Based on questions I’ve been asked by clients, most analysts prefer using the factor analysis procedures in their general statistical software to run a confirmatory factor analysis. While this can work in some situations, you’re losing out on some key information you’d get from a structural equation model. This article highlights one of these.

Confirmatory factor analysis (CFA) is the fundamental first step in running most types of SEM models. You want to do this first to verify the measurement quality of any and all latent constructs you’re using in your structural equation model.

In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running […]

One of the many decisions you have to make when model building is which form each predictor variable should take. One specific version of this decision is whether to combine categories of a categorical predictor. The greater the number of parameter estimates in a model the greater the number of observations that are needed to […]

Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion? I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in […]

Our analysis of linear regression focuses on parameter estimates, z-scores, p-values and confidence levels. Rarely in regression do we see a discussion of the estimates and F statistics given in the ANOVA table above the coefficients and p-values. And yet, they tell you a lot about your model and your data. Understanding the parts of […]

What is a Confidence Interval? Any sample-based findings used to generalize a population are subject to sampling error. In other words, sample statistics won’t exactly match the population parameters they estimate.

Odds is confusing in a different way than some of the other terms in this series. First, it’s a bit of an abstract concept, which I’ll explain below. But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term. I found this […]

Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately. Unfortunately, many of the graphs we see today confuse, mislead, or deceive the reader. These poor graphs result from two key limitations. One is a graph designer who isn’t familiar with the principles of effective graphs. The other is software with a […]

Multicollinearity is one of those terms in statistics that is often defined in one of two ways: 1. Very mathematical terms that make no sense — I mean, what is a linear combination anyway? 2. Completely oversimplified in order to avoid the mathematical terms — it’s a high correlation, right? So what is it really? […]