政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/156018

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 114895/145933 (79%)
Visitors : 53858452 Online Users : 319

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大機構典藏 > 教育學院 > 教育學系 > 學位論文 > Item 140.119/156018

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/156018

Title:	作答反應與反應時間聯合模式的擴展與應用 Extensions and applications of joint modeling for responses and response times
Authors:	蔡介文 Tsai, Jie-Wen
Contributors:	余民寧 Yu, Min-Ning 蔡介文 Tsai, Jie-Wen
Keywords:	試題反應模式反應時間模式 Gibbs抽樣 Pólya-Gamma分配不對稱 Laplace分配 item response modeling response time modeling Gibbs sampler Pólya-Gamma distribution asymmetric Laplace distribution
Date:	2025
Issue Date:	2025-03-03 14:42:24 (UTC+8)
Abstract:	本研究提出作答反應與反應時間聯合模式（RT-IRT 模式）的三個新擴展。這些擴展是貝氏統計中資料擴增 (DA) 策略的應用。具體來說，是利用 Pólya-Gamma (PG) 分配在 logistic 模式中實作 Gibbs 抽樣器，以及利用不對稱 Laplace 分配 (ALD) 在貝氏分量迴歸 (BQR) 中實作 Gibbs 抽樣器。本論文包含三項主要研究。第一項研究將 PG Gibbs 抽樣器應用於 logistic RT-IRT 模式、第二項研究將 BQR 引進潛在迴歸的 RT-IRT 模式、第三項研究將 BQR 加入具有交叉關係的 RT-IRT 模式。透過模擬研究和 TIMSS 2019 數學測驗的真實資料分析，本研究得到三個主要發現。第一，PG 方法比傳統方法（包括 MLE）表現更佳，尤其是在樣本較小（250 人）和測驗較短（15 題）的情況下。另外，在模擬條件下，BQR 在潛在迴歸模式和交叉關係模式上都表現有效。然而，對於 BQR 潛在迴歸模式，在不同分量位置上，迴歸係數並沒有明顯變化效果。相比之下，BQR 交叉關係模式在不同分量位置中顯示出明顯的交叉負荷，尤其在第 40 分量附近的模式適配效果更佳。三種模式都具有良好的收斂特性，所有參數都在 5000 次迭代的預熱期內達到穩定。整體來說，PG Gibbs 抽樣有助於整合 logistic IRT 模式，而 BQR 方法比平均迴歸模式更加靈活且有效。此外，本研究利用 Julia 開發的統計套件支援上述擴展，讓研究人員能夠分析教育測驗中的 RT-IRT 模式。 This study proposed three new extensions of the joint modeling for responses and response times (RT-IRT models). These extensions are applications of data augmentation (DA) strategies in the Bayesian statistics. Specifically, the study utilizes the Pólya-Gamma (PG) distribution for implementing Gibbs sampler in logistic models, and the asymmetric Laplace distribution (ALD) for implementing Gibbs sampler in Bayesian quantile regressions (BQR). Three major studies were conducted. The first study applied the PG Gibbs sampler to the logistic RT-IRT models, The second study introduced the BQR to the latent regression RT-IRT models, and the third study added the BQR to the RT-IRT models with cross-relations. Through simulation studies and real data analyses using TIMSS 2019 mathematics assessment, the research revealed three key findings. One is that the PG method demonstrated better performance than traditional approaches, including MLE, particularly with smaller samples (N=250) and shorter tests (15 items). Another is that the BQR model performed effectively under simulation conditions for both latent regression and cross-relation models. However, for the BQR-based latent regression, it showed no obvious change effects of regression coefficients in different quantile levels. In contrast, the BQR-based cross-relation model shows significant cross-loadings across quantile levels, with better model fit around the 40th quantile. All three models showed good convergence properties, with parameters stabilizing within the burn-in 5000 iterations. Overall, the PG Gibbs sampling facilitates the incorporation of logistic IRT models, while BQR approaches are more flexible and effective than mean regression models. Moreover, a statistical package developed in Julia supports these extensions, enabling researchers to analyze RT-IRT models in educational testing.
Reference:	Albert, J. H. (1992). Bayesian estimation of normal ogive item response curves using Gibbs sampling. Journal of Educational Statistics, 17(3), 251–269. https://doi.org/10.3102/10769986017003251 Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669–679. https://doi.org/10.1080/01621459.1993.10476321 Asparouhov, T., & Muthén, B. (2021). Expanding the Bayesian structural equation, multilevel and mixture models to logit, negative-binomial, and nominal variables. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 622–637. https://doi.org/10.1080/10705511.2021.1878896 Balamuta, J. J., & Culpepper, S. A. (2022). Exploratory restricted latent class models with monotonicity requirements under Pólya–Gamma data augmentation. Psychometrika, 87, 903–945. https://doi.org/10.1007/s11336-021-09815-9 Batchelder, W. H., & Riefer, D. M. (1999). Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin & Review, 6(1), 57–86. Becker, B., Weirich, S., Goldhammer, F., & Debeer, D. (2023). Controlling the speededness of assembled test forms: A generalization to the three-parameter lognormal response time model. Journal of Educational Measurement, 60(4), 551–574. Becker, B., Debeer, D., Weirich, S., & Goldhammer, F. (2021). On the speed sensitivity parameter in the lognormal model for response times and implications for high-stakes measurement practice. Applied Psychological Measurement, 45(6), 407–422. https://doi.org/10.1177/01466216211008530 Benoit, D. F., & Poel, D. Van den. (2017). bayesQR: A Bayesian approach to quantile regression. Journal of Statistical Software, 76(7), 1–32. https://doi.org/10.18637/jss.v076.i07 Beyerlein, A. (2014). Quantile regression—opportunities and challenges from a user's perspective. American Journal of Epidemiology, 180(3), 330–331. https://doi.org/10.1093/aje/kwu178 Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1), 65–98. https://doi.org/10.1137/141000671 Bockenholt, U. (2012). Modeling multiple response processes in judgment and choice. Psychological Methods, 17(4). https://doi.org/10.1037/a0028111 Bolsinova, M., & Maris, G. (2015). A test for conditional independence between response time and accuracy. British Journal of Mathematical and Statistical Psychology, 69(1), 62–79. https://doi.org/10.1111/bmsp.12059 Bolsinova, M., & Molenaar, D. (2018). Modeling nonlinear conditional dependence between response time and accuracy. Frontiers in Psychology, 9, 1525. https://doi.org/10.3389/fpsyg.2018.01525 Bolsinova, M., & Tijmstra, J. (2016). Posterior predictive checks for conditional independence between response time and accuracy. Journal of Educational and Behavioral Statistics, 41(2), 123–145. https://doi.org/10.3102/1076998616631746 Bolsinova, M., & Tijmstra, J. (2018). Improving precision of ability estimation: Getting more from response times. British Journal of Mathematical and Statistical Psychology, 71(1), 13–38. Bolsinova, M., Boeck, P. de, & Tijmstra, J. (2017a). Modelling conditional dependence between response time and accuracy. Psychometrika, 82, 1126–1148. https://doi.org/10.1002/s11336-016-9537-6 Bolsinova, M., Tijmstra, J., & Molenaar, D. (2017b). Response moderation models for conditional dependence between response time and response accuracy. British Journal of Mathematical and Statistical Psychology, 70(2), 257–279. https://doi.org/10.1111/bmsp.12076 Bolsinova, M., Tijmstra, J., Molenaar, D., & De Boeck, P. (2017c). Conditional dependence between response time and accuracy: An overview of its possible sources and directions for distinguishing between them. Frontiers in Psychology, 8. https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00202 Burgette, L. F., & Reiter, J. P. (2012). Modeling adverse birth outcomes via confirmatory factor quantile regression. Biometrics, 68(1), 92–100. Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1). https://doi.org/10.18637/jss.v080.i01 Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46(3), 167–174. Cho, S.-J., Brown-Schmidt, S., Boeck, P. D., & Shen, J. (2020). Modeling intensive polytomous time-series eye-tracking data: A dynamic tree-based item response model. Psychometrika, 85(1), 154–184. https://doi.org/10.1007/s11336-020-09694-6 Choi, H. M., & Hobert, J. P. (2013). The Polya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic. Electronic Journal of Statistics, 7, 2054–2064. https://doi.org/10.1214/13-EJS837 Curtis, S. M. (2010). BUGS code for item response theory. Journal of Statistical Software, 36(Code Snippet 1). https://doi.org/10.18637/jss.v036.c01 De Boeck, P., & Partchev, I. (2012). IRTrees: Tree-Based item response models of the GLMM family. Journal of Statistical Software, 48(1), 1–28. https://doi.org/10.18637/jss.v048.c01 De Boeck, P., & Jeon, M. (2019). An overview of models for response times and processes in cognitive tests. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00102 Debelak, R., Gittler, G., & Arendasy, M. (2014). On gender differences in mental rotation processing speed. Learning and Individual Differences, 29, 8–17. https://doi.org/10.1016/j.lindif.2013.10.003 DiTrapani, J., Jeon, M., De Boeck, P., & Partchev, I. (2016). Attempting to differentiate fast and slow intelligence: Using generalized item response trees to examine the role of speed on intelligence tests. Intelligence, 56, 82–92. https://doi.org/10.1016/j.intell.2016.02.012 Dolan, C. V., Maas, H. L. J. van der, & Molenaar, P. C. M. (2002). A framework for ML estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring. Behavior Research Methods, Instruments, & Computers, 34(3), 304–323. https://doi.org/10.3758/bf03195458 Erdfelder, E., Auer, T.-S., Hilbig, B. E., Aßfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift Für Psychologie/Journal of Psychology, 217(3), 108. https://doi.org/10.1027/0044-3409.217.3.108 Ferrando, P. J., & Lorenzo-Seva, U. (2007). An item response theory model for incorporating response time data in binary personality items. Applied Psychological Measurement, 31(6), 525–543. https://doi.org/10.1177/0146621606295197 Fox, J.-P., & Glas, C. A. (2001). Bayesian estimation of a multilevel IRT model using Gibbs sampling. Psychometrika, 66, 271–288. Fox, J.-P., & Marianti, S. (2016). Joint modeling of ability and differential speed using responses and response times. Multivariate Behavioral Research, 51(4), 540–553. https://doi.org/10.1080/00273171.2016.1171128 Fox, J.-P., Entink, R. K., & Linden, W. v. d. (2007). Modeling of responses and response times with the package cirt. Journal of Statistical Software, 20(7). https://doi.org/10.18637/jss.v020.i07 Fu, Z., & Lu, M. (2023). Bayesian inference for multidimensional graded response model using Pólya-Gamma latent variables. Journal of Statistical Computation and Simulation, 93(16), 2856–2887. https://doi.org/10.1080/00949655.2023.2212313 Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., & Rubin, D. (2013). Bayesian Data Analysis (3rd ed.). Taylor & Francis. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. https://doi.org/10.1214/ss/1177011136 Gelman, A., & Vehtari, A. (2021). What are the most important statistical ideas of the past 50 years? Journal of the American Statistical Association, 116(536), 2087–2097. https://doi.org/10.1080/01621459.2021.1938081 Glas, C. A. W., & Linden, W. J. van der. (2010). Marginal likelihood inference for a model for item responses and response times. British Journal of Mathematical and Statistical Psychology, 63(3), 603–626. https://doi.org/10.1348/000711009x481360 Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109. https://doi.org/10.1093/biomet/57.1.97 Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15. Huang, P.-H., Chen, H., & Weng, L.-J. (2017). A penalized likelihood method for structural equation modeling. Psychometrika, 82(2), 329–354. https://doi.org/10.1007/s11336-017-9566-9 Huang, Y., & Chen, J. (2016). Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features. Statistics in Medicine, 35, 5666–5685. https://doi.org/10.1002/sim.7092 Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(4), 555–566. https://doi.org/10.1080/10705511.2016.1154793 Jeon, M., & De Boeck, P. (2016). A generalized item response tree model for psychological assessments. Behavior Research Methods, 48(3), 1070–1085. https://doi.org/10.3758/s13428-015-0631-y Jiang, Z., & Carter, R. (2018). Using Hamiltonian Monte Carlo to estimate the log-linear cognitive diagnosis model via Stan. Behavior Research Methods, 51(2), 651–662. https://doi.org/10.3758/s13428-018-1069-9 Jiang, Z., & Templin, J. (2019). Gibbs samplers for logistic item response models via the Pólya—Gamma distribution: A computationally efficient data-augmentation strategy. Psychometrika, 84(2), 358–374. https://doi.org/10.1007/s11336-018-9641-x Jin, K.-Y., Wu, Y.-J., & Chen, H.-F. (2022). A new multiprocess IRT model with ideal points for Likert-type items. Journal of Educational and Behavioral Statistics, 47(3), 297–321. https://doi.org/10.3102/10769986211057160 Klein Entink, R. H., Fox, J.-P., & van der Linden, W. J. (2009a). A multivariate multilevel approach to the modeling of accuracy and speed of test takers. Psychometrika, 74, 21–48. https://doi.org/10.1007/s11336-008-9075-y Klein Entink, R. H., Kuhn, J.-T., Hornke, L. F., & Fox, J.-P. (2009b). Evaluating cognitive theory: A joint modeling approach using responses and response times. Psychological Methods, 14(1), 54–75. https://doi.org/10.1037/a0014877 Klein Entink, R. H., Linden, W. J. van der, & Fox, J.-P. (2009c). A Box-Cox normal model for response times. British Journal of Mathematical and Statistical Psychology, 62, 621–640. https://doi.org/10.1348/000711008X374126 Koenker, R. (2005). Quantile Regression. Cambridge University Press. Koenker, R., & Bassett, G. J. (1978). Regression quantiles. Econometrica, 46, 33–50. https://doi.org/10.2307/1913643 Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. https://doi.org/10.1080/00949655.2010.496117 Kyllonen, P. C., & Zu, J. (2016). Use of response time for measuring cognitive ability. Journal of Intelligence, 4(4), 14. https://doi.org/10.3390/jintelligence4040014 König, C., Becker, B., & Ulitzsch, E. (2023). Bayesian hierarchical response time modelling - A tutorial. The British Journal of Mathematical and Statistical Psychology, 76. https://doi.org/10.1111/bmsp.12302 Lee, Y.-H. (2007). Contributions to the statistical analysis of item response time in educational testing. Lee, Y.-H., & Chen, H. (2011). A review of recent response-time analyses in educational testing. Psychological Test and Assessment Modeling, 53(3), 359–379. Lindeløv, J. K. (2019, September). Reaction time distributions: An interactive overview. https://lindeloev.github.io/shiny-rt/#38_gamma Linden, W. J. van der. (2009). Conceptual issues in response-time modeling. Journal of Educational Measurement, 46(3), 247–272. https://doi.org/10.1111/j.1745-3984.2009.00080.x Linden, W. J. van der, & Glas, C. A. W. (2010). Statistical tests of conditional independence between responses and/or response times on test items. Psychometrika, 75(1), 120–139. https://doi.org/10.1007/s11336-009-9129-9 Luo, Y., & Jiao, H. (2017). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement, 78(3), 384–408. https://doi.org/10.1177/0013164417693666 Man, K., Harring, J. R., Jiao, H., & Zhan, P. (2019). Joint modeling of compensatory multidimensional item responses and response times. Applied Psychological Measurement, 43(8), 639–654. https://doi.org/10.1177/0146621618824853 Martin, A. D., Quinn, K. M., & Park, J. H. (2011). MCMCpack: Markov Chain Monte Carlo in R. Journal of Statistical Software, 42(9), 1–21. https://doi.org/10.18637/jss.v042.i09 McElreath, R. (2020). Statistical Rethinking (2nd ed.). Chapman, Hall/CRC. https://doi.org/10.1201/9780429029608 Meng, X.-B., Tao, J., & Chang, H.-H. (2015). A conditional joint modeling approach for locally dependent item responses and response times. Journal of Educational Measurement, 52(1), 1–27. https://doi.org/10.1111/jedm.12060 Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. https://doi.org/10.1063/1.1699114 Molenaar, D., & Boeck, P. de. (2018). Response mixture modeling: Accounting for heterogeneity in item characteristics across response times. Psychometrika, 83(2), 279–297. https://doi.org/10.1007/s11336-017-9602-9 Molenaar, D., & Bolsinova, M. (2017). A heteroscedastic generalized linear model with a non-normal speed factor for responses and response times. British Journal of Mathematical and Statistical Psychology, 70(2), 297–316. https://doi.org/10.1111/bmsp.12087 Molenaar, D., Bolsinova, M., & Vermunt, J. K. (2018). A semi-parametric within-subject mixture approach to the analyses of responses and response times. British Journal of Mathematical and Statistical Psychology, 71(2), 205–228. https://doi.org/10.1111/bmsp.12117 Molenaar, D., Bolsinova, M., Rozsa, S., & De Boeck, P. (2016b). Response mixture modeling of intraindividual differences in responses and response times to the Hungarian WISC-IV block design test. Journal of Intelligence, 4(3), 10. https://doi.org/10.3390/jintelligence4030010 Molenaar, D., Oberski, D., Vermunt, J., & Boeck, P. D. (2016a). Hidden Markov item response theory models for responses and response times. Multivariate Behavioral Research, 51(5), 606–626. https://doi.org/https://doi.org/10.1080/00273171.2016.1192983 Molenaar, D., Tuerlinckx, F., & Van Der Maas, H. L. J. (2015a). A bivariate generalized linear item response theory modeling framework to the analysis of responses and response times. Multivariate Behavioral Research, 50(1), 56–74. https://doi.org/10.1080/00273171.2014.962684 Molenaar, D., Tuerlinckx, F., & Van Der Maas, H. L. J. (2015b). A generalized linear factor model approach to the hierarchical framework for responses and response times. British Journal of Mathematical and Statistical Psychology, 68(2), 197–219. https://doi.org/10.1111/bmsp.12042 Muthén, L. K., & Muthén, B. O. (2017). Mplus: Statistical analysis with latent variables; user's guide;[version 8]. Muthén et Muthén. Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H. H., & Boker, S. M. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81(2), 535–549. https://doi.org/10.1007/s11336-014-9435-8 Oka, M., & Okada, K. (2023). Scalable Bayesian approach for the DINA Q-matrix estimation combining stochastic optimization and variational inference. Psychometrika, 88, 302–331. https://doi.org/10.1007/s11336-022-09884-4 Okumura, T. (2014). Empirical differences in omission tendency and reading ability in PISA: An application of tree-based item response models. Educational and Psychological Measurement, 74(4), 611–626. https://doi.org/10.1177/0013164413516976 Partchev, I., & De Boeck, P. (2012). Can fast and slow intelligence be differentiated? Intelligence, 40(1), 23–32. https://doi.org/10.1016/j.intell.2011.11.002 Plieninger, H., & Meiser, T. (2014). Validity of multiprocess IRT models for separating content and response styles. Educational and Psychological Measurement, 74(5), 875–899. https://doi.org/10.1177/0013164413514998 Plummer, M. (2022). JAGS Version 4.3.1 user manual. Lyon, France. Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian inference for logistic models using Pólya–Gamma latent variables. Journal of the American Statistical Association, 108(504), 1339–1349. https://doi.org/10.1080/01621459.2013.829001 Porter, S. R. (2015). Quantile regression: Analyzing changes in distributions instead of means. In M. Paulsen (Ed.), Higher Education: Handbook of Theory and Research: Vol. 30. Higher Education: Handbook of Theory and Research. Springer. https://doi.org/10.1007/978-3-319-12835-1_8 Ranger, J., & Ortner, T. (2012). The case of dependency of responses and response times: A modeling approach based on standard latent trait models. Psychological Test and Assessment Modeling, 54(2), 128–148. Ranger, J., Kuhn, J. T., & Ortner, T. M. (2020). Modeling responses and response times in tests with the hierarchical model and the three-parameter lognormal distribution. Educational and Psychological Measurement, 80(6), 1059–1089. https://doi.org/10.1177/0013164420908916 Ranger, J., Kuhn, J.-T., & Pohl, S. (2021). Effects of motivation on the accuracy and speed of responding in tests: The speed-accuracy tradeoff revisited. Measurement: Interdisciplinary Research and Perspectives, 19(1), 15–38. https://doi.org/10.1080/15366367.2020.1750934 Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356. https://doi.org/10.1111/1467-9280.00067 Rijn, P. W. van, & Ali, U. S. (2018). A Generalized Speed–Accuracy Response Model for Dichotomous Items. Psychometrika, 83, 109–131. https://doi.org/10.1007/s11336-017-9590-9 Rios-Avila, F., & Maroto, M. L. (2024). Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models With Fixed Effects. Sociological Methods & Research, 53(2), 639–682. https://doi.org/10.1177/00491241211036165 Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2). https://doi.org/10.18637/jss.v048.i02 Rouder, J. N., Lu, J., Speckman, P., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions. Psychonomic Bulletin & Review, 12(2), 195–223. https://doi.org/10.3758/bf03257252 Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B: Statistical Methodology, 64(4), 583–639. https://doi.org/10.1111/1467-9868.00353 Stan Development Team. (2022). Stan Modeling Language Users Guide and Reference Manual (Version 2.31). https://mc-stan.org/ Tijmstra, J., & Bolsinova, M. (2023). The Hierarchical Model for Response Times: Advantages, Limitations, and Risks of Its Use in Measurement Practice. In L. A. van der Ark, W. H. M. Emons, & R. R. Meijer (Eds.), Essays on Contemporary Psychometrics: Essays on Contemporary Psychometrics. Springer. https://doi.org/10.1007/978-3-031-10370-4_16 Ulitzsch, E., Davier, M. von, & Pohl, S. (2019a). A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response. British Journal of Mathematical and Statistical Psychology, 73(S1), 83–112. https://doi.org/10.1111/bmsp.12188 Ulitzsch, E., Davier, M. von, & Pohl, S. (2019b). Using response times for joint modeling of response and omission behavior. Multivariate Behavioral Research, 55(3), 425–453. https://doi.org/10.1080/00273171.2019.1643699 Ulitzsch, E., Pohl, S., Khorramdel, L., Kroehne, U., & Davier, M. von. (2023). Using response times for joint modeling of careless responding and attentive response styles. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986231173607 van der Linden, W. J. (2006). A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31(2), 181–204. https://doi.org/10.3102/10769986031002181 van der Linden, W. J. (2007). A hierarchical framework for modeling speed and accuracy on test items. Psychometrika, 72(3), 287–308. https://doi.org/10.1007/s11336-006-1478-z van der Linden, W. J. (2024). On the choice of parameters for the lognormal model for response times: Commentary on Becker et al.(2013). Journal of Educational Measurement, 61(4), 624–633. https://doi.org/10.1111/jedm.12411 Waldmann, E., & Kneib, T. (2015). Bayesian bivariate quantile regression. Statistical Modelling, 15(4), 326–344. https://doi.org/10.1177/1471082X14551247 Wang, C., & Xu, G. (2015). A mixture hierarchical model for response times and response accuracy. British Journal of Mathematical and Statistical Psychology, 68(3), 456–477. https://doi.org/10.1111/bmsp.12054 Wang, C. et al. (2019). Modeling response time and responses in multidimensional health measurement. Frontiers in Psychology, 10, 51. https://doi.org/10.3389/fpsyg.2019.00051 Wang, S. et al. (2019). A joint modeling framework of responses and response times to assess learning outcomes. Multivariate Behavioral Research, 55(1), 49–68. https://doi.org/10.1080/00273171.2019.1607238 Wang, T., & Hanson, B. A. (2005). Development and calibration of an item response model that incorporates response time. Applied Psychological Measurement, 29(5), 323–339. https://doi.org/10.1177/0146621605275984 Wang, X., & Dey, D. K. (2010). Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption. The Annals of Applied Statistics, 4(4). https://doi.org/10.1214/10-aoas354 Wang, Y., Feng, X.-N., & Song, X.-Y. (2016). Bayesian quantile structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 246–258. https://doi.org/10.1080/10705511.2015.1033057 Xue, M., & Chen, Y. (2023). A Stan tutorial on Bayesian IRTree models: Conventional models and explanatory extension. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02121-5 Yamaguchi, K., & Zhang, J. (2023). Fully Gibbs sampling algorithms for Bayesian variable selection in latent regression models. Journal of Educational Measurement, 60(2), 202–234. https://doi.org/10.1111/jedm.12348 Yang, M., Luo, S., & DeSantis, S. M. (2019). Bayesian quantile regression joint models: Inference and dynamic predictions. Statistical Methods in Medical Research, 28(8), 2524–2537. https://doi.org/10.1177/0962280218784757 Yu, K., & Moyeed, R. A. (2001). Bayesian quantile regression. Statistics and Probability Letters, 54, 437–447. https://doi.org/10.1016/S0167-7152(01)00124-9 Yu, K., Lu, Z., & Stander, J. (2003). Quantile regression: Applications and current research areas. Journal of the Royal Statistical Society: Series D (The Statistician), 52(3), 331–350. https://doi.org/10.1111/1467-9884.00363 Zhan, P., Jiao, H., & Liao, D. (2017). Cognitive diagnosis modelling incorporating item response times. British Journal of Mathematical and Statistical Psychology, 71(2), 262–286. https://doi.org/10.1111/bmsp.12114 Zhan, P., Jiao, H., Man, K., & Wang, L. (2019). Using JAGS for Bayesian cognitive diagnosis modeling: A tutorial. Journal of Educational and Behavioral Statistics, 44(4), 473–503. https://doi.org/10.3102/1076998619826040 Zhan, P., Jiao, H., Man, K., Wang, W.-C., & He, K. (2021). Variable speed across dimensions of ability in the joint model for responses and response times. Frontiers in Psychology, 12, 469196. https://doi.org/10.3389/fpsyg.2021.469196 Zhang, Z., Zhang, J., Lu, J., & Tao, J. (2020). Bayesian estimation of the DINA model with Pólya-Gamma Gibbs sampling. Frontiers in Psychology, 11, 384. https://doi.org/10.3389/fpsyg.2020.00384 Zhu, H., Gao, W., & Zhang, X. (2021). Bayesian analysis of a quantile multilevel item response theory model. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.607731 Štrumbelj, E., Bouchard-Côté, A., Corander, J., Gelman, A., Rue, H., Murray, L., Pesonen, H., Plummer, M., & Vehtari, A. (2023). Past, present, and future of software for Bayesian inference. Accepted by Statistical Science.
Description:	博士國立政治大學教育學系 109152512
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109152512
Data Type:	thesis
Appears in Collections:	[教育學系] 學位論文

Files in This Item:

File	Size	Format
251201.pdf	8194Kb	Adobe PDF	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback