Prism graphpad 89/9/2023 ![]() ![]() How to fit the mixed effects model any random factor is zero or negative When there are no missing values, report the familiar repeated measures ANOVA results. Report the fit to a mixed effects model only when there are missing values, when repeated measures ANOVA is impossible.If there are no missing values, the key results will be the same as repeated measures ANOVA but the results will be presented in an format unfamiliar to those used to repeated measures ANOVA. This will make all analyses be consistent, whether or not there are missing values. Prism is not "smart enough" to remove all data for a participant with missing values, but you could exclude all those values and rerun the ANOVA. This matches what Prism 7 and earlier did. If there are missing values, no results will be reported. The repeated measures tab of the ANOVA dialog (same for one-, two- and three-way data) gives you three choices: But the mixed effects model method can also fit data with missing values. As implemented in Prism 8, the two are completely equivalent when there are no missing values. In general, fitting a mixed effects model is a much more versatile method. Fitting a mixed model with missing values only makes sense when there is zero association between the treatments or time-points and the reason why some values are missing. For example, those results won't be helpful or meaningful if the values are missing because those participants were very sick, or those values were too high to measure (or too low to measure). The results will only be meaningful, of course, if the values are missing for random reasons. ![]() This analysis works fine even when there are some missing values. Prism can analyze repeated measures data in two ways: Two ways to analyze repeated measures data ![]()
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