Empirical Direction in Design and Analysis

By Norman H. Anderson | Go to book overview

Cognitive Algebra. Exact algebraic rules have been found in many areas of psychology (Chapter 21). Among other uses, these algebraic rules can provide theory control of confounding.

One application appears with the size-weight illusion. Apparent heaviness of a lifted weight is influenced through expectancy based on visual size, as was seen in Figure 5.3 (page 131). The traditional control was by elimination, placing the weights behind a screen so they could not be seen.

Theory control of the visual cue was obtained with the additive model, size + weight, demonstrated by the parallelism of Figure 5.3. The size-expectancy effect is thus fractionated out, leaving a pure measure of the sensory effect of the weight stimulus itself.

This model goes further to dissect the conscious sensation of heaviness into two nonconscious components, one for each stimulus determinant. It does this by shifting focus from the original sensory question to the more general question of how sensory and cognitive cues are integrated. This integration rule, aside from its own interest, gave a superior answer to the sensory question. b


NOTES

An exemplary book on confounding is the methods book of Underwood and Shaughnessy (1983), especially for its invaluable sets of exercises. Although these exercises are limited by their framework of traditional verbal learning and memory, this book deserves emulation in every field.

The undergraduate text, Experimental Psychology, by Solso, Johnson, and Beal (1998), is based on the theme that students learn best by concrete example. Numerous case examples are used to illustrate concepts of experimental method. These are expanded with 16 short articles reprinted from diverse fields, each with running commentary and with exercises for students. The material on the nature of experimental design and on control is well done and avoids the historical and philosophical didactics that deaden many texts. Although aimed at undergraduates, this book should be a useful resource for graduate classes; see also Ware and Brewer (1988), cited on page 778.

Among undergraduate texts concerned with experimental psychology, Levin and Hinrichs (1995) and Shaughnessy and Zechmeister (1997) are worthwhile. Useful texts more oriented toward social and field research include Jones' (1996) fine book, Aronson, Ellsworth, Carlsmith, and Gonzales (1990), and an admirable book by Light, Singer, and Willet (1990), concerned with observational studies of higher education.

I believe methods texts are a concern of the whole field, and deserve vigorous discussion and debate. Anyone who writes a text puts in enormous efforts, but seldom is there useful feedback. How to improve is a central question—and responsibility—yet virtually nothing is known about how. Many undergraduate methods texts suffer from over-involvement in statistics. At the graduate level, methods texts are almost nonexistent; what the unfortunate students get instead is statistics texts (see Chapter 23).

-250-

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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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