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monsmatglad
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Can i Use a standard F-test on longitudinal data for a linear multiple regression?
Mons
Mons
An F-test for longitudinal data is a statistical test used to compare the means of multiple groups over time. It is commonly used in longitudinal studies and can help determine if there are significant differences between group means at different time points.
An F-test for longitudinal data is different from other statistical tests because it takes into account the correlation between the repeated measures over time. This helps to reduce the risk of making a Type I error, which is the incorrect rejection of a true null hypothesis.
The main assumptions of an F-test for longitudinal data include: 1) the data is normally distributed within each group at each time point, 2) the variances of the groups are equal at each time point, and 3) the measurements are independent within and between groups.
The results of an F-test for longitudinal data will give you an F-statistic and a p-value. The F-statistic represents the ratio of between-group variability to within-group variability. A higher F-statistic indicates a larger difference between group means. The p-value measures the likelihood of obtaining the observed results by chance. A p-value less than 0.05 is typically considered statistically significant.
If the assumptions of an F-test for longitudinal data are not met, you may need to consider using a different statistical test or transforming your data to meet the assumptions. Alternatively, you can use non-parametric tests that do not require the same assumptions, such as the Friedman test. It is important to carefully consider the assumptions and choose the appropriate test for your data to ensure accurate results.