Comparing the Generalized Estimating Equations (GEE) to Mixed Analysis of Variance(ANOVA) Using Experimental Longitudinal Data of Depression
Psychological research is full of the longitudinal data in which a researcher is to compare two or more groups (experimental and control) over the time (pretest, posttest and follow-up). Mixed analysis of Variance is the most commonly used statistical method in such situation. This method can not meet a main assumption in which all correlations between each pair of observations within subjects must be same. An alternative method is GEE in which distribution assumption is robust, estimate for standard errors is more precise, and various covariance matrices can be chosen. To do this, using the data obtained from 40 depressed male patients (20 patients for Cognitive Behavioral Therapy and 20 patients for waiting list control group) in 3 time points (pretest, posttest and follow-up) were analyzed using Mixed ANOVA and GEE with SPSS-26 at the significance level of 0.05. The results indicated that GEE is more accurate than Mixed ANOVA (GEE-R squared =0.48 and Mixed ANOVA R squared=0.31). In this situation, GEE is more accurate and efficient than Mixed ANOVA.
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