The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
In this nearly 6-hour tutorial you will learn menu-based R libraries so you can use R without having to fuss with R code. These libraries don’t cover everything R can do, but they do quite a bit and can set you up to make running R much easier.
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The great majority of all regression modeling explores and tests the association between independent and dependent variables. We are not able to claim the independent variable(s) has a causal relationship with the dependent variable. There are five specific model types that allow us to test for causality. Difference in differences models are one of the five.
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Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions? [Read more…] about Member Training: Classic Experimental Designs
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
For nearly a hundred years the concept of “statistical significance” has been fundamental to statistics and to science. And for nearly that long, it has been controversial and misused as well. [Read more…] about Member Training: Moving to a World Beyond p<0.05
Missing data is a common problem in data analysis. One of the successful approaches is k-Nearest Neighbor (kNN), a simple approach that leverages known information to impute unknown values with a relatively high degree of accuracy. [Read more…] about Member Training: A (Gentle) Introduction to k-Nearest Neighbor