When I consult with researchers, a common part of that is going through their analysis together. Sometimes I notice that they're using some shortcut in SPSS that I had not known about. Or sometimes they could be saving themselves some headaches. So I thought I'd share three buttons you may not have noticed before that will make your data analysis more efficient.

A chi square test is often applied to two-way tables, like the one below. This table represents a sample of 1,322 individuals. Of these individuals, 687 are male, and 635 are female. Also 143 are union members, 159 are represented by unions, and 1,020 are not affiliated with a union. You might use a chi-square […]

In this 8-part tutorial, you will learn how to get started using Stata for data preparation, analysis, and graphing. This tutorial will give you the skills to start using Stata on your own. You will need a license to Stata and to have it installed before you begin.

I want to do a GLM (repeated measures ANOVA) with the valence of some actions of my test-subjects (valence = desirability of actions) as a within-subject factor. My subjects have to rate a number of actions/behaviours in a pre-set list of 20 actions from ‘very likely to do’ to ‘will never do this’ on a scale from 1 to 7,..

Spoiler alert, real data are seldom normally distributed. How does the population distribution influence the estimate of the population mean and its confidence interval? To figure this out, we randomly draw 100 observations 100 times from three distinct populations and plot the mean and corresponding 95% confidence interval of each sample.

Is it really ok to treat Likert items as continuous? And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately.

When is it important to use adjusted R-squared instead of R-squared? R², the Coefficient of Determination, is one of the most useful and intuitive statistics we have in linear regression. It tells you how well the model predicts the outcome and has some nice properties. But it also has one big drawback.

On a previous post (Why do I need to have knowledge of multiple regression to understand SEM?) we showed how a multiple regression model could be conceptualized using Structural Equation Model path diagrams. That's the simplest SEM you can create, but its real power lies in expanding on that regression model. Here I will discuss 4 ways to do that..

Bootstrapping is a methodology derived by Bradley Efron in the 1980s that provides a reasonable approximation to the sampling distribution of various “difficult” statistics. Difficult statistics are those where there is no mathematical theory to establish a distribution.

In most regression models, there is one response variable and one or more predictors. From the model’s point of view, it doesn’t matter if those predictors are there to predict, to moderate, to explain, or to control. All that matters is that they’re all Xs, on the right side of the equation.