User Guide

442
Chapter 29
Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity;
unrotated solution, including factor loadings, communalities, and eigenvalues; rotated
solution, i
ncluding rotated pattern matrix and transformation matrix; for oblique
rotations: rotated pattern and structure matrices; factor score coefficient matrix
and factor covariance matrix. Plots: scree plot of eigenvalues and loading plot of
first two o
r three factors.
Data. The variables should be quantitative at the interval or ratio level. Categorical
data (such as religion or country of origin) are not suitable for factor analysis.
Data for wh
ich Pearson correlation coefficients can sensibly be calculated should
be suitable for factor analysis.
Assumptions. The data should have a bivariate normal distribution for each pair
of variab
les, and observations should be independent. The factor analysis model
specifies that variables are determined by common factors (the factors estimated by
the model) and unique factors (which do not overlap between observed variables);
the compu
ted estimates are based on the assumption that all unique factors are
uncorrelated with each other and with the common factors.
Figure 2
9-1
Factor a
nalysis output
72.833 8.272 72
35.132 32.222 72
82.472 18.625 72
24.375 10.552 72
3.205 1.593 72
62.583 22.835 72
3.504 .608 72
1.697 1.156 72
3.577 2.313 72
8.038 3.174 72
4.153 .686 72
Average female life expectancy
Infant mortality (deaths per 1000 live
births)
People who read (%)
Birth rate per 1000 people
Fertility: average number of kids
People living in cities (%)
Log (base 10) of GDP_CAP
Population increase (% per year))
Birth to death ratio
Death rate per 1000 people
Log (base 10) of Population
Mean
Std.
Deviation
Analysis
N
Descriptive Statistics