Читать книгу Applied Univariate, Bivariate, and Multivariate Statistics - Daniel J. Denis - Страница 2
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7 1 PRELIMINARY CONSIDERATIONS 1.1 THE PHILOSOPHICAL BASES OF KNOWLEDGE: RATIONALISTIC VERSUS EMPIRICIST PURSUITS 1.2 WHAT IS A “MODEL”? 1.3 SOCIAL SCIENCES VERSUS HARD SCIENCES 1.4 IS COMPLEXITY A GOOD DEPICTION OF REALITY? ARE MULTIVARIATE METHODS USEFUL? 1.5 CAUSALITY 1.6 THE NATURE OF MATHEMATICS: MATHEMATICS AS A REPRESENTATION OF CONCEPTS 1.7 AS A SCIENTIST, HOW MUCH MATHEMATICS DO YOU NEED TO KNOW? 1.8 STATISTICS AND RELATIVITY 1.9 EXPERIMENTAL VERSUS STATISTICAL CONTROL 1.10 STATISTICAL VERSUS PHYSICAL EFFECTS 1.11 UNDERSTANDING WHAT “APPLIED STATISTICS” MEANS Review Exercises Further Discussion and Activities
8 2 INTRODUCTORY STATISTICS 2.1 DENSITIES AND DISTRIBUTIONS 2.2 CHI‐SQUARE DISTRIBUTIONS AND GOODNESS‐OF‐FIT TEST 2.3 SENSITIVITY AND SPECIFICITY 2.4 SCALES OF MEASUREMENT: NOMINAL, ORDINAL, INTERVAL, RATIO 2.5 MATHEMATICAL VARIABLES VERSUS RANDOM VARIABLES 2.6 MOMENTS AND EXPECTATIONS 2.7 ESTIMATION AND ESTIMATORS 2.8 VARIANCE 2.9 DEGREES OF FREEDOM 2.10 SKEWNESS AND KURTOSIS 2.11 SAMPLING DISTRIBUTIONS 2.12 CENTRAL LIMIT THEOREM 2.13 CONFIDENCE INTERVALS 2.14 MAXIMUM LIKELIHOOD 2.15 AKAIKE'S INFORMATION CRITERIA 2.16 COVARIANCE AND CORRELATION 2.17 PSYCHOMETRIC VALIDITY, RELIABILITY: A COMMON USE OF CORRELATION COEFFICIENTS 2.18 COVARIANCE AND CORRELATION MATRICES 2.19 OTHER CORRELATION COEFFICIENTS 2.20 STUDENT'S t DISTRIBUTION 2.21 STATISTICAL POWER 2.22 POWER ESTIMATION USING R AND G*POWER 2.23 PAIRED‐SAMPLES t‐TEST: STATISTICAL TEST FOR MATCHED‐PAIRS (ELEMENTARY BLOCKING) DESIGNS 2.24 BLOCKING WITH SEVERAL CONDITIONS 2.25 COMPOSITE VARIABLES: LINEAR COMBINATIONS 2.26 MODELS IN MATRIX FORM 2.27 GRAPHICAL APPROACHES 2.28 WHAT MAKES A p‐VALUE SMALL? A CRITICAL OVERVIEW AND PRACTICAL DEMONSTRATION OF NULL HYPOTHESIS SIGNIFICANCE TESTING 2.29 CHAPTER SUMMARY AND HIGHLIGHTS Review Exercises Further Discussion and Activities
9 3 ANALYSIS OF VARIANCE: FIXED EFFECTS MODELS 3.1 WHAT IS ANALYSIS OF VARIANCE? FIXED VERSUS RANDOM EFFECTS 3.2 HOW ANALYSIS OF VARIANCE WORKS: A BIG PICTURE OVERVIEW 3.3 LOGIC AND THEORY OF ANOVA: A DEEPER LOOK 3.4 FROM SUMS OF SQUARES TO UNBIASED VARIANCE ESTIMATORS: DIVIDING BY DEGREES OF FREEDOM 3.5 EXPECTED MEAN SQUARES FOR ONE‐WAY FIXED EFFECTS MODEL: DERIVING THE F‐RATIO 3.6 THE NULL HYPOTHESIS IN ANOVA 3.7 FIXED EFFECTS ANOVA: MODEL ASSUMPTIONS 3.8 A WORD ON EXPERIMENTAL DESIGN AND RANDOMIZATION 3.9 A PREVIEW OF THE CONCEPT OF NESTING 3.10 BALANCED VERSUS UNBALANCED DATA IN ANOVA MODELS 3.11 MEASURES OF ASSOCIATION AND EFFECT SIZE IN ANOVA: MEASURES OF VARIANCE EXPLAINED 3.12 THE F‐TEST AND THE INDEPENDENT SAMPLES t‐TEST 3.13 CONTRASTS AND POST‐HOCS 3.14 POST‐HOC TESTS 3.15 SAMPLE SIZE AND POWER FOR ANOVA: ESTIMATION WITH R AND G*POWER 3.16 FIXED EFFECTS ONE‐WAY ANALYSIS OF VARIANCE IN R: MATHEMATICS ACHIEVEMENT AS A FUNCTION OF TEACHER 3.17 ANALYSIS OF VARIANCE VIA R’s lm 3.18 KRUSKAL–WALLIS TEST IN R AND THE MOTIVATION BEHIND NONPARAMETRIC TESTS 3.19 ANOVA IN SPSS: ACHIEVEMENT AS A FUNCTION OF TEACHER 3.20 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
10 4 FACTORIAL ANALYSIS OF VARIANCE 4.1 WHAT IS FACTORIAL ANALYSIS OF VARIANCE? 4.2 THEORY OF FACTORIAL ANOVA: A DEEPER LOOK 4.3 COMPARING ONE‐WAY ANOVA TO TWO‐WAY ANOVA: CELL EFFECTS IN FACTORIAL ANOVA VERSUS SAMPLE EFFECTS IN ONE‐WAY ANOVA 4.4 PARTITIONING THE SUMS OF SQUARES FOR FACTORIAL ANOVA: THE CASE OF TWO FACTORS 4.5 INTERPRETING MAIN EFFECTS IN THE PRESENCE OF INTERACTIONS 4.6 EFFECT SIZE MEASURES 4.7 THREE‐WAY, FOUR‐WAY, AND HIGHER MODELS 4.8 SIMPLE MAIN EFFECTS 4.9 NESTED DESIGNS 4.10 ACHIEVEMENT AS A FUNCTION OF TEACHER AND TEXTBOOK: EXAMPLE OF FACTORIAL ANOVA IN R 4.11 INTERACTION CONTRASTS 4.12 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES
11 5 INTRODUCTION TO RANDOM EFFECTS AND MIXED MODELS 5.1 WHAT IS RANDOM EFFECTS ANALYSIS OF VARIANCE? 5.2 THEORY OF RANDOM EFFECTS MODELS 5.3 ESTIMATION IN RANDOM EFFECTS MODELS 5.4 DEFINING NULL HYPOTHESES IN RANDOM EFFECTS MODELS 5.5 COMPARING NULL HYPOTHESES IN FIXED VERSUS RANDOM EFFECTS MODELS: THE IMPORTANCE OF ASSUMPTIONS 5.6 ESTIMATING VARIANCE COMPONENTS IN RANDOM EFFECTS MODELS: ANOVA, ML, REML ESTIMATORS 5.7 IS ACHIEVEMENT A FUNCTION OF TEACHER? ONE‐WAY RANDOM EFFECTS MODEL IN R 5.8 R ANALYSIS USING REML 5.9 ANALYSIS IN SPSS: OBTAINING VARIANCE COMPONENTS 5.10 Factorial Random Effects: A Two‐Way Model 5.11 FIXED EFFECTS VERSUS RANDOM EFFECTS: A WAY OF CONCEPTUALIZING THEIR DIFFERENCES 5.12 CONCEPTUALIZING THE TWO‐WAY RANDOM EFFECTS MODEL: THE MAKE‐UP OF A RANDOMLY CHOSEN OBSERVATION 5.13 SUMS OF SQUARES AND EXPECTED MEAN SQUARES FOR RANDOM EFFECTS: THE CONTAMINATING INFLUENCE OF INTERACTION EFFECTS 5.14 YOU GET WHAT YOU GO IN WITH: THE IMPORTANCE OF MODEL ASSUMPTIONS AND MODEL SELECTION 5.15 MIXED MODEL ANALYSIS OF VARIANCE: INCORPORATING FIXED AND RANDOM EFFECTS 5.16 MIXED MODELS IN MATRICES 5.17 MULTILEVEL MODELING AS A SPECIAL CASE OF THE MIXED MODEL: INCORPORATING NESTING AND CLUSTERING 5.18 CHAPTER SUMMARY AND HIGHLIGHTS Review Exercises
12 6 RANDOMIZED BLOCKS AND REPEATED MEASURES 6.1 WHAT IS A RANDOMIZED BLOCK DESIGN? 6.2 RANDOMIZED BLOCK DESIGNS: SUBJECTS NESTED WITHIN BLOCKS 6.3 THEORY OF RANDOMIZED BLOCK DESIGNS 6.4 TUKEY TEST FOR NONADDITIVITY 6.5 ASSUMPTIONS FOR THE COVARIANCE MATRIX 6.6 INTRACLASS CORRELATION 6.7 REPEATED MEASURES MODELS: A SPECIAL CASE OF RANDOMIZED BLOCK DESIGNS 6.8 INDEPENDENT VERSUS PAIRED‐SAMPLES t‐TEST 6.9 THE SUBJECT FACTOR: FIXED OR RANDOM EFFECT? 6.10 MODEL FOR ONE‐WAY REPEATED MEASURES DESIGN 6.11 ANALYSIS USING R: ONE‐WAY REPEATED MEASURES: LEARNING AS A FUNCTION OF TRIAL 6.12 ANALYSIS USING SPSS: ONE‐WAY REPEATED MEASURES: LEARNING AS A FUNCTION OF TRIAL 6.13 SPSS TWO‐WAY REPEATED MEASURES ANALYSIS OF VARIANCE MIXED DESIGN: ONE BETWEEN FACTOR, ONE WITHIN FACTOR 6.14 Chapter Summary and Highlights Review Exercises
13 7 LINEAR REGRESSION 7.1 BRIEF HISTORY OF REGRESSION 7.2 REGRESSION ANALYSIS AND SCIENCE: EXPERIMENTAL VERSUS CORRELATIONAL DISTINCTIONS 7.3 A MOTIVATING EXAMPLE: CAN OFFSPRING HEIGHT BE PREDICTED? 7.4 THEORY OF REGRESSION ANALYSIS: A DEEPER LOOK 7.5 MULTILEVEL YEARNINGS 7.6 THE LEAST‐SQUARES LINE 7.7 MAKING PREDICTIONS WITHOUT REGRESSION 7.8 MORE ABOUT εi 7.9 MODEL ASSUMPTIONS FOR LINEAR REGRESSION 7.10 ESTIMATION OF MODEL PARAMETERS IN REGRESSION 7.11 NULL HYPOTHESES FOR REGRESSION 7.12 SIGNIFICANCE TESTS AND CONFIDENCE INTERVALS FOR MODEL PARAMETERS 7.13 OTHER FORMULATIONS OF THE REGRESSION MODEL 7.14 THE REGRESSION MODEL IN MATRICES: ALLOWING FOR MORE COMPLEX MULTIVARIABLE MODELS 7.15 ORDINARY LEAST‐SQUARES IN MATRICES 7.16 ANALYSIS OF VARIANCE FOR REGRESSION 7.17 MEASURES OF MODEL FIT FOR REGRESSION: HOW WELL DOES THE LINEAR EQUATION FIT? 7.18 ADJUSTED R2 7.19 WHAT “EXPLAINED VARIANCE” MEANS AND MORE IMPORTANTLY, WHAT IT DOES NOT MEAN 7.20 VALUES FIT BY REGRESSION 7.21 LEAST‐SQUARES REGRESSION IN R: USING MATRIX OPERATIONS 7.22 LINEAR REGRESSION USING R 7.23 REGRESSION DIAGNOSTICS: A CHECK ON MODEL ASSUMPTIONS 7.24 REGRESSION IN SPSS: PREDICTING QUANTITATIVE FROM VERBAL 7.25 POWER ANALYSIS FOR LINEAR REGRESSION IN R 7.26 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
14 8 MULTIPLE LINEAR REGRESSION 8.1 THEORY OF PARTIAL CORRELATION 8.2 SEMIPARTIAL CORRELATIONS 8.3 MULTIPLE REGRESSION 8.4 SOME PERSPECTIVE ON REGRESSION COEFFICIENTS: “EXPERIMENTAL COEFFICIENTS”? 8.5 MULTIPLE REGRESSION MODEL IN MATRICES 8.6 ESTIMATION OF PARAMETERS 8.7 CONCEPTUALIZING MULTIPLE R 8.8 INTERPRETING REGRESSION COEFFICIENTS: CORRELATED VERSUS UNCORRELATED PREDICTORS 8.9 ANDERSON’S IRIS DATA: PREDICTING SEPAL LENGTH FROM PETAL LENGTH AND PETAL WIDTH 8.10 FITTING OTHER FUNCTIONAL FORMS: A BRIEF LOOK AT POLYNOMIAL REGRESSION 8.11 MEASURES OF COLLINEARITY IN REGRESSION: VARIANCE INFLATION FACTOR AND TOLERANCE 8.12 R‐SQUARED AS A FUNCTION OF PARTIAL AND SEMIPARTIAL CORRELATIONS: THE STEPPING STONES TO FORWARD AND STEPWISE REGRESSION 8.13 MODEL‐BUILDING STRATEGIES: SIMULTANEOUS, HIERARCHICAL, FORWARD, STEPWISE 8.14 POWER ANALYSIS FOR MULTIPLE REGRESSION 8.15 INTRODUCTION TO STATISTICAL MEDIATION: CONCEPTS AND CONTROVERSY 8.16 BRIEF SURVEY OF RIDGE AND LASSO REGRESSION: PENALIZED REGRESSION MODELS AND THE CONCEPT OF SHRINKAGE 8.17 CHAPTER SUMMARY AND HIGHLIGHTS Review Exercises Further Discussion and Activities
15 9 INTERACTIONS IN MULTIPLE LINEAR REGRESSION 9.1 THE ADDITIVE REGRESSION MODEL WITH TWO PREDICTORS 9.2 WHY THE INTERACTION IS THE PRODUCT TERM xizi: DRAWING AN ANALOGY TO FACTORIAL ANOVA 9.3 A MOTIVATING EXAMPLE OF INTERACTION IN REGRESSION: CROSSING A CONTINUOUS PREDICTOR WITH A DICHOTOMOUS PREDICTOR 9.4 ANALYSIS OF COVARIANCE 9.5 CONTINUOUS MODERATORS 9.6 SUMMING UP THE IDEA OF INTERACTIONS IN REGRESSION 9.7 DO MODERATORS REALLY “MODERATE” ANYTHING? 9.8 INTERPRETING MODEL COEFFICIENTS IN THE CONTEXT OF MODERATORS 9.9 MEAN‐CENTERING PREDICTORS: IMPROVING THE INTERPRETABILITY OF SIMPLE SLOPES 9.10 MULTILEVEL REGRESSION: ANOTHER SPECIAL CASE OF THE MIXED MODEL 9.11 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES
16 10 LOGISTIC REGRESSION AND THE GENERALIZED LINEAR MODEL 10.1 NONLINEAR MODELS 10.2 GENERALIZED LINEAR MODELS 10.3 CANONICAL LINKS 10.4 DISTRIBUTIONS AND GENERALIZED LINEAR MODELS 10.5 DISPERSION PARAMETERS AND DEVIANCE 10.6 LOGISTIC REGRESSION 10.7 EXPONENTIAL AND LOGARITHMIC FUNCTIONS 10.8 ODDS AND THE LOGIT 10.9 PUTTING IT ALL TOGETHER: LOGISTIC REGRESSION 10.10 LOGISTIC REGRESSION IN R 10.11 CHALLENGER ANALYSIS IN SPSS 10.12 SAMPLE SIZE, EFFECT SIZE, AND POWER 10.13 FURTHER DIRECTIONS 10.14 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES
17 11 MULTIVARIATE ANALYSIS OF VARIANCE 11.1 A MOTIVATING EXAMPLE: QUANTITATIVE AND VERBAL ABILITY AS A VARIATE 11.2 CONSTRUCTING THE COMPOSITE 11.3 THEORY OF MANOVA 11.4 IS THE LINEAR COMBINATION MEANINGFUL? 11.5 MULTIVARIATE HYPOTHESES 11.6 ASSUMPTIONS OF MANOVA 11.7 HOTELLING’S T2: THE CASE OF GENERALIZING FROM UNIVARIATE TO MULTIVARIATE 11.8 THE COVARIANCE MATRIX S 11.9 FROM SUMS OF SQUARES AND CROSS‐PRODUCTS TO VARIANCES AND COVARIANCES 11.10 HYPOTHESIS AND ERROR MATRICES OF MANOVA 11.11 MULTIVARIATE TEST STATISTICS 11.12 EQUALITY OF COVARIANCE MATRICES 11.13 MULTIVARIATE CONTRASTS 11.14 MANOVA IN R AND SPSS 11.15 MANOVA OF FISHER’S IRIS DATA 11.16 POWER ANALYSIS AND SAMPLE SIZE FOR MANOVA 11.17 MULTIVARIATE ANALYSIS OF COVARIANCE AND MULTIVARIATE MODELS: A BIRD’S EYE VIEW OF LINEAR MODELS 11.18 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
18 12 DISCRIMINANT ANALYSIS 12.1 WHAT IS DISCRIMINANT ANALYSIS? THE BIG PICTURE ON THE IRIS DATA 12.2 THEORY OF DISCRIMINANT ANALYSIS 12.3 LDA IN R AND SPSS 12.4 DISCRIMINANT ANALYSIS FOR SEVERAL POPULATIONS 12.5 DISCRIMINATING SPECIES OF IRIS: DISCRIMINANT ANALYSES FOR THREE POPULATIONS 12.6 A NOTE ON CLASSIFICATION AND ERROR RATES 12.7 DISCRIMINANT ANALYSIS AND BEYOND 12.8 CANONICAL CORRELATION 12.9 MOTIVATING EXAMPLE FOR CANONICAL CORRELATION: HOTELLING’S 1936 DATA 12.10 CANONICAL CORRELATION AS A GENERAL LINEAR MODEL 12.11 THEORY OF CANONICAL CORRELATION 12.12 CANONICAL CORRELATION OF HOTELLING’S DATA 12.13 CANONICAL CORRELATION ON THE IRIS DATA: EXTRACTING CANONICAL CORRELATION FROM REGRESSION, MANOVA, LDA 12.14 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
19 13 PRINCIPAL COMPONENTS ANALYSIS 13.1 HISTORY OF PRINCIPAL COMPONENTS ANALYSIS 13.2 HOTELLING 1933 13.3 THEORY OF PRINCIPAL COMPONENTS ANALYSIS 13.4 EIGENVALUES AS VARIANCE 13.5 PRINCIPAL COMPONENTS AS LINEAR COMBINATIONS 13.6 EXTRACTING THE FIRST COMPONENT 13.7 EXTRACTING THE SECOND COMPONENT 13.8 EXTRACTING THIRD AND REMAINING COMPONENTS 13.9 THE EIGENVALUE AS THE VARIANCE OF A LINEAR COMBINATION RELATIVE TO ITS LENGTH 13.10 DEMONSTRATING PRINCIPAL COMPONENTS ANALYSIS: PEARSON’S 1901 ILLUSTRATION 13.11 SCREE PLOTS 13.12 PRINCIPAL COMPONENTS VERSUS LEAST‐SQUARES REGRESSION LINES 13.13 COVARIANCE VERSUS CORRELATION MATRICES: PRINCIPAL COMPONENTS AND SCALING 13.14 PRINCIPAL COMPONENTS ANALYSIS USING SPSS 13.15 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
20 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
