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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/148545


    Title: 限制式下的潛在空間試題反應理論模型
    Constrained Latent Space Item Response Model
    Authors: 林韋成
    Contributors: 張育瑋
    林韋成
    Keywords: 貝氏估計
    fused lasso
    IRT 模型
    Latent Space IRT 模型
    local dependence
    Bayesian estimation
    fused lasso
    IRT model
    Latent Space IRT model
    local dependence
    Date: 2023
    Issue Date: 2023-12-01 13:59:39 (UTC+8)
    Reference: 1. Amthauer, R. (1953). Intelligenz-Struktur-Test (IST) [Intelligence Structure Test IST]. Göttingen, Germany: Hogrefe.

    2. Amthauer, R. (1970). Intelligenz-Struktur-Test (IST-70) [Intelligence Structure Test IST70]. Göttingen, Germany: Hogrefe.

    3. Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (2001). Intelligenz-StrukturTest 2000 R (I-S-T 2000 R) [Intelligence Structure Test IST 2000 R]. Göttingen, Germany: Hogrefe.

    4. Bradlow, E. T., Wainer, H., & Wang, X. (1999). A Bayesian random effects model for testlets. Psychometrika, 64, 153-168.

    5. Edwards, M. C., Houts, C. R., & Cai, L. (2018). A Diagnostic Procedure to Detect Departures From Local Independence in Item Response Theory Models. Psychological Methods, 23, 138-149.

    6. Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.

    7. Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40, 33–51.

    8. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109.

    9. Hoff, P., Raftery, A., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090–1098.

    10. Janssen, A. B. & Geiser, C. (2010). On the relationship between solution strategies in two mental rotation tasks. Learning and Individual Differences, 20, 473-478.

    11. Jeon, M., Jin, I. H., Schweinberger, M., & Baugh, S. (2021). Mapping Unobserved Item–Respondent Interactions: A Latent Space Item Response Model with Interaction Map. Psychometrika, 86, 378–403.

    12. Kyung, M., Gill, J., Ghosh, M., & Casella, G. (2010). Penalized Regression, Standard Errors, and Bayesian Lassos. Bayesian Analysis, 5, 369–412.

    13. Lee, H. & Smith, W. Z. (2020). A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats. Educational and Psychological Measurement, 80, 578–603.

    14. Liu, Y. & Maydeu-Olivares, A. (2012). Local Dependence Diagnostics in IRT Modeling of Binary Data. Educational and Psychological Measurement, 73, 254–274.

    15. Morillo, D., Leenen, I., Abad, F. J., Hontangas, P. de la Torre, J., & Ponsoda, V. (2016). A dominance variant under the multi-unidimensional pairwise-preference framework: Model formulation and Markov Chain Monte Carlo estimation. Applied Psychological Measurement, 40, 500-516.

    16. Putz-Osterloh, W. (1977). Über Problemlöseprozesse bei dem Test Würfelaufgaben aus dem Intelligenzstrukturtest IST und IST-70 von Amthauer [On solution processes in the test cube comparisons from Amthauer’s Intelligence Structure Test IST and IST-70]. Diagnostica, 23, 252−265.

    17. Rasch, G. (1961). On general laws and meaning of measurement in psychology. In Proceedings of the fourth Berkeley symposium on mathematical statistics and probability (volume 4) (pp. 321–333).

    18. Rost, J. (1990). Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis. Applied Psychological Measurement, 14, 271-282.

    19. Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society, Series B, 58, 267-288.

    20. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., & Knight, K. (2005). Sparsity and Smoothness via the Fused Lasso. Journal of the Royal Statistical Society, Series B, 67, 91-108.

    21. Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet response theory and its applications. New York, NY: Cambridge University Press.
    Description: 碩士
    國立政治大學
    統計學系
    110354024
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110354024
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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