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Terminology and Concepts in Analysis of Variance (ANOVA), Quizzes of Psychology

Definitions for key terms and concepts in analysis of variance (anova), including main effect, interaction effect, factorial design, planned comparison, post hoc comparison, factor, independent variable, subject variable, f ratio, structural model equation, and assumptions of two-way anova. It also explains the difference between a priori and post hoc comparisons and provides examples of the number of factors and cells in a 2x3x3 design.

Typology: Quizzes

2010/2011

Uploaded on 04/13/2011

princev1125
princev1125 🇺🇸

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Download Terminology and Concepts in Analysis of Variance (ANOVA) and more Quizzes Psychology in PDF only on Docsity! TERM 1 main effect DEFINITION 1 impact of one factor on the dependent variable averaged across the factors TERM 2 interaction effect DEFINITION 2 Situation in a factorial ANOVA in which a combination of variables has an effect that could not be predicted from the effects of the two variables individually TERM 3 factorial design DEFINITION 3 Way of organizing a study in which the influence of two or more variables is studied at once by setting up the situation so that a different group of people are tested for each combination of the levels of the variables TERM 4 planned comparison DEFINITION 4 comparisons made before collecting data TERM 5 post hoc comparison DEFINITION 5 comparisons made after collecting data TERM 6 factor (in analysis of variance) DEFINITION 6 independent variable or subject variable implies only in situation of analysis and variance TERM 7 independent variable DEFINITION 7 directly manipulated by the researchers TERM 8 subject variable DEFINITION 8 characteristic someone brings to a study, which is not directly manipulated TERM 9 What is the basic concept of the F ratio? DEFINITION 9 Explained varience divided by error variance TERM 10 Write and label the terms of the structural model equation for a single score in one-way, between groups, univariate ANOVA. DEFINITION 10 X GM) = (X M) + (M GM)(X GM) = How much the score deviates from the grand mean(X M) = Deviation of sample score from sample mean(M GM) = Deviation of sample from grand mean due to the independent variable
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