Chia-Wen Chen

• Kim, Jiyoun; Chen, Chia-Wen & Wu, Yi-Jhen (2024). Exploration of the linear and nonlinear relationships between learning strategies and mathematics achievement in South Korea using the nominal response model : PISA 2012. Large-scale Assessments in Education. ISSN 2196-0739. 12(1), s. 1–27. doi: 10.1186/s40536-024-00198-8.
• Chen, Chia-Wen & Chen-Wei, Liu (2023). Online Parameter Estimation for Student Evaluation of Teaching. Applied Psychological Measurement. ISSN 0146-6216. doi: 10.1177/01466216231165314.
• Reis Costa, Denise & Chen, Chia-Wen (2023). Exploring the relationship between process data and contextual variables among Scandinavian students on PISA 2012 mathematics tasks. Large-scale Assessments in Education. ISSN 2196-0739. 11:5, s. 1–28. doi: 10.1186/s40536-023-00155-x. Fulltekst i vitenarkiv
• Chen, Chia-Wen; Andersson, Björn & Zhu, Jinxin (2022). A Factor Mixture Model for Item Responses and Certainty of Response Indices to Identify Student Knowledge Profiles. Journal of Educational Measurement. ISSN 0022-0655. doi: 10.1111/jedm.12344. Fulltekst i vitenarkiv
• Chen, Chia-Wen; Wang, Wen-Chung; Mok, Magdalena Mo Ching & Scherer, Ronny (2021). A Lognormal Ipsative Model for Multidimensional Compositional Items. Frontiers in Psychology. ISSN 1664-1078. 12, s. 1–19. doi: 10.3389/fpsyg.2021.573252. Fulltekst i vitenarkiv Vis sammendrag

  Compositional items – a form of forced-choice items – require respondents to allocate a fixed total number of points to a set of statements. To describe the responses to these items, the Thurstonian item response theory (IRT) model was developed. Despite its prominence, the model requires that items composed of parts of statements result in a factor loading matrix with full rank. Without this requirement, the model cannot be identified, and the latent trait estimates would be seriously biased. Besides, the estimation of the Thurstonian IRT model often results in convergence problems. To address these issues, this study developed a new version of the Thurstonian IRT model for analyzing compositional items – the lognormal ipsative model (LIM) – that would be sufficient for tests using items with all statements positively phrased and with equal factor loadings. We developed an online value test following Schwartz’s values theory using compositional items and collected response data from a sample size of N = 512 participants with ages from 13 to 51 years. The results showed that our LIM had an acceptable fit to the data, and that the reliabilities exceeded 0.85. A simulation study resulted in good parameter recovery, high convergence rate, and the sufficient precision of estimation in the various conditions of covariance matrices between traits, test lengths and sample sizes. Overall, our results indicate that the proposed model can overcome the problems of the Thurstonian IRT model when all statements are positively phrased and factor loadings are similar.

• Chen, Chia-Wen; Wang, Wen-Chung; Ming Ming, Chiu & Ro, Sage (2019). Item Selection and Exposure Control Methods for Computerized Adaptive Testing with Multidimensional Ranking Items . Journal of Educational Measurement. ISSN 0022-0655. 57(2), s. 343–369. doi: 10.1111/jedm.12252.

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