The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
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.
[Read more…] about Why Adding Values on a Scale Can Lead to Measurement Error
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.
[Read more…] about July Member Training: A Guide to Latent Variable Models
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.
[Read more…] about First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
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.
[Read more…] about Correlated Errors in Confirmatory Factor Analysis
by Christos Giannoulis, PhD
Many times in science we are intrigued to measure an underlying characteristic that cannot be observed or measured directly. This measure is hypothesized to exist to explain variables, such as behavior, that can be observed.
The measurable variables are called manifest variables. The unmeasurable are called latent variables.
Latent variables are often called factors, especially in the context of factor analysis.