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Robust maximum marginal likelihood (RMML) estimation for item response theory models.

Maxwell R Hong¹, Ying Cheng²

¹Department of Psychology, University of Notre Dame, Notre Dame, IN, USA.

Behavior Research Methods

|October 24, 2018

Summary

This study introduces a robust estimation method to identify careless responders in psychological research. The robust maximum marginal likelihood (RMML) estimation improves detection rates and reduces bias in item parameters.

Keywords:

Careless responses Item response theory Person fit Robust estimation Robust maximum marginal likelihood

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Area of Science:

Psychometrics
Statistical Modeling
Survey Methodology

Background:

Self-report data are widely used in psychological and survey research.
Unmotivated or careless responses can significantly compromise data quality and analysis.
Existing methods may not adequately detect or account for such response styles.

Purpose of the Study:

To propose and evaluate a robust estimation method for detecting careless responders.
To leverage item response theory (IRT) person-fit statistics within a robust estimation framework.
To enhance the accuracy of item parameter estimates in the presence of careless responses.

Main Methods:

Developed a general framework for robust estimation tailored to IRT models.
Conducted a comprehensive simulation study across various conditions to assess performance.
Applied the proposed robust maximum marginal likelihood (RMML) estimation method to real-world data.

Main Results:

RMML estimation significantly improved the detection rates of careless responders.
The method effectively reduced bias in item parameter estimates across different simulation conditions.
Application to real data demonstrated the practical utility and effectiveness of the proposed approach.

Conclusions:

Robust estimation, when combined with IRT person-fit statistics, provides a powerful tool for identifying careless respondents.
This approach leads to more accurate item parameter estimates, improving overall research integrity.
The findings support the routine use of robust estimation methods in survey and psychological research to mitigate data quality issues.