Multilevel and longitudinal modeling using stata:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
College Station, Tex.
Stata Press
2008
|
| Edition: | 2. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXXIII, 562 S. graph. Darst., Kt. |
| ISBN: | 1597180408 9781597180405 |
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MARC
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| 100 | 1 | |a Rabe-Hesketh, Sophia |e Verfasser |0 (DE-588)13573116X |4 aut | |
| 245 | 1 | 0 | |a Multilevel and longitudinal modeling using stata |c Sophia Rabe-Hesketh ; Anders Skrondal |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a College Station, Tex. |b Stata Press |c 2008 | |
| 300 | |a XXXIII, 562 S. |b graph. Darst., Kt. | ||
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| adam_text | CONTENTS LIST OF TABLES XXI LIST OF FIGURES XXV PREFACE XXXI I
PRELIMINARIES 1 1 REVIEW OF LINEAR REGRESSION 3 1.1 INTRODUCTION 3 1.2
IS THERE GENDER DISCRIMINATION IN FACULTY SALARIES? 3 1.3
INDEPENDENT-SAMPLES T TEST 4 1.4 ONE-WAY ANALYSIS OF VARIANCE 8 1.5
SIMPLE LINEAR REGRESSION 11 1.6 DUMMY VARIABLES 18 1.7 MULTIPLE LINEAR
REGRESSION 20 1.8 INTERACTIONS 26 1.9 DUMMIES FOR MORE THAN TWO GROUPS
29 1.10 OTHER TYPES OF INTERACTIONS 34 1.10.1 INTERACTION BETWEEN DUMMY
VARIABLES 34 1.10.2 INTERACTION BETWEEN CONTINUOUS COVARIATES 35 1.11
NONLINEAR EFFECTS 36 1.12 RESIDUAL DIAGNOSTICS 38 1.13 SUMMARY AND
FURTHER READING , 39 1.14 EXERCISES 40 II TWO-LEVEL LINEAR MODELS 49 2
VARIANCE-COMPONENTS MODELS 51 VIII CONTENTS 2.1 INTRODUCTION 51 2.2 HOW
RELIABLE ARE PEAK-EXPIRATORY-FLOW MEASUREMENTS? 52 2.3 THE
VARIANCE-COMPONENTS MODEL 54 2.3.1 MODEL SPECIFICATION AND PATH DIAGRAM
54 2.3.2 ERROR COMPONENTS, VARIANCE COMPONENTS, AND RELIABILITY ... 58
2.3.3 INTRACLASS CORRELATION 58 2.4 FIXED VERSUS RANDOM EFFECTS 61 2.5
ESTIMATION USING STATA 62 2.5.1 DATA PREPARATION 62 2.5.2 USING XTREG 63
2.5.3 USING XTMIXEDT 65 2.5.4 USING GLLAMM 66 2.6 HYPOTHESIS TESTS AND
CONFIDENCE INTERVALS 68 2.6.1 HYPOTHESIS TEST AND CONFIDENCE INTERVAL
FOR THE POPULATION MEAN 68 2.6.2 HYPOTHESIS TEST AND CONFIDENCE INTERVAL
FOR THE BETWEEN- CLUSTER VARIANCE 69 2.7 MORE ON STATISTICAL INFERENCE
71 2.7.1 *** DIFFERENT ESTIMATION METHODS 71 2.7.2 INFERENCE FOR (3 72
ESTIMATE AND STANDARD ERROR: BALANCED CASE 72 ESTIMATE: UNBALANCED CASE
74 2.8 CROSSED VERSUS NESTED EFFECTS 75 2.9 ASSIGNING VALUES TO THE
RANDOM INTERCEPTS 77 2.9.1 MAXIMUM LIKELIHOOD ESTIMATION 78
IMPLEMENTATION VIA OLS REGRESSION 78 IMPLEMENTATION VIA THE MEAN TOTAL
RESIDUAL 79 2.9.2 EMPIRICAL BAYES PREDICTION 80 2.9.3 *** EMPIRICAL
BAYES VARIANCES 83 2.10 SUMMARY AND FURTHER READING 85 2.11 EXERCISES 86
CONTENTS IX 3 RANDOM-INTERCEPT MODELS WITH COVARIATES 91 3.1
INTRODUCTION 91 3.2 DOES SMOKING DURING PREGNANCY AFFECT BIRTHWEIGHT? 91
3.3 THE LINEAR RANDOM-INTERCEPT MODEL WITH COVARIATES 94 3.3.1 MODEL
SPECIFICATION 94 3.3.2 RESIDUAL VARIANCE AND INTRACLASS CORRELATION 96
3.4 ESTIMATION USING STATA 97 3.4.1 USING XTREG 97 3.4.2 USING XTMIXED
99 3.4.3 USING GLLAMM 100 3.5 COEFFICIENTS OF DETERMINATION OR VARIANCE
EXPLAINED 102 3.6 HYPOTHESIS TESTS AND CONFIDENCE INTERVALS 104 3.6.1
HYPOTHESIS TESTS FOR REGRESSION COEFFICIENTS 104 HYPOTHESIS TESTS FOR
INDIVIDUAL REGRESSION COEFFICIENTS .... 105 JOINT HYPOTHESIS TESTS FOR
SEVERAL REGRESSION COEFFICIENTS . . . 105 3.6.2 PREDICTED MEANS AND
CONFIDENCE INTERVALS 107 3.6.3 HYPOTHESIS TEST FOR BETWEEN-CLUSTER
VARIANCE 108 3.7 BETWEEN AND WITHIN EFFECTS 109 3.7.1 BETWEEN-MOTHER
EFFECTS 109 3.7.2 WITHIN-MOTHER EFFECTS ILL 3.7.3 RELATIONS AMONG
ESTIMATORS 113 3.7.4 ENDOGENEITY AND DIFFERENT WITHIN- AND
BETWEEN-MOTHER EFFECTS 114 3.7.5 HAUSMAN ENDOGENEITY TEST 122 3.8 FIXED
VERSUS RANDOM EFFECTS REVISITED 124 3.9 RESIDUAL DIAGNOSTICS 125 3.10
MORE ON STATISTICAL INFERENCE FOR REGRESSION COEFFICIENTS 129 3.10.1
CONSEQUENCES OF USING ORDINARY REGRESSION FOR CLUSTERED DATA 129 3.10.2
*** POWER AND SAMPLE-SIZE DETERMINATION 130 3.11 SUMMARY AND FURTHER
READING . ; 132 3.12 EXERCISES 132 CONTENTS RANDOM-COEFFICIENT MODELS
141 4.1 INTRODUCTION 141 4.2 HOW EFFECTIVE ARE DIFFERENT SCHOOLS? 141
4.3 SEPARATE LINEAR REGRESSIONS FOR EACH SCHOOL 142 4.4 SPECIFICATION
AND INTERPRETATION OF A RANDOM-COEFFICIENT MODEL .... 146 4.4.1
SPECIFICATION OF RANDOM-COEFFICIENT MODEL 146 4.4.2 INTERPRETATION OF
THE RANDOM-EFFECTS VARIANCES AND COVARIANCES 150 4.5 ESTIMATION USING
STATA 153 4.5.1 USING XTMIXED 153 RANDOM-INTERCEPT MODEL 153
RANDOM-COEFFICIENT MODEL 155 4.5.2 USING GLLAMM 157 RANDOM-INTERCEPT
MODEL 157 RANDOM-COEFFICIENT MODEL 157 4.6 TESTING THE SLOPE VARIANCE
159 4.7 INTERPRETATION OF ESTIMATES 159 4.8 ASSIGNING VALUES TO THE
RANDOM INTERCEPTS AND SLOPES 161 4.8.1 MAXIMUM LIKELIHOOD ESTIMATION 161
4.8.2 EMPIRICAL BAYES PREDICTION 162 4.8.3 MODEL VISUALIZATION 164 4.8.4
RESIDUAL DIAGNOSTICS 165 4.8.5 INFERENCES FOR INDIVIDUAL SCHOOLS 167 4.9
TWO-STAGE MODEL FORMULATION 168 4.10 SOME WARNINGS ABOUT
RANDOM-COEFFICIENT MODELS 171 4.11 SUMMARY AND FURTHER READING 173 4.12
EXERCISES 173 LONGITUDINAL, PANEL, AND GROWTH-CURVE MODELS 179 5.1
INTRODUCTION 179 5.2 HOW AND WHY DO WAGES CHANGE OVER TIME? 180 5.3 DATA
STRUCTURE 181 CONTENTS XI 5.3.1 MISSING DATA 181 5.3.2 TIME-VARYING AND
TIME-CONSTANT VARIABLES 181 5.4 TIME SCALES IN LONGITUDINAL DATA 182 5.5
RANDOM- AND FIXED-EFFECTS APPROACHES 185 5.5.1 CORRELATED RESIDUALS 185
5.5.2 FIXED-INTERCEPT MODEL 186 USING XTREG 186 USING ANOVA 189 5.5.3
RANDOM-INTERCEPT MODEL 192 5.5.4 RANDOM-COEFFICIENT MODEL 194 5.5.5
MARGINAL MEAN AND COVARIANCE STRUCTURE INDUCED BY RAN- DOM EFFECTS 196
MARGINAL MEAN AND COVARIANCE STRUCTURE FOR RANDOM-INTERCEPT MODELS 196
MARGINAL MEAN AND COVARIANCE STRUCTURE FOR RANDOM-COEFFICIENT MODELS 198
5.6 MARGINAL MODELING 199 5.6.1 COVARIANCE STRUCTURES 199 COMPOUND
SYMMETRIC OR EXCHANGEABLE STRUCTURE 200 RANDOM-COEFFICIENT STRUCTURE 201
AUTOREGRESSIVE RESIDUAL STRUCTURE 201 UNSTRUCTURED COVARIANCE MATRIX 201
5.6.2 MARGINAL MODELING USING STATA 202 5.7 AUTOREGRESSIVE- OR
LAGGED-RESPONSE MODELS 204 5.8 HYBRID APPROACHES 206 5.8.1
AUTOREGRESSIVE RESPONSE AND RANDOM EFFECTS 206 5.8.2 *** AUTOREGRESSIVE
RESPONSES AND AUTOREGRESSIVE RESIDUALS . . 206 5.8.3 AUTOREGRESSIVE
RESIDUALS AND RANDOM OR FIXED EFFECTS .... 207 5.9 MISSING DATA 207
5.9.1 *** MAXIMUM LIKELIHOOD ESTIMATION UNDER MAR: A SIMULATION 207 5.10
HOW DO CHILDREN GROW? 210 XII CONTENTS 5.10.1 OBSERVED GROWTH
TRAJECTORIES 210 5.11 GROWTH-CURVE MODELING 211 5.11.1 RANDOM-INTERCEPT
MODEL 211 5.11.2 RANDOM-COEFFICIENT MODEL 213 5.11.3 TWO-STAGE MODEL
FORMULATION 216 5.12 PREDICTION OF TRAJECTORIES FOR INDIVIDUAL CHILDREN
. . . .* 217 5.13 PREDICTION OF MEAN GROWTH TRAJECTORY AND 95% BAND 219
5.14 *** COMPLEX LEVEL-1 VARIATION OR HETEROSKEDASTICITY 220 5.15
SUMMARY AND FURTHER READING 222 5.16 EXERCISES 223 1 III TWO-LEVEL
GENERALIZED LINEAR MODELS 229 6 DICHOTOMOUS OR BINARY RESPONSES 231 6.1
INTRODUCTION 231 6.2 SINGLE-LEVEL MODELS FOR DICHOTOMOUS RESPONSES 231
6.2.1 GENERALIZED LINEAR MODEL FORMULATION 232 6.2.2 LATENT-RESPONSE
FORMULATION 238 LOGISTIC REGRESSION 238 PROBIT REGRESSION 239 6.3 WHICH
TREATMENT IS BEST FOR TOENAIL INFECTION? ., 242 6.4 LONGITUDINAL DATA
STRUCTURE 242 6.5 POPULATION-AVERAGED OR MARGINAL PROBABILITIES 243 6.6
RANDOM-INTERCEPT LOGISTIC REGRESSION 247 6.7 ESTIMATION OF LOGISTIC
RANDOM-INTERCEPT MODELS 248 6.7.1 USING XTLOGIT 248 6.7.2 USING
XTMELOGIT 251 6.7.3 USING GLLAMM 251 6.8 INFERENCE FOR LOGISTIC
RANDOM-INTERCEPT MODELS 252 6.9 SUBJECT-SPECIFIC VS. POPULATION-AVERAGED
RELATIONSHIPS 254 6.10 MEASURES OF DEPENDENCE AND HETEROGENEITY 256
CONTENTS XIII 6.10.1 CONDITIONAL OR RESIDUAL INTRACLASS CORRELATION OF
THE LATENT RESPONSES 256 6.10.2 MEDIAN ODDS RATIO 257 6.11 MAXIMUM
LIKELIHOOD ESTIMATION 258 6.11.1 *** ADAPTIVE QUADRATURE 258 6.11.2 SOME
SPEED CONSIDERATIONS 261 ADVICE FOR SPEEDING UP GLLAMM 263 6.12
ASSIGNING VALUES TO RANDOM EFFECTS 264 6.12.1 MAXIMUM LIKELIHOOD
ESTIMATION 264 6.12.2 EMPIRICAL BAYES PREDICTION 265 6.12.3 EMPIRICAL
BAYES MODAL PREDICTION 266 6.13 DIFFERENT KINDS OF PREDICTED
PROBABILITIES 267 6.13.1 PREDICTED POPULATION-AVERAGED PROBABILITIES 267
6.13.2 PREDICTED SUBJECT-SPECIFIC PROBABILITIES 268 PREDICTIONS FOR
HYPOTHETICAL SUBJECTS 268 PREDICTIONS FOR THE SUBJECTS IN THE SAMPLE 269
6.14 OTHER APPROACHES TO CLUSTERED DICHOTOMOUS DATA 271 6.14.1
CONDITIONAL LOGISTIC REGRESSION 271 6.14.2 GENERALIZED ESTIMATING
EQUATIONS (GEE) 273 6.15 SUMMARY AND FURTHER READING 274 6.16 EXERCISES
275 7 ORDINAL RESPONSES 287 7.1 INTRODUCTION 287 7.2 SINGLE-LEVEL
CUMULATIVE MODELS FOR ORDINAL RESPONSES 287 7.2.1 GENERALIZED LINEAR
MODEL FORMULATION 287 7.2.2 LATENT-RESPONSE FORMULATION 288 7.2.3
PROPORTIONAL ODDS 292 7.2.4 * IDENTIFICATION 292 7.3 ARE ANTIPSYCHOTIC
DRUGS EFFECTIVE FOR PATIENTS WITH SCHIZOPHRENIA? . . 295 7.4 *
LONGITUDINAL DATA STRUCTURE AND GRAPHS 295 XIV CONTENTS 7A.I
LONGITUDINAL DATA STRUCTURE 296 7.4.2 PLOTTING CUMULATIVE PROPORTIONS
296 7.4.3 PLOTTING ESTIMATED CUMULATIVE LOGITS AND TRANSFORMING THE TIME
SCALE 298 7.5 A SINGLE-LEVEL PROPORTIONAL ODDS MODEL 299 7.5.1 MODEL
SPECIFICATION , 299 7.5.2 ESTIMATION USING STATA 300 7.6 A
RANDOM-INTERCEPT PROPORTIONAL ODDS MODEL 303 7.6.1 MODEL SPECIFICATION
303 7.6.2 ESTIMATION USING STATA 303 7.7 A RANDOM-COEFFICIENT
PROPORTIONAL ODDS MODEL 305 7.7.1 MODEL SPECIFICATION 305 7.7.2
ESTIMATION USING GLLAMM 305 7.8 DIFFERENT KINDS OF PREDICTED
PROBABILITIES 307 7.8.1 PREDICTED POPULATION-AVERAGED PROBABILITIES 307
7.8.2 PREDICTED PATIENT-SPECIFIC PROBABILITIES 309 7.9 DO EXPERTS DIFFER
IN THEIR GRADING OF STUDENT ESSAYS? 312 7.10 A RANDOM-INTERCEPT PROBIT
MODEL WITH GRADER BIAS 312 7.10.1 MODEL SPECIFICATION 312 7.10.2
ESTIMATION . 313 7.11 INCLUDING GRADER-SPECIFIC MEASUREMENT ERROR
VARIANCES 315 7.11.1 MODEL SPECIFICATION 315 7.11.2 ESTIMATION 315 7.12
*** INCLUDING GRADER-SPECIFIC THRESHOLDS 317 7.12.1 MODEL SPECIFICATION
317 7.12.2 ESTIMATION 318 7.13 SUMMARY AND FURTHER READING 323 7.14
EXERCISES 324 8 DISCRETE-TIME SURVIVAL 331 8.1 INTRODUCTION 331 CONTENTS
XV 8.1.1 CENSORING AND TRUNCATION 331 8.1.2 TIME-VARYING COVARIATES AND
DIFFERENT TIME-SCALES 333 8.1.3 DISCRETE- VERSUS CONTINUOUS-TIME
SURVIVAL DATA 334 8.2 SINGLE-LEVEL MODELS FOR DISCRETE-TIME SURVIVAL
DATA 334 8.2.1 DISCRETE-TIME HAZARD AND DISCRETE-TIME SURVIVAL 334 8.2.2
DATA EXPANSION FOR DISCRETE-TIME SURVIVAL ANALYSIS 336 8.2.3 ESTIMATION
VIA REGRESSION MODELS FOR DICHOTOMOUS RESPONSES 338 8.2.4 INCLUDING
COVARIATES 342 TIME-CONSTANT COVARIATES 342 TIME-VARYING COVARIATES 346
8.2.5 HANDLING LEFT-TRUNCATED DATA 350 8.3 HOW DOES BIRTH HISTORY AFFECT
CHILD MORTALITY? 352 8.4 DATA EXPANSION 352 8.5 *** PROPORTIONAL HAZARDS
AND INTERVAL CENSORING 354 8.6 COMPLEMENTARY LOG-LOG MODELS 356 8.7 A
RANDOM-INTERCEPT COMPLEMENTARY LOG-LOG MODEL 360 8.7.1 MODEL
SPECIFICATION 360 8.7.2 ESTIMATION USING STATA 361 8.8 *** MARGINAL AND
CONDITIONAL SURVIVAL PROBABILITIES 362 8.9 SUMMARY AND FURTHER READING
366 8.10 EXERCISES 367 9 COUNTS 373 9.1 INTRODUCTION 373 9.2 WHAT ARE
COUNTS? 373 9.2.1 COUNTS VERSUS PROPORTIONS 373 9.2.2 COUNTS AS
AGGREGATED EVENT-HISTORY DATA 374 9.3 SINGLE-LEVEL POISSON MODELS FOR
COUNTS 374 9.4 DID THE GERMAN HEALTH-CARE REFORM REDUCE THE NUMBER OF
DOCTOR VISITS? 376 9.5 LONGITUDINAL DATA STRUCTURE 377 XVI CONTENTS
9.6 SINGLE-LEVEL POISSON REGRESSION 378 9.6.1 MODEL SPECIFICATION 378
9.6.2 ESTIMATION USING STATA ; 378 9.7 RANDOM-INTERCEPT POISSON
REGRESSION 380 9.7.1 MODEL SPECIFICATION 380 9.7.2 ESTIMATION USING
STATA * 381 USING XTPOISSON 382 USING XTMEPOISSON 383 USING GLLAMM 384
9.8 RANDOM-COEFFICIENT POISSON REGRESSION 385 9.8.1 MODEL SPECIFICATION
385 9.8.2 ESTIMATION USING STATA 386 USING XTMEPOISSON 386 USING GLLAMM
387 9.8.3 INTERPRETATION OF ESTIMATES . 388 9.9 OVERDISPERSION IN
SINGLE-LEVEL MODELS 389 9.9.1 NORMALLY DISTRIBUTED RANDOM INTERCEPT 389
9.9.2 NEGATIVE BINOMIAL MODELS 390 MEAN DISPERSION OR NB2 391 CONSTANT
DISPERSION OR NB1 392 9.9.3 QUASILIKELIHOOD OR ROBUST STANDARD ERRORS
392 9.10 LEVEL-1 OVERDISPERSION IN TWO-LEVEL MODELS 394 9.11 OTHER
APPROACHES TO TWO-LEVEL COUNT DATA 395 9.11.1 CONDITIONAL POISSON
REGRESSION 395 9.11.2 CONDITIONAL NEGATIVE BINOMIAL REGRESSION 396
9.11.3 GENERALIZED ESTIMATING EQUATIONS 396 9.11.4 MARGINAL AND
CONDITIONAL ESTIMATES WHEN RESPONSES ARE MAR 397 * SIMULATION 397 9.12
HOW DOES BIRTH HISTORY AFFECT CHILD MORTALITY? 401 9.12.1 SIMPLE
PIECEWISE EXPONENTIAL SURVIVAL MODEL 401 CONTENTS XVII 9.12.2 PIECEWISE
EXPONENTIAL SURVIVAL MODEL WITH COVARIATES AND FRAILTY 404 9.13 WHICH
SCOTTISH COUNTIES HAVE A HIGH RISK OF LIP CANCER? 407 9.14 STANDARDIZED
MORTALITY RATIOS 407 9.15 RANDOM-INTERCEPT POISSON REGRESSION 410 9.15.1
MODEL SPECIFICATION 410 9.15.2 ESTIMATION USING GLLAMM 410 9.15.3
PREDICTION OF STANDARDIZED MORTALITY RATIOS 411 9.16 V NONPARAMETRIC
MAXIMUM LIKELIHOOD ESTIMATION 414 9.16.1 SPECIFICATION 414 9.16.2
ESTIMATION USING GLLAMM 414 9.16.3 PREDICTION 419 9.17 SUMMARY AND
FURTHER READING 419 9.18 EXERCISES 420 IV MODELS WITH NESTED AND CROSSED
RANDOM EFFECTS 429 10 HIGHER-LEVEL MODELS WITH NESTED RANDOM EFFECTS 431
10.1 INTRODUCTION 431 10.2 DO PEAK-EXPIRATORY-FLOW MEASUREMENTS VARY
BETWEEN METHODS? . . . 432 10.3 TWO-LEVEL VARIANCE-COMPONENTS MODELS 433
10.3.1 MODEL SPECIFICATION 433 10.3.2 ESTIMATION USING XTMIXED 433 10.4
THREE-LEVEL VARIANCE-COMPONENTS MODELS 436 10.4.1 MODEL SPECIFICATION ^
436 10.4.2 DIFFERENT TYPES OF INTRACLASS CORRELATION 438 10.4.3
THREE-STAGE FORMULATION 439 10.4.4 ESTIMATION USING XTMIXED 439 10.4.5
EMPIRICAL BAYES PREDICTION USING XTMIXED 442 10.5 DID THE GUATEMALAN
IMMUNIZATION CAMPAIGN WORK? 443 10.6 A THREE-LEVEL LOGISTIC
RANDOM-INTERCEPT MODEL 444 10.6.1 MODEL SPECIFICATION 444 XVIII
CONTENTS 10.6.2 DIFFERENT TYPES OF INTRACLASS CORRELATIONS FOR THE
LATENT RE- SPONSES . 445 10.6.3 DIFFERENT KINDS OF MEDIAN ODDS RATIOS
445 10.6.4 THREE-STAGE FORMULATION 446 10.7 ESTIMATION OF THREE-LEVEL
LOGISTIC RANDOM-INTERCEPT MODELS USING STATA 446 10.7.1 USING GLLAMM 446
10.7.2 USING XTMELOGIT 450 10.8 A THREE-LEVEL LOGISTIC
RANDOM-COEFFICIENT MODEL 453 10.9 ESTIMATION OF THREE-LEVEL LOGISTIC
RANDOM-COEFFICIENT MODELS USING STATA 454 10.9.1 USING GLLAMM 454 10.9.2
USING XTMELOGIT 458 10.10 PREDICTION OF RANDOM EFFECTS 459 10.10.1
EMPIRICAL BAYES PREDICTION 459 10.10.2 EMPIRICAL BAYES MODAL PREDICTION
460 10.11 DIFFERENT KINDS OF PREDICTED PROBABILITIES 461 10.11.1
PREDICTED MARGINAL PROBABILITIES 461 10.11.2 PREDICTED MEDIAN OR
CONDITIONAL PROBABILITIES 461 10.11.3 PREDICTED POSTERIOR MEAN
PROBABILITIES 462 10.12 SUMMARY AND FURTHER READING 463 10.13 EXERCISES
463 11 CROSSED RANDOM EFFECTS 473 11.1 INTRODUCTION 473 11.2 HOW DOES
INVESTMENT DEPEND ON EXPECTED PROFIT AND CAPITAL STOCK? . 474 11.3 A
TWO-WAY ERROR-COMPONENTS MODEL 475 11.3.1 MODEL SPECIFICATION 475 11.3.2
RESIDUAL INTRACLASS CORRELATIONS 476 11.3.3 ESTIMATION 476 11.3.4
PREDICTION . 478 11.4 HOW MUCH DO PRIMARY AND SECONDARY SCHOOLS AFFECT
ATTAINMENT AT AGE 16? 481 CONTENTS XIX 11.5 AN ADDITIVE CROSSED
RANDOM-EFFECTS MODEL 483 11.5.1 SPECIFICATION 483 11.5.2 ESTIMATION
USING XTMIXED 484 11.6 INCLUDING A RANDOM INTERACTION 485 11.6.1 MODEL
SPECIFICATION 485 11.6.2 INTRACLASS CORRELATIONS 485 11.6.3 ESTIMATION
USING XTMIXED 486 11.6.4 SOME DIAGNOSTICS 488 11.7 *** A TRICK REQUIRING
FEWER RANDOM EFFECTS 489 11.8 DO SALAMANDERS FROM DIFFERENT POPULATIONS
MATE SUCCESSFULLY? .... 493 11.9 CROSSED RANDOM-EFFECTS LOGISTIC
REGRESSION 495 11.10 SUMMARY AND FURTHER READING 500 11.11 EXERCISES 501
A SYNTAX FOR GLLAMM, EQ, AND GLLAPRED: THE BARE ESSENTIALS 509 B SYNTAX
FOR GLLAMM 515 C SYNTAX FOR GLLAPRED 527 D SYNTAX FOR GLLASIM 531
REFERENCES 535 AUTHOR INDEX 549 SUBJECT INDEX 555
|
| adam_txt |
CONTENTS LIST OF TABLES XXI LIST OF FIGURES XXV PREFACE XXXI I
PRELIMINARIES 1 1 REVIEW OF LINEAR REGRESSION 3 1.1 INTRODUCTION 3 1.2
IS THERE GENDER DISCRIMINATION IN FACULTY SALARIES? 3 1.3
INDEPENDENT-SAMPLES T TEST 4 1.4 ONE-WAY ANALYSIS OF VARIANCE 8 1.5
SIMPLE LINEAR REGRESSION 11 1.6 DUMMY VARIABLES 18 1.7 MULTIPLE LINEAR
REGRESSION 20 1.8 INTERACTIONS 26 1.9 DUMMIES FOR MORE THAN TWO GROUPS
29 1.10 OTHER TYPES OF INTERACTIONS 34 1.10.1 INTERACTION BETWEEN DUMMY
VARIABLES 34 1.10.2 INTERACTION BETWEEN CONTINUOUS COVARIATES 35 1.11
NONLINEAR EFFECTS 36 1.12 RESIDUAL DIAGNOSTICS 38 1.13 SUMMARY AND
FURTHER READING , 39 1.14 EXERCISES 40 II TWO-LEVEL LINEAR MODELS 49 2
VARIANCE-COMPONENTS MODELS 51 VIII CONTENTS 2.1 INTRODUCTION 51 2.2 HOW
RELIABLE ARE PEAK-EXPIRATORY-FLOW MEASUREMENTS? 52 2.3 THE
VARIANCE-COMPONENTS MODEL 54 2.3.1 MODEL SPECIFICATION AND PATH DIAGRAM
54 2.3.2 ERROR COMPONENTS, VARIANCE COMPONENTS, AND RELIABILITY . 58
2.3.3 INTRACLASS CORRELATION 58 2.4 FIXED VERSUS RANDOM EFFECTS 61 2.5
ESTIMATION USING STATA 62 2.5.1 DATA PREPARATION 62 2.5.2 USING XTREG 63
2.5.3 USING XTMIXEDT 65 2.5.4 USING GLLAMM 66 2.6 HYPOTHESIS TESTS AND
CONFIDENCE INTERVALS 68 2.6.1 HYPOTHESIS TEST AND CONFIDENCE INTERVAL
FOR THE POPULATION MEAN 68 2.6.2 HYPOTHESIS TEST AND CONFIDENCE INTERVAL
FOR THE BETWEEN- CLUSTER VARIANCE 69 2.7 MORE ON STATISTICAL INFERENCE
71 2.7.1 *** DIFFERENT ESTIMATION METHODS 71 2.7.2 INFERENCE FOR (3 72
ESTIMATE AND STANDARD ERROR: BALANCED CASE 72 ESTIMATE: UNBALANCED CASE
74 2.8 CROSSED VERSUS NESTED EFFECTS 75 2.9 ASSIGNING VALUES TO THE
RANDOM INTERCEPTS 77 2.9.1 MAXIMUM LIKELIHOOD ESTIMATION 78
IMPLEMENTATION VIA OLS REGRESSION 78 IMPLEMENTATION VIA THE MEAN TOTAL
RESIDUAL 79 2.9.2 EMPIRICAL BAYES PREDICTION 80 2.9.3 *** EMPIRICAL
BAYES VARIANCES 83 2.10 SUMMARY AND FURTHER READING 85 2.11 EXERCISES 86
CONTENTS IX 3 RANDOM-INTERCEPT MODELS WITH COVARIATES 91 3.1
INTRODUCTION 91 3.2 DOES SMOKING DURING PREGNANCY AFFECT BIRTHWEIGHT? 91
3.3 THE LINEAR RANDOM-INTERCEPT MODEL WITH COVARIATES 94 3.3.1 MODEL
SPECIFICATION 94 3.3.2 RESIDUAL VARIANCE AND INTRACLASS CORRELATION 96
3.4 ESTIMATION USING STATA 97 3.4.1 USING XTREG 97 3.4.2 USING XTMIXED
99 3.4.3 USING GLLAMM 100 3.5 COEFFICIENTS OF DETERMINATION OR VARIANCE
EXPLAINED 102 3.6 HYPOTHESIS TESTS AND CONFIDENCE INTERVALS 104 3.6.1
HYPOTHESIS TESTS FOR REGRESSION COEFFICIENTS 104 HYPOTHESIS TESTS FOR
INDIVIDUAL REGRESSION COEFFICIENTS . 105 JOINT HYPOTHESIS TESTS FOR
SEVERAL REGRESSION COEFFICIENTS . . . 105 3.6.2 PREDICTED MEANS AND
CONFIDENCE INTERVALS 107 3.6.3 HYPOTHESIS TEST FOR BETWEEN-CLUSTER
VARIANCE 108 3.7 BETWEEN AND WITHIN EFFECTS 109 3.7.1 BETWEEN-MOTHER
EFFECTS 109 3.7.2 WITHIN-MOTHER EFFECTS ILL 3.7.3 RELATIONS AMONG
ESTIMATORS 113 3.7.4 ENDOGENEITY AND DIFFERENT WITHIN- AND
BETWEEN-MOTHER EFFECTS 114 3.7.5 HAUSMAN ENDOGENEITY TEST 122 3.8 FIXED
VERSUS RANDOM EFFECTS REVISITED 124 3.9 RESIDUAL DIAGNOSTICS 125 3.10
MORE ON STATISTICAL INFERENCE FOR REGRESSION COEFFICIENTS 129 3.10.1
CONSEQUENCES OF USING ORDINARY REGRESSION FOR CLUSTERED DATA 129 3.10.2
*** POWER AND SAMPLE-SIZE DETERMINATION 130 3.11 SUMMARY AND FURTHER
READING . ; 132 3.12 EXERCISES 132 CONTENTS RANDOM-COEFFICIENT MODELS
141 4.1 INTRODUCTION 141 4.2 HOW EFFECTIVE ARE DIFFERENT SCHOOLS? 141
4.3 SEPARATE LINEAR REGRESSIONS FOR EACH SCHOOL 142 4.4 SPECIFICATION
AND INTERPRETATION OF A RANDOM-COEFFICIENT MODEL . 146 4.4.1
SPECIFICATION OF RANDOM-COEFFICIENT MODEL 146 4.4.2 INTERPRETATION OF
THE RANDOM-EFFECTS VARIANCES AND COVARIANCES 150 4.5 ESTIMATION USING
STATA 153 4.5.1 USING XTMIXED 153 RANDOM-INTERCEPT MODEL 153
RANDOM-COEFFICIENT MODEL 155 4.5.2 USING GLLAMM 157 RANDOM-INTERCEPT
MODEL 157 RANDOM-COEFFICIENT MODEL 157 4.6 TESTING THE SLOPE VARIANCE
159 4.7 INTERPRETATION OF ESTIMATES 159 4.8 ASSIGNING VALUES TO THE
RANDOM INTERCEPTS AND SLOPES 161 4.8.1 MAXIMUM LIKELIHOOD ESTIMATION 161
4.8.2 EMPIRICAL BAYES PREDICTION 162 4.8.3 MODEL VISUALIZATION 164 4.8.4
RESIDUAL DIAGNOSTICS 165 4.8.5 INFERENCES FOR INDIVIDUAL SCHOOLS 167 4.9
TWO-STAGE MODEL FORMULATION 168 4.10 SOME WARNINGS ABOUT
RANDOM-COEFFICIENT MODELS 171 4.11 SUMMARY AND FURTHER READING 173 4.12
EXERCISES 173 LONGITUDINAL, PANEL, AND GROWTH-CURVE MODELS 179 5.1
INTRODUCTION 179 5.2 HOW AND WHY DO WAGES CHANGE OVER TIME? 180 5.3 DATA
STRUCTURE 181 CONTENTS XI 5.3.1 MISSING DATA 181 5.3.2 TIME-VARYING AND
TIME-CONSTANT VARIABLES 181 5.4 TIME SCALES IN LONGITUDINAL DATA 182 5.5
RANDOM- AND FIXED-EFFECTS APPROACHES 185 5.5.1 CORRELATED RESIDUALS 185
5.5.2 FIXED-INTERCEPT MODEL 186 USING XTREG 186 USING ANOVA 189 5.5.3
RANDOM-INTERCEPT MODEL 192 5.5.4 RANDOM-COEFFICIENT MODEL 194 5.5.5
MARGINAL MEAN AND COVARIANCE STRUCTURE INDUCED BY RAN- DOM EFFECTS 196
MARGINAL MEAN AND COVARIANCE STRUCTURE FOR RANDOM-INTERCEPT MODELS 196
MARGINAL MEAN AND COVARIANCE STRUCTURE FOR RANDOM-COEFFICIENT MODELS 198
5.6 MARGINAL MODELING 199 5.6.1 COVARIANCE STRUCTURES 199 COMPOUND
SYMMETRIC OR EXCHANGEABLE STRUCTURE 200 RANDOM-COEFFICIENT STRUCTURE 201
AUTOREGRESSIVE RESIDUAL STRUCTURE 201 UNSTRUCTURED COVARIANCE MATRIX 201
5.6.2 MARGINAL MODELING USING STATA 202 5.7 AUTOREGRESSIVE- OR
LAGGED-RESPONSE MODELS 204 5.8 HYBRID APPROACHES 206 5.8.1
AUTOREGRESSIVE RESPONSE AND RANDOM EFFECTS 206 5.8.2 *** AUTOREGRESSIVE
RESPONSES AND AUTOREGRESSIVE RESIDUALS . . 206 5.8.3 AUTOREGRESSIVE
RESIDUALS AND RANDOM OR FIXED EFFECTS . 207 5.9 MISSING DATA 207
5.9.1 *** MAXIMUM LIKELIHOOD ESTIMATION UNDER MAR: A SIMULATION 207 5.10
HOW DO CHILDREN GROW? 210 XII CONTENTS 5.10.1 OBSERVED GROWTH
TRAJECTORIES 210 5.11 GROWTH-CURVE MODELING 211 5.11.1 RANDOM-INTERCEPT
MODEL 211 5.11.2 RANDOM-COEFFICIENT MODEL 213 5.11.3 TWO-STAGE MODEL
FORMULATION 216 5.12 PREDICTION OF TRAJECTORIES FOR INDIVIDUAL CHILDREN
. . . .* 217 5.13 PREDICTION OF MEAN GROWTH TRAJECTORY AND 95% BAND 219
5.14 *** COMPLEX LEVEL-1 VARIATION OR HETEROSKEDASTICITY 220 5.15
SUMMARY AND FURTHER READING 222 5.16 EXERCISES 223 1 III TWO-LEVEL
GENERALIZED LINEAR MODELS 229 6 DICHOTOMOUS OR BINARY RESPONSES 231 6.1
INTRODUCTION 231 6.2 SINGLE-LEVEL MODELS FOR DICHOTOMOUS RESPONSES 231
6.2.1 GENERALIZED LINEAR MODEL FORMULATION 232 6.2.2 LATENT-RESPONSE
FORMULATION 238 LOGISTIC REGRESSION 238 PROBIT REGRESSION 239 6.3 WHICH
TREATMENT IS BEST FOR TOENAIL INFECTION? ., 242 6.4 LONGITUDINAL DATA
STRUCTURE 242 6.5 POPULATION-AVERAGED OR MARGINAL PROBABILITIES 243 6.6
RANDOM-INTERCEPT LOGISTIC REGRESSION 247 6.7 ESTIMATION OF LOGISTIC
RANDOM-INTERCEPT MODELS 248 6.7.1 USING XTLOGIT 248 6.7.2 USING
XTMELOGIT 251 6.7.3 USING GLLAMM 251 6.8 INFERENCE FOR LOGISTIC
RANDOM-INTERCEPT MODELS 252 6.9 SUBJECT-SPECIFIC VS. POPULATION-AVERAGED
RELATIONSHIPS 254 6.10 MEASURES OF DEPENDENCE AND HETEROGENEITY 256
CONTENTS XIII 6.10.1 CONDITIONAL OR RESIDUAL INTRACLASS CORRELATION OF
THE LATENT RESPONSES 256 6.10.2 MEDIAN ODDS RATIO 257 6.11 MAXIMUM
LIKELIHOOD ESTIMATION 258 6.11.1 *** ADAPTIVE QUADRATURE 258 6.11.2 SOME
SPEED CONSIDERATIONS 261 ADVICE FOR SPEEDING UP GLLAMM 263 6.12
ASSIGNING VALUES TO RANDOM EFFECTS 264 6.12.1 MAXIMUM LIKELIHOOD
ESTIMATION 264 6.12.2 EMPIRICAL BAYES PREDICTION 265 6.12.3 EMPIRICAL
BAYES MODAL PREDICTION 266 6.13 DIFFERENT KINDS OF PREDICTED
PROBABILITIES 267 6.13.1 PREDICTED POPULATION-AVERAGED PROBABILITIES 267
6.13.2 PREDICTED SUBJECT-SPECIFIC PROBABILITIES 268 PREDICTIONS FOR
HYPOTHETICAL SUBJECTS 268 PREDICTIONS FOR THE SUBJECTS IN THE SAMPLE 269
6.14 OTHER APPROACHES TO CLUSTERED DICHOTOMOUS DATA 271 6.14.1
CONDITIONAL LOGISTIC REGRESSION 271 6.14.2 GENERALIZED ESTIMATING
EQUATIONS (GEE) 273 6.15 SUMMARY AND FURTHER READING 274 6.16 EXERCISES
275 7 ORDINAL RESPONSES 287 7.1 INTRODUCTION 287 7.2 SINGLE-LEVEL
CUMULATIVE MODELS FOR ORDINAL RESPONSES 287 7.2.1 GENERALIZED LINEAR
MODEL FORMULATION 287 7.2.2 LATENT-RESPONSE FORMULATION 288 7.2.3
PROPORTIONAL ODDS 292 7.2.4 * IDENTIFICATION 292 7.3 ARE ANTIPSYCHOTIC
DRUGS EFFECTIVE FOR PATIENTS WITH SCHIZOPHRENIA? . . 295 7.4 *
LONGITUDINAL DATA STRUCTURE AND GRAPHS 295 XIV CONTENTS 7A.I
LONGITUDINAL DATA STRUCTURE 296 7.4.2 PLOTTING CUMULATIVE PROPORTIONS
296 7.4.3 PLOTTING ESTIMATED CUMULATIVE LOGITS AND TRANSFORMING THE TIME
SCALE 298 7.5 A SINGLE-LEVEL PROPORTIONAL ODDS MODEL 299 7.5.1 MODEL
SPECIFICATION , 299 7.5.2 ESTIMATION USING STATA 300 7.6 A
RANDOM-INTERCEPT PROPORTIONAL ODDS MODEL 303 7.6.1 MODEL SPECIFICATION
303 7.6.2 ESTIMATION USING STATA 303 7.7 A RANDOM-COEFFICIENT
PROPORTIONAL ODDS MODEL 305 7.7.1 MODEL SPECIFICATION 305 7.7.2
ESTIMATION USING GLLAMM 305 7.8 DIFFERENT KINDS OF PREDICTED
PROBABILITIES 307 7.8.1 PREDICTED POPULATION-AVERAGED PROBABILITIES 307
7.8.2 PREDICTED PATIENT-SPECIFIC PROBABILITIES 309 7.9 DO EXPERTS DIFFER
IN THEIR GRADING OF STUDENT ESSAYS? 312 7.10 A RANDOM-INTERCEPT PROBIT
MODEL WITH GRADER BIAS 312 7.10.1 MODEL SPECIFICATION 312 7.10.2
ESTIMATION . 313 7.11 INCLUDING GRADER-SPECIFIC MEASUREMENT ERROR
VARIANCES 315 7.11.1 MODEL SPECIFICATION 315 7.11.2 ESTIMATION 315 7.12
*** INCLUDING GRADER-SPECIFIC THRESHOLDS 317 7.12.1 MODEL SPECIFICATION
317 7.12.2 ESTIMATION 318 7.13 SUMMARY AND FURTHER READING 323 7.14
EXERCISES 324 8 DISCRETE-TIME SURVIVAL 331 8.1 INTRODUCTION 331 CONTENTS
XV 8.1.1 CENSORING AND TRUNCATION 331 8.1.2 TIME-VARYING COVARIATES AND
DIFFERENT TIME-SCALES 333 8.1.3 DISCRETE- VERSUS CONTINUOUS-TIME
SURVIVAL DATA 334 8.2 SINGLE-LEVEL MODELS FOR DISCRETE-TIME SURVIVAL
DATA 334 8.2.1 DISCRETE-TIME HAZARD AND DISCRETE-TIME SURVIVAL 334 8.2.2
DATA EXPANSION FOR DISCRETE-TIME SURVIVAL ANALYSIS 336 8.2.3 ESTIMATION
VIA REGRESSION MODELS FOR DICHOTOMOUS RESPONSES 338 8.2.4 INCLUDING
COVARIATES 342 TIME-CONSTANT COVARIATES 342 TIME-VARYING COVARIATES 346
8.2.5 HANDLING LEFT-TRUNCATED DATA 350 8.3 HOW DOES BIRTH HISTORY AFFECT
CHILD MORTALITY? 352 8.4 DATA EXPANSION 352 8.5 *** PROPORTIONAL HAZARDS
AND INTERVAL CENSORING 354 8.6 COMPLEMENTARY LOG-LOG MODELS 356 8.7 A
RANDOM-INTERCEPT COMPLEMENTARY LOG-LOG MODEL 360 8.7.1 MODEL
SPECIFICATION 360 8.7.2 ESTIMATION USING STATA 361 8.8 *** MARGINAL AND
CONDITIONAL SURVIVAL PROBABILITIES 362 8.9 SUMMARY AND FURTHER READING
366 8.10 EXERCISES 367 9 COUNTS 373 9.1 INTRODUCTION 373 9.2 WHAT ARE
COUNTS? 373 9.2.1 COUNTS VERSUS PROPORTIONS 373 9.2.2 COUNTS AS
AGGREGATED EVENT-HISTORY DATA 374 9.3 SINGLE-LEVEL POISSON MODELS FOR
COUNTS 374 9.4 DID THE GERMAN HEALTH-CARE REFORM REDUCE THE NUMBER OF
DOCTOR VISITS? 376 9.5 ' LONGITUDINAL DATA STRUCTURE 377 XVI CONTENTS
9.6 SINGLE-LEVEL POISSON REGRESSION 378 9.6.1 MODEL SPECIFICATION 378
9.6.2 ESTIMATION USING STATA ; 378 9.7 RANDOM-INTERCEPT POISSON
REGRESSION 380 9.7.1 MODEL SPECIFICATION 380 9.7.2 ESTIMATION USING
STATA * 381 USING XTPOISSON 382 USING XTMEPOISSON 383 USING GLLAMM 384
9.8 RANDOM-COEFFICIENT POISSON REGRESSION 385 9.8.1 MODEL SPECIFICATION
385 9.8.2 ESTIMATION USING STATA 386 USING XTMEPOISSON 386 USING GLLAMM
387 9.8.3 INTERPRETATION OF ESTIMATES . 388 9.9 OVERDISPERSION IN
SINGLE-LEVEL MODELS 389 9.9.1 NORMALLY DISTRIBUTED RANDOM INTERCEPT 389
9.9.2 NEGATIVE BINOMIAL MODELS 390 MEAN DISPERSION OR NB2 391 CONSTANT
DISPERSION OR NB1 ' 392 9.9.3 QUASILIKELIHOOD OR ROBUST STANDARD ERRORS
392 9.10 LEVEL-1 OVERDISPERSION IN TWO-LEVEL MODELS 394 9.11 OTHER
APPROACHES TO TWO-LEVEL COUNT DATA 395 9.11.1 CONDITIONAL POISSON
REGRESSION 395 9.11.2 CONDITIONAL NEGATIVE BINOMIAL REGRESSION 396
9.11.3 GENERALIZED ESTIMATING EQUATIONS 396 9.11.4 MARGINAL AND
CONDITIONAL ESTIMATES WHEN RESPONSES ARE MAR 397 * SIMULATION 397 9.12
HOW DOES BIRTH HISTORY AFFECT CHILD MORTALITY? 401 9.12.1 SIMPLE
PIECEWISE EXPONENTIAL SURVIVAL MODEL 401 CONTENTS XVII 9.12.2 PIECEWISE
EXPONENTIAL SURVIVAL MODEL WITH COVARIATES AND FRAILTY 404 9.13 WHICH
SCOTTISH COUNTIES HAVE A HIGH RISK OF LIP CANCER? 407 9.14 STANDARDIZED
MORTALITY RATIOS 407 9.15 RANDOM-INTERCEPT POISSON REGRESSION 410 9.15.1
MODEL SPECIFICATION 410 9.15.2 ESTIMATION USING GLLAMM 410 9.15.3
PREDICTION OF STANDARDIZED MORTALITY RATIOS 411 9.16 V NONPARAMETRIC
MAXIMUM LIKELIHOOD ESTIMATION 414 9.16.1 SPECIFICATION 414 9.16.2
ESTIMATION USING GLLAMM 414 9.16.3 PREDICTION 419 9.17 SUMMARY AND
FURTHER READING 419 9.18 EXERCISES 420 IV MODELS WITH NESTED AND CROSSED
RANDOM EFFECTS 429 10 HIGHER-LEVEL MODELS WITH NESTED RANDOM EFFECTS 431
10.1 INTRODUCTION 431 10.2 DO PEAK-EXPIRATORY-FLOW MEASUREMENTS VARY
BETWEEN METHODS? . . . 432 10.3 TWO-LEVEL VARIANCE-COMPONENTS MODELS 433
10.3.1 MODEL SPECIFICATION 433 10.3.2 ESTIMATION USING XTMIXED 433 10.4
THREE-LEVEL VARIANCE-COMPONENTS MODELS 436 10.4.1 MODEL SPECIFICATION ^
436 10.4.2 DIFFERENT TYPES OF INTRACLASS CORRELATION 438 10.4.3
THREE-STAGE FORMULATION 439 10.4.4 ESTIMATION USING XTMIXED 439 10.4.5
EMPIRICAL BAYES PREDICTION USING XTMIXED 442 10.5 DID THE GUATEMALAN
IMMUNIZATION CAMPAIGN WORK? 443 10.6 A THREE-LEVEL LOGISTIC
RANDOM-INTERCEPT MODEL 444 ' 10.6.1 MODEL SPECIFICATION 444 XVIII
CONTENTS 10.6.2 DIFFERENT TYPES OF INTRACLASS CORRELATIONS FOR THE
LATENT RE- SPONSES . 445 10.6.3 DIFFERENT KINDS OF MEDIAN ODDS RATIOS
445 10.6.4 THREE-STAGE FORMULATION 446 10.7 ESTIMATION OF THREE-LEVEL
LOGISTIC RANDOM-INTERCEPT MODELS USING STATA 446 10.7.1 USING GLLAMM 446
10.7.2 USING XTMELOGIT 450 10.8 A THREE-LEVEL LOGISTIC
RANDOM-COEFFICIENT MODEL 453 10.9 ESTIMATION OF THREE-LEVEL LOGISTIC
RANDOM-COEFFICIENT MODELS USING STATA 454 10.9.1 USING GLLAMM 454 10.9.2
USING XTMELOGIT 458 10.10 PREDICTION OF RANDOM EFFECTS 459 10.10.1
EMPIRICAL BAYES PREDICTION 459 10.10.2 EMPIRICAL BAYES MODAL PREDICTION
460 10.11 DIFFERENT KINDS OF PREDICTED PROBABILITIES 461 10.11.1
PREDICTED MARGINAL PROBABILITIES 461 10.11.2 PREDICTED MEDIAN OR
CONDITIONAL PROBABILITIES 461 10.11.3 PREDICTED POSTERIOR MEAN
PROBABILITIES 462 10.12 SUMMARY AND FURTHER READING 463 10.13 EXERCISES
463 11 CROSSED RANDOM EFFECTS 473 11.1 INTRODUCTION 473 11.2 HOW DOES
INVESTMENT DEPEND ON EXPECTED PROFIT AND CAPITAL STOCK? . 474 11.3 A
TWO-WAY ERROR-COMPONENTS MODEL 475 11.3.1 MODEL SPECIFICATION 475 11.3.2
RESIDUAL INTRACLASS CORRELATIONS 476 11.3.3 ESTIMATION 476 11.3.4
PREDICTION '. 478 11.4 HOW MUCH DO PRIMARY AND SECONDARY SCHOOLS AFFECT
ATTAINMENT AT AGE 16? 481 CONTENTS XIX 11.5 AN ADDITIVE CROSSED
RANDOM-EFFECTS MODEL 483 11.5.1 SPECIFICATION 483 11.5.2 ESTIMATION
USING XTMIXED 484 11.6 INCLUDING A RANDOM INTERACTION 485 11.6.1 MODEL
SPECIFICATION 485 11.6.2 INTRACLASS CORRELATIONS 485 11.6.3 ESTIMATION
USING XTMIXED 486 11.6.4 SOME DIAGNOSTICS 488 11.7 *** A TRICK REQUIRING
FEWER RANDOM EFFECTS 489 11.8 DO SALAMANDERS FROM DIFFERENT POPULATIONS
MATE SUCCESSFULLY? . 493 11.9 CROSSED RANDOM-EFFECTS LOGISTIC
REGRESSION 495 11.10 SUMMARY AND FURTHER READING 500 11.11 EXERCISES 501
A SYNTAX FOR GLLAMM, EQ, AND GLLAPRED: THE BARE ESSENTIALS 509 B SYNTAX
FOR GLLAMM 515 C SYNTAX FOR GLLAPRED 527 D SYNTAX FOR GLLASIM 531
REFERENCES 535 AUTHOR INDEX 549 SUBJECT INDEX 555 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Rabe-Hesketh, Sophia Skrondal, Anders 1961- |
| author_GND | (DE-588)13573116X (DE-588)14306097X |
| author_facet | Rabe-Hesketh, Sophia Skrondal, Anders 1961- |
| author_role | aut aut |
| author_sort | Rabe-Hesketh, Sophia |
| author_variant | s r h srh a s as |
| building | Verbundindex |
| bvnumber | BV023307557 |
| callnumber-first | Q - Science |
| callnumber-label | QA278 |
| callnumber-raw | QA278.6 |
| callnumber-search | QA278.6 |
| callnumber-sort | QA 3278.6 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | MR 2100 QH 212 QH 234 |
| ctrlnum | (OCoLC)191245415 (DE-599)OBVAC06648871 |
| dewey-full | 519.5/35 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 |
| dewey-search | 519.5/35 |
| dewey-sort | 3519.5 235 |
| dewey-tens | 510 - Mathematics |
| discipline | Soziologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Soziologie Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV023307557 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:49:05Z |
| indexdate | 2024-07-09T21:15:30Z |
| institution | BVB |
| isbn | 1597180408 9781597180405 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016491899 |
| oclc_num | 191245415 |
| open_access_boolean | |
| owner | DE-706 DE-19 DE-BY-UBM DE-N2 DE-473 DE-BY-UBG DE-945 DE-20 DE-M382 DE-11 DE-384 DE-91 DE-BY-TUM DE-188 |
| owner_facet | DE-706 DE-19 DE-BY-UBM DE-N2 DE-473 DE-BY-UBG DE-945 DE-20 DE-M382 DE-11 DE-384 DE-91 DE-BY-TUM DE-188 |
| physical | XXXIII, 562 S. graph. Darst., Kt. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Stata Press |
| record_format | marc |
| spelling | Rabe-Hesketh, Sophia Verfasser (DE-588)13573116X aut Multilevel and longitudinal modeling using stata Sophia Rabe-Hesketh ; Anders Skrondal 2. ed. College Station, Tex. Stata Press 2008 XXXIII, 562 S. graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf Multi-level-Verfahren (DE-588)4344428-3 gnd rswk-swf Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Mehrebenenoptimierung (DE-588)4634607-7 gnd rswk-swf Regressionsmodell (DE-588)4127980-3 s Multi-level-Verfahren (DE-588)4344428-3 s Stata (DE-588)4617285-3 s DE-604 Längsschnittuntersuchung (DE-588)4034036-3 s Mehrebenenoptimierung (DE-588)4634607-7 s Statistik (DE-588)4056995-0 s 1\p DE-604 Skrondal, Anders 1961- Verfasser (DE-588)14306097X aut SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016491899&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rabe-Hesketh, Sophia Skrondal, Anders 1961- Multilevel and longitudinal modeling using stata Längsschnittuntersuchung (DE-588)4034036-3 gnd Multi-level-Verfahren (DE-588)4344428-3 gnd Regressionsmodell (DE-588)4127980-3 gnd Statistik (DE-588)4056995-0 gnd Stata (DE-588)4617285-3 gnd Mehrebenenoptimierung (DE-588)4634607-7 gnd |
| subject_GND | (DE-588)4034036-3 (DE-588)4344428-3 (DE-588)4127980-3 (DE-588)4056995-0 (DE-588)4617285-3 (DE-588)4634607-7 |
| title | Multilevel and longitudinal modeling using stata |
| title_auth | Multilevel and longitudinal modeling using stata |
| title_exact_search | Multilevel and longitudinal modeling using stata |
| title_exact_search_txtP | Multilevel and longitudinal modeling using stata |
| title_full | Multilevel and longitudinal modeling using stata Sophia Rabe-Hesketh ; Anders Skrondal |
| title_fullStr | Multilevel and longitudinal modeling using stata Sophia Rabe-Hesketh ; Anders Skrondal |
| title_full_unstemmed | Multilevel and longitudinal modeling using stata Sophia Rabe-Hesketh ; Anders Skrondal |
| title_short | Multilevel and longitudinal modeling using stata |
| title_sort | multilevel and longitudinal modeling using stata |
| topic | Längsschnittuntersuchung (DE-588)4034036-3 gnd Multi-level-Verfahren (DE-588)4344428-3 gnd Regressionsmodell (DE-588)4127980-3 gnd Statistik (DE-588)4056995-0 gnd Stata (DE-588)4617285-3 gnd Mehrebenenoptimierung (DE-588)4634607-7 gnd |
| topic_facet | Längsschnittuntersuchung Multi-level-Verfahren Regressionsmodell Statistik Stata Mehrebenenoptimierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016491899&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rabeheskethsophia multilevelandlongitudinalmodelingusingstata AT skrondalanders multilevelandlongitudinalmodelingusingstata |