Авторизация
Поиск по указателям
Reise S.P., Duan N. — Multilevel modeling: methodological advances, issues, and applications
Обсудите книгу на научном форуме Нашли опечатку?
Название: Multilevel modeling: methodological advances, issues, and applicationsАвторы: Reise S.P., Duan N. Аннотация: Researchers in multilevel modeling (MLM) report on new statistical advances, methodological issues, and applications in MLM, and examine problems that occur when trying to use MLM in applied research in areas such as power, experimental design, and model violations. The book will be of interest to researchers in advanced statistical training and experience in applying MLMs, especially in the areas of education, clinical intervention, psychology, and other behavioral sciences. The book can also be used as a supplement for an introductory graduate course. Reise teaches measurement at the University of California-Los Angeles. Duan teaches psychiatry and biostatistics at the University of California-Los Angeles.
Язык: Рубрика: Математика /Вероятность /Статистика и приложения /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Год издания: 2002Количество страниц: 314Добавлена в каталог: 12.06.2005Операции: Положить на полку |
Скопировать ссылку для форума | Скопировать ID
Предметный указатель
Adams, D. C. 109 111 Adaptive robust estimates 43 Additive linearly constant effects (ALICE) 128 Aggregated group-level effects (AGLEs) 219—223 Aitkin, M. 188 206 268 279 294 296 Alpert, A. 64 68 Altman, D. G. 253 Alwavs-taker 114 Alwin, D. F. 258 279 Analysis of verbal learning experiment 15—18 Anderson, D. A 188 206 268 279 294 296 Angrist, J. D. 113—115 136 137 Annandale, E. 233 236 253 Arbuckle, J. L. 257 279 Arminger, G. 75 88 Assumption of independence 262 Asymptotic normality 60 Average causal effect (ACE) 116 Baca, L. M. 159 178 Bachmann, N. 157 160 161 163 174 178 Bandura, A. 144 154 Banspach, S. 144 145 147 154 Barcikowski, R. S. 294—296 Barnett, V. 229 230 252 Barrera, M. 159 178 Bartholomew, L. K. 144 154 Basen — Engquist, K. 144 145 147 154 Basford, K. E. 80 88 Bates, D. M. 2 3 20 22 73 88 Baum, A. 159 179 Baumert, J. 285 297 Baumler, E. 140 147 154 Becker, P. 162 178 Belfield, C. 198 206 Belin, T. 47 49 Bell, R. A. 171 178 Benson, J. 258 279 Bentler, P. M. 53 55 64 67 68 70 Bernstein, I. H. 101 111 Berry, S. H. 286 297 Berta, D. 157 161 174 178 Best, N. G. 27 34 43 49 51 144 156 Bias correction 216 218—219 223 226—227 Bibby, J. M. 266 281 Biggerstaff, B. J. 108 111 Blair, R. C. 294 296 Bli 220 Bloom, H. S. 113 136 Bock, R. D. 2 21 Bois, F. 48 50 Bootstrap and errors in variables 211 Bootstrap and multilevel modeling 211 213 Bootstrap and random variables 211—212 Bootstrap, bias correction 218—219 223 226—227 Bootstrap, comparison of methods 225—227 Bootstrap, conditional semiparametric (CSP) 213 214 218 221 226—227 Bootstrap, convergence 218 221—223 Bootstrap, estimated standard error 219 Bootstrap, level 2 Bootstrap, nonparametric 212 215 217—218 Bootstrap, nonparametric maximum likelihood estimate 217—218 Bootstrap, only 218 223—227 Bootstrap, paradigms 212 Bootstrap, parametric 213 223—227 Bootstrap, residuals 213 227 Bootstrap, simple random resampling (SRR) 215—216 219—220 Bootstrap, two stage (TSRR) 217—218 220 222 225—226 Bootstrap, units 215 226—227 Boscardin, C. K. 71 81 89 Bosker, R. J. 91 111 287 288 Box, G. E. P. 31 32 49 Boyles, R. A. 68 Bozzette, S. A. 286 297 Bradbury, T. N. 160 178 Bradway, K. 12 13 15 16 21 Brant, L. J. 13 22 Brenner, S. 0 273 279 Brown, C. H. 114 118 123 129 137 138 295 296 Brown, J. A. 286 297 Browne, W. 188 197 199 207 235 253 255 259 263 271 280 282 Bryk, A. S. 1 21 26 27 30 31 34 46 49 51 74 88 91—94 100 106—109 111 140 141 144 154 159 178 188 206 255 257 279 285 296 Burda, P. C. 159 178 Burden, R. L. 3 21 Burnam, M. A. 286 297 Busing, F. M. T. A. 144 154 211 228 Cameron, B. 259 263 282 Carlin, B. 26 31 38 49 50 Carlin, J. B. 32 50 114 118 129 138 Carpenter, B. N. 159 163 179 Carpenter, J. 211 215 227 Carter, H. B. 13 22 Carter, N. L. 256 280 Carter, R. L. 20 22 Carvajal, S. C. 140 147 154 Categorical latent variable 116 categorical variables 210 Caterpillar diagrams 196 Chambers, J. M. 3 21 Chan, W. 154 Chang, H. 47 50 Charlton, C. 259 263 282 Cheong, Y. F. 107 Chinchilli, V. M. 2 22 Choi, K. 25 27—29 51 Choice of estimand 289—290 Clayton, D. J. 204 206 Clustering 80 147—148 288 293—296 Cochran, W. G. 288 296 Cohn, S. E. 286 297 Collins, J. 147 154 Common metric completely standardized solution 276 Compiler average causal effect (CACE) 113—114 Compiler example 123—128 130—134 Compiler growth mixture CACE modeling 113 115 Compiler ML — EM algorithmll 5—118 Complementary log-log 185 Complete data 56 Complier 114 Composite construct 273 Composite variable 256 257 259 270 274 275 278 Composite variable, maximally reliable 257 Composite variable, reliability 258 Compositional effect 158 174 Computer programs, CODA. 34 48 Computer programs, EQS 64 Computer programs, GLIM 4 188 Computer programs, HLM 31 34 47 73 92 107 109 188 Computer programs, LISREL 258 Computer programs, META 96 Computer programs, MIXOR 73 144 188 Computer programs, MIXREG. 144 Computer programs, MLn 73 165 211 218 227 259 Computer programs, MLwiN 92 107—199 235 259 Computer programs, Mplus 64—66 80 117 Computer programs, PRELIS. 258 Computer programs, SAS PROC MIXED. 73 144 Computer programs, VACRL. 144 Computer programs, VkHlm 108—109 Computer programs, WinBUGS 27 34 43 46 48 108 144 Confirmatory factor analysis 257 Congdon, R. T. 107 108 110 111 144 154 Conn, M. 256 280 Contextual conditions 158 Contextual effect 158—160 174 Continuous latent variable 113 115 Convergence (see bootstrap convergence) 221—223 Cooper, C. L. 273 279 Cooper, H. 99 110 Cornell, J. 93 110 Covariance level 1 Covariance level, error 210 217 Covariance level, level 2 Covariance structure model 273 Cowles, M. 34 49 Coyle, K. 144 145 147 154 Cramer, L. A. 171 178 Cronbach, L. J. 279 Cross-classification 197 Crossed effects 202 Cudeck, R. 13 21 255 279 Curran, P. J. 74 77 89 295 297 Cut point 194 Cut point estimates 203 Cut point parameter 193 Cutoff 234 249 250 Cutoff criterion 236 Cuttance, P. 255 256 269 279 Data augmentation 204 Davidian, M. 2 4 21 Davila, J. 160 178 de Leeuw, J. 2 22 285 295 297 Defier 114 Dempster, A. P. 26 50 56 68 117 136 Dennis, J. E. 3 9 21 Dey, D. 47 50 Di, G. 235 253 Diggle, P. 230 235 252 Dingle, N. L. 256 280 Direct effect 268 275 Discordant 230 Discordant observation 232 Distributional assumptions 271 Dixon, W. 241 252 Donner, A. P. 291 297 Dose-response curve 129 Draper, D. 31 43 47 50 188 197 199 207 235 253 255 271 280 282 Draper, N. R. 212 228 266 279 Drew, J. B. 159 163 179 du Toit, M. 109 110 255 257 269 279 281 du Toit, S. 1 21 23 109 110 255 257 269 279 281 Duan, N. 286 297 Dummy variable 232 237 242 DuMouchel, W. H. 110 Duncan, C. 159 165 178 Duncan, S. C. 64 68 Duncan, T. E. 64 68 Duval, R. D. 212 214 228 Dwyer, J. 291 297 Ecob, R. 231 233 236 252 253 255 256 279 Efron, B. 129 136 Eggli, P. 157 161 174 178 Elliot, P. R. 154 155 Elliott, P. R. 281 EM gradient 56 60 Empirical Bayes estimation 30 Endogenous 275 Equal interval scales 182 Errors-in-variables model 211—213 Exclusion restriction 115 135 Exogenous 275 Experimental design in MLM 291—293 Experimental design in MLM, factorial design 292 Experimental design in MLM, split plot design 292 Extramultinomial 189 Extramultinomial variation 183 204 Ezzett, F. 188 199 206 Factor score 258 Factor score regression coefficient 258 Factor score regression coefficients proportionally weighted 259 260 Factor score regression weights 257 260 Faires, J. D. 3 21 Feldman, D. 129 136 Feldman, H. A. 291 297 Fielding, A. 181 186 187 189 190 193 194 198 204 Finite mixture modeling 116 Fishbein, M. 144 155 Fisicaro, S. A. 159 163 179 Fitz — Gibbon, C. T. 195 207 Fleishman, J. 258 279 Fleming, R. 159 179 Francis, D. 71 72 75 76 81 88 89 Frankel, M. R. 286 297 Freedy, J. R. 158 179 Fuller, W. A. 212 228 Fully Bayesian analysis 31 Functional model 212 Fydrich, T. 162 180 Gauss-hermite quadrature 6 24 Gelfand, A. E. 31 32 46 47 50 52 Gelman, A. 32 33 43 47 48 50 General latent variable model 113 Generalized linear mixed models 182 187 199 204 Gibbons, R. D. 109 110 144 154 155 188 195 207 295 297 Gibbs sampling/sampler 27 31 34 47—48 Gilks, W. 27 32 50 51 144 156 Giltinan, D. M. 2 4 21 Glaisher, J. 250 253 Glass, G. V. 90 93 97 110 Gleser, L. J. 107 110 Goldman, D. P. 286 297 Goldman, N. 155 Goldstein, H. 1 21 53 69 74 88 91 103 107 110 140—144 149 154—156 159 165 176 179 180 185 186 188 189 195 200 206—211 215 218 227—230 233 235 250 253 255—257 259 262 269 271 280 282 285 297 Grade level scores 197 Graham, J. W. 141 155 Graubard. B. 1 291 297 Greene, J. 28 29 51 Greenhouse, J. B. 103 110 Growth mixture modeling 79 113 115 Growth modeling 73—76 229 Growth modeling for the request behavior data 29—30 Growth modeling with multiple processes 76—77 Growth trajectory 73 Gurevitch, J. 109 111 Gutscher, H. 158 179 Hagley, F. 219 228 Hamagami, F. 12 14 22 256 281 Hansson, R 0 159 163 179 Harrist, R. 140 147 154 Hart, P. M. 256 273 280 Harville, D. A. 188 207 Hastie, T. J. 3 21 Haukka, J. M. 211 228 Hawkins, D. 229 230 253 Hays, R. B. 159 179 Healy, M. J. R. 188 196 197 199 207 235 253 255 257 259 263 271 280 282 Heath, A. 198 208 Heck, R, H. 55 69 Hedeker, D. 109 110 144 154 155 186 188 195 207 295 297 Hedges, L. V. 90—92 94—97 99 100 106 108 110 Heterogeneity of variance 235 251 Higgins, J. J. 294 296 Hilden — Milton, J. 229 253 Hill, P. W. 256 258 280 283 Hills, S. 31 50 Hinde, J. 268 279 294 296 Hirano, K. 115 135 136 Hitchcock, P. 144 155 Hoadley, B. 60 69 Hobfoll, S. E. 158 179 Hodges, J. 229 253 Hoeksma, J. 229 253 Hogg, R. 46 50 Holland, P. W. 114 128 137 Hollis, M. 75 89 Holmes — Smith, P. 256 258 280 283 Hops, H. 64 68 Hornung, R. 157 158 161 174 178 179 Hox, J. J. 53 66 69 91 110 255 280 Huber, L. 159 178 179 Hunt, K. 233 236 253
Реклама
|
|
О проекте