interaction. It emphasizes the importance of conducting follow-up tests to understand the specific differences between the groups at each time point.

The final paragraph concludes the screencast by outlining how to report the results of a mixed design ANOVA. It provides guidance on presenting the F values, degrees of freedom, and probability values for each effect and interaction. The paragraph also advises on the presentation of results in written format, figures, or tables, and stresses the importance of focusing on the significant effects and their implications. It concludes with a summary of the main findings from the study, highlighting the changes in body fat percentage over time and the differences between the groups.