Empirical Direction in Design and Analysis

By Norman H. Anderson | Go to book overview

PREFACE
Everyone recognizes the need to go beyond the significance test, especially to assess size and importance of effects. Unfortunately, most of the popular statistical indexes for assessing size and importance of effects not only fail to illuminate but actually obscure the data. This is not surprising since size and importance usually need to be assessed in empirical, extrastatistical terms. This issue is discussed in Section 18.1 on size and importance and continued in Section 18.3 on curve shape.One useful way to go beyond the significance test is with the mean-plus-confidence interval. Section 18.2 presents this approach to analysis of contrasts designed to test specific effects.Section 18.4 takes up the following seven topics.
1. Concept of Validity. This section relates the discussion of validity begun in Section 1.2 to two major positions, one from test theory, one from quasi-experimental design. Neither position suffices for experimental analysis.
2. Multivariate Analysis of Variance. Multivariate analysis of variance is a needed tool to handle multiple different measures on each subject. For common repeated measures designs, however, ɛ-adjusted Anova is generally preferable.
3. Partial Analysis. Valuable simplification of multi-factor design can be obtained by using extrastatistical knowledge systems to strike out most of the high-way statistical interaction residuals.
4. Pooling. Pooling interaction terms with error is sometimes appropriate when done a priori, but only in exceptional circumstances when done post hoc.
5. Regression Artifact. The ubiquitous, insidious regression artifact needs to be understood by everyone; see especially the Exercises.
6. Multiplication Model for Repeated Measures. With repeated measures variables, transformation of the data has a twofold potential benefit: Transformation can increase the size of the effect and also decrease the error term.
7. General Linear Model. All most of us need to know about the general linear model in 1 ½ easy pages.

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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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