A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats
Bibliographic Data
| ID | 20282909 |
|---|---|
| Authors | HyeSun Lee (0000-0002-0826-4655, California State University Channel Islands, Camarillo, CA, USA, corresponding author), Weldon Z Smith (California State University Channel Islands, Camarillo, CA, USA) |
| Year | 2020 |
| Volume | 80 |
| Issue | 3 |
| Pages | 578-603 |
| Publication date | 2020-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164419871659 |
| PMID | 32425220 |
| OpenAlex | W2971330264 |
| Language | EN |
| References cited | 66 |
Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items
Bayesian probability · Block (permutation group theory) · Covariate · Econometrics · Item analysis · Item response theory · Markov chain Monte Carlo · Meta-analysis · Psychometrics · Random effects model · Statistical model · Statistics · Trait · Two-alternative forced choice · Advanced Statistical Modeling Techniques · Cognitive Abilities and Testing · Computer Science · Mathematics · Psychometric Methodologies and Testing
Explanatory Item Response Models
Bayesian Item Response Modeling
A law of comparative judgment.
Rank Analysis of Incomplete Block Designs
Local Dependence Indexes for Item Pairs Using Item Response Theory
Higher-Order Latent Trait Models for Cognitive Diagnosis
Cutoff criteria for fit indexes in covariance structure analysis
Inference from Iterative Simulation Using Multiple Sequences
On the Statistical and Practical Limitations of Thurstonian IRT Models
Comparison of Single-Response Format and Forced-Choice Format Instruments Using Thurstonian Item Response Theory
Item Response Modeling of Forced-Choice Questionnaires
Spuriouser and spuriouser
The ubiquity of common method variance
Modeling Subjective Health Outcomes
Some properties of ipsative, normative, and forced-choice normative measures
The validity and utility of selection methods in personnel psychology
| Citation velocity | historical |
|---|---|
| Highly cited | No |