href="#uc94f1c7b-f614-5575-943f-b3683904e622">14 FACTOR ANALYSIS
14.1 HISTORY OF FACTOR ANALYSIS
14.2 FACTOR ANALYSIS AT A GLANCE
14.3 EXPLORATORY VERSUS CONFIRMATORY FACTOR ANALYSIS
14.4 THEORY OF FACTOR ANALYSIS: THE EXPLORATORY FACTOR‐ANALYTIC MODEL
14.5 THE COMMON FACTOR‐ANALYTIC MODEL
14.6 ASSUMPTIONS OF THE FACTOR‐ANALYTIC MODEL
14.7 WHY MODEL ASSUMPTIONS ARE IMPORTANT
14.8 THE FACTOR MODEL AS AN IMPLICATION FOR THE COVARIANCE MATRIX ∑
14.9 AGAIN, WHY IS ∑ = ΛΛ′ + ψ SO IMPORTANT A RESULT?
14.10 THE MAJOR CRITIQUE AGAINST FACTOR ANALYSIS: INDETERMINACY AND THE NONUNIQUENESS OF SOLUTIONS
14.11 HAS YOUR FACTOR ANALYSIS BEEN SUCCESSFUL?
14.12 ESTIMATION OF PARAMETERS IN EXPLORATORY FACTOR ANALYSIS
14.13 PRINCIPAL FACTOR
14.14 MAXIMUM LIKELIHOOD
14.15 THE CONCEPTS (AND CRITICISMS) OF FACTOR ROTATION
14.16 VARIMAX AND QUARTIMAX ROTATION
14.17 SHOULD FACTORS BE ROTATED? IS THAT NOT CHEATING?
14.18 SAMPLE SIZE FOR FACTOR ANALYSIS
14.19 PRINCIPAL COMPONENTS ANALYSIS VERSUS FACTOR ANALYSIS: TWO KEY DIFFERENCES
14.20 PRINCIPAL FACTOR IN SPSS: PRINCIPAL AXIS FACTORING
14.21 BARTLETT TEST OF SPHERICITY AND KAISER–MEYER–OLKIN MEASURE OF SAMPLING ADEQUACY (MSA)
14.22 FACTOR ANALYSIS IN R: HOLZINGER AND SWINEFORD (1939)
14.23 CLUSTER ANALYSIS
14.24 WHAT IS CLUSTER ANALYSIS? THE BIG PICTURE
14.25 MEASURING PROXIMITY
14.26 HIERARCHICAL CLUSTERING APPROACHES
14.27 NONHIERARCHICAL CLUSTERING APPROACHES
14.28 K‐MEANS CLUSTER ANALYSIS IN R
14.29 GUIDELINES AND WARNINGS ABOUT CLUSTER ANALYSIS
14.30 A BRIEF LOOK AT MULTIDIMENSIONAL SCALING
14.31 CHAPTER SUMMARY AND HIGHLIGHTS
REVIEW EXERCISES
Further Discussion and Activities
21 15 PATH ANALYSIS AND STRUCTURAL EQUATION MODELING 15.1 PATH ANALYSIS: A MOTIVATING EXAMPLE—PREDICTING IQ ACROSS GENERATIONS 15.2 PATH ANALYSIS AND “CAUSAL MODELING” 15.3 EARLY POST‐WRIGHT PATH ANALYSIS: PREDICTING CHILD'S IQ (Burks, 1928) 15.4 DECOMPOSING PATH COEFFICIENTS 15.5 PATH COEFFICIENTS AND WRIGHT'S CONTRIBUTION 15.6 PATH ANALYSIS IN R—A QUICK OVERVIEW: MODELING GALTON'S DATA 15.7 CONFIRMATORY FACTOR ANALYSIS: THE MEASUREMENT MODEL 15.8 STRUCTURAL EQUATION MODELS 15.9 DIRECT, INDIRECT, AND TOTAL EFFECTS 15.10 THEORY OF STATISTICAL MODELING: A DEEPER LOOK INTO COVARIANCE STRUCTURES AND GENERAL MODELING 15.11 THE DISCREPANCY FUNCTION AND CHI‐SQUARE 15.12 IDENTIFICATION 15.13 DISTURBANCE VARIABLES 15.14 MEASURES AND INDICATORS OF MODEL FIT 15.15 OVERALL MEASURES OF MODEL FIT 15.16 MODEL COMPARISON MEASURES: INCREMENTAL FIT INDICES 15.17 WHICH INDICATOR OF MODEL FIT IS BEST? 15.18 STRUCTURAL EQUATION MODEL IN R 15.19 HOW ALL VARIABLES ARE LATENT: A SUGGESTION FOR RESOLVING THE MANIFEST‐LATENT DISTINCTION 15.20 THE STRUCTURAL EQUATION MODEL AS A GENERAL MODEL: SOME CONCLUDING THOUGHTS ON STATISTICS AND SCIENCE 15.21 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
22 REFERENCES
23 INDEX
List of Tables
1 Chapter 2Table 2.1 Contingency Table for 2 × 2 DesignTable 2.2 Contingency Table for 2 × 2 × 2 Design...Table 2.3 Contingency Table for 2 × 2 Diagnostic DesignTable
