📸 Linear Mixed Models menu path in SPSS
Why Use Linear Mixed Models?
Linear Mixed Models (LMM) handle data with complex dependency structures — repeated measurements on the same person, nested designs (students within classrooms), or longitudinal data with missing values. Unlike repeated measures ANOVA, LMM handles missing data via maximum likelihood estimation without listwise deletion.
Running LMM in SPSS
Step 1: Analyze → Mixed Models → Linear.
Step 2: Specify Subjects and Repeated: add the subject ID to Subjects, time variable to Repeated. Choose covariance type: Unstructured or AR(1).
Step 3: Add the DV to Dependent. Add fixed effects (treatment, time, interaction) to Fixed. Add random effects to Random if applicable.
Step 4: Statistics: Parameter estimates, Tests for covariance parameters → OK.
📸 LMM fixed effects table — significant interaction
APA Reporting
A linear mixed model with unstructured covariance revealed a significant Treatment × Time interaction, F(2, 182.4)=12.47, p<.001, indicating that treatment groups showed different trajectories of change over time.