Factor Analysis

How Big of a Sample Size do you need for Factor Analysis?

Most of the time when we plan a sample size for a data set, it’s based on obtaining reasonable statistical power for a key analysis of that data set. These power calculations figure out how big a sample you need so that a certain width of a confidence interval or p-value will coincide with a scientifically meaningful effect size.

But that’s not the only issue in sample size, and not every statistical analysis uses p-values.

Why Adding Values on a Scale Can Lead to Measurement Error

by Jeff Meyer Leave a 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.

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.

Member Training: Reporting Structural Equation Modeling Results

by Jeff Meyer

The last, and sometimes hardest, step for running any statistical model is writing up results.

As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.

Any good statistical report includes enough information that someone else could replicate your results with your data.

How to Reduce the Number of Variables to Analyze

by Christos Giannoulis 1 Comment

by Christos Giannoulis

Many data sets contain well over a thousand variables. Such complexity, the speed of contemporary desktop computers, and the ease of use of statistical analysis packages can encourage ill-directed analysis.

It is easy to generate a vast array of poor ‘results’ by throwing everything into your software and waiting to see what turns up. [Read more…] about How to Reduce the Number of Variables to Analyze