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Multivariate Analysis with LISREL

E-BookPDF1 - PDF WatermarkE-Book
557 Seiten
Englisch
Springer International Publishingerschienen am17.10.20161st ed. 2016
This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.



Karl G. Jöreskog is Professor Emeritus at Uppsala University, Sweden, and Senior Professor at the BI Norwegian School of Business in Oslo. He has received three honorary doctorates: from the Faculty of Economics and Statistics at the University of Padua, Italy, 1993, from the Norwegian School of Economics, Bergen, Norway, 1996, and from the Faculty of Psychology at the Friedrich-Schiller-Universität, Jena, Germany, 2004. Professor Jöreskog is a member of the Swedish Royal Academy of Sciences, a Fellow of the American Statistical Association, and an Honorary Fellow of the Royal Statistical Society. He has received many awards including the American Psychological Association Distinguished Award for the Applications of Psychology and the Psychometric Society Award for Career Achievement to Educational Measurement. Together with Dag Sörbom he developed the LISREL computer program.

Ulf H. Olsson is Professor at Department of Economics and Provost at BI Norwegian Business School in Oslo with responsibility for research and academic resources. He has worked on structural equation modeling, statistical modeling and psychometrics and published several research articles in leading statistics and psychometric journals. Dr. Olsson has also authored textbooks on statistics and mathematics. In 2003 Olsson was awarded the BI Norwegian Business School's research prize.

Fan Y. Wallentin is Professor of Statistics at Uppsala University, Sweden. She received her Ph.D. in Statistics in 1997. She is a recipient of the Arnberg Prize from the Swedish Royal Academy of Sciences. Dr. Wallentin's program of research is on the theory and applications of latent variable modeling and other types of multivariate statistical analysis, particularly their applications in the social and behavioral sciences. She has published research articles in several leading statistics and psychometrics journals. She has taught courses on Structural Equation Models in Sweden, USA, China and several European countries. She has broad experience in statistical consultation for researchers in social and behavioral sciences.mehr
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Produkt

KlappentextThis book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.



Karl G. Jöreskog is Professor Emeritus at Uppsala University, Sweden, and Senior Professor at the BI Norwegian School of Business in Oslo. He has received three honorary doctorates: from the Faculty of Economics and Statistics at the University of Padua, Italy, 1993, from the Norwegian School of Economics, Bergen, Norway, 1996, and from the Faculty of Psychology at the Friedrich-Schiller-Universität, Jena, Germany, 2004. Professor Jöreskog is a member of the Swedish Royal Academy of Sciences, a Fellow of the American Statistical Association, and an Honorary Fellow of the Royal Statistical Society. He has received many awards including the American Psychological Association Distinguished Award for the Applications of Psychology and the Psychometric Society Award for Career Achievement to Educational Measurement. Together with Dag Sörbom he developed the LISREL computer program.

Ulf H. Olsson is Professor at Department of Economics and Provost at BI Norwegian Business School in Oslo with responsibility for research and academic resources. He has worked on structural equation modeling, statistical modeling and psychometrics and published several research articles in leading statistics and psychometric journals. Dr. Olsson has also authored textbooks on statistics and mathematics. In 2003 Olsson was awarded the BI Norwegian Business School's research prize.

Fan Y. Wallentin is Professor of Statistics at Uppsala University, Sweden. She received her Ph.D. in Statistics in 1997. She is a recipient of the Arnberg Prize from the Swedish Royal Academy of Sciences. Dr. Wallentin's program of research is on the theory and applications of latent variable modeling and other types of multivariate statistical analysis, particularly their applications in the social and behavioral sciences. She has published research articles in several leading statistics and psychometrics journals. She has taught courses on Structural Equation Models in Sweden, USA, China and several European countries. She has broad experience in statistical consultation for researchers in social and behavioral sciences.
Details
Weitere ISBN/GTIN9783319331539
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2016
Erscheinungsdatum17.10.2016
Auflage1st ed. 2016
ReiheSpringer Series in Statistics
Seiten557 Seiten
SpracheEnglisch
IllustrationenXV, 557 p. 155 illus., 89 illus. in color.
Artikel-Nr.2103757
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;6
2;Contents;8
3;About the Authors;15
4;1Getting Started;16
4.1;1.1 Importing Data;16
4.2;1.2 Graphs;19
4.3;1.3 Splitting the Data into Two Groups;24
4.4;1.4 Introduction to LISREL Syntaxes;26
4.5;1.5 Estimating Covariance or Correlation Matrices;30
4.6;1.6 Missing Values;33
4.7;1.7 Data Management;41
5;2Regression Models;49
5.1;2.1 Linear Regression;49
5.1.1;2.1.1 Estimation and Testing;51
5.1.2;2.1.2 Example: Cholesterol;53
5.1.3;2.1.3 Importing Data;53
5.1.4;2.1.4 Checking the Assumptions;59
5.1.5;2.1.5 The Effect of Increasing the Sample Size;66
5.1.6;2.1.6 Regression using Means, Variances, and Covariances;66
5.1.7;2.1.7 Standardized Solution;67
5.1.8;2.1.8 Predicting y When ln(y) is Used as the Dependent Variable;69
5.1.9;2.1.9 Example: Income;69
5.1.10;2.1.10 ANOVA and ANCOVA;72
5.1.11;2.1.11 Example: Biology;73
5.1.12;2.1.12 Conditional Regression;75
5.1.13;2.1.13 Example: Birthweight;75
5.1.14;2.1.14 Testing Equal Regressions;77
5.1.15;2.1.15 Example: Math on Reading by Career;78
5.1.16;2.1.16 Instrumental Variables and Two-Stage Least Squares;84
5.1.17;2.1.17 Example: Income and Money Supply;86
5.1.18;2.1.18 Example: Tintner´s Meat Market Model;89
5.1.19;2.1.19 Example: Klein´s Model I of US Economy;90
5.2;2.2 General Principles of SIMPLIS Syntax;93
5.2.1;2.2.1 Example: Income and Money Supply Using SIMPLIS Syntax;100
5.2.2;2.2.2 Example: Prediction of Grade Averages;102
5.2.3;2.2.3 Example: Prediction of Test Scores;104
5.2.4;2.2.4 Example: Union Sentiment of Textile Workers;106
5.3;2.3 The General Multivariate Linear Model;109
5.3.1;2.3.1 Introductory LISREL Syntax;111
5.3.2;2.3.2 Univariate Regression Model;112
5.3.3;2.3.3 Multivariate Linear Regression;115
5.3.4;2.3.4 Example: Prediction of Test Scores with LISREL Syntax;116
5.3.5;2.3.5 Recursive Systems;119
5.3.6;2.3.6 Example: Union Sentiment of Textile Workers with LISREL Syntax;119
5.3.7;2.3.7 Non-Recursive Systems;121
5.3.8;2.3.8 Example: Income and Money Supply with LISREL syntax;121
5.3.9;2.3.9 Direct, Indirect, and Total Effects;123
5.4;2.4 Logistic and Probit Regression;126
5.4.1;2.4.1 Continuous Predictors;126
5.4.2;2.4.2 Example: Credit Risk;127
5.4.3;2.4.3 Pseudo-R2s;129
5.4.4;2.4.4 Categorical Predictors;129
5.4.5;2.4.5 Example: Death Penalty Verdicts;130
5.4.6;2.4.6 Extensions of Logistic and Probit Regression;133
5.5;2.5 Censored Regression;133
5.5.1;2.5.1 Censored Normal Variables;134
5.5.2;2.5.2 Censored Normal Regression;136
5.5.3;2.5.3 Example: Affairs;137
5.5.4;2.5.4 Example: Reading and Spelling Tests;140
5.6;2.6 Multivariate Censored Regression;141
5.6.1;2.6.1 Example: Testscores;144
6;3Generalized Linear Models;148
6.1;3.1 Components of Generalized Linear Models;148
6.2;3.2 Exponential Family Distributions;149
6.2.1;3.2.1 Distributions and Link Functions;149
6.3;3.3 The Poisson-Log Model;150
6.3.1;3.3.1 Example: Smoking and Coronary Heart Disease;152
6.3.2;3.3.2 Example: Awards;157
6.4;3.4 The Binomial-Logit/Probit Model;161
6.4.1;3.4.1 Example: Death Penalty Verdicts Revisited;162
6.5;3.5 Log-linear Models;165
6.5.1;3.5.1 Example: Malignant Melanoma;166
6.6;3.6 Nominal Logistic Regression;169
6.6.1;3.6.1 Example: Program Choices 1;171
6.6.2;3.6.2 Example: Program Choices 2;175
6.7;3.7 Ordinal Logistic Regression;177
6.7.1;3.7.1 Example: Mental Health;178
6.7.2;3.7.2 Example: Car Preferences;180
7;4Multilevel Analysis;183
7.1;4.1 Basic Concepts and Issues in Multilevel Analysis;183
7.1.1;4.1.1 Multilevel Data and Multilevel Analysis;183
7.1.2;4.1.2 Examples of Multilevel Data;183
7.1.3;4.1.3 Terms Used for Two-level Models;184
7.1.4;4.1.4 Multilevel Analysis vs Linear Regression;184
7.1.5;4.1.5 Other Terminology;185
7.1.6;4.1.6 Populations and Subgroups;185
7.1.7;4.1.7 The Interaction Question;185
7.2;4.2 Within and Between Group Variation;186
7.2.1;4.2.1 Univariate Analysis;186
7.2.2;4.2.2 Example: Netherlands Schools, Univariate Case;186
7.2.3;4.2.3 Multivariate Analysis;193
7.2.4;4.2.4 Example: Netherlands Schools, Multivariate Case;193
7.3;4.3 The Basic Two-Level Model;195
7.3.1;4.3.1 Example: Math on Reading with Career-Revisited;197
7.4;4.4 Two-Level Model with Cross-Level Interaction;201
7.5;4.5 Likelihood, Deviance, and Chi-Square;202
7.5.1;4.5.1 Example: Math Achievement and Socioeconomic Status;203
7.6;4.6 Multilevel Analysis of Repeated Measurements;209
7.6.1;4.6.1 Example: Treatment of Prostate Cancer;210
7.6.2;4.6.2 Example: Learning Curves of Air Traffic Controllers;213
7.6.3;4.6.3 Example: Growth Curves for the Weight of Mice;220
7.6.4;4.6.4 Example: Growth Curves for Weight of Chicks on Four Diets;222
7.7;4.7 Multilevel Generalized Linear Models;229
7.7.1;4.7.1 Example: Social Mobility;229
7.8;4.8 The Basic Three-Level Model;235
7.8.1;4.8.1 Example: CPC Survey Data;236
7.9;4.9 Multivariate Multilevel Analysis;240
7.9.1;4.9.1 Example: Analysis of the Junior School Project Data (JSP);242
8;5Principal Components (PCA);248
8.1;5.1 Principal Components of a Covariance Matrix;248
8.1.1;5.1.1 Example: Five Meteorological Variables;252
8.2;5.2 Principal Components vs Factor Analysis;259
8.3;5.3 Principal Components of a Data Matrix;263
8.3.1;5.3.1 Example: PCA of Nine Psychological Variables;264
8.3.2;5.3.2 Example: Stock Market Prices;266
9;6Exploratory Factor Analysis (EFA);268
9.1;6.1 The Factor Analysis Model and Its Estimation;269
9.2;6.2 A Population Example;276
9.2.1;6.2.1 Example: A Numeric Illustration;276
9.3;6.3 EFA with Continuous Variables;279
9.3.1;6.3.1 Example: EFA of Nine Psychological Variables (NPV);279
9.4;6.4 EFA with Ordinal Varaibles;284
9.4.1;6.4.1 EFA of Binary Test Items;285
9.4.2;6.4.2 Example: Analysis of LSAT6 Items;285
9.4.3;6.4.3 EFA of Polytomous Tests and Survey Items;288
9.4.4;6.4.4 Example: Attitudes Toward Science and Technology;289
10;7Confirmatory Factor Analysis(CFA);294
10.1;7.1 General Model Framework;295
10.2;7.2 Measurement Models;297
10.2.1;7.2.1 The Congeneric Measurement Model;297
10.2.2;7.2.2 Congeneric, parallel, and tau-equivalent measures;298
10.2.3;7.2.3 Example: Analysis of Reader Reliability in Essay Scoring;299
10.3;7.3 CFA with Continuous Variables;301
10.3.1;7.3.1 Continuous Variables without Missing Values;301
10.3.2;7.3.2 Example: CFA of Nine Psychological Variables;302
10.3.3;7.3.3 Estimating the Model by Maximum Likelihood;303
10.3.4;7.3.4 Analyzing Correlations;315
10.3.5;7.3.5 Continuous Variables with Missing Values;322
10.3.6;7.3.6 Example: Longitudinal Data on Math and English Scores;322
10.4;7.4 CFA with Ordinal Variables;329
10.4.1;7.4.1 Ordinal Variables without Missing Values;329
10.4.2;7.4.2 Ordinal Variables with Missing Values;339
10.4.3;7.4.3 Example: Measurement of Political Efficacy;340
11;8Structural Equation Models (SEM) with Latent Variables;351
11.1;8.1 Example: Hypothetical Model;351
11.1.1;8.1.1 Hypothetical Model with SIMPLIS Syntax;352
11.2;8.2 The General LISREL Model in LISREL Format;353
11.3;8.3 General Framework;354
11.3.1;8.3.1 Scaling of Latent Variables;355
11.3.2;8.3.2 Notation for LISREL Syntax;356
11.4;8.4 Special Cases of the General LISREL Model;357
11.4.1;8.4.1 Matrix Specification of the Hypothetical Model;357
11.4.2;8.4.2 LISREL syntax for the Hypothetical Model;359
11.5;8.5 Measurement Errors in Regression;360
11.5.1;8.5.1 Example: Verbal Ability in Grades 4 and 5;360
11.5.2;8.5.2 Example: Role Behavior of Farm Managers;361
11.6;8.6 Second-Order Factor Analysis;365
11.6.1;8.6.1 Example: Second-Order Factor of Nine Psychological Variables;367
11.7;8.7 Analysis of Correlation Structures;369
11.7.1;8.7.1 Example: CFA Model for NPV Estimated from Correlations;370
11.8;8.8 MIMIC Models;373
11.8.1;8.8.1 Example: Peer Influences and Ambition;373
11.8.2;8.8.2 Example: Continuous Causes and Ordinal Indicators;377
11.9;8.9 A Model for the Theory of Planned Behavior;381
11.9.1;8.9.1 Example: Attitudes to Drinking and Driving;381
11.10;8.10 Latent Variable Scores;384
11.10.1;8.10.1 Example: Panel Model for Political Democracy;384
12;9Analysis of Longitudinal Data;389
12.1;9.1 Two-wave Models;389
12.1.1;9.1.1 Example: Stability of Alienation;389
12.1.2;9.1.2 Example: Panel Model for Political Efficacy;394
12.2;9.2 Simplex Models;406
12.2.1;9.2.1 Example: A Simplex Model for Academic Performance;408
12.3;9.3 Latent Curve Models;409
12.3.1;9.3.1 Example: Treatment of Prostate Cancer;412
12.3.2;9.3.2 Example: Learning Curves for of Traffic Controllers;423
12.4;9.4 Latent Growth Curves and Dyadic Data;430
12.4.1;9.4.1 Example: Quality of Marriages;430
13;10Multiple Groups;437
13.1;10.1 Factorial Invariance;437
13.2;10.2 Multiple Groups with Continuous Variables;439
13.2.1;10.2.1 Equal Regressions;439
13.2.2;10.2.2 Example: STEP Reading and Writing Tests in Grades 5 and 7;439
13.2.3;10.2.3 Estimating Means of Latent Variables;442
13.2.4;10.2.4 Confirmatory Factor Analysis with Multiple Groups;446
13.2.5;10.2.5 Example: Chicago Schools Data;446
13.2.6;10.2.6 MIMIC Models for Multiple Groups;449
13.2.7;10.2.7 Twin Data Models;454
13.2.8;10.2.8 Example: Heredity of BMI;457
13.3;10.3 Multiple Groups with Ordinal Variables;464
13.3.1;10.3.1 Example: The Political Action Survey;464
13.3.2;10.3.2 Data Screening;465
13.3.3;10.3.3 Multigroup Models;468
14;11Appendix A: Basic Matrix Algebra and Statistics;478
14.1;11.1 Basic Matrix Algebra;478
14.2;11.2 Basic Statistical Concepts;486
14.3;11.3 Basic Multivariate Statistics;488
14.4;11.4 Measurement Scales;489
15;12Appendix B: Testing Normality;490
15.1;12.1 Univariate Skewness and Kurtosis;490
15.2;12.2 Multivariate Skewness and Kurtosis;493
16;13Appendix C: Computational Notes on Censored Regression;495
16.1;13.1 Computational Notes on Univariate Censored Regression;495
16.2;13.2 Computational Notes on Multivariate Censored Regression;497
17;14Appendix D: Normal Scores;499
18;15Appendix E: Asessment of Fit;500
18.1;15.1 From Theory to Statistical Model;500
18.2;15.2 Nature of Inference;502
18.3;15.3 Three Situations;502
18.4;15.4 Selection of One of Several Specified Models;504
18.5;15.5 Model Assessment and Modification;505
18.6;15.6 Chi-squares;506
18.7;15.7 Goodness-of-Fit Indices;507
18.8;15.8 Population Error of Approximation;507
18.9;15.9 Other Fit Indices;508
19;16Appendix F: General Statistical Theory;510
19.1;16.1 Continuous Variables;510
19.1.1;16.1.1 Data and Sample Statistics;510
19.1.2;16.1.2 The Multivariate Normal Distribution;510
19.1.3;16.1.3 The Multivariate Normal Likelihood;511
19.1.4;16.1.4 Likelihood, Deviance, and Chi-square;513
19.1.5;16.1.5 General Covariance Structures;514
19.1.6;16.1.6 The Independence Model;518
19.1.7;16.1.7 Mean and Covariance Structures;518
19.1.8;16.1.8 Augmented Moment Matrix;520
19.1.9;16.1.9 Multiple Groups;520
19.1.10;16.1.10 Maximum Likelihood with Missing Values (FIML);522
19.1.11;16.1.11 Multiple Imputation;523
19.2;16.2 Ordinal Variables;523
19.2.1;16.2.1 Estimation by FIML;524
19.2.2;16.2.2 Estimation via Polychorics;526
20;17Appendix G: Iteration Algorithms;529
20.1;17.1 General Definitions;529
20.2;17.2 Technical Parameters;530
20.3;17.3 The Davidon-Fletcher-Powell Method;532
20.4;17.4 Convergence Criterion;532
20.5;17.5 Line Search;532
20.6;17.6 Interpolation and Extrapolation Formulas;538
21;Bibliography;540
22;Subject Index;555mehr

Autor

Karl G. Jöreskog is Professor Emeritus at Uppsala University, Sweden, and Senior Professor at the BI Norwegian School of Business in Oslo. He has received three honorary doctorates: from the Faculty of Economics and Statistics at the University of Padua, Italy, 1993, from the Norwegian School of Economics, Bergen, Norway, 1996, and from the Faculty of Psychology at the Friedrich-Schiller-Universität, Jena, Germany, 2004. Professor Jöreskog is a member of the Swedish Royal Academy of Sciences, a Fellow of the American Statistical Association, and an Honorary Fellow of the Royal Statistical Society. He has received many awards including the American Psychological Association Distinguished Award for the Applications of Psychology and the Psychometric Society Award for Career Achievement to Educational Measurement. Together with Dag Sörbom he developed the LISREL computer program.

Ulf H. Olsson is Professor at Department of Economics and Provost at BI Norwegian Business School in Oslo with responsibility for research and academic resources. He has worked on structural equation modeling, statistical modeling and psychometrics and published several research articles in leading statistics and psychometric journals. Dr. Olsson has also authored textbooks on statistics and mathematics. In 2003 Olsson was awarded the BI Norwegian Business School's research prize.
Fan Y. Wallentin is Professor of Statistics at Uppsala University, Sweden. She received her Ph.D. in Statistics in 1997. She is a recipient of the Arnberg Prize from the Swedish Royal Academy of Sciences. Dr. Wallentin's program of research is on the theory and applications of latent variable modeling and other types of multivariate statistical analysis, particularly their applications in the social and behavioral sciences. She has published research articles in several leading statistics and psychometrics journals. She has taught courses on Structural Equation Models in Sweden, USA, China and several European countries. She has broad experience in statistical consultation for researchers in social and behavioral sciences.
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