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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? (more…)

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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. (more…)

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If you’ve been doing data analysis for very long, you’ve certainly come across terms, concepts, and processes of matrix algebra.  Not just matrices, but:

• Matrix addition and multiplication
• Traces and determinants
• Eigenvalues and Eigenvectors
• Inverting and transposing
• Positive and negative definite

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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. (more…)

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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. (more…)

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