Centering variables is common practice in some areas, and rarely seen in others. That being the case, it isn’t always clear what are the reasons for centering variables. Is it only a matter of preference, or does centering variables help with analysis and interpretation? [Read more…] about Member Training: Centering
Analysis of Means (ANOM) is an underappreciated methodology that has relevance to quality control and institutional comparisons.
Post-hoc tests, pairwise or other linear contrasts, are typical in an analysis of variance (ANOVA) setting to understand which group means differ. They incorporate p-value adjustments to avoid concluding that group means differ when they actually do not. There are several adjustments that can be considered for conducting multiple post-hoc tests, including single-step and stepwise adjustments. [Read more…] about Member Training: ANOVA Post-hoc Tests: Practical Considerations
Statistical contrasts are a tool for testing specific hypotheses and model effects, particularly comparing specific group means.
A key part of the output in any linear model is the ANOVA table. It has many names in different software procedures, but every regression or ANOVA model has a table with Sums of Squares, degrees of freedom, mean squares, and F tests. Many of us were trained to skip over this table, but
1. Because she was making you calculate everything by hand. Sums of squares require a different formula* if sample sizes are unequal, but statistical software will automatically use the right formula. So we’re not too concerned. We’re definitely using software.
2. Nice properties in ANOVA such as the Grand Mean being the intercept in an effect-coded regression model don’t hold when data are unbalanced. Instead of the grand mean, you need to use a weighted mean. That’s not a big deal if you’re aware of it. [Read more…] about When Unequal Sample Sizes Are and Are NOT a Problem in ANOVA