Analysis of variance (ANOVA) is a widely used statistical test in the behavioral and social sciences.
In a nutshell, ANOVA is used to evaluate differences between (at least) three group means to determine whether there is a “statistically significant” difference somewhere among them (i.e., a difference that is unlikely due to chance factors).
ANOVA is commonly used in conjunction with an experimental research design, in which a researcher randomly assigns participants to one of several groups and tests to see whether an experimental treatment variable leads to group differences on a given dependent measure.
Read more “Analyzing Analysis of Variance: Violation of Assumptions”
This is the first in a new series of posts exploring assumptions behind various statistical tests and measures, with a focus on understanding what happens when those assumptions are violated.
In this first post, I’ll take a look at the use of Analysis of Covariance (ANCOVA) to statistically “control for” and “remove” the effects of an extraneous, third variable from a general linear model that describes the relationship between a dichotomous predictor variable and a continuous dependent measure.
Specifically, I’ll take a look at the appropriateness of using ANCOVA to help answer the following question:
Are people more likely to relocate to a new town or city if there is a four-year college or university within the same county?
Read more “Do Four-Year Colleges & Universities Increase Migration to a Region?”