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Auteur : G. David Garson
Catégorie : Boutique Kindle,Ebooks Kindle,Ebooks en langues étrangères
Broché : * pages
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Langue : Français, Anglais

VARIANCE COMPONENTS ANALYSISOverviewVariance components models are a way to assess the amount of variation in a dependent variable that is associated with one or more random-effects variables. The central output is a variance components table which shows the proportion of variance attributable to a random effects variable's main effect and, optionally, the random variable's interactions with other factors. Random effects variables are categorical variables (factors) whose categories (levels) are conceived as a random sample of all categories. Examples might include grouping variables like schools in a study of students, days of the month in a marketing study, or subject id in repeated measures studies. Variance components analysis will show whether such random school-level effects, day-of-month effects, or subject effects are important or if they may be discounted. It should be noted that variance components analysis has largely been superceded by linear mixed models and generalized linear mixed models analysis. The variance components procedure is often an adjunct to these procedures. Unlike them, the variance components procedure estimates only variance components, not model regression coefficients. Variance components analysis may be seen as a more computationally efficient procedure useful for models in special designs, such as split plot, univariate repeated measures, random block, and other mixed effects designs. The full content is now available from Statistical Associates Publishers. http://www.statisticalassociates.comBelow is the unformatted table of contents.Table of ContentsOverview5Key Concepts and Terms6Variables6Example6Types of variables7Variable entry in SPSS8Models10Purpose10Model entry in SPSS10Balanced vs. unbalanced models11Multilevel models11Repeated measures models11Intercept12Saved variables12Variance components12Procedures12SPSS12Estimation12Overview12MINQUE estimation method13Anova estimation method13Maximum likelihood estimation14Restricted maximum likelihood estimation15Variance components output and interpretation16Overview16SPSS output16Anova estimation16MINQUE estimation output19Maximum likelihood estimation output20REML estimation output23Reconciling variance components estimates25SAS PROC VARCOMP25Overview25SAS Syntax25SAS interface27TYPE1 estimation in SAS28ML, REML, and MIVQUE0 estimation in SAS29Assumptions31Random effects31Homoscedastic random effects31Uncorrelated random effects31Uncorrelated residuals31Well distributed residuals31Normality31Linearity32Correct priors32Frequently Asked Questions32What is the syntax for the variance components procedure in SPSS?32How does variance components (VARCOMP) analysis compare to linear mixed models (LMM, aka MIXED) and general linear models (GLM) for the same data?33Bibliography33

Télécharger Variance Components (Statistical Associates Blue Book Series 21) (English Edition) de G. David Garson Pdf Ebook

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Variance components > Precision > Measurement systems ~ Variance components are estimates of a part of the total variability accounted for by a specified source of variability. Random factors are factors where a number of levels are randomly sampled from the population, and the intention is to make inferences about the population. For example, a study might examine the precision of a measurement procedure in different laboratories. In this case .

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