Cramming Sam's top tips from chapter 13
Click on the topic to read Sam's tips from the book
- When the linear model is used to compare several means adjusted for the effect of one or more other variables (called covariates) it can be referred to as analysis of covariance (ANCOVA).
- Before the analysis check that the covariate(s) are independent of any independent variables by seeing whether those independent variables predict the covariate (i.e., the covariate should not differ across groups).
- In the table labelled Tests of Between-Subjects Effects, assuming you’re using an alpha of 0.05, look to see if the value in the column labelled Sig. is below 0.05 for both the covariate and the independent variable. If it is for the covariate then this variable has a significant relationship to the outcome variable; if it is for the independent variable then the means (adjusted for the effect of the covariate) are significantly different across categories of this variable.
- If you have generated specific hypotheses before the experiment use planned contrasts; if not, use post hoc tests.
- For parameters and post hoc tests, look at the columns labelled Sig. to discover if your comparisons are significant (they will be if the significance value is less than 0.05). Use bootstrapping to get robust versions of these tests.
- In addition to the assumptions in Chapter 6, test for homogeneity of regression slopes by customizing the model to look at the independent variable × covariate interaction.