多层统计分析模型：SAS与应用_在线百科全书查询
多层统计分析模型：SAS与应用
图书信息
出版社: 高等教育出版社; 第1版 (2009年6月1日)
外文书名: Multilevel Models: Applications Using SAS
丛书名: 当代科学前沿论丛
平装: 264页
正文语种: 简体中文
开本: 16
ISBN: 9787040275681
条形码: 9787040275681
尺寸: 26 x 18.6 x 2.2 cm
重量: 839 g
作者简介
作者：王济川 谢海义 (美国) 费舍余 (Fisher.J.)
Dr. Jichuan Wang is a professor in the Center for Clinical and Community Research, the Children''s National Medical Center, the George Washington Universty School of Medicine.
Dr. Haiyi Xie is an associate professor of Community and Family Medicine, Dartmouth Medical School, Dartmouth College.
Dr. James Henry Fisher is a senior planner for the HancockCounty Planning Commission in EIIsworth, Maine.
内容简介
《多层统计分析模型:SAS与应用》讲述了：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.
目录
Chapter 1 Introduction
1.1 Conceptual framework of multilevel modeling
1.2 Hierarchically structured data
1.3 Variables in multilevel data
1.4 Analytical problems with multilevel data
1.5 Advantages and limitations of multilevel modeling
1.6 Computer software for multilevel modeling
Chapter 2 Basics of Linear Multilevel Models
2.1 Intraclass correlation coefficient (ICC)
2.2 Formulation of two-level multilevel models
2.3 Model assumptions
2.4 Fixed and random regression coefficients
2.5 Cross-level interactions
2.6 Measurement centering
2.7 Model estimation
2.8 Model fit, hypothesis testing, and model comparisons
2.8.1 Model fit
2.8.2 Hypothesis testing
2.8.3 Model comparisons
2.9 Explained level-1 and level-2 variances
2.10 Steps for building multilevel models
2.11 Higher-level multilevel models
Chapter 3 Application of Two-level Linear Multilevel Models
3.1 Data
3.2 Empty model
3.3 Predicting between-group variation
3.4 Predicting within-group variation
3.5 Testing random level-1 slopes
3.6 Across-level interactions
3.7 Other issues in model development
Chapter 4 Application of Multilevel Modeling to Longitudinal Data
4.1 Features of longitudinal data
4.2 Limitations of traditional approaches for modeling longitudinal data
4.3 Advantages of multilevel modeling for longitudinal data
4.4 Formulation of growth models
4.5 Data description and manipulation
4.6 Linear growth models
4.6.1 The shape of average outcome change over time
4.6.2 Random intercept growth models
4.6.3 Random intercept and slope growth models
4.6.4 Intercept and slope as outcomes
4.6.5 Controlling for individual background variables in models
4.6.6 Coding time score
4.6.7 Residual variance/covariance structures
4.6.8 Time-varying covariates
4.7 Curvilinear growth models
4.7.1 Polynomial growth model
4.7.2 Dealing with collinearity in higher order polynomial growth model
4.7.3 Piecewise (linear spline) growth model
Chapter 5 Multilevel Models for Discrete Outcome Measures
5.1 Introduction to generalized linear mixed models
5.1.1 Generalized linear models
5.1.2 Generalized linear mixed models
5.2 SAS Procedures for multilevel modeling with discrete outcomes
5.3 Multilevel models for binary outcomes
5.3.1 Logistic regression models
5.3.2 Probit models
5.3.3 Unobserved latent variables and observed binary outcome measures
5.3.4 Multilevel logistic regression models
5.3.5 Application of multilevel logistic regression models
5.3.6 Application of multilevel logit models to longitudinal data
5.4 Multilevel models for ordinal outcomes
5.4.1 Cumulative logit models
5.4.2 Multilevel cumulative logit models
5.5 Multilevel models for nominal outcomes
5.5.1 Multinomial logit models
5.5.2 Multilevel multinomial logit models
5.5.3 Application of multilevel multinomial logit models
5.6 Multilevel models for count outcomes
5.6.1 Poisson regression models
5.6.2 Poisson regression with over-dispersion and a negative binomial model
5.6.3 Multilevel Poisson and negative binomial models
5.6.4 Application of multilevel Poisson and negative binomial models
Chapter 6 Other Applications of Multilevel Modeling and Related Issues
6.1 Multilevel zero-inflated models for count data with extra zeros
6.1.1 Fixed-effect ZIP model
6.1.2 Random effect zero-inflated Poisson (RE-ZIP) models
6.1.3 Random effect zero-inflated negative binomial (RE-ZINB) models
6.1.4 Application of RE-ZIP and RE-ZINB models
6.2 Mixed-effect mixed-distribution models for semi-continuous outcomes
6.2.1 Mixed-effects mixed distribution model
6.2.2 Application of the Mixed-Effect mixed distribution model
6.3 Bootstrap multilevel modeling
6.3.1 Nonparametric residual bootstrap multilevel modeling
6.3.2 Parametric residual bootstrap multilevel modeling
6.3.3 Application of nonparametric residual bootstrap multilevel modeling
6.4 Group-based models for longitudinal data analysis
6.4.1 Introduction to group-based model
6.4.2 Group-based logit model
6.4.3 Group-based zero-inflated Poisson (ZIP) model
6.4.4 Group-based censored normal models
6.5 Missing values issue
6.5.1 Missing data mechanisms and their implications
6.5.2 Handling missing data in longitudinal data analyses
6.6 Statistical power and sample size for multilevel modeling
6.6.1 Sample size estimation for two-level designs
6.6.2 Sample size estimation for longitudinal data analysis
Reference
Index
