Empirical Direction in Design and Analysis

By Norman H. Anderson | Go to book overview

PREFACE

Two axioms are basic to construction of unified science of psychology. The axiom of purposiveness recognizes that behavior is goal-directed. Purposiveness is evident in our evolutionary heritage of sensory-motor systems—survival tools to approach and to avoid goals. The axiom of multiple determination recognizes that perception, thought, and action result from multiple influences, including changing motivations of the organism and a variable environment.

Each axiom corresponds to a capability of organisms. The first corresponds to capability for evaluating multiple aspects of the environment in relation to operative motivations. The second corresponds to capability for integrating these multiple values into a unified resultant, manifest in goal-oriented behavior.

General theory must possess the two corresponding capabilities: To measure values of aspects of the environment within the functional goal currency of individual organisms; and to analyze operative rules of value integration.

Both capabilities have been developed in information integration theory (IIT). A solution to the long-controversial issue of true psychological measurement was obtained in this way—functional measurement—distinguished from other measurement theories by empirical success.

Multiple determination has been found to obey simple algebraic rules in many areas, from person cognition through developmental psychology and judgment-decision to animal behavior. These algebraic rules allow measurement of functional, goal-relevant values for the individual organism.

The main value of algebraic psychology lies in conceptual implications, beyond the algebraic structure. One conceptual implication concerns independence of valuation and integration—a key to functional analysis. With this goes the implication of meaning invariance. A third implication is the functional conception of memory, quite different from the traditional conception of reproductive memory. In addition, functional measurement provides a ladder to go beyond algebraic models to study nonalgebraic and configural integration.

IIT is a general, unified theory that has been developed through empirical applications of functional measurement theory. IIT is general in being empirically grounded across diverse areas. IIT is unified in that the same concepts and methods apply in all these areas. Although incomplete in many ways, IIT offers a self-sufficient approach to functional cognition, an approach near the level of the perception, thought, and action of everyday life.

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Empirical Direction in Design and Analysis
Table of contents

Table of contents

  • Title Page iii
  • Dedication v
  • Foreword vi
  • Contents vii
  • Preface xvi
  • Chapter 1 - Scientific Inference 1
  • Preface 30
  • Chapter 2 - Statistical Inference 31
  • How to Do Exercises 54
  • Exercises for Chapter 2 54
  • Preface 58
  • Chapter 3 - Elements of Analysis of Variance I 59
  • Notes 75
  • Appendix: How to Randomize 77
  • Exercises for Chapter 3 84
  • Preface 90
  • Chapter 4 - Elements of Analysis of Variance II 91
  • Notes 111
  • Exercises for Chapter 4 113
  • Preface 118
  • Chapter 5 - Factorial Design 119
  • Notes 145
  • Appendix: Hand Calculation for Factorial Design 148
  • Exercises for Chapter 5 151
  • Preface 158
  • Chapter 6 - Repeated Measures Design 159
  • Notes 177
  • Exercises for Chapter 6 181
  • Preface 188
  • Chapter 7 - Understanding Interactions 189
  • Notes 209
  • Exercises for Chapter 7 214
  • Preface 218
  • Chapter 8 - Confounding 219
  • Notes 250
  • Preface 258
  • Chapter 9 - Regression and Correlation 259
  • Notes 280
  • Exercises for Chapter 9 282
  • Preface 286
  • Chapter 10 - Frequency Data and Chi-Square 287
  • Notes 300
  • Exercises for Chapter 10 302
  • Preface 306
  • Chapter 11 - Single Subject Design 307
  • Notes 338
  • Exercises for Chapter 11 345
  • Preface 350
  • Chapter 12 - Nonnormal Data and Unequal Variance 351
  • Notes 373
  • Exercises for Chapter 12 378
  • Preface 382
  • Chapter 13 - Analysis of Covariance 383
  • Notes 395
  • Exercises for Chapter 13 397
  • Preface 400
  • Chapter 14 - Design Topics I 401
  • Notes 431
  • Exercises for Chapter 14 437
  • Preface 442
  • Chapter 15 - Design Topics II 443
  • Notes 475
  • Exercises for Chapter 15 481
  • Preface 484
  • Chapter 16 - Multiple Regression 485
  • Notes 514
  • Exercises for Chapter 16 520
  • Preface 524
  • Chapter 17 - Multiple Comparisons 525
  • Notes 546
  • Exercises for Chapter 17 548
  • Preface 550
  • Chapter 18 - Sundry Topics 551
  • Notes 589
  • Exercises for Chapter 18 596
  • Preface 602
  • Chapter 19 - Foundations of Statistics 603
  • Notes 637
  • Preface 646
  • Chapter 20 - Mathematical Models for Process Analysis 647
  • Notes 677
  • Exercises for Chapter 20 681
  • Preface 688
  • Chapter 21 - Toward Unified Theory 689
  • Notes 729
  • Exercises for Chapter 21 742
  • Preface 750
  • Chapter 22 - Principles and Tactics of Writing Papers 751
  • Notes 761
  • Preface 764
  • Chapter 23 - Lifelong Learning 765
  • Notes 780
  • Preface 782
  • Chapter 0 - Basic Statistical Concepts 783
  • Notes 803
  • Exercises for Chapter 0 805
  • Statistical Tables 808
  • References 820
  • Author Index 847
  • Subject Index 854
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