Author/Title Index (Volume 30) Afshartous, David. Prediction in Multilevel Models, No. 2, p. 109 Bonett, Douglas G. Inferential Methods for the Tetrachoric Correlation Coeffi cient, No. 2 p 213 De Boeck, Paul. See Van den Noortgate, Wim de la Torre, Jimmy. Making the Most of What We Have: A Practical Application of Multidimensional Item Response Theory in Test Scoring, No. 3, p. 295 Dodhia, Rahul M. A Review of Applied Multiple Regression/Correlation Anal) € Behavioral Sciences (3rd ed.). No. 25. Pp. 2927 07 -an-Paul Randomize d Item Re SPONSE Theory Mode ls, No 2, p 189 Gitelman, Alix I. Estimating Causal Effects From Multilevel Group-Allocation Data, No. 4, p. 397 Holland, Paul W. Profiles in Research Interview by Dan Robinson, No. 3, p. 343 Hornik, Robert. See Zanutto, Elaine Kaplan, David. Finite Mixture Dynamic Regression Modeling of Panel Data With Implications for Dynamic Re sponse Analysis, No. 2. p. 169 Kennedy, Benjamin B. See Scott, Marc A Kong, Nan. See von Davier, Alina A Lee, Sik- Yum. Maximum Likelihood Analysis of a Two-Level Nonlinear Structural Equation Model With Fixed Covariates, No. 1, p. | Leeuw, Jan de. See Afshartous, David Lu, Bo. See Zanutto, Elaine Maier, Kimberly S. See Miyazaki, Yasuo Masyn, Katherine. See Muthen, Bengt Maxwell, Scott. See Yuan, Ke-Hai Miyazaki, Yasuo. Johnson-Neyman Type Technique in Hierarchical Linear Mod els, No. 3, p. 233 Mutheén, Bengt. Discrete-Time Survival Mixture Analysis, No. 1, p. 27 Patz. Richard J. See de la Torre, Jimmy Price, Robert M. See Bonett, Douglas G Scott, Mare A. Pitfalls in Pathways: Some Perspectives on Competing Risks Event History Analysis in Education Research, No. 4, p. 413 Segawa, Eisuke. A Growth Model for Multilevel Ordinal Data, No. 4, p. 369 Shaffer, Juliet Popper. Profiles in Research Interview by Dan Robinson, No. |, p. 93 477 Sklar, Jeffrey C. See Zwick, Rebecca Song, Xin- Yuan. See Lee, Sik-Yum Spence, lan. No Humble Pie: The Origins and Usage of a Statistical Chart, No. 4, p. 353 Van den Noortgate,Wim. Assessing and Explaining Differential Item Functioning Using Logistic Mixed Models, No. 4, p. 443 Viechtbauer, Wolfgang. Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model, No. 3, p 261 von Davier, Alina A. A Unified Approach to Linear Equating for the Nonequiva- lent Groups Design, No. 3, p. 313 ) Wainer, Howard. Profiles in Research Interview by Dan Robinson, No. 4, p. 465 Wang, Xiang Bo. A Review of Automatic Essay Scoring: A Cross-Disciplinary Pe rspec five. No l. p 105 Yuan, Ke-Hai. On the Post Hoc Power in Testing Mean Differences, No. 2, p. 141 Zanutto, Elaine. Using Propensity Score Subclassification for Multiple Treatment Doses to Evaluate a National Antidrug Media Campaign, No. 1, p. 59 Zwick, Rebecca. A Note on Standard Errors for Survival Curves in Discrete-Time Survival AnalysisN,o . | |T 75y=‘ 5 Keyword Index (Volume 30) ability estimation 3:295 Johnson-Neyman technique 3:233 analysis of covariance analysis of variance 2 latent classes Leibniz 4:353 Bayesian est‘ imation 23.:920965 Levine observed-score equating >3 :3 24% Bayesian hierarchical models 4:369 Llull 4:353 Bayesian information criterion |:] lloogg ic diagy rams 41:-325523 .24 bi IS ciel |l ogistic mixed models 4:443 Bruno 4 Z Markov chain Monte Carlo (MCMC) causal modeling 4:39 2-189. 3:2905 chhaian in lilinneeaarr eeqquuaattiinngg 3:313 maximum likelihood | circle chart 4:353 : : 5 icp McCullagh’s ordinal logit model 1:59 college graduation 1:75 : “— , ieee meta-analysis 3:26] ompeting risks 4:413 , yieitis ah Metropolis-Hastings algorithm 1:1 complex Survey 1:59 missing data 4:369 Monte Carlo 2:109 delta method 3:313 Monte Carlo EM algorithm 1:1 differential item functioning 4:443 multidimensionality 3:295 dynamic regression 2:169 . ’ y multi-indicator model 4:369 dynamic response modeling 2:169 multilevel model 2: 109, 4:397 effect size 2 ee nonequivalent groups anchor test (NEAT) Euler 4:353 Fes event : history ,| design 3:313 event history analysis 4 : observational study 1:59 observed power 2:14 finite mixture modeling 2 »bserved power 2:14] odds ratio 2:213 Gibbs sampler 1:1 group allocation 4:397 path sampling | group dynamic 4:397 pie chart 4:353 growth mixture modeling 1:27 Playfair 4:353 point estimation 2:213 hazard models 1:75 prediction 2:109 heterogeneity estimation 3:26] propensity score 1:59 hierarchical linear models 3:233 random effects 4:443 interval estimation 2:213 random effects randomized response item bias 4:443 2:189 item response theory 3:295, 4:443 random-effects model 3:261 item response theory model 2:189 reliability 2:213 sample size requirement 2:213 test equating 3:313 standard error 22:-2 713 tetrachoric approximation 2:213 standard error of equating 3 Tucker equating 3:313 subclassification 1:59 survey weights 1:59 Wald statistic 3:233 survival analysis 1:75, 4:413