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 […]

Imputation as an approach to missing data has been around for decades. You probably learned about mean imputation in methods classes, only to be told to never do it for a variety of very good reasons. Mean imputation, in which each missing value is replaced, or imputed, with the mean of observed values of that […]

Effect size statistics are extremely important for interpreting statistical results. The emphasis on reporting them has been a great development over the past decade.

Data analysts can get away without ever understanding matrix algebra, certainly. But there are times when having even a basic understanding of how matrix algebra works and what it has to do with data can really make your analyses make a little more sense.

The Estimated Marginal Means in SPSS GLM are the means of each factor or interaction you specify, adjusted for any other variables in the model.

The practice of choosing predictors for a regression model, called model building, is an area of real craft. There are many possible strategies and approaches and they all work well in some situations. Every one of them requires making a lot of decisions along the way. As you make decisions, one danger to look out […]

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.

It’s easy to make things complex without meaning to. Especially in statistical analysis. Sometimes that complexity is unavoidable. You have ethical and practical constraints on your study design and variable measurement. Or the data just don’t behave as you expected. Or the only research question of interest is one that demands many variables. But sometimes […]

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.

Even if you’ve never heard the term Generalized Linear Model, you may have run one. It’s a term for a family of models that includes logistic and Poisson regression, among others. It’s a small leap to generalized linear models, if you already understand linear models. Many, many concepts are the same in both types of […]