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Computerized Adaptive Testing System of Functional Assessment of Stroke
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Compensatory and non-compensatory multidimensional randomized item response models.

Jean-Paul Fox1, Rinke Klein Entink2, Marianna Avetisyan1

  • 1University of Twente, The Netherlands.

The British Journal of Mathematical and Statistical Psychology
|May 30, 2013
PubMed
Summary

Randomized response (RR) models enhance data collection on sensitive behaviors. Using a new multidimensional model (MRIRT), researchers found RR questioning yielded higher scores for alcohol problems and expectancies compared to direct questioning.

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Area of Science:

  • Psychometrics
  • Survey Methodology
  • Statistics

Background:

  • Randomized response (RR) models are established for univariate sensitive data analysis.
  • Existing RR models have been extended to individual unidimensional behavior measurement.
  • There's a need to measure multiple, potentially compensatory, sensitive factors simultaneously.

Purpose of the Study:

  • To extend randomized response modeling for multivariate sensitive data.
  • To introduce a confirmatory multidimensional randomized item response theory (MRIRT) model.
  • To analyze the relationships between sensitive behaviors and background variables.

Main Methods:

  • Development of a confirmatory multidimensional randomized item response theory (MRIRT) model.
  • Utilizing a Markov chain Monte Carlo algorithm for simultaneous parameter estimation.
  • Application of the MRIRT model to college student data on alcohol problems and expectancies.

Main Results:

  • The MRIRT model allows computation of individual true item response probabilities.
  • Students interviewed using RR techniques reported significantly higher alcohol-related problems and expectancies.
  • Alcohol problems and sexual enhancement expectancies showed a moderate positive correlation, varying by gender and university.

Conclusions:

  • The proposed MRIRT model effectively analyzes multivariate randomized response data.
  • Randomized response questioning appears to elicit higher self-reported sensitive behaviors.
  • The study highlights the utility of MRIRT for understanding complex sensitive behaviors and their correlates.