Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    REWEIGHTING DATA IN THE SPIRIT OF TUKEY: USING BAYESIAN POSTERIOR PROBABILITIES AS RASCH RESIDUALS FOR STUDYING MISFIT

    Thumbnail
    View/Open
    Dardick_umd_0117E_11646.pdf (3.461Mb)
    No. of downloads: 646

    Date
    2010
    Author
    Dardick, William Ross
    Advisor
    Mislevy, Robert J
    Metadata
    Show full item record
    Abstract
    A new variant of the iterative "data = fit + residual" data-analytical approach described by Mosteller and Tukey is proposed and implemented in the context of item response theory psychometric models. Posterior probabilities from a Bayesian mixture model of a Rasch item response theory model and an unscalable latent class are expressed as weights for the original data. The data weighted by the units' posterior probabilities for the unscalable class is used for further exploration of structures. Data were generated in accordance with departures from the Rasch model that have been studied in the literature. Factor analysis models are compared with the original data and the data as reweighted by the posterior probabilities for the unscalable class. Eigenvalues are compared with Horn's parallel analysis corresponding to each class of factor models to determine the number of factors in a dataset. In comparing two weighted data sets, the Rasch weighted data and the data were considered unscalable, and clear differences are manifest. Pattern types are detected for the Rasch baselines that have different patterns than that of random or systematic contamination. The Rasch baseline patterns are strongest around item difficulties that are closest to the mean generating value of è's. Patterns in baseline conditions are weaker as they depart from a item difficulty of zero and move toward extreme values of ±6. The random contamination factor patterns are typically flat and near zero regardless of the item difficulty with which it is associated. Systematic contamination using reversed Rasch generated data produces alternate patterns to the Rasch baseline condition and in some conditions shows an opposite effect when compared to the Rasch patterns. Differences can also be detected within the residually weighted data between the Rasch generated subtest and contaminated subtest. In conditions that have identified factors, the Rasch subtest often had Rasch patterns and the contaminated subtest has some form of random/flat or systematic/reversed pattern.
    URI
    http://hdl.handle.net/1903/11096
    Collections
    • Human Development & Quantitative Methodology Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility