Statistics

ANOVA vs. Regression

ANOVA (Analysis of Variance) and regression are both statistical methods used to analyze the relationship between variables. ANOVA compares means across groups, while regression assesses the relationship between dependent and independent variables to make predictions.

ANOVA tends to reported effects coding, so —> main effects

Regression tends to default to dummy coding, so —> simple effects

Assumptions of ANOVA

include independence of observations, normality of residuals, and homogeneity of variances across groups.

Assumptions of MANCOVA

The assumptions of MANCOVA (Multivariate Analysis of Covariance) include multivariate normality, homogeneity of variance-covariance matrices, and the independence of observations. Additionally, it requires that the covariates are linearly related to the dependent variables.

Running power analyses in G Power

numerator df

denominator df

groups

most powerful

least powerful

Measurement invariance vs. reliability

Reliability = WITHIN group

Measurment invariance = BETWEEN groups (checks to make sure same construct is actually being measured across different groups / countries)

Measurement invariance isn’t reported that commonly in cross-cultural work, despite it being used to enable comparison of the means ACROSS groups (countries).