Orthogonal Rotations

 

The n-dimensional space, as e.g., the two dimensional space of our example, can be described by a system of n coordinates. There are two basic coordinate systems. Orthogonal coordinate system with angles separating coordinates from each other equal to 90 degrees and oblique coordinate systems with an angle separating the coordinates not equal to 90 degrees.

The orientation of coordinates is often arbitrary and the coordinates can be rotated, as to optimize some property of the measured system. There is an infinite number of possible orientations of a coordinate system so selection of a particular orientation depends to a degree on the researcher's perception of properties of the solution to be stressed. The arbitrariness of a system of coordinates can be well illustrated by considering the geographical system of latitudes and longitudes. The passage of the zero meridian through Greenwich is optimal only to the England home based navigators.

In factor analysis, reference axes are rotated to increase interpretability of factors. Factor loadings can be rotated; i.e., described by a different system of coordinates, either visually or analytically. Depending on angular separation of the reference axes, the rotation can be either orthogonal or oblique. The best orthogonal analytic rotation method is Kaiser's Varimax. Other orthogonal rotation methods are quartimax and equimax. Oblique analytical methods of rotation show far greater variety of methods than the orthogonal rotation methods.  Among the better known techniques of oblique rotations are promax, maxplan and direct and indirect oblimin.

 

Transformation Matrix

To rotate a system of Cartesian coordinates counterclockwise around the origin, use a transformation matrix

 

 

Consider a point with Cartesian coordinates (1,1), as shown below, to be rotated 45 degrees, counterclockwise.

 

 Both the cosine and sine of 45 degrees are .71, thus

 

 

results in the vector of the new coordinates of the point (0,1.4). The Pythagorean theorem can easily verify the new coordinates. The hypotenuse of the triangle connecting the original point with the origin of the Cartesian coordinates equals ; i.e., ; which is 1.4.

 

Trans-temporal Cognitive Matching

It was suggested that the Zeitgeist period of the 1960's and 1970's paralleled the period of European romanticism around the turn of the 18th and 19th centuries. Hypotheses of this type are frequently made within the non-quantitative areas of the social sciences as history or philosophy.

The following experimental investigation attempted to quantify this hypothesis. To measure a societal climate, sentences from popular books, expressing opinions, were converted into agree-disagree test items and administered to a group of 63 subjects.

The societal climate of the classical romantic period was assessed by Goethe's book Die Leiden Des Jungen Werthers (Goethe). The contemporary social climate was represented by two bestsellers; by Reich's book The Greening of America and by Piersig's book Zen and the Art of Motorcycle Maintenance (Zen). Sentences from Reich's book were used to construct the neo-romantic Consciousness III scale (CIII) and Consciousness I and II scales (CI and CII), indexing the non-romantic, analytical and rational set of attitudes.

Additional scales were added to the analysis, indexing the normative attitudes (Normative), openness to self-absorbing and self-altering experiences (Absorption) and measuring cognitive styles characteristics of the Euro-American (West) and Oriental (East) Civilizations.

Correlation Matrix

The scores on all nine scales analyzed were correlated using product-moment coefficients of correlations, as shown in the following table.

 

 

2

3

4

5

6

7

8

9

1. Goethe

.47

-.09

-.04

.59

-.10

.40

.65

-.13

2. Zen

 

.14

.01

.54

.05

.36

.66

.07

3. Consciousness I

 

 

.63

.17

.42

.23

.07

.38

4. Consciousness II

 

 

 

-.02

.51

.27

.11

.30

5. Consciousness III

 

 

 

 

-.02

.42

.56

.04

6. Normative

 

 

 

 

 

-.04

-.14

.26

7. Absorption

 

 

 

 

 

 

.41

-.03

8. East

 

 

 

 

 

 

 

-.19

9. West

 

 

 

 

 

 

 

 

 

The above correlation matrix was factor-analyzed. Results of this analysis are described in the following paragraphs.

Bartlett's Sphericity Test

The first statistics of interest from the generated output is the determinant of the correlation matrix. For the example, it was computed as .03 and used for computation of the Bartlett's sphericity test

 

 

where n equals the number of subjects and k is the number of variables. The Bartlett's sphericity test tests the null hypothesis that the population correlation matrix is an identity matrix. If the obtained chi square value is significant, then the correlation matrix to be analyzed is non-random. The better procedure is to use Humphrey and Ilgen's parallel test.

 

Shared Variance

Another interesting part of the output is the inverse of the correlation matrix. Reciprocals of its diagonal entries are equal to the coefficients of multiple non-determination (alienation); i.e., to the proportion of variance a variable is not sharing with other variables. Thus, for our example, the first element in the diagonal of the inverted correlation matrix equals 2.24. The coefficient of alienation can be computed as 1 / 2.24 that is .45. The coefficient of determination can be computed as 1 - .44 that is .55. The multiple R can be computed as a square root of the coefficient of determination. For the example R = .74. The inverted correlation matrix can be used for the initial communality estimates.

 

Principal Factors Solution

Communality Estimates and Eigenvalues

The squared multiple correlations, obtained from the inverted matrix of inter-correlations were used as initial communality estimates. The communalities and eigenvalues are shown in the following table. 

 

Components

Communalities

Eigenvalues

1

.546

2.627

2

.542

1.796

3

.497

.337

4

.564

.213

5

.531

.061

6

.404

.021

 

Number of Factors to be Extracted 

Eigenvalues are plotted below where Cattell's scree can be observed.

 

Both the scree of eigenvalues and Kaiser's rule indicated that two factors should be extracted.

Matrix of Factor Loadings

Following iterations, a matrix of factor loadings was obtained as

 

 

F I

F II

East

.84

-09

Goethe

.74

-14

Zen

.71

-07

Consciousness III

.73

.02

Absorption

.56

.16

Consciousness II

.14

.77

Consciousness I

.09

.75

Normative

-.04

.61

West

-.05

.47

 

Varimax Rotation

Thurstone's Criteria

For many years, Thurstone's criteria of simple structure were used for graphic rotations. These criteria consisted of rules describing favorable properties of the rotated solution as, e.g., that each row of the matrix of factor loadings should have at least one zero and that each factor should have a distinct set of factor loadings close to zero. However, the graphic rotations were partly subjective and did not lead to unequivocal solutions.

Kaiser's Varimax

Analytic rotations approximate Thurstone's criteria better. Among them, Kaiser's Varimax is the most widely accepted method for analytical rotation. This method uses the iterative maximization of column variances of the factor loadings. For the example, the transformation matrix, obtained form the application of Kaiser's Varimax is

 

 

Since  equals about 5 degrees, as well as , the adjustment of factor loadings for our example will be only minor.

The Varimax rotated factor loadings are shown in the figure below.

 

The East, Goethe, Zen, Consciousness III and Absorption scales are located in the top cluster. The Consciousness II, Consciousness I, Normative, and West scales are clustered toward the right of the above figure. During the rotation, communalities of the rotated and the unrotated solutions remain identical. However, the variance of the columns of the factor matrix increases. The Varimax rotation facilitates interpretation of factors by increasing their variance and thus their information content.