本书是一本英文版介绍统计分析应用SAS软件的书籍，是国内第一本系统介绍各种多层模型的教学和科研参考书。书中采用国际通用的著名统计软件SAS来演示各种多层模型的应用，结合具体的实例，由浅入深地逐步介绍如何使用不同的SAS程序，如Proc MIXED，Proc NLMIXED和Proc GLIMMIX，来进行各种多层资料的模型分析。本书可作为综合性大学，医学院、财经大学，师范院校等相应专业的研究生或本科生教材，也可供实际应用工作者参考。本书德国著名的沃尔特·德·格鲁伊特（Walter de Gruyter）出版社和中国高等教育出版社共同出版。
        王济川，1947年出生。1982年四川大学经济系毕业。1986年获美国康乃尔大学社会学硕士学位，1990年获该校社会学博士学位。1989年9月至1990年8月于美国密西根大学人口中心作博士后研究。1991年9月任职美国俄亥俄州怀特州立大学医学院社区卫生系，2000年7月至今任该系教授。2002年被聘为山东大学客座教授，2006年被聘为山东大学流行病与卫生统计学专业博士研究生兼职导师。王济川博士的主要研究领域为社会科学定量分析方法、人口分析方法及公共卫生和疾病预防研究。http://baike.baidu.com/view/1566397.htm

社会科学总论>统计学>统计方法
王济川，谢海义，（美）James Henry Fisher.
出版社: Walter de Gruyter & Co (2011年12月23日)
精装: 264页
语种：英语


PDF高清版本，可以复制里面的SAS程序
Preface ………………………………………………………………………………………………. v
1 Introduction ..............................................................................................................................1
1.1 Conceptual framework of multilevel modeling ................................................................. 1
1.2 Hierarchically structured data........................................................................................... 3
1.3 Variables in multilevel data .............................................................................................. 4
1.4 Analytical problems with multilevel data .......................................................................... 6
1.5 Advantages and limitations of multilevel modeling .......................................................... 8
1.6 Computer software for multilevel modeling .................................................................... 10
2 Basics of linear multilevel models .........................................................................................13
2.1 Intraclass correlation coefficient (ICC).......................................................................... 13
2.2 Formulation of two-level multilevel models .................................................................. 15
2.3 Model assumptions ......................................................................................................... 17
2.4 Fixed and random regression coefficients...................................................................... 18
2.5 Cross-level interactions................................................................................................... 20
2.6 Measurement centering................................................................................................... 21
2.7 Model estimation.............................................................................................................23
2.8 Model fit, hypothesis testing, and model comparisons.................................................. 27
2.8.1 Model fit .............................................................................................................. 27
2.8.2 Hypothesis testing ............................................................................................... 28
2.8.3 Model comparisons ............................................................................................. 30
2.9 Explained level-1 and level-2 variances......................................................................... 30
2.10 Steps for building multilevel models............................................................................ 33
2.11 Higher-level multilevel models .................................................................................... 37
3 Application of two-level linear multilevel models................................................................39
3.1 Data ................................................................................................................................. 39
3.2 Empty model ...................................................................................................................42
3.3 Predicting between-group variation ............................................................................... 48
3.4 Predicting within-group variation................................................................................... 53
3.5 Testing level-1 random................................................................................................... 57
3.6 Across-level interactions ................................................................................................ 62
3.7 Other issues in model development................................................................................ 66
4 Application of multilevel modeling to longitudinal data...................................................73
4.1 Features of longitudinal data............................................................................................ 73
4.2 Limitations of traditional approaches for modeling longitudinal data ............................. 74
4.3 Advantages of multilevel modeling for longitudinal data................................................ 75
4.4 Formulation of growth models......................................................................................... 75
4.5 Data and variable description........................................................................................... 77
4.6 Linear growth models ...................................................................................................... 79
4.6.1 The shape of average outcome change over time ................................................. 80
4.6.2 Random intercept growth models......................................................................... 80
4.6.3 Random intercept-slope growth models ............................................................... 84
4.6.4 Intercept and slope as outcomes ........................................................................... 86
4.6.5 Controlling for individual background variables in models ................................. 88
4.6.6 Coding time score................................................................................................. 89
4.6.7 Residual variance/covariance structures............................................................... 91
4.6.8 Time-varying covariates....................................................................................... 95
4.7 Curvilinear growth models .............................................................................................. 98
4.7.1 Polynomial growth model .................................................................................... 98
4.7.2 Dealing with collinearity in higher order polynomial growth model ................. 100
4.7.3 Piecewise (linear spline) growth model.............................................................. 106
5 Multilevel models for discrete outcome measures ........................................................... 113
5.1 Introduction to generalized linear mixed models......................................................... 113
5.1.1 Generalized linear models................................................................................. 113
5.1.2 Generalized linear mixed models...................................................................... 115
5.2 SAS Procedures for multilevel modeling with discrete outcomes .............................. 116
5.3 Multilevel models for binary outcomes........................................................................ 117
5.3.1 Logistic regression models................................................................................ 117
5.3.2 Probit models..................................................................................................... 118
5.3.3 Unobserved latent variables and observed binary outcome measures ............. 119
5.3.4 Multilevel logistic regression models .............................................................. 119
5.3.5 Application of multilevel logistic regression models....................................... 120
5.3.6 Application of multilevel logit models to longitudinal data ............................ 136
5.4 Multilevel models for ordinal outcomes....................................................................... 139
5.4.1 Cumulative logit models ................................................................................... 139
5.4.2 Multilevel cumulative logit models .................................................................. 141
。。。
Index............................................................................................................................................. 259