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Philip Hyland Bifactor Modelling in Mplus University of Ulster参考
SUBSCALES Provides a method for determining the usefulness of creating subscales. Because the general and group/nuisance factors are uncorrelated in a bifactor model, inspection of the factor loadings on the general and group/nuisance factors is informative. If factor loadings are high on the general factor and low on the group/nuisance factors - makes little sense to create subscales. If factor loadings are high for both the general factor and the group/nuisance factors - subscale creation should be considered. APPLICATIONS Reise, Moore, and Haviland (2010) - main problem in traditional psychometric evaluation of scales is that the wrong default model is used. A unidimensional model is normally used as the default model and is also frequently a researcher’s ideal solution. Yet, item response data drawn from complex measures are rarely, if ever, strictly unidimensional. APPLICATIONS In a way it’s also not desirable to achieve such a solution To achieve unidimensionality one essentially has to write a set of items with very narrow conceptual bandwidth (i.e., the same item written over and over in slightly different ways) Results in poor predictive power or theoretical usefulness. Reise et al. (2010) argue that a bifactor model, which contains a single common trait but also allows for multidimensionality due to item content diversity, provides a better foundational model for conceptualizing dimensionality. STRENGTHS Ability to maintain a unidimensional conceptualisation while also recognising issues of multidimensionality. What else? So far we have focused on the role on grouping factors. Factors that are present within a scale that arise from intentionally attempting to capture complexities of a particular psychological construct (e.g. the cognitive-affective and somatic-performance components of depression). But, what about nuisance factors? STRENGTHS Factors that are present within a scale which arise unintentionally when attempting to capture the prima
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