OptinMon 15 - Principal Component Analysis

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

August 21st, 2020 by

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.


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…)

Rescaling Sets of Variables to Be on the Same Scale

December 11th, 2018 by

by Christos Giannoulis, PhD

Attributes are often measured using multiple variables with different upper and lower limits. For example, we may have five measures of political orientation, each with a different range of values.

Each variable is measured in a different way. The measures have a different number of categories and the low and high scores on each measure are different.


Life After Exploratory Factor Analysis: Estimating Internal Consistency

June 25th, 2018 by

by Christos Giannoulis, PhD

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.


Four Common Misconceptions in Exploratory Factor Analysis

June 5th, 2018 by

by Christos Giannoulis, PhD

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.


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…)