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Related Experiment Videos

Two strategies for fitting real data to Rasch polytomous models.

Antonio J Rojas Tejada1, Andres Gonzalez Gomez, Jose L Padilla Garcia

  • 1Area de Metodología de las Ciencias del Comportamiento, University of Almería, 04120 Almería, Spain. arojas@ual.es

Journal of Applied Measurement
|May 16, 2002
PubMed
Summary

Two methods for fitting data to Latent Trait Theory Models were compared. The Total-Persons-Items strategy prioritizes person fit, while Total-Items-Persons prioritizes item fit, impacting model evaluation.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Latent Trait Theory (LTT) models are crucial for understanding measurement properties.
  • Assessing data fit to LTT models is essential for valid interpretations.
  • Existing strategies for data fitting may yield different results based on their procedural order.

Purpose of the Study:

  • To comparatively analyze two distinct strategies for fitting data to Latent Trait Theory Models.
  • To investigate the impact of procedural order (person-fit first vs. item-fit first) on model evaluation.
  • To propose a method for controlling the sensitivity of fit statistics to sample size.

Main Methods:

  • A comparative study was conducted using two fitting strategies: Total-Persons-Items (TPI) and Total-Items-Persons (TIP).

Related Experiment Videos

  • Data from 821 persons on a 30-item religious attitude scale were analyzed.
  • The Partial Credit Model and the Rating Scale Model were employed.

    • Main Results:

    • The TPI strategy maximized the number of persons demonstrating good fit to the models.
    • The TIP strategy maximized the number of items demonstrating good fit to the models.
    • A procedure to control for sample size's influence on fit assessment was developed.

    Conclusions:

    • The choice between TPI and TIP strategies significantly influences whether person or item fit is prioritized.
    • Researchers must consider the implications of each strategy for their specific research questions.
    • The proposed method aids in robustly evaluating model fit across different sample sizes.