When reporting the results of a repeated measures ANOVA (Analysis of Variance) in APA (American Psychological Association) style, you generally include a table and a concise narrative summary. Here's a step-by-step guide on how to present the results:

Table 1 Descriptive Statistics and Repeated Measures ANOVA Results

The table should include the following columns:

• Descriptive Statistics: Present the means and standard deviations (SD) for each condition or time point.
• Mauchly's Test of Sphericity: If you have more than two levels of the within-subjects factor, include the results of the Mauchly's test to assess the assumption of sphericity. Report the degrees of freedom (df) and the p-value.
• Greenhouse-Geisser Correction: If the assumption of sphericity is violated, include the Greenhouse-Geisser correction results, which adjust the degrees of freedom and p-values.
• Tests of Within-Subjects Effects: Present the main effects and interaction effects. Include the degrees of freedom (df), the F-value, the p-value, and effect size measures like partial eta-squared (ηp²) or epsilon-squared (ε²). Report the effect size values alongside the F-value and p-value.

• Describe the purpose of the analysis and the variables involved.
• State whether the assumption of sphericity was met. If it was violated, mention that the Greenhouse-Geisser correction was applied.
• Report the main effects and interaction effects, including the relevant F-values, degrees of freedom, p-values, and effect size measures.
• Interpret the significant effects and provide a concise summary of the findings. Focus on the direction and magnitude of the effects.
• If appropriate, discuss any post hoc tests or planned comparisons conducted following the ANOVA. Highlight significant pairwise comparisons and any patterns observed.

Here's an example narrative summary:

"The repeated measures ANOVA revealed a significant main effect of time, F(2, 30) = 7.21, p < .001, ηp² = .32. Post hoc tests using the Bonferroni correction indicated that the mean scores at time point 3 (M = 8.56, SD = 1.21) were significantly higher than at time point 1 (M = 5.34, SD = 0.98, p < .01) and time point 2 (M = 6.12, SD = 1.05, p < .05). However, there was no significant main effect of condition, F(1, 15) = 1.89, p = .18, ηp² = .11. Additionally, the interaction between time and condition was not significant, F(2, 30) = 1.45, p = .25, ηp² = .09. These results suggest that time had a significant impact on the variable of interest, with scores increasing significantly from time point 1 to time point 3."

Remember to adapt the example to fit your specific study and its findings.

For your design statisticians (e.g. Senn, 2006) would advise to use an analysis of covariance. It is essetially analysis of variance in which differences between levels of a factor (experimental group verus control) are tested after controlling for other variables , termed covariates. It is a regression:

post = pre + group (in that order).

Seen, S. 2006. Change from baseline and analysis of covariance revisited. Statistics in Medicine 25:4334-4344.

For the APA style, see:

https://apastyle.apa.org/

Based on your study design with two groups (Control and Experimental) and two states (Pre and Post), a Repeated Measures ANOVA is an appropriate statistical test to compare the differences between the groups over time. This test allows you to examine the main effects of states, the States*group interaction, and the error(states).

To present your findings in a result table following APA guidelines, it is common to create separate tables for descriptive statistics and the ANOVA results

If your research work will measure online two means, that is, at baseline and endline, I will suggest you use paired t-test to check effect. But if you're checking for the effect of time, which may lead to having two or more mean then RM ANOVA is okay and Pankaj Yadav answer above will suffice. ab