Analysis of Variance and Sundry

Here I will write very briefly about some analysis.

This test compares the means to two groups. The goal is to determine if they are statistically different from one another.

Analysis of Variance
These include the MANOVA, ANOVA and MANCOVA and ANCOVA.

The MANOVA test compares the multivariate means of multiple groups. If this test is significant, then it is possible to do an ANOVA test for each DV.

In particular, the MANOVA:

  • Has more than one DV
  • Uses an omnibus F-test
  • The IV is categorical
  • Identifies significant DVs
  • Assumes normality, linearity


  • Can be used for each DV after MANOVA F-test is significant.
The MANCOVA and ANCOVA test includes covariates. The covariates are used when you want to control for this variables in the analysis of variance.
The purpose of a regression is to estimate the relationship between the independent (predictor/explanatory) variables and the dependent (response./outcome) variable. Assumptions of regressions include:
  • No specification error (no omitted variables)
  • No measurement error
  • There is no multicollinearity (variables are independent)
  • Errors are independent (no omitted variables)
  • Errors are normally distributed
  • Normality
Hierarchical Regression
Are a type of regression models. This analysis builds successive linear regression models, each time adding more predictors. Here, the order of input matters. Generally, the oder is:
  • Controls
  • Main
  • Interactions and higher order
(Adapted from course notes)
(Flashcards and other resources here)

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