ArticleEducational and psychological measurement2022
A Multilevel Mixture IRT Framework for Modeling Response Times as Predictors or Indicators of Response Engagement in IRT Models.
Article in Educational and psychological measurement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Unveiling local dependencies in accuracy and speed: A mixture hierarchical modeling approach.Behavior research methods · 2026Article
- Dependent latent class partial credit models for careless and insufficient effort responding in online survey data.Behavior research methods · 2026Article
- Faking in High-Stakes Personality Assessments: A Response-Time-Based Latent Response Mixture Modeling Approach.Educational and psychological measurement · 2026Article
- Misclassification Produced by Rapid-Guessing Identification Methods and Their Suitability Under Various Conditions.Educational and psychological measurement · 2026Article
- A Two-Step Method Based on lz* for Identifying Effortful Respondents.Journal of Intelligence · 2026Article
- Comparing Different Approaches of (Not) Accounting for Rapid Guessing in Plausible Values Estimation.Educational and psychological measurement · 2026Article
- Bayesian factor mixture modeling with response time for detecting careless respondents.Behavior research methods · 2025Article
- Accounting for Persistence in Tests with Linear Ballistic Accumulator Models.Psychometrika · 2025Article
- Polytomous explanatory item response models for item discrimination: Assessing negative-framing effects in social-emotional learning surveys.Behavior research methods · 2025Article
- A response time-based mixture item response theory model for dynamic item-response strategies.Behavior research methods · 2025Article
- The Impact of Insufficient Effort Responses on the Order of Category Thresholds in the Polytomous Rasch Model.Educational and psychological measurement · 2024Article
- A Comparison of Response Time Threshold Scoring Procedures in Mitigating Bias From Rapid Guessing Behavior.Educational and psychological measurement · 2024Article
- Identifying Disengaged Responding in Multiple-Choice Items: Extending a Latent Class Item Response Model With Novel Process Data Indicators.Educational and psychological measurement · 2024Article
- Accounting for careless and insufficient effort responding in large-scale survey data-development, evaluation, and application of a screen-time-based weighting procedure.Behavior research methods · 2024Article
- Testing Replicability and Generalizability of the Time on Task Effect.Journal of Intelligence · 2023Article
- State-Aware Deep Item Response Theory using student facial features.Frontiers in artificial intelligence · 2023Article
- Investigating the Effect of Differential Rapid Guessing on Population Invariance in Equating.Applied psychological measurement · 2022Article
- A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data.Psychometrika · 2022Article
- The Role of Response Times on the Measurement of Mental Ability.Frontiers in psychology · 2022Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based procedures for classifying response engagement and IRT models for response engagement are based on common ideas, and we propose the distinction between independent and dependent latent class IRT models. In all IRT models considered, response engagement is represented by an item-level latent class variable, but the models assume that response times either reflect or predict engagement. We summarize existing IRT models that belong to each group and extend them to increase their flexibility. Furthermore, we propose a flexible multilevel mixture IRT framework in which all IRT models can be estimated by means of marginal maximum likelihood. The framework is based on the widespread Mplus software, thereby making the procedure accessible to a broad audience. The procedures are illustrated on the basis of publicly available large-scale data. Our results show that the different IRT models for response engagement provided slightly different adjustments of item parameters of individuals' proficiency estimates relative to a conventional IRT model.
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