StatisticsChapter 16Mixed designs Situations where we combine repeated-measures and independent designs.Mixed designs: when a design includes some independent variables that were measured using different entities and others that used repeated measures.A mixed design requires at least two independent variables.Because by adding independent variables we’re simply adding predictors to the linear model, you can have virtually any number of independent variables if your sample size is gin enough.We’re still essentially using the linear model.Because there are repeated measures involved, people typically use an ANOVA-style model. Mixed ANOVA All the sources of potential bias in chapter 6 apply.homogeneity of variancesphericityYou can apply the Greenhouse-Geisser correction and forget about sphericity. Mixed designs compare several means when there are two or more independent variables, and at least one of them has been measured using the same entities and at least one other has been measured using different entiteis.Correct for deviations from sphericity for the repeated-measures variable(s) by routinely interpreting the Greenhouse-Geisser corrected effects.The table labelled Tests of Within-Subject Effects shows the F-statistic(s) for any repeated-measures variables and all of the interaction effects. For each effect, read the row labelled Greenhouse-Geisser or Huynh-Feldt...