What is the appropriate statistical test to analyze 20 time points between 4 groups? repeated measure ANOVA?

To analyze the effect of treatment in four groups at 20 time points, a repeated measures ANOVA (Analysis of Variance) is a suitable choice if you have repeated measurements on the same subjects or experimental units across those 20 time points. However, there are a few considerations to take into account:

Assumption of Sphericity: Repeated measures ANOVA assumes sphericity, which means that the variances of the differences between all possible pairs of time points are equal. If this assumption is violated, you may need to consider correcting for it using methods like Greenhouse-Geisser or Huynh-Feldt corrections.

Post-hoc Tests: If your repeated measures ANOVA shows a significant effect, you will likely want to perform post-hoc tests to determine which groups and time points are significantly different from each other. Common post-hoc tests include Tukey's HSD (Honestly Significant Difference), Bonferroni correction, or pairwise comparisons.

Sample Size and Power: Ensure that you have an adequate sample size for your analysis, as repeated measures ANOVA can be sensitive to sample size. Additionally, you may want to perform a power analysis to determine if your sample size is sufficient to detect the effects you are interested in.

Data Distribution: Check the distribution of your data. Repeated measures ANOVA assumes that the residuals are normally distributed. If your data deviates significantly from normality, you might consider using a non-parametric alternative, such as the Friedman test.

Effect Size: Consider reporting effect sizes along with p-values. Effect sizes help provide a practical understanding of the magnitude of differences between groups or time points.

Data Preprocessing: Depending on the specifics of your study, you may need to perform data preprocessing, such as handling missing data or outliers.

Time Dependency: Make sure that your measurements at different time points are genuinely dependent on each other. If there is no temporal relationship between measurements, a regular ANOVA may be more appropriate.

In summary, a repeated measures ANOVA is a suitable choice for analyzing the effect of treatment in four groups at 20 time points, but you should consider the assumptions, conduct appropriate post-hoc tests, and ensure your data meets the necessary requirements for the analysis. If assumptions are violated or your data doesn't meet the criteria for repeated measures ANOVA, you might need to explore alternative statistical methods. Consulting with a statistician or data analyst can be helpful in making the most appropriate choice for your specific study.

Medhat Elsahookie

If I did understand your question correctly, I will suggest to you to use RCBD , at the

same time you still have the chance to analyze data for regression, for each 20 points and/or 80 collected together. Regards.

Muhammad Zeeshan

Input your data into statistical software (e.g., R, Python with statistical packages, SPSS) and conduct a repeated measures ANOVA with the treatment as the independent variable and time points as the repeated measures.

Best guidance can be given if you share you data set.

Ashi Saif

thank you Hugo Assunção Muhammad Zeeshan Medhat Elsahookie

Wadie Abu Dahoud

I think it would be better to use Mixed linear models, depending on your assumptions and hypotheses.

Best Regards,

Wadie

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