Archive:ANCOVA

File:Wikademia.logo.png Resource type: this resource contains a tutorial or tutorial notes. Template:0%done This tutorial teaches use of analysis of covariance (ANCOVA) techniques, with practical exercises based on using SPSS.

Overview

An ANCOVA evaluates whether population means on the DV, adjusted for differences on the covariate(s), differ across the levels of the IVs.

Covariates

• Typically included in an experimental design to remove extraneous influences from the DV, thus decreasing the within-group variance
• Including covariates is appropriate in order to:
  1. Eliminate some systematic variance outside the control of the researcher that can bias the results.
  2. Account for differences in response due to unique characteristics of the respondents.

  This is usually achieved in experimental designs by random assignment to groups, however, in quasi-experimental designs problems related to non-random assignment can be minimised by statistically controlling for the effects of covariates.

Example

If you are interested in testing the effect of computer experience on the attitude towards use of internet shopping, and you suspect that those with more positive attitudes toward shopping in general are more likely to have positive attitudes towards internet shopping, you may include attitude toward shopping as a covariate so as to remove its influence from the attitude towards internet shopping measure.

Assumptions

Assumptions to be met are those for ANOVA, plus:

1. Covariates must be linearly related to the DV
2. Covariates must have a homogeneity of regression effect (must have equal effects on the dependent variable across the groups) )if there is a significant interaction between the covariate and the factor – you cannot use this procedure)

See also

• ANCOVA (Wikipedia)

External links

• ANCOVA (ucspace)
  • ANCOVA Notes (Handout)