Repeated measurement analysis of variance is a special type of two way ANOVA, named after one of the grouping factors being time or frequency, used to test the differences between its dependent variables at different times or frequencies.

    Data description:

    Repeated measurement analysis of variance is a statistical method used to study the changes in the same group of subjects under different processing conditions at a given time. It takes into account the differences between the internal variations of the subjects and the treatment conditions, and provides a way to evaluate whether the treatment conditions significantly affect the results. This analysis can help researchers determine significant differences between different treatment conditions and the impact of interactions on the results.

    This study conducted a repeated measurement analysis of variance. This analysis was conducted in an experiment on the SAS total score. Repetitive measurement refers to the repeated measurement of a subject's response multiple times in the same experiment when exposed to different processing conditions.

    The analysis results are as follows:

    The results showed that there were two independent variables with significant differences in the dependent variable: the p-value of item A1 was 0.0<0.05, indicating a significant difference in the dependent variable between different A1 items (F=16.428, p=0.0). The p-value of item A2 is 0.001<0.05, indicating significant differences in the dependent variable among different A2 items (F=6.274, p=0.001).

• Factor A1 has a significant impact on the total SAS score (F=16.428, p < 0.001 * * *). The specific comparison results show that there is a significant difference in the average values between A1 levels 1 and 3, as well as between A1 levels 1 and 4.
• Factor A2 also has a significant impact on the total SAS score (F=6.274, p=0.001 * *). The specific comparison results show that the average difference between level 1 and level 4 of A2 is significant.
• The interaction between A1 and A2 did not have a significant impact on the total SAS score (F=1.648, p=0.120).
• Based on the results of multiple post hoc comparisons, we found that the average difference in SAS total scores at the A1 level was not significant compared to other levels.
• In the A2 level, the total SAS score significantly increased compared to the A1 and A3 levels.
• In the A3 level, the total score of SAS significantly increased compared to the A1 level.

In summary, based on the results of repeated measurement analysis of variance and post hoc multiple comparisons, we can conclude that A1 and A2 factors have a significant impact on SAS total score, while the interaction between A1 and A2 has no significant impact on SAS total score.

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