In Factor Analysis, How Do We Decide Whether to Have Rotated or Unrotated Factors?

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: How do we decide whether to have rotated or unrotated factors?


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…)

Incorporating Graphs in Regression Diagnostics with Stata

May 24th, 2016 by

by Jeff MeyerStage 2

You put a lot of work into preparing and cleaning your data. Running the model is the moment of excitement.

You look at your tables and interpret the results. But first you remember that one or more variables had a few outliers. Did these outliers impact your results? (more…)

Linear Regression in Stata: Missing Data and the Stories it Might Tell

May 18th, 2016 by

by Jeff MeyerStage 2

In a previous post , Using the Same Sample for Different Models in Stata, we examined how to use the same sample when comparing regression models. Using different samples in our models could lead to erroneous conclusions when interpreting results.

But excluding observations can also result in inaccurate results.

The coefficient for the variable “frequent religious attendance” was negative 58 in model 3 (more…)