Why should you, as a researcher in Psychology, Education, or Agriculture, who is trained in ANOVA, need to learn linear regression? There are 3 main reasons.

A Median Split is one method for turning a continuous variable into a categorical one.  Essentially, the idea is to find the median of the continuous variable.  Any value below the median is put it the category “Low” and every value above it is labeled “High.” This is a very common practice in many social […]

The steps you take to analyze data are just as important as the statistics you use. Mistakes and frustration in statistical analysis come as much, if not more, from poor process than from using the wrong statistical method. Benjamin Earnhart of the University of Iowa has written a short (and humorous) article entitled “Respect Your […]

As a statistical consultant, these are the four questions I need answered in order to help a client decide which statistical approach to take. Explicitly answering these questions for you or your consultant makes your statistical consultation more efficient and your analyses more accurate.

Centering is often used to improve the interpretability of regression coefficients. When should a data analyst not center? This article gives 3 necessary conditions.

I just discovered something in SPSS GLM that I never knew. When you have an interaction in the model, the order you put terms into the Model statement affects which parameters SPSS gives you. The default in SPSS is to automatically create interaction terms among all the categorical predictors.  But if you want fewer than […]

There are many ways to approach missing data. The most common, I believe, is to ignore it. But making no choice means that your statistical software is choosing for you. Most of the time, your software is choosing listwise deletion. Listwise deletion may or may not be a bad choice, depending on why and how […]

Proportions as dependent variables can be tricky. You can run a linear regression model, a logistic regression model, or a tobit model, depending on your data and variables.

Poisson Regression Models and its extensions (Zero-Inflated Poisson, Negative Binomial Regression, etc.) are used to model counts and rates. A few examples of count variables include: – Number of words an eighteen month old can say – Number of aggressive incidents performed by patients in an impatient rehab center Most count variables follow one of […]

Adding interaction terms to a regression model has real benefits. It greatly expands your understanding of the relationships among the variables in the model. And you can test more specific hypotheses.  But interpreting interactions in regression takes understanding of what each coefficient is telling you. The example from Interpreting Regression Coefficients was a model of […]