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multi-statistical analysis model: SAS and application(Chinese Edition) - Softcover

WANG JI CHUAN

 
9787040275681: multi-statistical analysis model: SAS and application(Chinese Edition)
Softcover
ISBN 10: 7040275686 ISBN 13: 9787040275681
Publisher: Higher Education Press Pub. Date :2009-06, 2009
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Jichuan Wang
ISBN 10: 7040275686 ISBN 13: 9787040275681
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Book Description paperback. Condition: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Language: English. Pub. Date: 2009. Multilevel Models: Appfications Using SAS is written in nontechnical terms. focuses on the methods and applications of various multilevel models. including liner multilevel models. multilevel logistic regression models. multilevel Poisson regression models. multilevel negative binomial models. as well as some cutting-edge applications. such as multilevel zero-inflated Poisson (ZIP) model. random effect zero-inflated negative binomial model (RE-ZINB). mixed-effect mixed-distribution models. bootstrapping multilevel models. and group-based trajectory models. Readers will learn to build and apply multilevel models for hierarchically structured cross-sectional data and longitudinal data using the internationally distributed software package Statistics Analysis System (SAS). Detailed SAS syntax and output are provided for model applications. providing students. research scientists and data analysts with ready templates for their applications. Contents: Chapter 1 Introduction1.1 Conceptual framework of multilevel modeling1.2 Hierarchically structured data1.3 Variables in multilevel data1.4 Analytical problems with multilevel data1.5 Advantages and limitations of multilevel modeling1.6 Computer software for multilevel modelingChapter 2 Basics of Linear Multilevel Models2.1 Intraclass correlation coefficient (ICC) 2.2 Formulation of two-level multilevel models2.3 Model assumptions2.4 Fixed and random regression coefficients2.5 Cross-level interactions2.6 Measurement centering2.7 Model estimation2.8 Model fit. hypothesis testing. and model comparisons2.8.1 Model fit2.8.2 Hypothesis testing2.8.3 Model comparisons2.9 Explained level-1 and level-2 variances2.10 Steps for building multilevel models2 .11 Higher-level multilevel modelsChapter 3 Application of Two-level Linear Multilevel Models3.1 Data3.2 Empty model3.3 Predicting between-group variation3.4 Predicting within-group variation3.5 Testing random level-1 slopes3.6 Across-level interactions3.7 Other issues in model developmentChapter 4 Application of Multilevel Modeling to Longitudinal Data4.1 Features of longitudinal data4.2 Limitations of traditional approaches for modeling longitudinal data4.3 Advantages of multilevel modeling for longitudinal data4.4 Formulation of growth models4.5 Data description and manipulation4.6 Linear growth models4.6.1 The shape of average outcome change over time4.6.2 Random intercept growth models4.6.3 Random intercept and slope growth models4.6.4 Intercept and slope as outcomes4.6.5 Controlling for individual background variables in models4.6.6 Coding time score4.6.7 Residual variance / covariance structures4.6.8 Time-varying covariates4.7 Curvilinear growth models4.7.1 Polynomial growth model4.7.2 Dealing with collinearity in higher order polynomial growth model4.7.3 Piecewise (linear spline) growth modelChapter 5 Multilevel Models for Discrete Outcome Measures5.1 Introduction to generalized linear mixed models5.1.1 Generalized linear models5.1.2 Generalized linear mixed models5.2 SAS Procedures for multilevel modeling with discrete outcomes5.3 Multilevel models for binary outcomes5.3.1 Logistic regression models5.3.2 Probit models5.3.3 Unobserved latent variables and observed binary outcome measures5.3.4 Multilevel logistic regression models5.3.5 Application of multilevel logistic regression models5.3.6 Application of multilevel logit models to longitudinal data5.4 Multilevel models for ordinal outcomes5.4.1 Cumulative logit models5.4.2 Multilevel cumulative logit models5 .5 Multilevel models for nominal outcomes5.5.1 Multinomial logit models5.5.2 Multilevel multinomial logit models5.5.3 Application of multilevel multinomial logit models5.6 Multilevel models for count outcomes5.6.1 Poisson regression models5.6.2 Poisson regression with over-dispersion and a negative binomial model5.6.3 Multilevel Poi. Seller Inventory # 099145

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