Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： In recent years, response time has received a rapidly growing amount of attention in psychometric research, likely due to the increasing availability of (item-level) response time data through computer-based testing and online survey data collection. Compared to the conventional item response data that are often dichotomous or polytomous, the response time is continuous and can provide much more information. Aberrant response behaviors are frequently encountered during testing. It could cause various negative effects. Change point analysis (CPA) is a well-established statistical process control method to detect changes in a sequence, and it has provided testing professionals a new lens through to understand test-taking behavior at both the examinee and item levels. In this paper, we took test speededness as an example to illustrate how the CPA method can be used to detect aberrant behavior using item response time data. Response time under speededness was simulated using the gradual-change log-normal model for response time. Two CPA-based test statistics, the Likelihood Ratio Test and Wald Test, were used to detect aberrant response behaviors. The critical values were obtained through Monte Carlo simulations and compared with the approximate critical values in a previous study. Based on the chosen critical values, we examined the performance of the likelihood ratio test and Wald test in detecting speeded responses, specifically in terms of power and empirical Type-I error. On the one hand, the critical values are almost identical for Wald and the likelihood ratio test. They vary substantially at different nominal α levels, but do not differ much across different test lengths. On the other hand, compared to approximate critical values, the critical values are not too far away from them but are different. That may be because the approximate critical values are suitable for situations where the change point appears in the middle of the test. Results indicate that the proposed method is much more powerful based on the critical values than conventional methods that use item response data. The power was close to 1 for most of the conditions while keeping the type-I error rate well-controlled. Real data analysis also demonstrates the performance of the method. This study uses CPA with response time data and offers a very promising approach to detecting aberrant response behavior. Through the simulation study, we demonstrated that it is possible to use fixed critical values in different test lengths, which makes the application of the method straightforward. It also means that it is unnecessary to reconduct the simulation to update critical values when small changes occur in the test. CPA is very flexible. This study assumed that the log-normal model fits the response time data, but the method is not bounded by that assumption.
Subjects: Psychology >> Psychological Measurement submitted time 2022-05-14
Abstract：
In recent years, response time has received a rapidly growing amount of attention in psychometric research, likely due to the increasing availability of (item-level) response time data through computer-based testing and online survey data collection. Compared to the conventional item response data that are often dichotomous or polytomous, the response time is continuous and can provide much more information. Aberrant response behaviors are frequently encountered during testing. It could cause various negative effects. Change point analysis (CPA) is a well-established statistical process control method to detect changes in a sequence, and it has provided testing professionals a new lens through to understand test-taking behavior at both the examinee and item levels.
In this paper, we took test speededness as an example to illustrate how the CPA method can be used to detect aberrant behavior using item response time data. Response time under speededness was simulated using the gradual-change log-normal model for response time. Two CPA-based test statistics, the Likelihood Ratio Test and Wald Test, were used to detect aberrant response behaviors. The critical values were obtained through Monte Carlo simulations and compared with the approximate critical values in a previous study. Based on the chosen critical values, we examined the performance of the likelihood ratio test and Wald test in detecting speeded responses, specifically in terms of power and empirical Type-I error.
On the one hand, the critical values are almost identical for Wald and the likelihood ratio test. They vary substantially at different nominal α levels, but do not differ much across different test lengths. On the other hand, compared to approximate critical values, the critical values are not too far away from them but are different. That may be because the approximate critical values are suitable for situations where the change point appears in the middle of the test. Results indicate that the proposed method is much more powerful based on the critical values than conventional methods that use item response data. The power was close to 1 for most of the conditions while keeping the type-I error rate well-controlled. Real data analysis also demonstrates the performance of the method.
This study uses CPA with response time data and offers a very promising approach to detecting aberrant response behavior. Through the simulation study, we demonstrated that it is possible to use fixed critical values in different test lengths, which makes the application of the method straightforward. It also means that it is unnecessary to reconduct the simulation to update critical values when small changes occur in the test. CPA is very flexible. This study assumed that the log-normal model fits the response time data, but the method is not bounded by that assumption.
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Awaiting Review
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