ArticlePsychometrika2018
A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing.
Article in Psychometrika, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 79 citations in OpenAlex.
- A Two-Step Method Based on lz* for Identifying Effortful Respondents.Journal of Intelligence · 2026Article
- Joint modeling with generalized item response theory model family and response time model: Enhancing model structural flexibility and data-fitting adequacy.Behavior research methods · 2025Article
- Few and Different: Detecting Examinees With Preknowledge Using Extended Isolation Forests.Applied psychological measurement · 2025Article
- Optimizing Large-Scale Educational Assessment with a "Divide-and-Conquer" Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models.Psychometrika · 2024Article
- Modeling item revisiting behavior in computer-based testing: Exploring the effect of item revisitations as collateral information.Behavior research methods · 2024Article
- A Mixture Fluency model using responses and response times with cognitive diagnosis model framework.Behavior research methods · 2024Article
- A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data.Psychometrika · 2023Article
- A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times.Psychometrika · 2023Article
- Compromised item detection: A Bayesian change-point perspective.The British journal of mathematical and statistical psychology · 2023Article
- Application of Change Point Analysis of Response Time Data to Detect Test Speededness.Educational and psychological measurement · 2022Article
- Two New Models for Item Preknowledge.Applied psychological measurement · 2022Article
- Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model.Psychometrika · 2022Article
- Bayesian Analysis of Aberrant Response and Response Time Data.Frontiers in psychology · 2022Article
- Parameter Estimation Accuracy of the Effort-Moderated Item Response Theory Model Under Multiple Assumption Violations.Educational and psychological measurement · 2021Article
- A Novel and Highly Effective Bayesian Sampling Algorithm Based on the Auxiliary Variables to Estimate the Testlet Effect Models.Frontiers in psychology · 2021Article
- Modeling Responses and Response Times in Tests With the Hierarchical Model and the Three-Parameter Lognormal Distribution.Educational and psychological measurement · 2020Article
- Using Response Times and Response Accuracy to Measure Fluency Within Cognitive Diagnosis Models.Psychometrika · 2020Article
- Identifying Effortful Individuals With Mixture Modeling Response Accuracy and Response Time Simultaneously to Improve Item Parameter Estimation.Educational and psychological measurement · 2020Article
- Methods of Detecting Insufficient Effort Responding: Comparisons and Practical Recommendations.Educational and psychological measurement · 2020Article
- A General Three-Parameter Logistic Model With Time Effect.Frontiers in psychology · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches. In the first stage, a mixture hierarchical model is fitted to the response and response time data to distinguish normal and aberrant behaviors using Markov chain Monte Carlo (MCMC) algorithm. In the second stage, a further distinction between rapid guessing and cheating behavior is made at a person level using a Bayesian residual index. Simulation results show that the two-stage method yields accurate item and person parameter estimates, as well as high true detection rate and low false detection rate, under different manipulated conditions mimicking NAEP parameters. A real data example is given in the end to illustrate the potential application of the proposed method.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.