Purchase Solution

How to write up Exploratory Factor Analysis.

Not what you're looking for?

An example of how to write up Exploratory Factor Analysis

Solution Summary

How to write up Exploratory Factor Analysis. An example of the format for writing up the analysis. Includes Principal Components Analysis and raw data.

Solution Preview

Autumn Assignment: Exploratory Factor Analysis

A Principal Components Analysis was initially carried out on the data set to try and establish the number of factors that needed to be extracted. The scree plot below displays the eigenvalues for each of the principal components. The graph suggests that 4 factors should be extracted.

Figure 1. Scree plot in PCA to show eigenvalues

To confirm that only four factors should be extracted O'Connor's parallel method was employed, this calculated the random and raw eigenvalues. It was therefore decided to extract 4 factors.

Figure 2. Graph to show raw and random eigenvalues

Exploratory factor analysis was then carried out to assess which of the commonalities could be removed from the data. Seven items were discarded (see appendix) as they shared very little variance with the other items. The factor analysis was run again with these items omitted, making the structure much clearer, direct oblimin rotation was also applied to achieve 'simple structure'. The pattern matrix was used to determine what the four factors corresponded to (see appendix). The four factors are named as:

Factor 1. Negative views of Christmas
Factor 2. Spirituality
Factor 3. Materialism
Factor 4. Commercialism

Pearson's correlation revealed a significant result between factor 3 and materialism, r = .294, p = .008 (see appendix). The graph below displays the correlation between factor three and materialism.

Figure 3. Graph to show correlation between factor 3 and materialism

A further correlation between the 'satisfaction with life scale (SWLS)' and the four factors was conducted which revealed that SWLS is significantly negatively correlated with factor 3 (materialism) r = -.423, p = .000. This means that people who are more materialistic are generally going to have higher SWLS scores (suggesting they are happier with their life), whereas people who are less materialistic have lower SWLS scores (see appendix). The correlation also revealed that factor 1 (negative views of Christmas) was significantly negatively correlated with ...

Terms and Definitions for Statistics

This quiz covers basic terms and definitions of statistics.