Sometimes it makes sense to change the scale of predictor variables so that interpretations of parameter estimates, including odds ratios, make sense. It is generally done by multiplying the values of a predictor by a constant, often a factor of 10. Since parameter estimates and odds ratios tell you the effect of a one unit […]
If you’ve been doing data analysis for long, you’ve probably had the ‘AHA’ moment where you realized statistical practice is a craft and not just a science. As with any craft, there are best practices that will save you a lot of pain and suffering and elevate the quality of your work. And yet, it’s […]
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 […]
Updated 9/20/2021 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 […]
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 […]