A principal components factor analysis was then conducted to determine whether the remaining items all loaded a single factor based on both the slope
of scree plot as well as examination of the eigenvalues.
However, an inspection
of the scree plot indicated that a three - factor solution would provide a best fit for the data, as inflexions justified the removal of components 4, 5, 6, and 7; retained factors are presented in Table 1.
Not exact matches
Choice
of number
of factors was based on a combination
of theory (based on previous literature) and points
of inflection on the
scree plots.
Decisions on the number
of components to extract were based on parallel analysis, Kaiser's eigenvalue - greater - than - one rule, total proportion
of variance explained and Cattell's
scree plot.
Although a
scree plot has a limitation in terms
of its subjectivity, it is considered more suitable than the Kaiser method [25].
Two component solutions were examined: (1) component extraction based on a parallel analysis, proportion
of variance explained, Kaiser's eigenvalue - greater - than - one rule and on the examination
of Cattell's
scree plot and (2) a three - component solution as originally conceptualised in the VAWI.
Factor analysis
of the 30 remaining items was then conducted; the
scree plot indicated a one - factor solution, having an eigenvalue
of 13.1 and accounting for 43.5 %
of the variance.
We selected factors using eigenvalue
scree plots, and chose a factor - loading threshold
of 0.3, taking the higher - loaded variable where there was cross-loading (Table 1).