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A. J. Leroux & S. N. Beretvas, Cross-Classified Multiple Membership LVR Model
Seltzer, M., Choi, K., & Thum, Y. (2003). Examining relationships between where students start and how rapidly they progress: Using new developments in growth modeling to gain insight into the distribution of achievement within schools. Education Evaluation and Policy Analysis, 25, 263–286. https://doi.org/ 10.3102/01623737025003263 Seltzer, M. H., Frank, K. A., & Bryk, A. S. (1994). The metric matters: The sensitivity of conclusions about growth in student achievement to choice of metric. Educational Evaluation and Policy Analysis, 16, 41–49. https://doi.org/10.3102/ 01623737016001041 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, 583–639. https://doi.org/10.1111/1467-9868.00353 Su, Y., & Yajima, M. (2015). Package “R2jags”. Retrieved from http://cran.r-project.org/web/packages/R2jags/index.html US Census Bureau. (2015). Geographic mobility: 2013 to 2014. Retrieved from http://www.census.gov/hhes/migration/data/ cps/cps2014.html US Government Accounting Office. (2010). K-12 education: Many challenges arise in educating students who change schools frequently (GAO Publication No. 11–40). Washington, DC: US Government Printing Office.
Audrey J. Leroux Department of Educational Policy Studies Georgia State University P.O. Box 3977 Atlanta, GA 30302-3977 USA aleroux@gsu.edu
Audrey J. Leroux is an Assistant Professor of Research, Measurement, and Statistics at Georgia State University. Her research is focused on evaluating innovative models and procedures within item exposure controls and stopping rules in computerized adaptive testing, as well as in extensions to conventional multilevel modeling that handle individual mobility.
S. Natasha Beretvas is a Professor in the Quantitative Methods program at The University of Texas at Austin. Her research is currently focused on assessing meta-analytic techniques as well as examining extensions to the multilevel model that are intended to handle student mobility and other sources of data structure complexities.
Received July 22, 2015 Revision received October 14, 2016 Accepted August 28, 2017 Published online April 23, 2018
Appendix A JAGS Code for the Unconditional CCMM-LVR Model ##T = total number of time-points; I = total number of students; J = total number of initial schools; K = total number of subsequent schools## model { ##Level-1 Model## for(t in 1:T) { MATH[t] dnorm(mu[t],tauinv_e) mu[t] <- pi0[STU[t]] + pi1[STU[t]] * TIME[t] } ##Level-2 Model## for (i in 1:I) { pi0[i] dnorm(beta00[SCH[i]],tauinv_r0) pi1[i] dnorm(stu_growth[i],tauinv_r1) stu_growth[i] <- beta10[SCH[i],1] + beta10[SCH[i],2] * (pi0[i] – beta00[SCH[i]]) + WGT2[i] * u1J2[SCH2[i]] + WGT3[i] * u1J2[SCH3[i]] + WGT4[i] * u1J2[SCH4[i]] } ##Level-3 Model## for (j in 1:J) { beta00[j] dnorm(gamma0000,tauinv_u0) beta10[j,1:2] dmnorm(sch_growth[j,1:2],tauinv_u1[1:2,1:2]) sch_growth[j,1] <- gamma1000 + Bb * (beta00[j] - gamma0000) sch_growth[j,2] <- Bw0 + Bw1 * (beta00[j] - gamma0000) } Methodology (2018), 14(1), 30–44
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