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Fitting a Thurstonian IRT model to forced-choice data using Mplus.

Anna Brown¹, Alberto Maydeu-Olivares

¹Department of Psychiatry, University of Cambridge, Cambridge, UK. A.A.Brown@kent.ac.uk

Behavior Research Methods

|June 27, 2012

Summary

This summary is machine-generated.

This study introduces a user-friendly method for analyzing forced-choice survey data, overcoming psychometric challenges. The approach simplifies the use of forced-choice designs, making them as accessible as traditional rating scales.

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Area of Science:

Psychometrics
Psychological Measurement
Item Response Theory

Background:

Rating scales (Likert scales) can cause response distortions.
Forced-choice formats present items comparatively, asking respondents to rank them.
Traditional scoring of forced-choice data yields ipsative data, posing psychometric challenges.

Purpose of the Study:

To provide a tutorial for coding forced-choice responses.
To specify a Thurstonian item response theory model for forced-choice designs.
To demonstrate how to assess model fit and score individuals.

Main Methods:

Utilizing a model based on Thurstone's law of comparative judgment.
Employing Mplus software for estimation and scoring.
Providing an Excel macro to generate Mplus input files for forced-choice designs.

Main Results:

The proposed Thurstonian item response theory model effectively addresses the psychometric issues of ipsative data.
The provided tools (Mplus and Excel macro) simplify the analysis of forced-choice data.
The study demonstrates that forced-choice designs can be implemented as easily as rating scales.

Conclusions:

The Brown and Maydeu-Olivares model offers a robust solution for analyzing forced-choice data.
The accessibility of forced-choice data analysis is significantly enhanced.
This facilitates more accurate psychological attribute measurement by overcoming response distortions.