Data analysis using regression and multilevel, hierarchical models:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2007
|
| Edition: | 1. publ. |
| Series: | Analytical methods for social research
|
| Subjects: | |
| Online Access: | Publisher description Table of contents only Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke. |
| Physical Description: | XXII, 625 S. graph. Darst. |
| ISBN: | 0521867061 052168689X 9780521867061 9780521686891 |
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| 100 | 1 | |a Gelman, Andrew |d 1965- |e Verfasser |0 (DE-588)128832592 |4 aut | |
| 245 | 1 | 0 | |a Data analysis using regression and multilevel, hierarchical models |c Andrew Gelman ; Jennifer Hill |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2007 | |
| 300 | |a XXII, 625 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Analytical methods for social research | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke. | ||
| 650 | 4 | |a Analyse de régression | |
| 650 | 4 | |a Modèles multiniveaux (Statistique) | |
| 650 | 7 | |a Multiniveau-analyse |2 gtt | |
| 650 | 7 | |a Regressieanalyse |2 gtt | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Multilevel models (Statistics) | |
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| 700 | 1 | |a Hill, Jennifer |e Verfasser |4 aut | |
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| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0668/2006040566-t.html |3 Table of contents only | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015428351&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-015428351 | ||
Record in the Search Index
| _version_ | 1804136198891896832 |
|---|---|
| adam_text | Contents
List of examples page
xvii
Preface
xix
1
Why?
1
1.1
What is multilevel regression modeling?
1
1.2
Some examples from our own research
3
1.3
Motivations for multilevel modeling
б
1.4
Distinctive features of this book
8
1.5
Computing
9
2
Concepts and methods from basic probability and statistics
13
2.1
Probability distributions
13
2.2
Statistical inference
16
2.3
Classical confidence intervals
18
2.4
Classical hypothesis testing
20
2.5
Problems with statistical significance
22
2.6 55,000
residents desperately need your help!
23
2.7
Bibliographic note
26
2.8
Exercises
26
Part 1A: Single-level regression
29
3
Linear regression: the basics
31
3.1
One predictor
31
3.2
Multiple predictors
32
3.3
Interactions
34
3.4
Statistical inference
37
3.5
Graphical displays of data and fitted model
42
3.6
Assumptions and diagnostics
45
3.7
Prediction and validation
47
3.8
Bibliographic note
49
3.9
Exercises
49
4
Linear regression: before and after fitting the model
53
4.1
Linear transformations
53
4.2
Centering and standardizing, especially for models with interactions
55
4.3
Correlation and regression to the mean
57
4.4
Logarithmic transformations
59
4.5
Other transformations
65
4.6
Building regression models for prediction
68
4.7
Fitting a series of regressions
73
χ
CONTENTS
4.8 Bibliographie
note
74
4.9
Exercises
74
5
Logistic regression
79
5.1
Logistic regression with a single predictor
79
5.2
Interpreting the logistic regression coefficients
81
5.3
Latent-data formulation
85
5.4
Building a logistic regression model: wells in Bangladesh
86
5.5
Logistic regression with interactions
92
5.6
Evaluating, checking, and comparing fitted logistic regressions
97
5.7
Average predictive comparisons on the probability scale
101
5.8
Identifiability and separation
104
5.9
Bibliographic note
105
5.10
Exercises
105
6
Generalized linear models
109
6.1
Introduction
109
6.2
Poisson
regression, exposure, and overdispersion
110
6.3
Logistic-binomial model
116
6.4
Probit
regression: normally distributed latent data
118
6.5
Ordered and unordered categorical regression
119
6.6
Robust regression using the
í
model
124
6.7
Building more complex generalized linear models
125
6.8
Constructive choice models
127
6.9
Bibliographic note
131
6.10
Exercises
132
Part IB: Working with regression inferences
135
7
Simulation of probability models and statistical inferences
137
7.1
Simulation of probability models
137
7.2
Summarizing linear regressions using simulation: an informal
Bayesian approach
140
7.3
Simulation for nonlinear predictions: congressional elections
144
7.4
Predictive simulation for generalized linear models
148
7.5
Bibliographic note
151
7.6
Exercises
152
8
Simulation for checking statistical procedures and model fits
155
8.1
Fake-data simulation
155
8.2
Example: using fake-data simulation to understand residual plots
157
8.3
Simulating from the fitted model and comparing to actual data
158
8.4
Using predictive simulation to check the fit of a time-series model
163
8.5
Bibliographic note
165
8.6
Exercises
165
9
Causal inference using regression on the treatment variable
167
9.1
Causal inference and predictive comparisons
167
9.2
The fundamental problem of causal inference
170
9.3
Randomized experiments
172
9.4
Treatment interactions and
poststratification
178
CONTENTS xi
9.5
Observational studies
181
9.6
Understanding causal inference in observational studies
186
9.7
Do not control for post-treatment variables
188
9.8
Intermediate outcomes and causal paths
190
9.9
Bibliographic note
194
9.10
Exercises
194
10
Causal inference using more advanced models
199
10.1
Imbalance and lack of complete overlap
199
10.2
Subclassification:
effects and estimates for different
subpopulations
204
10.3
Matching: subsetting the data to get overlapping and balanced
treatment and control groups
206
10.4
Lack of overlap when the assignment mechanism is known:
regression discontinuity
212
10.5
Estimating causal effects indirectly using instrumental variables
215
10.6
Instrumental variables in a regression framework
220
10.7
Identification strategies that make use of variation within or between
groups
226
10.8
Bibliographic note
229
10.9
Exercises
231
Part 2A: Multilevel regression
235
11
Multilevel structures
237
11.1
Varying-intercept and varying-slope models
237
11.2
Clustered data: child support enforcement in cities
237
11.3
Repeated measurements, time-series cross sections, and other
non-nested structures
241
11.4
Indicator variables and fixed or random effects
244
11.5
Costs and benefits of multilevel modeling
246
11.6
Bibliographic note
247
11.7
Exercises
248
12
Multilevel linear models: the basics
251
12.1
Notation
251
12.2
Partial pooling with no predictors
252
12.3
Partial pooling with predictors
254
12.4
Quickly fitting multilevel models in
R
259
12.5
Five ways to write the same model
262
12.6
Group-level predictors
265
12.7
Model building and statistical significance
270
12.8
Predictions for new observations and new groups
272
12.9
How many groups and how many observations per group are
needed to fit a multilevel model?
275
12.10
Bibliographic note
276
12.11
Exercises
277
13
Multilevel linear models: varying slopes, non-nested models, and
other complexities
279
13.1
Varying intercepts and slopes
279
13.2
Varying slopes without varying intercepts
283
xii CONTENTS
13.3
Modeling
multiple
varying coefficients using the scaled inverse-
Wishart distribution
284
13.4
Understanding correlations between group-level intercepts and
slopes
287
13.5
Non-nested models
289
13.6
Selecting, transforming, and combining regression inputs
293
13.7
More complex multilevel models
297
13.8
Bibliographic note
297
13.9
Exercises
298
14
Multilevel logistic regression
301
14.1
State-level opinions from national polls
301
14.2
Red states and blue states: what s the matter with Connecticut?
310
14.3
Item-response and ideal-point models
314
14.4
Non-nested overdispersed model for death sentence reversals
320
14.5
Bibliographic note
321
14.6
Exercises
322
15
Multilevel generalized linear models
325
15.1
Overdispersed
Poisson
regression: police stops and ethnicity
325
15.2
Ordered categorical regression: storable votes
331
15.3
Non-nested negative-binomial model of structure in social networks
332
15.4
Bibliographic note
342
15.5
Exercises
342
Part 2B: Fitting multilevel models
343
16
Multilevel modeling in Bugs and R: the basics
345
16.1
Why you should learn Bugs
345
16.2
Bayesian inference and prior distributions
345
16.3
Fitting and understanding a varying-intercept multilevel model
using
R
and Bugs
348
16.4
Step by step through a Bugs model, as called from
R
353
16.5
Adding individual- and group-level predictors
359
16.6
Predictions for new observations and new groups
361
16.7
Fake-data simulation
363
16.8
The principles of modeling in Bugs
366
16.9
Practical issues of implementation
369
16.10
Open-ended modeling in Bugs
370
16.11
Bibliographic note
373
16.12
Exercises
373
17
Fitting multilevel linear and generalized linear models in Bugs
and
R
375
17.1
Varying-intercept, varying-slope models
375
17.2
Varying intercepts and slopes with group-level predictors
379
17.3
Non-nested models
380
17.4
Multilevel logistic regression
381
17.5
Multilevel
Poisson
regression
382
17.6
Multilevel ordered categorical regression
383
17.7
Latent-data parameterizations of generalized linear models
384
CONTENTS xiii
17.8 Bibliographie
note
385
17.9
Exercises
385
18
Likelihood and Bayesian inference and computation
387
18.1
Least squares and maximum likelihood estimation
387
18.2
Uncertainty estimates using the likelihood surface
390
18.3
Bayesian inference for classical and multilevel regression
392
18.4
Gibbs sampler for multilevel linear models
397
18.5
Likelihood inference, Bayesian inference, and the Gibbs sampler:
the case of censored data
402
18.6
Metropolis algorithm for more general Bayesian computation
408
18.7
Specifying a log posterior density, Gibbs sampler, and Metropolis
algorithm in
R
409
18.8
Bibliographic note
413
18.9
Exercises
413
19
Debugging and speeding convergence
415
19.1
Debugging and confidence building
415
19.2
General methods for reducing computational requirements
418
19.3
Simple linear transformations
419
19.4
Redundant parameters and intentionally nonidentifiable models
419
19.5
Parameter expansion: multiplicative redundant parameters
424
19.6
Using redundant parameters to create an informative prior
distribution for multilevel variance parameters
427
19.7
Bibliographic note
434
19.8
Exercises
434
Part
3:
From data collection to model understanding to model
checking
435
20
Sample size and power calculations
437
20.1
Choices in the design of data collection
437
20.2
Classical power calculations: general principles, as illustrated by
estimates of proportions
439
20.3
Classical power calculations for continuous outcomes
443
20.4
Multilevel power calculation for cluster sampling
447
20.5
Multilevel power calculation using fake-data simulation
449
20.6
Bibliographic note
454
20.7
Exercises
454
21
Understanding and summarizing the fitted models
457
21.1
Uncertainty and variability
457
21.2
Superpopulation
and finite-population variances
459
21.3
Contrasts and comparisons of multilevel coefficients
462
21.4
Average predictive comparisons
466
21.5
R2 and explained variance
473
21.6
Summarizing the amount of partial pooling
477
21.7
Adding a predictor can increase the residual variance!
480
21.8
Multiple comparisons and statistical significance
481
21.9
Bibliographic note
484
21.10
Exercises
485
xiv CONTENTS
22
Analysis of variance
487
22.1
Classical analysis of variance
487
22.2
ANO VA
and multilevel linear and generalized linear models
490
22.3
Summarizing multilevel models using
ANO VA
492
22.4
Doing
ANOVA
using multilevel models
494
22.5
Adding predictors: analysis of covariance and contrast analysis
496
22.6
Modeling the variance parameters: a split-plot latin square
498
22.7
Bibliographic note
501
22.8
Exercises
501
23
Causal inference using multilevel models
503
23.1
Multilevel aspects of data collection
503
23.2
Estimating treatment effects in a multilevel observational study
506
23.3
Treatments applied at different levels
507
23.4
Instrumental variables and multilevel modeling
509
23.5
Bibliographic note
512
23.6
Exercises
512
24
Model checking and comparison
513
24.1
Principles of predictive checking
513
24.2
Example: a behavioral learning experiment
515
24.3
Model comparison and deviance
524
24.4
Bibliographic note
526
24.5
Exercises
527
25
Missing-data imputation
529
25.1
Missing-data mechanisms
530
25.2
Missing-data methods that discard data
531
25.3
Simple missing-data approaches that retain all the data
532
25.4
Random imputation of a single variable
533
25.5
Imputation of several missing variables
539
25.6
Model-based imputation
540
25.7
Combining inferences from multiple imputations
542
25.8
Bibliographic note
542
25.9
Exercises
543
Appendixes
545
A Six quick tips to improve your regression modeling
547
A.I Fit many models
547
A.
2
Do a little work to make your computations faster and more reliable
547
A.3 Graphing the relevant and not the irrelevant
548
A.
4
Transformations
548
A.
5
Consider all coefficients as potentially varying
549
A.6 Estimate causal inferences in a targeted way, not as a byproduct
of a large regression
549
В
Statistical graphics for research and presentation
551
B.I Reformulating a graph by focusing on comparisons
552
B.2 Scatterplots
553
B.3 Miscellaneous tips
559
CONTENTS xv
B.4 Bibliographie
note
562
B.5
Exercises
563
С
Software
565
C.I Getting started with
R,
Bugs, and a text editor
565
C.2 Fitting classical and multilevel regressions in
R
565
C.3 Fitting models in Bugs and
R
567
С.
4
Fitting multilevel models using R,
Stata,
SAS,
and other software
568
С
5
Bibliographic note
573
References
575
Author index
601
Subject index
607
|
| adam_txt |
Contents
List of examples page
xvii
Preface
xix
1
Why?
1
1.1
What is multilevel regression modeling?
1
1.2
Some examples from our own research
3
1.3
Motivations for multilevel modeling
б
1.4
Distinctive features of this book
8
1.5
Computing
9
2
Concepts and methods from basic probability and statistics
13
2.1
Probability distributions
13
2.2
Statistical inference
16
2.3
Classical confidence intervals
18
2.4
Classical hypothesis testing
20
2.5
Problems with statistical significance
22
2.6 55,000
residents desperately need your help!
23
2.7
Bibliographic note
26
2.8
Exercises
26
Part 1A: Single-level regression
29
3
Linear regression: the basics
31
3.1
One predictor
31
3.2
Multiple predictors
32
3.3
Interactions
34
3.4
Statistical inference
37
3.5
Graphical displays of data and fitted model
42
3.6
Assumptions and diagnostics
45
3.7
Prediction and validation
47
3.8
Bibliographic note
49
3.9
Exercises
49
4
Linear regression: before and after fitting the model
53
4.1
Linear transformations
53
4.2
Centering and standardizing, especially for models with interactions
55
4.3
Correlation and "regression to the mean"
57
4.4
Logarithmic transformations
59
4.5
Other transformations
65
4.6
Building regression models for prediction
68
4.7
Fitting a series of regressions
73
χ
CONTENTS
4.8 Bibliographie
note
74
4.9
Exercises
74
5
Logistic regression
79
5.1
Logistic regression with a single predictor
79
5.2
Interpreting the logistic regression coefficients
81
5.3
Latent-data formulation
85
5.4
Building a logistic regression model: wells in Bangladesh
86
5.5
Logistic regression with interactions
92
5.6
Evaluating, checking, and comparing fitted logistic regressions
97
5.7
Average predictive comparisons on the probability scale
101
5.8
Identifiability and separation
104
5.9
Bibliographic note
105
5.10
Exercises
105
6
Generalized linear models
109
6.1
Introduction
109
6.2
Poisson
regression, exposure, and overdispersion
110
6.3
Logistic-binomial model
116
6.4
Probit
regression: normally distributed latent data
118
6.5
Ordered and unordered categorical regression
119
6.6
Robust regression using the
í
model
124
6.7
Building more complex generalized linear models
125
6.8
Constructive choice models
127
6.9
Bibliographic note
131
6.10
Exercises
132
Part IB: Working with regression inferences
135
7
Simulation of probability models and statistical inferences
137
7.1
Simulation of probability models
137
7.2
Summarizing linear regressions using simulation: an informal
Bayesian approach
140
7.3
Simulation for nonlinear predictions: congressional elections
144
7.4
Predictive simulation for generalized linear models
148
7.5
Bibliographic note
151
7.6
Exercises
152
8
Simulation for checking statistical procedures and model fits
155
8.1
Fake-data simulation
155
8.2
Example: using fake-data simulation to understand residual plots
157
8.3
Simulating from the fitted model and comparing to actual data
158
8.4
Using predictive simulation to check the fit of a time-series model
163
8.5
Bibliographic note
165
8.6
Exercises
165
9
Causal inference using regression on the treatment variable
167
9.1
Causal inference and predictive comparisons
167
9.2
The fundamental problem of causal inference
170
9.3
Randomized experiments
172
9.4
Treatment interactions and
poststratification
178
CONTENTS xi
9.5
Observational studies
181
9.6
Understanding causal inference in observational studies
186
9.7
Do not control for post-treatment variables
188
9.8
Intermediate outcomes and causal paths
190
9.9
Bibliographic note
194
9.10
Exercises
194
10
Causal inference using more advanced models
199
10.1
Imbalance and lack of complete overlap
199
10.2
Subclassification:
effects and estimates for different
subpopulations
204
10.3
Matching: subsetting the data to get overlapping and balanced
treatment and control groups
206
10.4
Lack of overlap when the assignment mechanism is known:
regression discontinuity
212
10.5
Estimating causal effects indirectly using instrumental variables
215
10.6
Instrumental variables in a regression framework
220
10.7
Identification strategies that make use of variation within or between
groups
226
10.8
Bibliographic note
229
10.9
Exercises
231
Part 2A: Multilevel regression
235
11
Multilevel structures
237
11.1
Varying-intercept and varying-slope models
237
11.2
Clustered data: child support enforcement in cities
237
11.3
Repeated measurements, time-series cross sections, and other
non-nested structures
241
11.4
Indicator variables and fixed or random effects
244
11.5
Costs and benefits of multilevel modeling
246
11.6
Bibliographic note
247
11.7
Exercises
248
12
Multilevel linear models: the basics
251
12.1
Notation
251
12.2
Partial pooling with no predictors
252
12.3
Partial pooling with predictors
254
12.4
Quickly fitting multilevel models in
R
259
12.5
Five ways to write the same model
262
12.6
Group-level predictors
265
12.7
Model building and statistical significance
270
12.8
Predictions for new observations and new groups
272
12.9
How many groups and how many observations per group are
needed to fit a multilevel model?
275
12.10
Bibliographic note
276
12.11
Exercises
277
13
Multilevel linear models: varying slopes, non-nested models, and
other complexities
279
13.1
Varying intercepts and slopes
279
13.2
Varying slopes without varying intercepts
283
xii CONTENTS
13.3
Modeling
multiple
varying coefficients using the scaled inverse-
Wishart distribution
284
13.4
Understanding correlations between group-level intercepts and
slopes
287
13.5
Non-nested models
289
13.6
Selecting, transforming, and combining regression inputs
293
13.7
More complex multilevel models
297
13.8
Bibliographic note
297
13.9
Exercises
298
14
Multilevel logistic regression
301
14.1
State-level opinions from national polls
301
14.2
Red states and blue states: what's the matter with Connecticut?
310
14.3
Item-response and ideal-point models
314
14.4
Non-nested overdispersed model for death sentence reversals
320
14.5
Bibliographic note
321
14.6
Exercises
322
15
Multilevel generalized linear models
325
15.1
Overdispersed
Poisson
regression: police stops and ethnicity
325
15.2
Ordered categorical regression: storable votes
331
15.3
Non-nested negative-binomial model of structure in social networks
332
15.4
Bibliographic note
342
15.5
Exercises
342
Part 2B: Fitting multilevel models
343
16
Multilevel modeling in Bugs and R: the basics
345
16.1
Why you should learn Bugs
345
16.2
Bayesian inference and prior distributions
345
16.3
Fitting and understanding a varying-intercept multilevel model
using
R
and Bugs
348
16.4
Step by step through a Bugs model, as called from
R
353
16.5
Adding individual- and group-level predictors
359
16.6
Predictions for new observations and new groups
361
16.7
Fake-data simulation
363
16.8
The principles of modeling in Bugs
366
16.9
Practical issues of implementation
369
16.10
Open-ended modeling in Bugs
370
16.11
Bibliographic note
373
16.12
Exercises
373
17
Fitting multilevel linear and generalized linear models in Bugs
and
R
375
17.1
Varying-intercept, varying-slope models
375
17.2
Varying intercepts and slopes with group-level predictors
379
17.3
Non-nested models
380
17.4
Multilevel logistic regression
381
17.5
Multilevel
Poisson
regression
382
17.6
Multilevel ordered categorical regression
383
17.7
Latent-data parameterizations of generalized linear models
384
CONTENTS xiii
17.8 Bibliographie
note
385
17.9
Exercises
385
18
Likelihood and Bayesian inference and computation
387
18.1
Least squares and maximum likelihood estimation
387
18.2
Uncertainty estimates using the likelihood surface
390
18.3
Bayesian inference for classical and multilevel regression
392
18.4
Gibbs sampler for multilevel linear models
397
18.5
Likelihood inference, Bayesian inference, and the Gibbs sampler:
the case of censored data
402
18.6
Metropolis algorithm for more general Bayesian computation
408
18.7
Specifying a log posterior density, Gibbs sampler, and Metropolis
algorithm in
R
409
18.8
Bibliographic note
413
18.9
Exercises
413
19
Debugging and speeding convergence
415
19.1
Debugging and confidence building
415
19.2
General methods for reducing computational requirements
418
19.3
Simple linear transformations
419
19.4
Redundant parameters and intentionally nonidentifiable models
419
19.5
Parameter expansion: multiplicative redundant parameters
424
19.6
Using redundant parameters to create an informative prior
distribution for multilevel variance parameters
427
19.7
Bibliographic note
434
19.8
Exercises
434
Part
3:
From data collection to model understanding to model
checking
435
20
Sample size and power calculations
437
20.1
Choices in the design of data collection
437
20.2
Classical power calculations: general principles, as illustrated by
estimates of proportions
439
20.3
Classical power calculations for continuous outcomes
443
20.4
Multilevel power calculation for cluster sampling
447
20.5
Multilevel power calculation using fake-data simulation
449
20.6
Bibliographic note
454
20.7
Exercises
454
21
Understanding and summarizing the fitted models
457
21.1
Uncertainty and variability
457
21.2
Superpopulation
and finite-population variances
459
21.3
Contrasts and comparisons of multilevel coefficients
462
21.4
Average predictive comparisons
466
21.5
R2 and explained variance
473
21.6
Summarizing the amount of partial pooling
477
21.7
Adding a predictor can increase the residual variance!
480
21.8
Multiple comparisons and statistical significance
481
21.9
Bibliographic note
484
21.10
Exercises
485
xiv CONTENTS
22
Analysis of variance
487
22.1
Classical analysis of variance
487
22.2
ANO VA
and multilevel linear and generalized linear models
490
22.3
Summarizing multilevel models using
ANO VA
492
22.4
Doing
ANOVA
using multilevel models
494
22.5
Adding predictors: analysis of covariance and contrast analysis
496
22.6
Modeling the variance parameters: a split-plot latin square
498
22.7
Bibliographic note
501
22.8
Exercises
501
23
Causal inference using multilevel models
503
23.1
Multilevel aspects of data collection
503
23.2
Estimating treatment effects in a multilevel observational study
506
23.3
Treatments applied at different levels
507
23.4
Instrumental variables and multilevel modeling
509
23.5
Bibliographic note
512
23.6
Exercises
512
24
Model checking and comparison
513
24.1
Principles of predictive checking
513
24.2
Example: a behavioral learning experiment
515
24.3
Model comparison and deviance
524
24.4
Bibliographic note
526
24.5
Exercises
527
25
Missing-data imputation
529
25.1
Missing-data mechanisms
530
25.2
Missing-data methods that discard data
531
25.3
Simple missing-data approaches that retain all the data
532
25.4
Random imputation of a single variable
533
25.5
Imputation of several missing variables
539
25.6
Model-based imputation
540
25.7
Combining inferences from multiple imputations
542
25.8
Bibliographic note
542
25.9
Exercises
543
Appendixes
545
A Six quick tips to improve your regression modeling
547
A.I Fit many models
547
A.
2
Do a little work to make your computations faster and more reliable
547
A.3 Graphing the relevant and not the irrelevant
548
A.
4
Transformations
548
A.
5
Consider all coefficients as potentially varying
549
A.6 Estimate causal inferences in a targeted way, not as a byproduct
of a large regression
549
В
Statistical graphics for research and presentation
551
B.I Reformulating a graph by focusing on comparisons
552
B.2 Scatterplots
553
B.3 Miscellaneous tips
559
CONTENTS xv
B.4 Bibliographie
note
562
B.5
Exercises
563
С
Software
565
C.I Getting started with
R,
Bugs, and a text editor
565
C.2 Fitting classical and multilevel regressions in
R
565
C.3 Fitting models in Bugs and
R
567
С.
4
Fitting multilevel models using R,
Stata,
SAS,
and other software
568
С
5
Bibliographic note
573
References
575
Author index
601
Subject index
607 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Gelman, Andrew 1965- Hill, Jennifer |
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| callnumber-subject | HA - Statistics |
| classification_rvk | CM 4000 QH 234 SK 840 |
| classification_tum | MAT 628f |
| ctrlnum | (OCoLC)67375137 (DE-599)BVBBV022217074 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Psychologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Psychologie Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV022217074 |
| illustrated | Illustrated |
| index_date | 2024-07-02T16:27:46Z |
| indexdate | 2024-07-09T20:52:36Z |
| institution | BVB |
| isbn | 0521867061 052168689X 9780521867061 9780521686891 |
| language | English |
| lccn | 2006040566 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015428351 |
| oclc_num | 67375137 |
| open_access_boolean | |
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| physical | XXII, 625 S. graph. Darst. |
| publishDate | 2007 |
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| publishDateSort | 2007 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| series2 | Analytical methods for social research |
| spelling | Gelman, Andrew 1965- Verfasser (DE-588)128832592 aut Data analysis using regression and multilevel, hierarchical models Andrew Gelman ; Jennifer Hill 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2007 XXII, 625 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Analytical methods for social research Hier auch später erschienene, unveränderte Nachdrucke. Analyse de régression Modèles multiniveaux (Statistique) Multiniveau-analyse gtt Regressieanalyse gtt Regression analysis Multilevel models (Statistics) Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s DE-604 Hill, Jennifer Verfasser aut http://www.loc.gov/catdir/enhancements/fy0668/2006040566-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0668/2006040566-t.html Table of contents only Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015428351&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gelman, Andrew 1965- Hill, Jennifer Data analysis using regression and multilevel, hierarchical models Analyse de régression Modèles multiniveaux (Statistique) Multiniveau-analyse gtt Regressieanalyse gtt Regression analysis Multilevel models (Statistics) Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4129903-6 |
| title | Data analysis using regression and multilevel, hierarchical models |
| title_auth | Data analysis using regression and multilevel, hierarchical models |
| title_exact_search | Data analysis using regression and multilevel, hierarchical models |
| title_exact_search_txtP | Data analysis using regression and multilevel, hierarchical models |
| title_full | Data analysis using regression and multilevel, hierarchical models Andrew Gelman ; Jennifer Hill |
| title_fullStr | Data analysis using regression and multilevel, hierarchical models Andrew Gelman ; Jennifer Hill |
| title_full_unstemmed | Data analysis using regression and multilevel, hierarchical models Andrew Gelman ; Jennifer Hill |
| title_short | Data analysis using regression and multilevel, hierarchical models |
| title_sort | data analysis using regression and multilevel hierarchical models |
| topic | Analyse de régression Modèles multiniveaux (Statistique) Multiniveau-analyse gtt Regressieanalyse gtt Regression analysis Multilevel models (Statistics) Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Analyse de régression Modèles multiniveaux (Statistique) Multiniveau-analyse Regressieanalyse Regression analysis Multilevel models (Statistics) Regressionsanalyse |
| url | http://www.loc.gov/catdir/enhancements/fy0668/2006040566-d.html http://www.loc.gov/catdir/enhancements/fy0668/2006040566-t.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015428351&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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