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Factor & Model
Factor: IV/factor we’re interested in
Factorial: when using 2+ IVs/factors
Model: relationships between tested variables
Main & Interaction Effect
Main: effect of one IV averaged across levels of other IVs on a DV
Interaction: effect of one IV depends on value of another IV → IVs have combined impact on DV
Factorial Designs (GIS)
Greater IVs in factorial design, greater potential interactions
Increases statistical power by reducing error variance by modelling mutliple effects
Shows interactions, allow testing of main/interaction effects
ANCOVA (ARC)
ANOVA with an extraneous/covariate variable (uncontrolled factor that may impact results)
Reduces error variance as covariates explain part of residual variance & sometimes model variance
Covariate must be linearly related to DV → tested by scatterplot
Mixed ANOVA
Type of factorial ANOVA
At least one between-subjects and within-group IV
Can include covariates