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
In your statistics class, your professor made a big deal about unequal sample sizes in one-way Analysis of Variance (ANOVA) for two reasons.
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
Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?
I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.
We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.
Our analysis of linear regression focuses on parameter estimates, z-scores, p-values and confidence levels. Rarely in regression do we see a discussion of the estimates and F statistics given in the ANOVA table above the coefficients and p-values.
And yet, they tell you a lot about your model and your data. Understanding the parts of the table and what they tell you is important for anyone running any regression or ANOVA model.