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    Quantifying parsimony in structural equation modeling

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    preacher_2006b.pdf (885.4Kb)
    Issue Date
    2006
    Author
    Preacher, Kristopher J.
    Type
    Article
    Is part of series
    41
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    Abstract
    Fitting propensity (FP) is defined as a model's average ability to fit diverse data patterns, all else being equal. The relevance of FP to model selection is examined in the context of structural equation modeling (SEM). In SEM it is well known that the number of free model parameters influences FP, but other facets of FP are routinely excluded from consideration. It is shown that models possessing the same number of free parameters but different structures may exhibit different FPs. The consequences of this fact are demonstrated using illustrative examples and models culled from published research. The case is made that further attention should be given to quantifying FP in SEM and considering it in model selection. Practical approaches are suggested.
    URI
    http://hdl.handle.net/1808/1484
    Collections
    • Psychology Scholarly Works [311]
    Citation
    Multivariate Behavioral Research

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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