IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/kh7vq_v1.html
   My bibliography  Save this paper

Evaluating the Impact of Multidimensionality on Type I and Type II Error Rates Using the Q-Index Item Fit Statistic for the Rasch Model

Author

Listed:
  • Estrada, Samantha

Abstract

To understand the role of fit statistics in Rasch measurement is simple: applied researchers can only benefit from the desirable properties of the Rasch model when the data fit the model. The purpose of the current study was to assess the Q-Index robustness (Ostini and Nering, 2006), and its performance was compared to the current popular fit statistics known as MSQ Infit, MSQ Outfit, and standardized Infit and Outfit (ZSTDs) under varying conditions of test length, sample size, item difficulty (normal and uniform), and dimensionality utilizing a Monte Carlo simulation. The Type I and Type II error rates are also examined across fit indices. This study provides applied researchers guidelines the robustness and appropriateness of the use of the Q-Index, which is an alternative to the currently available item fit statistics. The Q-Index was slightly more sensitive to the levels of multidimensionality set in the study while MSQ Infit, Outfit, and standardized Infit and Outfit (ZSTDs) failed to identify the multidimensional conditions. The Type I error rate of the Q-Index was lower than the rest of the fit indices; however, the Type II error rate was higher than the anticipated β=.20 across all fit indices.

Suggested Citation

  • Estrada, Samantha, 2021. "Evaluating the Impact of Multidimensionality on Type I and Type II Error Rates Using the Q-Index Item Fit Statistic for the Rasch Model," OSF Preprints kh7vq_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:kh7vq_v1
    DOI: 10.31219/osf.io/kh7vq_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6074ebc4f2ad330579a735d2/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/kh7vq_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:kh7vq_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.