Confirmatory Factor Analysis

Measurement Invariance and Multiple Group Analysis

Creating a quality scale for a latent construct (a variable that cannot be directly measured with one variable) takes many steps. Structural Equation Modeling is set up well for this task.

One important step in creating scales is making sure the scale measures the latent construct equally well and the same way for different groups of individuals.

Why Adding Values on a Scale Can Lead to Measurement Error

by Jeff Meyer 1 Comment

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.

Member Training: A Guide to Latent Variable Models

by Jeff Meyer

An extremely useful area of statistics is a set of models that use latent variables: variables whole values we can’t measure directly, but instead have to infer from others. These latent variables can be unknown groups, unknown numerical values, or unknown patterns in trajectories.

One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model

by Jeff Meyer 9 Comments

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

by Jeff Meyer 4 Comments

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.

Correlated Errors in Confirmatory Factor Analysis

by Jeff Meyer 2 Comments

Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly.

But we can use a set of questions on a scale, called indicators, to represent the construct together by combining them into a latent factor.

Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.