Exploratory Factor Analysis

Measurement Invariance and Multiple Group Analysis

October 23rd, 2020 by

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

July 22nd, 2020 by

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.


Life After Exploratory Factor Analysis: Estimating Internal Consistency

June 25th, 2018 by

After you are done with the odyssey of exploratory factor analysis (aka a reliable and valid instrument)…you may find yourself at the beginning of a journey rather than the ending.

The process of performing exploratory factor analysis usually seeks to answer whether a given set of items form a coherent factor (or often several factors). If you decide on the number and type of factors, the next step is to evaluate how well those factors are measured.


Confirmatory Factor Analysis: How To Measure Something We Cannot Observe or Measure Directly

June 18th, 2018 by

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.


Four Common Misconceptions in Exploratory Factor Analysis

June 5th, 2018 by

Today, I would like to briefly describe four misconceptions that I feel are commonly perceived by novice researchers in Exploratory Factor Analysis:

Misconception 1: The choice between component and common factor extraction procedures is not so important.

In Principal Component Analysis, a set of variables is transformed into a smaller set of linear composites known as components. This method of analysis is essentially a method for data reduction.


Can You Use Principal Component Analysis with a Training Set Test Set Model?

January 20th, 2017 by

I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn’t get through all the questions. This is part of a series of answers to those questions.

If you missed it, you can get the webinar recording here.

Question: Can you use Principal Component Analysis with a Training Set Test Set Model?

Answer: Yes and no.

Principal Component Analysis specifically could be used with a training and test data set, but it doesn’t make as much sense as doing so for Factor Analysis.

That’s because PCA is really just about creating an index variable from a set of correlated predictors.

Factor Analysis is an actual model that is measuring a latent variable. Any time you’re creating some sort of scale to measure an underlying construct, you want to use Factor Analysis.

Factor Analysis is definitely best done with a training and test data set.

In fact, ideally, you’d run multiple rounds of training and test data sets, in which the variables included on your scale are updated after each test. (more…)