Repeated Measures ANOVA Used when the research design contains one factor on which participants are measured more than twice (dependent, or within- groups design). Similar to the paired- samples t -test

Computing Repeated Measures ANOVA in SPSS Go to Analyze  General Linear Model  Repeated Measures In the repeated measures define factor(s) window, name the factor and enter the number of levels  click Add  click Define In the Repeated Measures dialog box, click on the first level of your variable and move it to the __?__(1) space in the within-subjects variables window  continue to do this for all of the remaining levels of the variable Click Options  Move factor 1 to the Display Means for window and select Compare Main Effects  also select Descriptive Statistics and Estimates of Effect Size. Click Continue  Click OK

Interpreting the Output The descriptive statistics box provides the mean, standard deviation, and number of participants for each measurement time. This box is generated because three (or more) columns of measurements are being compared. This only needs to be interpreted when those columns of measurements correspond to separate variables (multivariate designs).

Main Analysis The row you are interested in is the row which has the name of your variable in it. The between df appear in this row; the within degrees of freedom appear in the error row. F is your test statistic, and Sig is its probability. Partial eta squared is the effect size statistic for the F-ratio.

Post Hoc Tests Pairwise Comparisons provide the mean difference between each measurement time and its significance.

Factorial ANOVA A special case of ANOVA in which there is more than one independent variable (IV) being explored. Because there are multiple IVs, factorial designs have multiple hypotheses which are analyzed by multiple F tests: one for each main effect (IV); and one for each possible interaction between the IVs.

Looking for Main Effects Main Effect : the action of a single IV in an experiment

Looking for Interactions Interaction : the effect of one IV changes across the levels of another IV Higher-Order Interaction : an interaction effect involving more than two IVs

Laying Out a Factorial Design Design Matrix : a visual representation of the research design Hint: If you can’t draw it, you can’t interpret it!

Describing the Design Shorthand Notation : a system that uses numbers to describe the design of a factorial study

Within-Subjects Factorial Designs Within-Subjects Factorial Design : a factorial design in which subjects receive all conditions in the experiment

Mixed Designs Mixed Design : a factorial design that combines within- subjects and between-subjects factors

Computing Factorial ANOVA in SPSS Analyze  General Linear Model  Univariate Move the independent variables to the Fixed Factor(s) box  Move the dependent variable to the Dependent Variable box Click Options  highlight the independent variables and the interaction term in the Factor(s) box and move it to the Display Means for box  Under Display, check descriptive statistics, homogeneity tests, and estimates of effect size. Note that the significance level is already set at Click Continue. Click OK.

Interpreting the Output The descriptive statistics box provides the means, standard deviations, and Ns for each main effect, as well as all interactions. Levene’s test is designed to compare the error variance of the dependent variable across groups. We do not want this result to be significant.

Main Analysis There are three hypotheses being tested here (one for each main effect and one for the interaction). Thus, there are three separate F-tests conducted. The between degrees of freedom, as well as the F-ratio, its significance, and associated effect size, are located on the rows with the variable names. The within degrees of freedom is located with the error term.