## Applied Analysis of Variance in Behavioral ScienceA reference devoted to the discussion of analysis of variance (ANOVA) techniques. It presents ANOVA as a research design, a collection of statistical models, an analysis model, and an arithmetic summary of data. Discussion focuses primarily on univariate data, but multivariate generalizations are to |

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### Contents

Analysis of Variance Overview | 1 |

References | 22 |

References | 59 |

Analysis of Repeated Measurements | 105 |

References | 142 |

An SAS GLM Program to Analyze Table 5 1 | 189 |

Confounded Factorial Designs | 197 |

Multivariate Analysis of Variance | 255 |

Unbalanced Data and Cell Means Models | 375 |

References | 420 |

References | 434 |

References | 453 |

Hierarchical Linear Models and Experimental Design | 459 |

References | 494 |

538 | |

References | 569 |

Output from SPSS MANOVA Analysis | 292 |

Getting the Software Modules | 339 |

Robustness in ANOVA | 345 |

References | 371 |

602 | |

Upper a Point of the Studentized Range Distribution with | 609 |

### Common terms and phrases

Amer analysis of covariance analysis of variance ANOVA applied approach assumption balanced Biometrics cell means model chapter classical coefficients column comparing computed contrasts correlation covariance matrix critical value defined degrees of freedom dependent variable discriminant function equal equations error rate error term example Experimental Design F test fixed effects grand mean groups Hierarchical Linear Model independent interaction Keselman Latin square Latin square design linear combination logit main effects mean squares method Multiple Comparisons multivariate nested nonparametric normal distribution null hypothesis observations orthogonal pairwise problem procedure Psych random effects randomized block regression repeated measures designs residuals response sample sizes SAS Institute scores Searle Section significant specific sphericity SPSS standard error subjects subset sum of squares SYSTAT Table test statistic treatment combinations treatment effects Type I error unbalanced data unequal univariate variance components vector within-subjects factor zero