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15,809 results on '"Lu, Jing"'

215. A Sequential Bayesian Changepoint Detection Procedure for Aberrant Behaviors in Computerized Testing

Author

Lu, Jing, Wang, Chun, Zhang, Jiwei, and Wang, Xue

Abstract

Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is pivotal to properly differentiate examinees' aberrant behaviors from solution behavior to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to monitor the locations of changepoints for response times in real time and, subsequently, further identify the types of aberrant behaviors in conjunction with response patterns. Two simulation studies were conducted to investigate the efficiency and accuracy of the proposed detection procedure in terms of identifying one or multiple change points at different locations. In addition to manipulating the number and locations of changepoints, two types of aberrant behaviors were also considered: rapid guessing behavior and cheating behavior. Simulation results indicate that ability estimates could be improved after removing responses from aberrant behaviors identified by our approach. Two empirical examples were analyzed to illustrate the application of proposed sequential Bayesian changepoint detection procedure. [This paper will be published in "British Journal of Mathematical and Statistical Psychology."]

Published
2023
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216. A Mixture Response Time Process Model for Aberrant Behaviors and Item Nonresponses

Author

Lu, Jing, Wang, Chun, and Shi, Ningzhong

Abstract

In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008; Wang & Xu, 2015): solution behavior, rapid guessing behavior, and cheating behavior. Oftentimes examinees do not always solve all items due to various reasons such as time limit or test-taking strategy. Item nonresponses may happen due to intentionally omitting some items (omitted responses) or due to lack of time (not-reached responses). Both types are related to latent abilities and hence the missingness is nonignorable. In this paper, we proposed an innovative mixture response time process model (1) to detect two most common aberrant behaviors: rapid guessing behavior and cheating behavior, and (2) to account for two types of item nonresponses: not-reached items and omitted items. The new model combines the two-stage approach of Wang, Xu, and Shang (2018) with Lu and Wang (2020)'s model. It also contains two steps: (1) a mixture response time process model is first fitted to the responses and response times data to distinguish normal and aberrant behaviors and to account for the missing data mechanism; and (2) a Bayesian residual index is used to further distinguish rapid guessing and cheating behaviors. Simulation results show that the two-stage method yields accurate item and person parameter estimates, as well as high detection of aberrant behaviors. A real data analysis was conducted to illustrate the potential application of the proposed method. [This paper was published in "Multivariate Behavioral Research" v58 n1 p71-89 2023.]

Published
2023
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217. Strict Kantorovich contractions for Markov chains and Euler schemes with general noise

Author

Huang, Lu-Jing, Majka, Mateusz B., and Wang, Jian

Subjects
Mathematics - Probability
Abstract

We study contractions of Markov chains on general metric spaces with respect to some carefully designed distance-like functions, which are comparable to the total variation and the standard $L^p$-Wasserstein distances for $p \ge 1$. We present explicit lower bounds of the corresponding contraction rates. By employing the refined basic coupling and the coupling by reflection, the results are applied to Markov chains whose transitions include additive stochastic noises that are not necessarily isotropic. This can be useful in the study of Euler schemes for SDEs driven by L\'evy noises. In particular, motivated by recent works on the use of heavy tailed processes in Markov Chain Monte Carlo, we show that chains driven by the $\alpha$-stable noise can have better contraction rates than corresponding chains driven by the Gaussian noise, due to the heavy tails of the $\alpha$-stable distribution., Comment: 24 pages

Published
2021

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