Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?
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Member Training: Explaining Logistic Regression Results to Non-Researchers
August 1st, 2020 by TAF Support
Why Adding Values on a Scale Can Lead to Measurement Error
July 22nd, 2020 by Jeff Meyer
Whenever you use a multi-item scale to measure a construct, a key step is to create a score for each subject in the data set.
This score is an estimate of the value of the latent construct (factor) the scale is measuring for each subject. In fact, calculating this score is the final step of running a Confirmatory Factor Analysis.
Three Designs that Look Like Repeated Measures, But Aren’t
June 19th, 2020 by Karen Grace-Martin
Repeated measures is one of those terms in statistics that sounds like it could apply to many design situations. In fact, it describes only one.
A repeated measures design is one where each subject is measured repeatedly over time, space, or condition on the dependent variable.
These repeated measurements on the same subject are not independent of each other. They’re clustered. They are more correlated to each other than they are to responses from other subjects. Even if both subjects are in the same condition. (more…)
One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
February 23rd, 2020 by Jeff Meyer
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.
First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
February 7th, 2020 by Jeff Meyer
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.
Member Training: A Gentle Introduction to Multilevel Models
January 31st, 2020 by guest contributer
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 example will be used to clarify the ideas and the meaning of the MLM results.