# 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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