search for books and compare prices
Tables of Contents for Multilevel Analysis
Chapter/Section Title
Page #
Page Count
Preface
ix
Introduction
1
5
Multilevel analysis
1
2
Probability models
2
1
This book
3
3
Prerequisites
5
1
Notation
5
1
Multilevel Theories, Multi-stage Sampling, and Multilevel Models
6
7
Dependence as a nuisance
6
1
Dependence as an interesting phenomenon
7
2
Macro-level, micro-level, and cross-level relations
9
4
Statistical Treatment of Clustered Data
13
25
Aggregation
13
2
Disaggregation
15
1
The intraclass correlation
16
6
Within-group and between-group variance
18
3
Testing for group differences
21
1
Design effects in two-stage samples
22
2
Reliability of aggregated variables
24
2
Within- and between-group relations
26
9
Regressions
27
4
Correlations
31
2
Estimation of within- and between-group correlations
33
2
Combination of within-group evidence
35
3
The Random Intercept Model
38
29
A regression model: fixed effects only
39
2
Variable intercepts: fixed or random parameters?
41
4
When to use random coefficient models?
43
2
Definition of the random intercept model
45
6
More explanatory variables
51
1
Within- and between-group regressions
52
4
Parameter estimation
56
2
`Estimating' random group effects: posterior means
58
5
Posterior confidence intervals
60
3
Three-level random intercept models
63
4
The Hierarchical Linear Model
67
19
Random slopes
67
5
Heteroscedasticity
68
1
Don't force τ01 to be 0!
69
1
Interpretation of random slope variances
70
2
Explanation of random intercepts and slopes
72
8
Cross-level interaction effects
73
6
A general formulation of fixed and random parts
79
1
Specification of random slope models
80
2
Centering variables with random slopes?
80
2
Estimation
82
1
Three and more levels
83
3
Testing and Model Specification
86
13
Tests for fixed parameters
86
2
Multi-parameter tests for fixed effects
88
1
Deviance tests
88
3
Halved p-values for variance parameters
90
1
Other tests for parameters in the random part
91
1
Model specification
91
8
Working upward from level one
94
2
Joint consideration of level-one and level-two variables
96
1
Concluding remarks about model specification
97
2
How Much Does the Model Explain?
99
11
Explained variance
99
6
Negative values of R2?
99
2
Definitions of proportions of explained variance in two-level models
101
3
Explained variance in three-level models
104
1
Explained variance in models with random slopes
104
1
Components of variance
105
5
Random intercept models
106
2
Random slope models
108
2
Heteroscedasticity
110
10
Heteroscedasticity at level one
110
9
Linear variance functions
110
4
Quadratic variance functions
114
5
Heteroscedasticity at level two
119
1
Assumptions of the Hierarchical Linear Model
120
20
Assumptions of the hierarchical linear model
120
1
Following the logic of the hierarchical linear model
121
3
Include contextual effects
122
1
Check whether variables have random effects
122
1
Explained variance
123
1
Specification of the fixed part
124
1
Specification of the random part
125
3
Testing for heteroscedasticity
126
2
What to do in case of heteroscedasticity
128
1
Inspection of level-one residuals
128
4
Residuals and influence at level two
132
7
Empirical Bayes residuals
132
2
Influence of level-two units
134
5
More general distributional assumptions
139
1
Designing Multilevel Studies
140
15
Some introductory notes on power
141
1
Estimating a population mean
142
1
Measurement of subjects
143
1
Estimating association between variables
144
7
Cross-level interaction effects
148
3
Exploring the variance structure
151
4
The intraclass correlation
151
3
Variance parameters
154
1
Crossed Random Coefficients
155
11
A two-level model with a crossed random factor
155
4
Random slopes of dummy variables
156
3
Crossed random effects in three-level models
159
1
Correlated random coefficients of crossed factors
160
6
Random slopes in a crossed design
160
1
Multiple roles
161
1
Social networks
162
4
Longitudinal Data
166
34
Fixed occasions
167
14
The compound symmetry model
168
3
Random slopes
171
2
The fully multivariate model
173
5
Multivariate regression analysis
178
1
Explained variance
179
2
Variable occasion designs
181
18
Populations of curves
181
1
Random functions
182
11
Explaining the functions
193
2
Changing covariates
195
4
Autocorrelated residuals
199
1
Multivariate Multilevel Models
200
7
The multivariate random intercept model
201
5
Multivariate random slope models
206
1
Discrete Dependent Variables
207
32
Hierarchical generalized linear models
207
1
Introduction to multilevel logistic regression
208
12
Heterogeneous proportions
208
3
The logit function: Log-odds
211
2
The empty model
213
2
The random intercept model
215
3
Estimation
218
1
Aggregation
219
1
Testing the random intercept
220
1
Further topics about multilevel logistic regression
220
9
Random slope model
220
3
Representation as a threshold model
223
1
Residual intraclass correlation coefficient
224
1
Explained variance
225
2
Consequences of adding effects to the model
227
2
Bibliographic remarks
229
1
Ordered categorical variables
229
5
Multilevel Poisson regression
234
5
Software
239
13
Special software for multilevel modeling
239
9
HLM
240
3
MLn/MLwiN
243
2
VARCL
245
2
MIXREG, MIXOR, MIXNO, MIXPREG
247
1
Modules in general purpose software packages
248
3
SAS, procedure MIXED
248
1
SPSS, command VARCOMP
249
1
BMDP-V modules
250
1
Stata
250
1
Other multilevel software
251
1
PinT
251
1
Mplus
251
1
MLA
251
1
BUGS
251
1
References
252
9
Index
261