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E-raamat: Multivariate Analysis with LISREL

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  • Formaat: PDF+DRM
  • Sari: Springer Series in Statistics
  • Ilmumisaeg: 17-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319331539
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Multivariate Analysis with LISRELSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Springer Series in Statistics
  • Ilmumisaeg: 17-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319331539
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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.

Arvustused

This is book presents all options of the latest version of Lisrel in terms of concise theoretical introductions followed by applications to empirical data. It was a pleasure to read this book because of its style of presentation in terms of many empirical examples and interesting plots. (Peter C. M. Molenaar, Structural Equation Modeling, Vol. 26 (2), 2019)

This book consists of 10 chapters and 7 appendices covering introductory and advanced topics in multivariate analysis. The book is primarily suitable for MS and Ph.D. students inthe areas of social and behavioral sciences, economists, and researchers in the field. this book covers a large number of introductory and advanced multivariate statistical topics suitable for students in graduate programs and researchers in social and behavioral sciences, and economics disciplines. (Morteza Marzjarani, Technometrics, Vol. 59 (4), November, 2017)

This book presents topics in multivariate statistics in ten chapters. audience for the book are researchers and graduate students in social sciences, public health, education, psychology, economics, and management. Because the book takes on a practical approach to multivariate statistics with numerous examples, graduate students will find an excellent source for hands-on practice. The inclusion of numerous color plots and graphics and a very clear and personal expository language make this book a friendly companion to advanced statistics. (Abdolvahab Khademi, Journal of Statistical Software, Vol. 78, June, 2017) 

1 Getting Started
1(34)
1.1 Importing Data
1(3)
1.2 Graphs
4(5)
1.3 Splitting the Data into Two Groups
9(2)
1.4 Introduction to LISREL Syntaxes
11(4)
1.5 Estimating Covariance or Correlation Matrices
15(3)
1.6 Missing Values
18(8)
1.7 Data Management
26(9)
2 Regression Models
35(100)
2.1 Linear Regression
35(44)
2.1.1 Estimation and Testing
37(2)
2.1.2 Example: Cholesterol
39(1)
2.1.3 Importing Data
39(6)
2.1.4 Checking the Assumptions
45(7)
2.1.5 The Effect of Increasing the Sample Size
52(1)
2.1.6 Regression using Means, Variances, and Covariances
52(1)
2.1.7 Standardized Solution
53(2)
2.1.8 Predicting y When ln(y) is Used as the Dependent Variable
55(1)
2.1.9 Example: Income
55(3)
2.1.10 ANOVA and ANCOVA
58(1)
2.1.11 Example: Biology
59(2)
2.1.12 Conditional Regression
61(1)
2.1.13 Example: Birthweight
61(2)
2.1.14 Testing Equal Regressions
63(1)
2.1.15 Example: Math on Reading by Career
64(6)
2.1.16 Instrumental Variables and Two-Stage Least Squares
70(2)
2.1.17 Example: Income and Money Supply
72(3)
2.1.18 Example: Tintner's Meat Market Model
75(1)
2.1.19 Example: Klein's Model I of US Economy
76(3)
2.2 General Principles of SIMPLIS Syntax
79(16)
2.2.1 Example: Income and Money Supply Using SIMPLIS Syntax
86(2)
2.2.2 Example: Prediction of Grade Averages
88(2)
2.2.3 Example: Prediction of Test Scores
90(2)
2.2.4 Example: Union Sentiment of Textile Workers
92(3)
2.3 The General Multivariate Linear Model
95(17)
2.3.1 Introductory LISREL Syntax
97(1)
2.3.2 Univariate Regression Model
98(3)
2.3.3 Multivariate Linear Regression
101(1)
2.3.4 Example: Prediction of Test Scores with LISREL Syntax
102(3)
2.3.5 Recursive Systems
105(1)
2.3.6 Example: Union Sentiment of Textile Workers with LISREL Syntax
105(2)
2.3.7 Non-Recursive Systems
107(1)
2.3.8 Example: Income and Money Supply with LISREL syntax
107(2)
2.3.9 Direct, Indirect, and Total Effects
109(3)
2.4 Logistic and Probit Regression
112(7)
2.4.1 Continuous Predictors
112(1)
2.4.2 Example: Credit Risk
113(2)
2.4.3 Pseudo-R2s
115(1)
2.4.4 Categorical Predictors
115(1)
2.4.5 Example: Death Penalty Verdicts
116(3)
2.4.6 Extensions of Logistic and Probit Regression
119(1)
2.5 Censored Regression
119(8)
2.5.1 Censored Normal Variables
120(2)
2.5.2 Censored Normal Regression
122(1)
2.5.3 Example: Affairs
123(3)
2.5.4 Example: Reading and Spelling Tests
126(1)
2.6 Multivariate Censored Regression
127(8)
2.6.1 Example: Testscores
130(5)
3 Generalized Linear Models
135(36)
3.1 Components of Generalized Linear Models
135(1)
3.2 Exponential Family Distributions
136(1)
3.2.1 Distributions and Link Functions
136(1)
3.3 The Poisson-Log Model
137(11)
3.3.1 Example: Smoking and Coronary Heart Disease
139(5)
3.3.2 Example: Awards
144(4)
3.4 The Binomial-Logit/Probit Model
148(4)
3.4.1 Example: Death Penalty Verdicts Revisited
149(3)
3.5 Log-linear Models
152(4)
3.5.1 Example: Malignant Melanoma
153(3)
3.6 Nominal Logistic Regression
156(8)
3.6.1 Example: Program Choices 1
158(4)
3.6.2 Example: Program Choices 2
162(2)
3.7 Ordinal Logistic Regression
164(7)
3.7.1 Example: Mental Health
165(2)
3.7.2 Example: Car Preferences
167(4)
4 Multilevel Analysis
171(66)
4.1 Basic Concepts and Issues in Multilevel Analysis
171(3)
4.1.1 Multilevel Data and Multilevel Analysis
171(1)
4.1.2 Examples of Multilevel Data
171(1)
4.1.3 Terms Used for Two-level Models
172(1)
4.1.4 Multilevel Analysis vs Linear Regression
172(1)
4.1.5 Other Terminology
173(1)
4.1.6 Populations and Subgroups
173(1)
4.1.7 The Interaction Question
173(1)
4.2 Within and Between Group Variation
174(9)
4.2.1 Univariate Analysis
174(1)
4.2.2 Example: Netherlands Schools, Univariate Case
174(7)
4.2.3 Multivariate Analysis
181(1)
4.2.4 Example: Netherlands Schools, Multivariate Case
181(2)
4.3 The Basic Two-Level Model
183(6)
4.3.1 Example: Math on Reading with Career-Revisited
185(4)
4.4 Two-Level Model with Cross-Level Interaction
189(1)
4.5 Likelihood, Deviance, and Chi-Square
190(7)
4.5.1 Example: Math Achievement and Socioeconomic Status
191(6)
4.6 Multilevel Analysis of Repeated Measurements
197(20)
4.6.1 Example: Treatment of Prostate Cancer
198(3)
4.6.2 Example: Learning Curves of Air Traffic Controllers
201(7)
4.6.3 Example: Growth Curves for the Weight of Mice
208(2)
4.6.4 Example: Growth Curves for Weight of Chicks on Four Diets
210(7)
4.7 Multilevel Generalized Linear Models
217(6)
4.7.1 Example: Social Mobility
217(6)
4.8 The Basic Three-Level Model
223(5)
4.8.1 Example: CPC Survey Data
224(4)
4.9 Multivariate Multilevel Analysis
228(9)
4.9.1 Example: Analysis of the Junior School Project Data (JSP)
230(7)
5 Principal Components (PCA)
237(20)
5.1 Principal Components of a Covariance Matrix
237(11)
5.1.1 Example: Five Meteorological Variables
241(7)
5.2 Principal Components vs Factor Analysis
248(4)
5.3 Principal Components of a Data Matrix
252(5)
5.3.1 Example: PCA of Nine Psychological Variables
253(2)
5.3.2 Example: Stock Market Prices
255(2)
6 Exploratory Factor Analysis (EFA)
257(26)
6.1 The Factor Analysis Model and Its Estimation
258(7)
6.2 A Population Example
265(3)
6.2.1 Example: A Numeric Illustration
265(3)
6.3 EFA with Continuous Variables
268(5)
6.3.1 Example: EFA of Nine Psychological Variables (NPV)
268(5)
6.4 EFA with Ordinal Varaibles
273(10)
6.4.1 EFA of Binary Test Items
274(1)
6.4.2 Example: Analysis of LSAT6 Items
274(3)
6.4.3 EFA of Polytomous Tests and Survey Items
277(1)
6.4.4 Example: Attitudes Toward Science and Technology
278(5)
7 Confirmatory Factor Analysis(CFA)
283(58)
7.1 General Model Framework
284(2)
7.2 Measurement Models
286(4)
7.2.1 The Congeneric Measurement Model
286(1)
7.2.2 Congeneric, parallel, and tau-equivalent measures
287(1)
7.2.3 Example: Analysis of Reader Reliability in Essay Scoring
288(2)
7.3 CFA with Continuous Variables
290(28)
7.3.1 Continuous Variables without Missing Values
290(1)
7.3.2 Example: CFA of Nine Psychological Variables
291(1)
7.3.3 Estimating the Model by Maximum Likelihood
292(12)
7.3.4 Analyzing Correlations
304(7)
7.3.5 Continuous Variables with Missing Values
311(1)
7.3.6 Example: Longitudinal Data on Math and English Scores
311(7)
7.4 CFA with Ordinal Variables
318(23)
7.4.1 Ordinal Variables without Missing Values
318(10)
7.4.2 Ordinal Variables with Missing Values
328(1)
7.4.3 Example: Measurement of Political Efficacy
329(12)
8 Structural Equation Models (SEM) with Latent Variables
341(38)
8.1 Example: Hypothetical Model
341(2)
8.1.1 Hypothetical Model with SIMPLIS Syntax
342(1)
8.2 The General LISREL Model in LISREL Format
343(1)
8.3 General Framework
344(3)
8.3.1 Scaling of Latent Variables
345(1)
8.3.2 Notation for LISREL Syntax
346(1)
8.4 Special Cases of the General LISREL Model
347(3)
8.4.1 Matrix Specification of the Hypothetical Model
347(2)
8.4.2 LISREL syntax for the Hypothetical Model
349(1)
8.5 Measurement Errors in Regression
350(5)
8.5.1 Example: Verbal Ability in Grades 4 and 5
350(1)
8.5.2 Example: Role Behavior of Farm Managers
351(4)
8.6 Second-Order Factor Analysis
355(4)
8.6.1 Example: Second-Order Factor of Nine Psychological Variables
357(2)
8.7 Analysis of Correlation Structures
359(4)
8.7.1 Example: CFA Model for NPV Estimated from Correlations
360(3)
8.8 MIMIC Models
363(8)
8.8.1 Example: Peer Influences and Ambition
363(4)
8.8.2 Example: Continuous Causes and Ordinal Indicators
367(4)
8.9 A Model for the Theory of Planned Behavior
371(3)
8.9.1 Example: Attitudes to Drinking and Driving
371(3)
8.10 Latent Variable Scores
374(5)
8.10.1 Example: Panel Model for Political Democracy
374(5)
9 Analysis of Longitudinal Data
379(48)
9.1 Two-wave Models
379(17)
9.1.1 Example: Stability of Alienation
379(5)
9.1.2 Example: Panel Model for Political Efficacy
384(12)
9.2 Simplex Models
396(3)
9.2.1 Example: A Simplex Model for Academic Performance
398(1)
9.3 Latent Curve Models
399(21)
9.3.1 Example: Treatment of Prostate Cancer
402(11)
9.3.2 Example: Learning Curves for of Traffic Controllers
413(7)
9.4 Latent Growth Curves and Dyadic Data
420(7)
9.4.1 Example: Quality of Marriages
420(7)
10 Multiple Groups
427(42)
10.1 Factorial Invariance
427(2)
10.2 Multiple Groups with Continuous Variables
429(25)
10.2.1 Equal Regressions
429(1)
10.2.2 Example: STEP Reading and Writing Tests in Grades 5 and 7
429(3)
10.2.3 Estimating Means of Latent Variables
432(4)
10.2.4 Confirmatory Factor Analysis with Multiple Groups
436(1)
10.2.5 Example: Chicago Schools Data
436(3)
10.2.6 MIMIC Models for Multiple Groups
439(5)
10.2.7 Twin Data Models
444(3)
10.2.8 Example: Heredity of BMI
447(7)
10.3 Multiple Groups with Ordinal Variables
454(15)
10.3.1 Example: The Political Action Survey
454(1)
10.3.2 Data Screening
455(3)
10.3.3 Multigroup Models
458(11)
11 Appendix A: Basic Matrix Algebra and Statistics
469(12)
11.1 Basic Matrix Algebra
469(8)
11.2 Basic Statistical Concepts
477(2)
11.3 Basic Multivariate Statistics
479(1)
11.4 Measurement Scales
480(1)
12 Appendix B: Testing Normality
481(6)
12.1 Univariate Skewness and Kurtosis
481(3)
12.2 Multivariate Skewness and Kurtosis
484(3)
13 Appendix C: Computational Notes on Censored Regression
487(4)
13.1 Computational Notes on Univariate Censored Regression
487(2)
13.2 Computational Notes on Multivariate Censored Regression
489(2)
14 Appendix D: Normal Scores
491(2)
15 Appendix E: Asessment of Fit
493(10)
15.1 From Theory to Statistical Model
493(2)
15.2 Nature of Inference
495(1)
15.3 Three Situations
495(2)
15.4 Selection of One of Several Specified Models
497(1)
15.5 Model Assessment and Modification
498(1)
15.6 Chi-squares
499(1)
15.7 Goodness-of-Fit Indices
500(1)
15.8 Population Error of Approximation
500(1)
15.9 Other Fit Indices
501(2)
16 Appendix F: General Statistical Theory
503(20)
16.1 Continuous Variables
503(13)
16.1.1 Data and Sample Statistics
503(1)
16.1.2 The Multivariate Normal Distribution
503(1)
16.1.3 The Multivariate Normal Likelihood
504(2)
16.1.4 Likelihood, Deviance, and Chi-square
506(1)
16.1.5 General Covariance Structures
507(4)
16.1.6 The Independence Model
511(1)
16.1.7 Mean and Covariance Structures
511(2)
16.1.8 Augmented Moment Matrix
513(1)
16.1.9 Multiple Groups
513(2)
16.1.10 Maximum Likelihood with Missing Values (FIML)
515(1)
16.1.11 Multiple Imputation
516(1)
16.2 Ordinal Variables
516(7)
16.2.1 Estimation by FIML
517(2)
16.2.2 Estimation via Polychorics
519(4)
17 Appendix G: Iteration Algorithms
523(12)
17.1 General Definitions
523(1)
17.2 Technical Parameters
524(2)
17.3 The Davidon-Fletcher-Powell Method
526(1)
17.4 Convergence Criterion
526(1)
17.5 Line Search
526(6)
17.6 Interpolation and Extrapolation Formulas
532(3)
Bibliography 535(16)
Subject Index 551
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 Schools 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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