Mixed designs - summary of chapter 16 of Statistics by A. Field (5th edition)

Chapter 16
Mixed designs

Mixed 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

Assumptions in mixed designs

All the sources of potential bias in chapter 6 apply.

  • homogeneity of variance
  • sphericity

You can apply the Greenhouse-Geisser correction and forget about sphericity.

Mixed designs

  • 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. If the values in the Sig column is less than 0.05 then the means are significantly different
  • The table labelled Test of Between-Subjects Effects shows the F-statistic(s) for any between-group variables. If the value in the Sig column is less than 0.05 then the means of the groups are significantly different
  • Break down the mean effects and interaction terms using contrasts. These contrasts appear in the table labelled Tests of Within-Subjects Contrasts. Again, look at the column labelled sig.
  • Look at the means, or draw graphs, to help you interpret contrasts.

Calculating effect sizes

Effect sizes are more useful when they summarize a focused effect.

A straightforward approach is to calculate effect sizes for your contrasts.

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