Linked e-resources
Details
Table of Contents
Intro; Foreword to the Second Edition of Structural Equation Models; Contents; 1 An Introduction to Structural Equation Models; 1.1 Latent Constructs as Organizing Principles of Science in the Twentieth Century; 1.2 Path Analysis in Genetics; 1.3 Sewall Wright's Path Analysis; 1.4 Networks and Cycles; 1.5 What Is a Path Coefficient?; 1.6 Applications and Evolution; 1.7 The Chicago School; 1.8 The Scandinavian School; 1.9 Limited and Full-Information Methods; 2 Partial Least Squares Path Analysis; 2.1 PLS Path Analysis Software: Functions and Objectives; 2.2 Path Regression
2.3 Hermann Wold' Contributions to Path Analysis2.4 Possible Choices for Path Coefficients: Covariance, Correlation, and Regression Coefficients; 2.4.1 Covariance and Variance; 2.4.2 Correlation; 2.4.3 Regression Coefficients; 2.5 Lohmöller's PCA-OLS Path Analysis Method; 2.6 PLS Path Analysis vs. PLS Regression; 2.7 Resampling; 2.8 Measures; 2.9 Limited Information; 2.10 Sample Size in PLS-PA; 2.11 PLS-PA: The Bottom Line; 3 Full-Information Covariance SEM; 3.1 LISREL; 3.2 Short History of LISREL; 3.3 LISREL Performance Statistics; 4 Systems of Regression Equations
4.1 The Birth of Structural Equation Modeling4.2 Simultaneous Regression Equation Models; 4.3 Estimation; 4.4 Comparing the Different SEM Methods; 5 Data Collection, Control, and Sample Size; 5.1 The Role of Data; 5.2 The Ancient Roots of Model-Data Duality; 5.3 Data: Model Fit; 5.4 Latent Variables; 5.5 Linear Models; 5.6 Hypothesis Tests and Data; 5.7 Data Adequacy in SEM; 5.7.1 Does Our Dataset Contain Sufficient Information for Model Analysis?; 5.7.2 The ``Black-Box'' Problem; 5.7.3 Minimum Effect Size and Correlation Metrics; 5.7.4 Minimum Sample Size for SEM Model Analysis
5.8 Can Resampling Recover Information Lost Through Likert Mapping?5.9 Data Screening; 5.10 Exploratory Specification Search; 6 Survey and Questionnaire Data; 6.1 Rensis Likert's Contribution to Social Research; 6.2 Likert Scales; 6.3 How Much Information Is Lost in Likert Responses?; 6.4 The Information Content of Items Measured on a Likert Scale; 6.5 Affective Technologies to Enhance the Information Content of Likert-Scaled Surveys; 6.6 Known Unknowns: What Is a Latent Variable?; 7 Research Structure and Paradigms; 7.1 The Quest for Truth; 7.2 Research Questions; 7.3 Models
7.4 Theory Building and Hypotheses7.5 Hypothesis Testing; 7.6 Model Specification and Confirmation; 7.7 How Many Alternative Models Should You Test?; 7.8 Distributional Assumptions; 7.9 Statistical Distributions: Are They Part of the Model or Are They Part of the Data?; 7.10 Causality; 7.11 The Risks of Received Wisdom; 7.12 Design of Empirical Studies; 7.12.1 Concepts; 7.12.2 Significance Testing; 7.12.3 Model Identification; 7.12.4 Negative Error Variance Estimates; 7.12.5 Heywood Cases; 7.12.6 Empirical Confirmation of Theory; 8 Frontiers in Latent Variable Analysis
2.3 Hermann Wold' Contributions to Path Analysis2.4 Possible Choices for Path Coefficients: Covariance, Correlation, and Regression Coefficients; 2.4.1 Covariance and Variance; 2.4.2 Correlation; 2.4.3 Regression Coefficients; 2.5 Lohmöller's PCA-OLS Path Analysis Method; 2.6 PLS Path Analysis vs. PLS Regression; 2.7 Resampling; 2.8 Measures; 2.9 Limited Information; 2.10 Sample Size in PLS-PA; 2.11 PLS-PA: The Bottom Line; 3 Full-Information Covariance SEM; 3.1 LISREL; 3.2 Short History of LISREL; 3.3 LISREL Performance Statistics; 4 Systems of Regression Equations
4.1 The Birth of Structural Equation Modeling4.2 Simultaneous Regression Equation Models; 4.3 Estimation; 4.4 Comparing the Different SEM Methods; 5 Data Collection, Control, and Sample Size; 5.1 The Role of Data; 5.2 The Ancient Roots of Model-Data Duality; 5.3 Data: Model Fit; 5.4 Latent Variables; 5.5 Linear Models; 5.6 Hypothesis Tests and Data; 5.7 Data Adequacy in SEM; 5.7.1 Does Our Dataset Contain Sufficient Information for Model Analysis?; 5.7.2 The ``Black-Box'' Problem; 5.7.3 Minimum Effect Size and Correlation Metrics; 5.7.4 Minimum Sample Size for SEM Model Analysis
5.8 Can Resampling Recover Information Lost Through Likert Mapping?5.9 Data Screening; 5.10 Exploratory Specification Search; 6 Survey and Questionnaire Data; 6.1 Rensis Likert's Contribution to Social Research; 6.2 Likert Scales; 6.3 How Much Information Is Lost in Likert Responses?; 6.4 The Information Content of Items Measured on a Likert Scale; 6.5 Affective Technologies to Enhance the Information Content of Likert-Scaled Surveys; 6.6 Known Unknowns: What Is a Latent Variable?; 7 Research Structure and Paradigms; 7.1 The Quest for Truth; 7.2 Research Questions; 7.3 Models
7.4 Theory Building and Hypotheses7.5 Hypothesis Testing; 7.6 Model Specification and Confirmation; 7.7 How Many Alternative Models Should You Test?; 7.8 Distributional Assumptions; 7.9 Statistical Distributions: Are They Part of the Model or Are They Part of the Data?; 7.10 Causality; 7.11 The Risks of Received Wisdom; 7.12 Design of Empirical Studies; 7.12.1 Concepts; 7.12.2 Significance Testing; 7.12.3 Model Identification; 7.12.4 Negative Error Variance Estimates; 7.12.5 Heywood Cases; 7.12.6 Empirical Confirmation of Theory; 8 Frontiers in Latent Variable Analysis