ArticleEducational and psychological measurement2022
Application of Change Point Analysis of Response Time Data to Detect Test Speededness.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Testing for a General Changepoint in Medical and Psychometric Studies: Changes Detection and Sample Size Planning.Statistics in medicine · 2025Article
- Human ratings take time: A hierarchical facets model for the joint analysis of ratings and rating times.Behavior research methods · 2024Article
- A Mixture Fluency model using responses and response times with cognitive diagnosis model framework.Behavior research methods · 2024Article
- Using Item Scores and Distractors to Detect Test Speededness.Applied psychological measurement · 2023Article
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Authors and funding
2 authors.
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Abstract
Computer-based and web-based testing have become increasingly popular in recent years. Their popularity has dramatically expanded the availability of response time data. Compared to the conventional item response data that are often dichotomous or polytomous, response time has the advantage of being continuous and can be collected in an unobstrusive manner. It therefore has great potential to improve many measurement activities. In this paper, we propose a change point analysis (CPA) procedure to detect test speededness using response time data. Specifically, two test statistics based on CPA, the likelihood ratio test and Wald test, are proposed to detect test speededness. A simulation study has been conducted to evaluate the performance of the proposed CPA procedure, as well as the use of asymptotic and empirical critical values. Results indicate that the proposed procedure leads to high power in detecting test speededness, while keeping the false positive rate under control, even when simplistic and liberal critical values are used. Accuracy of the estimation of the actual change point, however, is highly dependent on the true change point. A real data example is also provided to illustrate the utility of the proposed procedure and its contrast to the response-only procedure. Implications of the findings are discussed at the end.
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