3. Results
3.1. Data reduction
The first step in our data analysis was to factor analyze the 44
items in the first two sections of the survey in an attempt to reduce
the number of ‘‘dependent’’ variables. A screen test indicated that
the extraction of nine factors would provide optimal organization
of the data. Thus, using SPSS software, a principal components factor
analysis using varimax rotation was conducted on 44 items,
with nine factors being extracted. Only items with a factor loading
exceeding .50 were included in a factor, with two exceptions to be
described shortly. ‘‘Choice of the cutoff for size of factor loading to
be interpreted is a matter of researcher preference’’ (Tabachnick &
Fidell, 2007), and we elected to use the traditional factor loading
size that reflects a ‘‘good’’ amount of overlap in the variables (Comfrey
& Lee, 1992). The list of variables loading on each factor and
their corresponding values are presented in Table 1. The extracted
factors were labeled as follows: