I'm using SPSS to run a regression analysis in order to predict navigational performance (continuous dependent variable) from self-assessment scores (continuous predictor), sex and age group (both categorical predictors). I'm also interested in the interaction effects of (score x sex) and (score x age group). Categorial predictors were coded with 0 and 1 and all predictors were centered before computing the interaction terms.
Since the residual plots indicated heterogenity of variances (heteroscedasticity), I wanted to perform a Levene's test on the model. In order to do so I used the "Univariate" dialogue in SPSS and specified the same (custom) model as in my regression analysis:
- Main effects: score, age group, sex.
- Interaction effects: score x age group, score x sex.
In my understanding, regression analysis and AN(C)OVA are essentially the same linear models. However, using the "Univariate" option in SPSS with scores as a covariate I receive results differing from the regression analysis:
Sum of squares, R² and F-value of the whole model are identical between regression analysis and ANCOVA. p-values for the categorical predictors and interaction terms are identical as well.
The only difference I see is in the p-value of the main effect of score:
- Entering score as factor in the regression model: p = .182
- Entering score as covariate: p = .009
I'm just wondering how does this happen? Shouldn't both models be analoguous?