PSY325Week5DQ1 - In statistics, analysis of variance...

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In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation (Aron, Coups, and Aron, 2011). There are three classes of ANOVA models: 1. Fixed-effects models assume that the data came from normal populations , which may differ only in their means. (Model 1) 2. Random effects models assume that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy. (Model 2) 3. Mixed-effect models describe the situations where both fixed and random effects are present. (Model 3) In statistics, a fixed effects model is a statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non- random (Christensen, 2002). The fixed-effects model of analysis of variance applies to situations in which the experimenter applies one or more treatments to the subjects of the experiment to see if the response variable values change. In statistics, a random effect(s) model, also called a variance components model is a kind of hierarchical linear model (Christensen, 2002). In general, random effects is efficient, and should be used (over fixed effects) if the assumptions underlying it are believed to be satisfied. A mixed model
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This note was uploaded on 01/08/2012 for the course PSY325 325 taught by Professor Donnawall during the Spring '11 term at Ashford University.

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PSY325Week5DQ1 - In statistics, analysis of variance...

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