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    Sample size in factor analysis: The role of model error

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    maccallum_widaman_preacher_hong_2001.pdf (532.7Kb)
    Issue Date
    2001
    Author
    MacCallum, Robert C.
    Widaman, Keith F.
    Preacher, Kristopher J.
    Hong, Sehee
    Type
    Article
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    Abstract
    This article examines effects of sample size and other design features on correspondence between factors obtained from analysis of sample data and those present in the population from which the samples were drawn. We extend earlier work on this question by examining these phenomena in the situation in which the common factor model does not hold exactly in the population. We present a theoretical framework for representing such lack of fit and examine its implications in the population and sample. Based on this approach we hypothesize that lack of fit of the model in the population will not, on the average, influence recovery of population factors in analysis of sample data, regardless of degree of model error and regardless of sample size. Rather, such recovery will be affected only by phenomena related to sampling error which have been studied previously. These hypotheses are investigated and verified in two sampling studies, one using artificial data and one using empirical data.
    URI
    http://hdl.handle.net/1808/1499
    Collections
    • Psychology Scholarly Works [311]
    Citation
    Multivariate Behavioral Research, 36, 611-637.

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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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