Factor Analysis

1547 words - 7 pages

Factor Analysis in SPSS
To conduct a Factor Analysis, start from the “Analyze” menu. This procedure is intended to
reduce the complexity in a set of data, so we choose “Data Reduction” from the menu. And the
choice in this category is “Factor,” for factor analysis.

This dataset gives children’s scores on subtests of the Wechsler Intelligence Scale for Children
(WISC-III). The Wechsler scales are scored to give you a “verbal” and a “performance” IQ.
The question is whether we can reproduce the verbal vs. nonverbal distinction, with the
appropriate subtests grouping into each category, using factor analysis.

This dialog box will appear:
Factor analysis has no IVs and DVs, so ...view middle of the document...

You can set a more conservative stopping criterion by requiring each
factor to have a higher eigenvalue. Or, if you already know exactly how many factors you think
there will be, you can set the extraction method to a specific “Number of factors,” and then put
the number into this box.

Now go back to the main dialog box by clicking “Continue.” Once you’re there, click on the
button for “Rotation” to see the second sub-dialog:

This dialog allows you to choose a “rotation method” for your factor analysis. As discussed in
the class notes, a rotation method gets factors that are as different from each other as possible,
and helps you interpret the factors by putting each variable primarily on one of the factors.
However, you still need to decide whether you want an “orthogonal” solution (factors are not
highly correlated with each other), or an “oblique” solution (factors are correlated with one
another). If you want an oblique solution, the only choice SPSS gives you is “Direct Oblimin.”
All of the others are orthogonal solutions—the one that you’ll use most often from these choices
is the default value, “Varimax.” Most of the factor analyses you will see in published articles
use a Varimax rotation.
While you’re here, make sure that the check-box for a “rotated solution” is on. The rotated
solution gives you the factor loadings for each individual variable in your dataset, which are
what you use to interpret the meaning of (i.e., make up names for) the different factors.

Hit “Continue” in the sub-dialog, and then “OK” in the main dialog to see the output:

Factor Analysis
Communalities

INFO

Initial
1.000

COMP

1.000

ARITH

1.000

SIMIL

1.000

VOCAB

1.000

DIGIT

1.000

PICTCOMP

1.000

PARANG

1.000

BLOCK

1.000

OBJECT

1.000

CODING

1.000
Extraction Method: Principal Component Analysis.
Total Variance Explained

Component
1

Total
3.829

Initial Eigenvalues
Cumulative %
% of Variance

Rotation Sums of Squared Loadings

34.806

34.806

Total
3.023

% of Variance
27.485

Cumulative %
27.485

2

1.442

13.109

47.915

2.209

20.084

47.569

3

1.116

10.147

58.062

1.154

10.492

58.062

4

.890

8.092

66.153

5

.768

6.985

73.138

6

.633

5.753

78.891

7

.595

5.412

84.303

8

.522

4.749

89.051

9

.471

4.281

93.332

10

.419

3.806

97.138

11

.315

2.862

100.000

Extraction Method: Principal Component Analysis.

This table shows you the actual factors that were extracted. If you look at the section labeled
“Rotation Sums of Squared Loadings,” it shows you only those factors that met your cut-off
criterion (extraction method). In this case, there were three factors with eigenvalues greater than
1. SPSS always extracts as many factors initially as there are variables in the dataset, but the rest
of these didn’t make the grade....

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