Factor Analysis

Factor Analysis: A Short Introduction, Part 3-The Difference Between Confirmatory and Exploratory Factor Analysis

November 2nd, 2012 by

by Maike Rahn, PhD

An important question that the consultants at The Analysis Factor are frequently asked is:

What is the difference between a confirmatory and an exploratory factor analysis?

A confirmatory factor analysis assumes that you enter the factor analysis with a firm idea about the number of factors you will encounter, and about which variables will most likely load onto each factor.

Your expectations are usually based on published findings of a factor analysis.

An example is a fatigue scale that has previously been validated. You would like to make sure that the variables in your sample load onto the factors the same way they did in the original research.

In other words, you have very clear expectations about what you will find in your own sample. This means that (more…)


Factor Analysis: A Short Introduction, Part 2–Rotations

September 24th, 2012 by

by Maike Rahn, PhD

Rotations

An important feature of factor analysis is that the axes of the factors can be rotated within the multidimensional variable space. What does that mean?

Here is, in simple terms, what a factor analysis program does while determining the best fit between the variables and the latent factors: (more…)


Factor Analysis: A Short Introduction, Part 1

September 10th, 2012 by

Why use factor analysis?

Factor analysis is a useful tool for investigating variable relationships for complex concepts such as socioeconomic status, dietary patterns, or psychological scales.

It allows researchers to investigate concepts they cannot measure directly. It does this by using a large number of variables to esimate a few interpretable underlying factors.

What is a factor?

The key concept of factor analysis is that multiple observed variables have similar patterns of responses because they are all associated with a latent variable (i.e. not directly measured). (more…)


An Easy Way to Reverse Code Scale items

June 29th, 2012 by

Before you run a Cronbach’s alpha or factor analysis on scale items, it’s generally a good idea to reverse code items that are negatively worded so that a high value indicates the same type of response on every item.

So for example let’s say you have 20 items each on a 1 to 7 scale. For most items, a 7 may indicate a positive attitude toward some issue, but for a few items, a 1 indicates a positive attitude.

I want to show you a very quick and easy way to reverse code them using a single command line. This works in any software. (more…)


Confusing Statistical Term #6: Factor

April 27th, 2012 by

Factor is confusing much in the same way as hierarchical and beta, because it too has different meanings in different contexts.  Factor might be a little worse, though, because its meanings are related.

In both meanings, a factor is a variable.  But a factor has a completely different meaning and implications for use in two different contexts. (more…)


Computing Cronbach’s Alpha in SPSS with Missing Data

July 16th, 2010 by

I recently received this question:

I have scale which I want to run Chronbach’s alpha on.  One response category for all items is ‘not applicable’. I want to run  Chronbach’s alpha requiring that at least 50% of the items must be answered for the scale to be defined.  Where this is the case then I want all missing values on that scale replaced by the average of the non-missing items on that scale. Is this reasonable? How would I do this in SPSS?

My Answer:

In RELIABILITY, the SPSS command for running a Cronbach’s alpha, the only options for Missing Data (more…)