ANOVA (analysis of variance) tests if 3+ population means are all equal.
Example: do the pupils of schools A, B and C have equal mean IQ scores?
This super simple introduction quickly walks you through the basics such as assumptions, null hypothesis and post hoc tests.
Post hoc tests in ANOVA test if the difference between each possible pair of means is statistically significant.
This step-by-step tutorial quickly walks you through the entire procedure.
If Levene’s test is “Significant“ for an ANOVA, the Welch and Games-Howell tests are good alternatives.
This tutorial quickly walks you through these analyses.
In ANOVA and regression, an interaction effect means that some effect depends on another variable.
Example: women become happier but men become unhappier if they have children. So the effect of having children depends on sex.
This tutorial walks you through testing for and interpreting interaction effects in ANOVA.
Repeated measures ANOVA tests if 3+ variables have equal means in some population.
Example: are the mean scores on IQ tests A, B and C equal for all Dutch children?
This simple introduction quickly walks you through the basics.
Levene’s test examines if 2+ populations have equal variances on some variable.
This condition -known as the homogeneity of variance assumption- is required by t-tests and ANOVA.
So how to run and interpret this test in SPSS? This simple tutorial quickly walks you through.
ANCOVA (analysis of covariance) tests if 2+ population means are equal while controlling for 1+ background variables.
Example: do medicines A, B and C result in equal mean blood pressures when controlling for age?
ANCOVA basically combines ANOVA and regression. This tutorial walks you through the analysis with an example in SPSS.