Page 203 - Encyclopedia of Nursing Research

F

designing the latent constructs and the rela-
Factor analysis tionship between latent constructs.
The raw data should be at or applicable
to the interval level, such as the data obtained
Factor analysis is a multivariate technique with Likert-type measures. Next, a number of
for determining the underlying structure assumptions relating to the sample, variables,
and dimensionality of a set of variables. By and factors should be met. First, the sample
analyzing intercorrelations among variables, size must be sufficiently large to avoid erro-
factor analysis shows which variables clus- neous interpretations of random differences
ter together to form unidimensional con- in the magnitude of correlation coefficients.
structs. However, it involves a higher degree As a rule of thumb, a minimum of five cases
of subjective interpretation than is common for each observed variable is recommended;
with most other statistical methods. In nurs- however, Knapp and Brown (1995) reported
ing research, factor analysis is commonly that ratios as low as three subjects per var-
used for instrument development (Ferketich iable may be acceptable. Others generally
& Muller, 1990), theory development, and recommend that 100 to 200 cases is advisable
data reduction. Factor analysis is used for (Nunnally & Bernstein, 1994).
identifying the number, nature, and impor- Second, the observed variables need
tance of factors, comparing factor solutions to vary. In other words, one category of
for different groups, estimating scores on responses for a single observed variable
factors, and testing theories (Nunnally & should not contain more than 90% of the
Bernstein, 1994). responses for that specific variable. Third,
There are two major types of factor there should be no obvious miscodes or
analysis: exploratory and confirmatory. In outliers, as indicated in a review of the fre-
exploratory factor analysis, the data are quencies of the observed variables. Outliers
described and summarized by grouping among cases should be identified and their
together related variables. The variables influence reduced either by transformation
may or may not be selected with a particular or by replacing the outlying value with a less
purpose in mind. Exploratory factor analy- extreme score. Fourth, the observed variables
sis is commonly used in the early stages of should be normally distributed, with no sub-
research, when it provides a method for con- stantial evidence of skewness or kurtosis. For
solidating variables and generating hypothe- normality, Kline (2005) recommends absolute
ses about underlying processes that affect the values for skewness less than 3 and absolute
clustering of the variables. Confirmatory fac- values of kurtosis less than 8. Fifth, there
tor analysis is used in later stages of research should be little, if any, missing data for each
for theory testing related to latent processes observed variable. Sixth, use scatterplots
or to examine hypothesized differences in to determine if pairs of observed variables
latent processes among groups of subjects. are linearly related. Seventh, instances of
Confirmatory factor analysis is typically con- multicollinearity of the variables should be
ducted with structural equation modeling, in deleted. Multicollinearity can be tested using
which an investigator has complete control of regression and testing for tolerance levels less