common factor analysis

How to Reduce the Number of Variables to Analyze

July 10th, 2019 by

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. (more…)

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.