Multiple Comparisons, Issue 61
Multiple Comparisons demonstrates the most important methods of investigating differences between levels of an independent variable within an experimental design. The authors review the analysis of variance and hypothesis testing and describe the dimensions on which multiple comparisons vary. A feature is the use made of a famous experiment by Solomon Asch on group conformity. The authors demonstrate the statistical power of each method against this one experimental question.
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Series Editors Introduction
Variance Estimates or Mean Squares l
A Priori Comparisons
The Scheffe Test
Multiple Comparisons in Factorial Designs
Other editions - View all
analysis of variance analyze Behrens-Fisher problem Chapter comparison error rate comparison involves conducted confederates Conformity Example conformity experiment conservative control over Type critical trials critical value decision rule degrees of freedom described difference between Groups difference between means Duncan Duncan's Test evaluate experimentwise error rate F-distribution F-ratio harmonic mean independent variables interaction l20 degrees LSD Test mean square methods multiple comparisons multiple-comparison tests noncritical trials nonsignificant null hypothesis number of comparisons number of degrees number of means number of subjects omnibus test orthogonal comparisons pairs of means pairwise comparisons partial null hypotheses planned comparisons post hoc comparisons procedures protection level random variability range tests researcher result sample means sampling distribution Scheffe Test Significant Difference Test standard error statistic sum of squares tabled value tests of significance treatment effect treatment groups treatment means treatment sum trend Tukey's HSD Type I error unanimous groups Univarsity value of q within-groups
Page 85 - An Addendum to A Comparison of the Modified-Tukey and Scheffe Methods of Multiple Comparisons for Pairwise Contrasts.