an examination of the factor structure of the psychopathy checklist

3.3.1 PCL:YV Factor Structure
3.3 Primary Analyses
Confirmatory factor analysis (CFA) was used to test the three primary factor
models since it permits quantification of a factor models’ fit within a particular sample.
Although the distribution of PCL:YV total scores were approximately normally
distributed, D(142) = .073, p = .06, due to the ordinal nature of the individual items that
compose the PCL:YV, the items cannot have normal distributions. Consequently, the
impact of nonnormality was assessed through examination of the fit indices for each of
the models using the Generalized Least Squares (GLS) method. The GLS method is
suitable for nonnormal data (Hu, Bentler & Kano, 1992) and is one of two methods
available in Amos that are recommended for nonnormal data. The second available
estimation method available in Amos is the Asymptotically Distribution Free (ADF)
estimator but it has been shown to perform poorly with sample sizes under 2,500 (Hu et
al., 1992) and consequently was not used in this study. A requirement for the GLS
estimator to proceed is complete data (i.e., no missing values) so items that had
missing data were assigned values using regression imputation in Amos. This is a
sophisticated method for estimating missing values and is advantageous in that it is
more objective than the researchers guess but not as blind as simply inserting the grand
mean (i.e., mean substitution; Tabachnick & Fidell, 2007).
When evaluating CFA results, Hu and Bentler (1999) have shown that two model
fit indices, one relative and one absolute, are sufficient to determine the goodness of fit
of a model. Earlier, Kline (1998) suggested reporting the chi-square (X 2 ) goodness-of-fit
test, with associated degrees of freedom and p value, as well as an index that notes the
overall proportion of explained variance (e.g., Comparative Fit Index; CFI) and a similar
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