When the first edition of this Handbook was fields are likely to be hard reading, but anyone who wants to get in touch with the published in 1966 I scarcely gave thought to a future edition. Its whole purpose was to growing edges will find something to meet his inaugurate a radical new outlook on ex­ taste. perimental psychology, and if that could be Of course, this book will need teachers. As accomplished it was sufficient reward. In the it supersedes the narrow conceptions of 22 years since we have seen adequate-indeed models and statistics still taught as bivariate staggering-evidence that the growth of a new and ANOV A methods of experiment, in so branch of psychological method in science has many universities, those universities will need become established. The volume of research to expand their faculties with newly trained has grown apace in the journals and has young people. The old vicious circle of opened up new areas and a surprising increase obsoletely trained members turning out new of knowledge in methodology. obsoletely trained members has to be The credit for calling attention to the need recognized and broken. And wherever re­ for new guidance belongs to many members search deals with integral wholes-in per­ of the Society of Multivariate Experimental sonalities, processes, and groups-researchers Psychology, but the actual innervation is due will recognize the vast new future that to the skill and endurance of one man, John multivariate methods open up.

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I Multivariate Method and Theory Construction.- 1 Psychological Theory and Scientific Method.- 1. The Role of Methodology in Science.- 2. Design of This Book.- 3. Some Major Historical Springs of Methodological Tradition.- 4. What Is and What Might Be in Present-Day Research Method Concepts.- 5. The Nature of the Inductive-Hypothetico-Deductive (IHD) Method in Science.- 6. Summary.- References.- 2 The Principles of Experimental Design and Analysis in Relation to Theory Building.- 1. The Six Basic Parameters of Experimental Design.- 2. The Logically Possible and Practically Viable Types of Experimental Design.- 3. The Main Methods of Mathematico-Statistical Treatment.- 4. Definition of Theory, Law, Postulate, Hypothesis, and Reversibility-Irreversibility.- 5. Social and Psychological Influences in the Natural History of Scientific Theory.- 6. The Total Plan: Advantages and Disadvantages Guiding the Choice among Various Research Procedures.- 7. Creative Scientific Thinking in Relation to Multivariate and Bivariate Procedures.- 8. Summary.- References.- 3 The Data Box: Its Ordering of Total Resources in Terms of Possible Relational Systems.- 1. Relational System, Hypothesis, Design, and Method as the Four Panels of the Investigatory Plan.- 2. The Purpose of Developing the Covariation Chart into the BDRM or Data Box.- 3. Two Protoconstructs: Pattern Entity (Vector) and Attribute Scale (Scalar).- 4. The Ten Coordinates of the Hyperspace BDRM.- 5. The Nature and Definition of a BDRM Facet.- 6. Principles Governing "Entries": Aspects and Shifts.- 7. The Numbers and Varieties of Facets, and Associated Techniques.- 8. The Numbers and Varieties of Faces, Frames, and Grids.- 9. The Totality of Possible Direct and Derived Relational Analyses and Techniques.- 10. Sources of Variance and Covariance in the Data Box: Observable and Inherent (Ideal, Conceptual) Sources Contrasted.- 11. Scales and Standardizations: Normative, Ipsative, Abative.- 12. Superordinate Relational and Interactional Analysis Techniques: Including Superset and Interset Factor Analysis.- 13. Summary, Glossary, and Notation.- References.- 4 The Meaning and Strategic Use of Factor Analysis.- 1. Its Role and Relationships among Statistical Methods.- 2. The Basic Mathematical Propositions and Formulations.- 3. Alternative Models: Components and Factors.- 4. Properties and Formulas for the Full Factor Model.- 5. Unique Resolution and the Tests of Its Attainment.- 6. Factor Invariance, Identification, and Interpretation.- 7. Deciding the Number of Factors.- 8. The Reticular and Strata Models for Higher-Order Factors.- 9. Some Modifications, Developments, and Conditions of the Main Factor Model.- 10. Strategies in the Practical Use of Factor Analysis.- 11. Questions of Statistical Significance and Use of Computer Procedures.- 12. Summary (and Rationale of Notation).- References.- II Multivariate Modeling and Data Analysis.- 5 Analysis of Covariance Structures.- 1. Introduction.- 2. Some Types of Covariance Structures.- 3. General Approaches to Analysis of Covariance Structures.- 4. Analysis of the Examples.- 5. Generalizations.- References.- 6 Exploratory Factor Analysis.- 1. Introduction.- 2. Decision Points in Factoring.- 3. Implications: Some Designs for Exploratory Factor Analysis.- References.- 7 Confirmatory Factor Analysis.- 1. Philosophical Contrasts between Exploratory and Confirmatory Factor Analysis.- 2. The Fundamentals of Confirmatory Factor Analysis.- 3. Applications for Confirmatory Factor Analysis.- 4. Conclusion.- References.- 8 Multimode Factor Analysis.- 1. Multimode Experimental Design.- 2. Factor-Analytic Developments.- 3. Application: Spectrum of Affect.- 4. Comparisons and Contemplations.- References.- 9 Causal Modeling via Structural Equation Systems.- 1. Introduction.- 2. Structural Equations.- 3. Path Diagrams.- 4. Representation Systems.- 5. Estimation Systems.- 6. Examples.- 7. Future Directions.- References.- 10 Multivariate Analysis of Discrete Data.- 1. Introduction.- 2. One-Way Tables.- 3. Bivariate Data: Two-Way Tables.- 4. Multiway Tables.- 5. Latent Structure Models.- 6. Conclusion.- References.- 11 Some Multivariate Developments in Nonparametric Statistics.- 1. A Characterization of Nonparametric Statistics.- 2. Multivariate Perspective.- 3. Exploratory Nonparametric Analysis of All Analytical Units.- 4. Exploratory Nonparametric Analysis of Subsets of Analytical Units.- 5. Confirmatory Nonparametric Analysis.- 6. Discussion and Summary.- References.- 12 Multivariate Analysis of Variance.- 1. Classical Approach.- 2. General Linear Model Approach.- 3. Significance Tests.- 4. Discriminant Analysis.- References.- 13 Multidimensional Scaling.- 1. Introduction.- 2. Models and Methods.- 3. Important Findings.- 4. Classic Problems in MDS.- 5. Current Issues and Future Directions.- References.- 14 The Methods and Problems of Cluster Analysis.- 1. Introduction to Cluster Analysis.- 2. Cluster Analysis Methods.- 3. Similarity.- 4. Unresolved Problems of Cluster Analysis.- 5. Final Remarks.- References.- 15 Human Behavior Genetics.- 1. Introduction.- 2. The Development of Multivariate Human Behavior Genetic Analysis.- 3. Multivariate Generalization of Path Analysis.- 4. Application of Multivariate Path Analysis: Nuclear Family and Twin Design.- 5. Application of Multivariate Path Analysis: Full Adoption Design.- 6. Current Status of Multivariate Human Behavior Genetics.- 7. Multivariate Behavior Genetic Models of Development.- 8. Future Directions: Intergenerational Equilibrium?.- 9. Summary.- References.- 16 Multivariate Reliability Theory: Principles of Symmetry and Successful Validation Strategies.- 1. Introduction.- 2. Basic Concepts of Reliability Theory.- 3. Multivariate Extensions of Reliability Concepts.- 4. Foundations of a General Measurement and Research Strategy Synthesizing the Experimental and the Psychometric Traditions in Psychology.- 5. Paradoxes Revisited.- 6. Relationships to Other Approaches, Implications, and Conclusions.- References.- 17 Dynamic but Structural Equation Modeling of Repeated Measures Data.- 1. Introduction.- 2. Basic Features of a Latent Growth Curve Model.- 3. Dynamic Modeling with Latent Growth Curves.- 4. The Curve-of-Factors Model of Multivariate Growth.- 5. The Factor-of-Curves Model as a Multivariate Alternative.- 6. Discussion of Further Issues.- 7. Appendix: Assorted Technical Issues for LGM Programming.- References.- 18 N-Way Factor Analysis…