When you’re working with many correlated variables, they get too unwieldy to use individually.
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principal component analysis
Member Training: Principal Component Analysis
January 1st, 2026 by TAF SupportLinear Regression Analysis – 3 Common Causes of Multicollinearity and What Do to About Them
February 11th, 2022 by Karen Grace-MartinMulticollinearity in regression is one of those issues that strikes fear into the hearts of researchers. You’ve heard about its dangers in statistics 
classes, and colleagues and journal reviews question your results because of it. But there are really only a few causes of multicollinearity. Let’s explore them.Multicollinearity is simply redundancy in the information contained in predictor variables. If the redundancy is moderate, (more…)
classes, and colleagues and journal reviews question your results because of it. But there are really only a few causes of multicollinearity. Let’s explore them.Multicollinearity is simply redundancy in the information contained in predictor variables. If the redundancy is moderate, (more…)Member Training: Reporting Structural Equation Modeling Results
October 1st, 2019 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.
Four Common Misconceptions in Exploratory Factor Analysis
June 5th, 2018 by Christos GiannoulisToday, 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.
In Factor Analysis, How Do We Decide Whether to Have Rotated or Unrotated Factors?
January 20th, 2017 by Karen Grace-MartinI 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: How do we decide whether to have rotated or unrotated factors?
Answer:
Great question. Of course, the answer depends on your situation.
When you retain only one factor in a solution, then rotation is irrelevant. In fact, most software won’t even print out rotated coefficients and they’re pretty meaningless in that situation.
But if you retain two or more factors, you need to rotate.
Unrotated factors are pretty difficult to interpret in that situation. (more…)
Can You Use Principal Component Analysis with a Training Set Test Set Model?
January 20th, 2017 by Karen Grace-MartinI 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…)