Preface	6
	Contents	8
	About the Authors	15
	1Getting Started	16
	1.1 Importing Data	16
	1.2 Graphs	19
	1.3 Splitting the Data into Two Groups	24
	1.4 Introduction to LISREL Syntaxes	26
	1.5 Estimating Covariance or Correlation Matrices	30
	1.6 Missing Values	33
	1.7 Data Management	41
	2Regression Models	49
	2.1 Linear Regression	49
	2.1.1 Estimation and Testing	51
	2.1.2 Example: Cholesterol	53
	2.1.3 Importing Data	53
	2.1.4 Checking the Assumptions	59
	2.1.5 The Effect of Increasing the Sample Size	66
	2.1.6 Regression using Means, Variances, and Covariances	66
	2.1.7 Standardized Solution	67
	2.1.8 Predicting y When ln(y) is Used as the Dependent Variable	69
	2.1.9 Example: Income	69
	2.1.10 ANOVA and ANCOVA	72
	2.1.11 Example: Biology	73
	2.1.12 Conditional Regression	75
	2.1.13 Example: Birthweight	75
	2.1.14 Testing Equal Regressions	77
	2.1.15 Example: Math on Reading by Career	78
	2.1.16 Instrumental Variables and Two-Stage Least Squares	84
	2.1.17 Example: Income and Money Supply	86
	2.1.18 Example: Tintner’s Meat Market Model	89
	2.1.19 Example: Klein’s Model I of US Economy	90
	2.2 General Principles of SIMPLIS Syntax	93
	2.2.1 Example: Income and Money Supply Using SIMPLIS Syntax	100
	2.2.2 Example: Prediction of Grade Averages	102
	2.2.3 Example: Prediction of Test Scores	104
	2.2.4 Example: Union Sentiment of Textile Workers	106
	2.3 The General Multivariate Linear Model	109
	2.3.1 Introductory LISREL Syntax	111
	2.3.2 Univariate Regression Model	112
	2.3.3 Multivariate Linear Regression	115
	2.3.4 Example: Prediction of Test Scores with LISREL Syntax	116
	2.3.5 Recursive Systems	119
	2.3.6 Example: Union Sentiment of Textile Workers with LISREL Syntax	119
	2.3.7 Non-Recursive Systems	121
	2.3.8 Example: Income and Money Supply with LISREL syntax	121
	2.3.9 Direct, Indirect, and Total Effects	123
	2.4 Logistic and Probit Regression	126
	2.4.1 Continuous Predictors	126
	2.4.2 Example: Credit Risk	127
	2.4.3 Pseudo-R2s	129
	2.4.4 Categorical Predictors	129
	2.4.5 Example: Death Penalty Verdicts	130
	2.4.6 Extensions of Logistic and Probit Regression	133
	2.5 Censored Regression	133
	2.5.1 Censored Normal Variables	134
	2.5.2 Censored Normal Regression	136
	2.5.3 Example: Affairs	137
	2.5.4 Example: Reading and Spelling Tests	140
	2.6 Multivariate Censored Regression	141
	2.6.1 Example: Testscores	144
	3Generalized Linear Models	148
	3.1 Components of Generalized Linear Models	148
	3.2 Exponential Family Distributions	149
	3.2.1 Distributions and Link Functions	149
	3.3 The Poisson-Log Model	150
	3.3.1 Example: Smoking and Coronary Heart Disease	152
	3.3.2 Example: Awards	157
	3.4 The Binomial-Logit/Probit Model	161
	3.4.1 Example: Death Penalty Verdicts Revisited	162
	3.5 Log-linear Models	165
	3.5.1 Example: Malignant Melanoma	166
	3.6 Nominal Logistic Regression	169
	3.6.1 Example: Program Choices 1	171
	3.6.2 Example: Program Choices 2	175
	3.7 Ordinal Logistic Regression	177
	3.7.1 Example: Mental Health	178
	3.7.2 Example: Car Preferences	180
	4Multilevel Analysis	183
	4.1 Basic Concepts and Issues in Multilevel Analysis	183
	4.1.1 Multilevel Data and Multilevel Analysis	183
	4.1.2 Examples of Multilevel Data	183
	4.1.3 Terms Used for Two-level Models	184
	4.1.4 Multilevel Analysis vs Linear Regression	184
	4.1.5 Other Terminology	185
	4.1.6 Populations and Subgroups	185
	4.1.7 The Interaction Question	185
	4.2 Within and Between Group Variation	186
	4.2.1 Univariate Analysis	186
	4.2.2 Example: Netherlands Schools, Univariate Case	186
	4.2.3 Multivariate Analysis	193
	4.2.4 Example: Netherlands Schools, Multivariate Case	193
	4.3 The Basic Two-Level Model	195
	4.3.1 Example: Math on Reading with Career-Revisited	197
	4.4 Two-Level Model with Cross-Level Interaction	201
	4.5 Likelihood, Deviance, and Chi-Square	202
	4.5.1 Example: Math Achievement and Socioeconomic Status	203
	4.6 Multilevel Analysis of Repeated Measurements	209
	4.6.1 Example: Treatment of Prostate Cancer	210
	4.6.2 Example: Learning Curves of Air Traffic Controllers	213
	4.6.3 Example: Growth Curves for the Weight of Mice	220
	4.6.4 Example: Growth Curves for Weight of Chicks on Four Diets	222
	4.7 Multilevel Generalized Linear Models	229
	4.7.1 Example: Social Mobility	229
	4.8 The Basic Three-Level Model	235
	4.8.1 Example: CPC Survey Data	236
	4.9 Multivariate Multilevel Analysis	240
	4.9.1 Example: Analysis of the Junior School Project Data (JSP)	242
	5Principal Components (PCA)	248
	5.1 Principal Components of a Covariance Matrix	248
	5.1.1 Example: Five Meteorological Variables	252
	5.2 Principal Components vs Factor Analysis	259
	5.3 Principal Components of a Data Matrix	263
	5.3.1 Example: PCA of Nine Psychological Variables	264
	5.3.2 Example: Stock Market Prices	266
	6Exploratory Factor Analysis (EFA)	268
	6.1 The Factor Analysis Model and Its Estimation	269
	6.2 A Population Example	276
	6.2.1 Example: A Numeric Illustration	276
	6.3 EFA with Continuous Variables	279
	6.3.1 Example: EFA of Nine Psychological Variables (NPV)	279
	6.4 EFA with Ordinal Varaibles	284
	6.4.1 EFA of Binary Test Items	285
	6.4.2 Example: Analysis of LSAT6 Items	285
	6.4.3 EFA of Polytomous Tests and Survey Items	288
	6.4.4 Example: Attitudes Toward Science and Technology	289
	7Confirmatory Factor Analysis(CFA)	294
	7.1 General Model Framework	295
	7.2 Measurement Models	297
	7.2.1 The Congeneric Measurement Model	297
	7.2.2 Congeneric, parallel, and tau-equivalent measures	298
	7.2.3 Example: Analysis of Reader Reliability in Essay Scoring	299
	7.3 CFA with Continuous Variables	301
	7.3.1 Continuous Variables without Missing Values	301
	7.3.2 Example: CFA of Nine Psychological Variables	302
	7.3.3 Estimating the Model by Maximum Likelihood	303
	7.3.4 Analyzing Correlations	315
	7.3.5 Continuous Variables with Missing Values	322
	7.3.6 Example: Longitudinal Data on Math and English Scores	322
	7.4 CFA with Ordinal Variables	329
	7.4.1 Ordinal Variables without Missing Values	329
	7.4.2 Ordinal Variables with Missing Values	339
	7.4.3 Example: Measurement of Political Efficacy	340
	8Structural Equation Models (SEM) with Latent Variables	351
	8.1 Example: Hypothetical Model	351
	8.1.1 Hypothetical Model with SIMPLIS Syntax	352
	8.2 The General LISREL Model in LISREL Format	353
	8.3 General Framework	354
	8.3.1 Scaling of Latent Variables	355
	8.3.2 Notation for LISREL Syntax	356
	8.4 Special Cases of the General LISREL Model	357
	8.4.1 Matrix Specification of the Hypothetical Model	357
	8.4.2 LISREL syntax for the Hypothetical Model	359
	8.5 Measurement Errors in Regression	360
	8.5.1 Example: Verbal Ability in Grades 4 and 5	360
	8.5.2 Example: Role Behavior of Farm Managers	361
	8.6 Second-Order Factor Analysis	365
	8.6.1 Example: Second-Order Factor of Nine Psychological Variables	367
	8.7 Analysis of Correlation Structures	369
	8.7.1 Example: CFA Model for NPV Estimated from Correlations	370
	8.8 MIMIC Models	373
	8.8.1 Example: Peer Influences and Ambition	373
	8.8.2 Example: Continuous Causes and Ordinal Indicators	377
	8.9 A Model for the Theory of Planned Behavior	381
	8.9.1 Example: Attitudes to Drinking and Driving	381
	8.10 Latent Variable Scores	384
	8.10.1 Example: Panel Model for Political Democracy	384
	9Analysis of Longitudinal Data	389
	9.1 Two-wave Models	389
	9.1.1 Example: Stability of Alienation	389
	9.1.2 Example: Panel Model for Political Efficacy	394
	9.2 Simplex Models	406
	9.2.1 Example: A Simplex Model for Academic Performance	408
	9.3 Latent Curve Models	409
	9.3.1 Example: Treatment of Prostate Cancer	412
	9.3.2 Example: Learning Curves for of Traffic Controllers	423
	9.4 Latent Growth Curves and Dyadic Data	430
	9.4.1 Example: Quality of Marriages	430
	10Multiple Groups	437
	10.1 Factorial Invariance	437
	10.2 Multiple Groups with Continuous Variables	439
	10.2.1 Equal Regressions	439
	10.2.2 Example: STEP Reading and Writing Tests in Grades 5 and 7	439
	10.2.3 Estimating Means of Latent Variables	442
	10.2.4 Confirmatory Factor Analysis with Multiple Groups	446
	10.2.5 Example: Chicago Schools Data	446
	10.2.6 MIMIC Models for Multiple Groups	449
	10.2.7 Twin Data Models	454
	10.2.8 Example: Heredity of BMI	457
	10.3 Multiple Groups with Ordinal Variables	464
	10.3.1 Example: The Political Action Survey	464
	10.3.2 Data Screening	465
	10.3.3 Multigroup Models	468
	11Appendix A: Basic Matrix Algebra and Statistics	478
	11.1 Basic Matrix Algebra	478
	11.2 Basic Statistical Concepts	486
	11.3 Basic Multivariate Statistics	488
	11.4 Measurement Scales	489
	12Appendix B: Testing Normality	490
	12.1 Univariate Skewness and Kurtosis	490
	12.2 Multivariate Skewness and Kurtosis	493
	13Appendix C: Computational Notes on Censored Regression	495
	13.1 Computational Notes on Univariate Censored Regression	495
	13.2 Computational Notes on Multivariate Censored Regression	497
	14Appendix D: Normal Scores	499
	15Appendix E: Asessment of Fit	500
	15.1 From Theory to Statistical Model	500
	15.2 Nature of Inference	502
	15.3 Three Situations	502
	15.4 Selection of One of Several Specified Models	504
	15.5 Model Assessment and Modification	505
	15.6 Chi-squares	506
	15.7 Goodness-of-Fit Indices	507
	15.8 Population Error of Approximation	507
	15.9 Other Fit Indices	508
	16Appendix F: General Statistical Theory	510
	16.1 Continuous Variables	510
	16.1.1 Data and Sample Statistics	510
	16.1.2 The Multivariate Normal Distribution	510
	16.1.3 The Multivariate Normal Likelihood	511
	16.1.4 Likelihood, Deviance, and Chi-square	513
	16.1.5 General Covariance Structures	514
	16.1.6 The Independence Model	518
	16.1.7 Mean and Covariance Structures	518
	16.1.8 Augmented Moment Matrix	520
	16.1.9 Multiple Groups	520
	16.1.10 Maximum Likelihood with Missing Values (FIML)	522
	16.1.11 Multiple Imputation	523
	16.2 Ordinal Variables	523
	16.2.1 Estimation by FIML	524
	16.2.2 Estimation via Polychorics	526
	17Appendix G: Iteration Algorithms	529
	17.1 General Definitions	529
	17.2 Technical Parameters	530
	17.3 The Davidon-Fletcher-Powell Method	532
	17.4 Convergence Criterion	532
	17.5 Line Search	532
	17.6 Interpolation and Extrapolation Formulas	538
	Bibliography	540
	Subject Index	555