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Future Blog Post
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Cross Estimation Network
Schematic diagrams of CEN 1
publications
Change point analysis: A new method to detect aberrant responses in psychological and educational testing
Published in Advances in Psychological Science《心理科学进展》, 2020
The change point analysis (CPA), as one of the most widely used methods for statistical process control, is introduced to psychological and educational measurement for detection of aberrant response patterns in recent years. CPA outperforms the traditional method as follows: In addition to detecting aberrant response patterns, it can also pinpoint the locations of change points, contributing to efficient cleansing of response data. The method is employed to determine whether there is a point so that the complete sequence can be divided into two parts with different statistical properties, where person-fit statistics (PFS) is needed for quantifying the difference between two sub-sequences. Future researchers should pay more attention to multiple change points detection, making full use of other effective information like response time data, developing non-parametric indices as well as reforming the exiting person-fit statistics for polytomous and multidimensional tests, so as to enhance its applicability and power.
Recommended citation: Zhang, L., Wang, X., Cai, Y., & TU, D. (2020). Change point analysis: A new method to detect aberrant responses in psychological and educational testing. Advances in Psychological Science, 28(9), 1462.
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Factor structure and psychometric properties of the purpose in life test (PIL) in a sample of Chinese college students: An application of confirmatory factor analysis and item response theory
Published in Current Psychology, 2021
The Purpose in Life test (PIL) is the best-known measure of meaning in life and has attracted widespread attention for decades. The current study aimed to determine the optimal version of the PIL and to investigate its psychometric properties in the Chinese context. This study was conducted with a Chinese college student sample (N = 986) using confirmatory factor analysis (CFA) and item response theory (IRT). The results indicated that Morgan and Farsides’ two-factor solution (PIL-10) showed the best fit to the data among all fifteen PIL versions. In addition, the PIL-10 was demonstrated to be a cross-culturally sound measure with good reliability and validity and has high precision over most of the latent trait range. However, the findings from category response curves showed some problematic options in some items. These findings may serve as references for revision of the PIL-10.
Recommended citation: Zhang, L., Lin, J., Liu, K., Cai, Y., & Tu, D. (2021). Factor structure and psychometric properties of the purpose in life test (PIL) in a sample of Chinese college students: An application of confirmatory factor analysis and item response theory. Current Psychology, 1-20.
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A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation network
Published in Behavior Research Methods, 2024
This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two subnetworks: the person network (PN) and the item network (IN). The PN processes the response pattern of individual respondent and generates an estimate of the underlying ability, while the IN takes in the response pattern of individual item and outputs the estimates of the item parameters. Four simulation studies and an empirical study were comprehensively and rigorously conducted to investigate the performance of CEN on parameter estimation of the two-parameter logistic model under various testing scenarios. Results showed that CEN effectively fit the training data and produced accurate estimates of both person and item parameters. The trained PN and IN adhered to AI principles and acted as intelligent agents, delivering commendable evaluations for even unseen patterns of new respondents and items.
Recommended citation: Zhang, L., & Chen, P. (2024). A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation network. Behavior Research Methods, 1-33.
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talks
To Be Announced
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.