Empirical Direction in Design and Analysis

By Norman H. Anderson | Go to book overview

PREFACE

Generality is a prime goal of scientific inference because science depends on evidence from samples. In psychology, experimental results are typically obtained from a small sample of subjects tested in one narrow experimental situation, with a very small sample of stimulus conditions and usually a single task and a single measure of behavior. These results have value only to the extent that they generalize. No one is interested, for example, in those particular infant monkeys in Harlow's experiments on mother love; their behavior is of interest only insofar as it generalizes to other infants, especially human infants, across a wider range of test situations than Harlow used. The four kinds of generality implicit in the previous sentence are discussed on pages 20–24.

Reliability, or replicability, is one aspect of generality. Different samples of subjects will yield different results. Perhaps the effect observed in your sample is merely a chance accident of which subjects chanced to get into your particular sample. Any claim for a real effect should be prefaced by evidence that it is reliable—not likely to be produced by chance alone. Statistics can help assess reliability. Not less important, statistics can help you plan your experiments to get more reliability for less cost (pages 16–20).

Validity, which is far more important than reliability, is primarily an extra-statistical issue—which must be answered in terms of substantive knowledge. The ubiquitous threat to validity is confounding. Confounding arises because the experimenter employs some concrete stimulus manipulation that is intended to elicit a specified process. But this manipulation may also elicit some other process that undercuts the interpretation. The classic example of confounding is the placebo effect, in which the suggestion produced by giving a medicine has beneficial effects even though the medicine itself is worthless. Validity is more complex, however, as discussed on pages 8–16.

Generality, reliability, and validity are mainly extrastatistical problems. Statistics can furnish valuable assistance with some aspects of these problems, but effective use of statistics depends on integration of statistics with extra-statistical, empirical knowledge.

Six levels of knowledge are distinguished in the Experimental Pyramid (page 3). Each lower level is more important. Statistics, although mainly applicable at the top level, can also help at each lower level. Your labors will be more productive the more you learn how to integrate statistical inference into an empirical framework of extrastatistical, scientific inference. This empirical direction is the main theme of this initial chapter and of the entire book.

-xvi-

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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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