655
Views
10
CrossRef citations to date
0
Altmetric
Original Articles

Incorporating Mobility in Growth Modeling for Multilevel and Longitudinal Item Response Data

&

References

  • Adams, R. J., Wilson, M., & Wu, M. (1997). Multilevel item response models: An approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22(1), 47–76. doi:10.3102/10769986022001047
  • Asparouhov, T., & Muthén, B. (2012). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. Paper presented at the annual meeting of the National Council on Measurement in Education, Vancouver, British Columbia.
  • Bolt, D. M., Cohen, A. S., & Wollack, J. A. (2002). Item parameter estimation under conditions of test speededness: Application of a mixture Rasch model with ordinal constraints. Journal of Educational Measurement, 39(4), 331–348. doi:10.1111/j.1745-3984.2002.tb01146.x
  • Browne, W. J., Goldstein, H., & Rasbash, J. (2001). Multiple membership multiple classification (MMMC) models. Statistical Modelling, 1(2), 103–124. doi:10.1177/1471082X0100100202
  • Cho, S.-J., & Cohen, A. S. (2010). A multilevel mixture IRT model with an application to DIF. Journal of Educational and Behavioral Statistics, 35(3), 336–370. doi:10.3102/1076998609353111
  • Cho, S.-J., Cohen, A. S., & Kim, S.-H. (2013). Markov chain Monte Carlo estimation of a mixture item response theory model. Journal of Statistical Computation and Simulation, 38(2), 278–306. doi:10.1080/00949655.2011.603090
  • Chung, H., & Beretvas, S. N. (2011). The impact of ignoring multiple membership data structures in multilevel models. British Journal of Mathematical and Statistical Psychology, 65(2), 185–200. doi:10.1111/j.2044-8317.2011.02023.x
  • Cohen, A. S., & Bolt, D. M. (2005). A mixture model analysis of differential item functioning. Journal of Educational Measurement, 42(2), 133–148. doi:10.1111/j.1745-3984.2005.00007
  • Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Erlbaum.
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian data analysis (3rd ed.). Boca Raton, FL: Chapman & Hall/CRC.
  • Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. doi:10.1214/ss/1177011136
  • Goldstein, H. (2003). Multilevel statistical models (3rd ed.). London, England: Anorld.
  • Grady, M. W., & Beretvas, S. N. (2010). Incorporating student mobility in achievement growth modeling: A cross-classified multiple membership growth curve model. Multivariate Behavioral Research, 45(3), 393–419. doi:10.1080/00273171.2010.483390
  • Heinlein, L. M., & Shinn, M. (2000). School mobility and student achievement in an urban setting. Psychology in the Schools, 37(4), 349–357. doi:10.1002/1520-6807(200007)37:4<359::AID-PITS6>3.0.CO;2-1
  • Hill, P. W., & Goldstein, H. (1998). Multilevel modeling of educational data with cross-classification and missing identification for units. Journal of Educational and Behavioral Statistics, 23(2), 117–128. doi:10.3102/10769986023002117
  • Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26(3), 329–367. doi:10.1177/0049124198026003003
  • Hung, L. F., & Wang, W.-C. (2012). The generalized multilevel facets model for longitudinal data. Journal of Educational and Behavioral Statistics, 37(2), 231–255. doi:10.3102/1076998611402503
  • Jeon, M., & Rabe-Hesketh, S. (2012). Profile-likelihood approach for estimating generalized linear mixed models with factor structures. Journal of Educational and Behavioral Statistics, 37(4), 518–542. doi:10.3102/1076998611417628
  • Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational Measurement, 38(1), 79–93. doi:10.2307/1435439
  • Kelcey, B., McGinn, D., & Hill, H. (2014). Approximate measurement invariance in cross-classified rater-mediated assessments. [Methods]. Frontiers in Psychology, 5, 1469. doi:10.3389/fpsyg.2014.01469
  • Leckie, G. (2009). The complexity of school and neighbourhood effects and movements of pupils on school differences in models of educational achievement. Journal of the Royal Statistical Society: Series A, 172(3), 537–554. doi:10.1111/j.1467-985X.2008.00577.x
  • Li, F., Duncan, T. E., Duncan, S. C., & Hops, H. (2001). Piecewise growth mixture modeling of adolescent alcohol use data. Structural Equation Modeling, 8(2), 175–204. doi:10.1207/S15328007SEM0802_2
  • Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., & Schabenberger, O. (2006). SAS for mixed models (vol. 840). Cary, NC: SAS Institute.
  • Lockwood, J. R., McCaffrey, D. F., Mariano, L. T., & Setodji, C. (2007). Bayesian methods for scalable multivariate value-added assessment. Journal of Educational and Behavioral Statistics, 32(2), 125–150. doi:10.3102/1076998606298039
  • Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10(4), 325–337. doi:10.1023/A:1008929526011
  • Luo, W., & Kwok, O.-M. (2009). The impacts of ignoring a crossed factor in analyzing cross-classified data. Multivariate Behavioral Research, 44(2), 182–212. doi:10.1080/00273170902794214
  • Luo, W., & Kwok, O.-M. (2012). The consequences of ignoring individuals' mobility in multilevel growth models: A Monte Carlo study. Journal of Educational and Behavioral Statistics, 37(1), 31–56. doi:10.3102/1076998610394366
  • MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84–99. doi:10.1037/1082-989X.4.1.84
  • Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174. doi:10.1007/BF02296272
  • McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (Vol. 2, pp. 561–614). New York, NY: Plenum Press.
  • McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58(1), 110–133. doi:10.3102/10769986029001067
  • McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–101. doi:10.3102/10769986029001067
  • Meyers, J. L., & Beretvas, S. N. (2006). The impact of inappropriate modeling of cross-classified data structures. Multivariate Behavioral Research, 41(4), 473–497. doi:10.1207/s15327906mbr4104_3
  • Mislevy, R. J., & Bock, R. D. (1989). A hierarchical item-response model for educational testing. In R. Bock (Ed.), Multilevel analysis of educational data (pp. 57–74). San Diego, CA: Springer.
  • Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176. doi:10.1002/j.2333-8504.1992.tb01436.x
  • Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user's guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
  • National Youth Policy Institute. (2009). Korean Youth Panel Survey (KYPS) user's guide for the 1st-5th year of panel study of the second year middle school students. Seoul, Korea: National Youth Policy Institute.
  • Palardy, G. J. (2010). The multilevel crossed random effects growth model for estimating teacher and school effects: Issues and extensions. Educational and Psychological Measurement, 70(3), 401–419. doi:10.1177/0013164409355693
  • Pastor, D. A., & Beretvas, S. N. (2006). Longitudinal Rasch modeling in the context of psychotherapy outcomes assessment. Applied Psychological Measurement, 30(2), 100–120. doi:10.1177/0146621605279761
  • R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org
  • Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modeling. Psychometrika, 69(2), 167–190. doi:10.1007/BF02295939
  • Rasbash, J., & Browne, W. J. (2001). Non-hierarchical multilevel models. In A. Leyland & H. Goldstein (Eds.), Multilevel modelling of health statistics (pp. 93–103). New York: John Wiley.
  • Rasbash, J., & Goldstein, H. (1994). Efficient analysis of mixed hierarchical and cross-classified random structures using a multilevel model. Journal of Educational and Behavioral Statistics, 19(4), 337–350. doi:10.3102/10769986019004337
  • Rasbash, J., Steele, F., Browne, W. J., & Goldstein, H. (2012). A User's Guide to MLwiN, v2.26. University of Bristol: Centre for Multilevel Modelling.
  • Raudenbush, S. W. (1993). A crossed random effects model for unbalanced data with applications in cross-sectional and longitudinal research. Journal of Educational and Behavioral Statistics, 18(4), 321–349. doi:10.3102/10769986018004321
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage.
  • Raudenbush, S. W., & Liu, X.-F. (2001). Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods, 6(4), 387–401. doi:10.1037/1082-989X.6.4.387
  • Rumberger, R. W. (2003). The causes and consequences of student mobility. Journal of Negro Education, 72(1), 6–21. doi:10.2307/3211287
  • Rumberger, R. W., & Larson, K. A. (1998). Student mobility and the increased risk of high school dropout. American Journal of Education, 107(1), 1–35. doi:10.2307/1085729
  • Segawa, E. (2005). A growth model for multilevel ordinal data. Journal of Educational and Behavioral Statistics, 30(4), 369–396. doi:10.3102/10769986030004369
  • Sinharay, S., Johnson, M. S., & Stern, H. S. (2006). Posterior predictive assessment of item response theory models. Applied Psychological Measurement, 30(4), 298–321. doi:10.1177/0146621605285517
  • 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. doi:10.1111/1467-9868.00353
  • Temple, J. A., & Reynolds, A. J. (2000). School mobility and achievement: Longitudinal findings from an urban cohort. Journal of School Psychology, 37(4), 355–377. doi:10.1016/S0022-4405(99)00026-6
  • U.S. Government Accounting Office. (1994). Elementary school children: Many change schools frequently, harming their education (GAO/HEHS publication no. 94–45). Washington, DC: U.S. Government Printing Office.
  • Wilson, M. (2004). Constructing Measures: An item response modeling approach. Mahwah, NJ: Lawrence Earlbaum Associates.
  • Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. Chicago, IL: MESA press.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.