Using propensity scores in quasi-experimental designs:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles [u.a.]
SAGE
2014
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XVII, 340 S. graph. Darst. |
| ISBN: | 9781452205267 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV040966498 | ||
| 003 | DE-604 | ||
| 005 | 20141029 | ||
| 007 | t | ||
| 008 | 130424s2014 xxud||| |||| 00||| eng d | ||
| 010 | |a 2013005294 | ||
| 020 | |a 9781452205267 |c pbk. : alk. paper |9 978-1-4522-0526-7 | ||
| 035 | |a (OCoLC)856797291 | ||
| 035 | |a (DE-599)BVBBV040966498 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
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| 084 | |a QH 236 |0 (DE-625)141551: |2 rvk | ||
| 100 | 1 | |a Holmes, William M. |d 1931- |e Verfasser |0 (DE-588)1042783349 |4 aut | |
| 245 | 1 | 0 | |a Using propensity scores in quasi-experimental designs |c William M. Holmes |
| 264 | 1 | |a Los Angeles [u.a.] |b SAGE |c 2014 | |
| 300 | |a XVII, 340 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Statistical decision | |
| 650 | 4 | |a Experimental design | |
| 650 | 0 | 7 | |a Statist |0 (DE-588)4618562-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Experiment |0 (DE-588)4015999-1 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Experiment |0 (DE-588)4015999-1 |D s |
| 689 | 0 | 1 | |a Statist |0 (DE-588)4618562-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025944772&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-025944772 | ||
Datensatz im Suchindex
| _version_ | 1804150279302545408 |
|---|---|
| adam_text | Brief Contents
rrerace
xu
Acknowledgments
xvi
About the Author
xvii
1.
Quasi-Experiments and
Nonequivalent
Groups
1
2.
Causal Inference Using Control Variables
24
3.
Causal Inference Using Counterfactual Designs
51
4.
Propensity Approaches for Quasi-Experiments
77
5.
Propensity Matching
103
6.
Propensity Score Optimized Matching
128
7.
Propensities and Weighted Least Squares Regression
147
8.
Propensities and Covariate Controls
169
9.
Use With Generalized Linear Models
191
10.
Propensity With Correlated Samples
216
11.
Handling Missing Data
245
12.
Repairing Broken Experiments
272
Appendixes
299
Appendix
A: Stata
Commands for Propensity Use
300
Appendix
B: R
Commands for Propensity Use
304
Appendix C: SPSS Commands for Propensity Use
309
Appendix
D: SAS
Commands for Propensity Use
315
References
320
Author Index
327
Subject Index
330
Detailed Contents
Preface
Approach of the Book
Example Data
The Genera! Social Survey Panel
The Health and Retirement Study
Improving Knowledge
Acknowledgments
About the Author
xii
xii
xiii
xiv
XV
XV
xvi
xvii
1.
QUASI-EXPERIMENTS AND
NONEQUIVALENT
GROUPS
1
Experiments and Inference
1
The Classic Experiment
6
Quasi-Experiments and Inference
10
Threats to Valid Inference
11
Propensity Scores
12
Quasi-Experiments and Observational Studies
15
Cross-Sectional Designs
15
Ρ
re-Post Comparison Groups
17
Dose-Response Designs
17
Panel Studies
18
Longitudinal Studies
19
Broken Experiments
19
Adequacy and Sufficiency of Causal Inference
20
2.
CAUSAL INFERENCE USING CONTROL VARIABLES
24
Controlling Confoundedness
25
Matching as Controlling
27
Stratifying as Controlling
28
Weighting as Controlling
28
Adjusting as Controlling
29
Multivariate Models for Controlling
29
Selecting Control Variables
31
Theory-Selected Controls
31
Research-Selected Controls
31
Ad Hoc Controls
32
Pretest Controls
33
Misspecification in Causal Models
33
Consistency in Using Controls
34
Getting Consistent Estimates
35
Instrumental Variable Controls
38
Estimating With Instrumental Variables
39
Two-Stage Least Squares
42
Detecting Selection Bias
44
Removing Selection Bias
45
Checking for Misspecification
48
Summary
50
3.
CAUSAL INFERENCE USING
COUNTERFACTUAL DESIGNS
51
Controlled Experiments
51
Random Assignment
52
Criteria Assignment
52
Dropping Out
53
Challenges to Counterfactual Designs
56
Natural Experiments
57
Matching Samples
58
Propensity Matching
59
Key Variable Matching
65
Identifying Key Variables
66
Using Key Variables and Propensity Scores
66
Distance Matching
61
Assessing Matching Results
68
Sample Weighting
72
Adequacy and Sufficiency of Matching
74
Causal Inference With Matching
74
4.
PROPENSITY APPROACHES FOR QUASI-EXPERIMENTS
77
Estimating Propensity Scores
77
Regression Estimation of Propensities
80
Logistic Estimation of Propensities
83
Discriminant Analysis Estimation
86
Linking Estimation and Analysis
87
Estimation Complications
90
Checking Imbalance Reduction
91
Standard Deviation Criteria
92
Percent Reduction
92
Clinical/Substantive Criteria
93
Graphical Criteria
93
Improving Imbalance Reduction
94
Propensity Score Uses
95
Matching
95
Stratifying
97
Regressing
98
Adequacy and Sufficiency of Propensity Estimates
100
5.
PROPENSITY MATCHING
103
One-to-One Matching
107
Matching Similar Propensities
109
Using Calipers
112
Using Distance Criteria
113
Dealing With Dropped Cases
114
Computer Programs for One-to-One Matching
117
One-to-Many Matching
119
Greedy Matching
120
Nongreedy Matching
122
Using Calipers
123
Using Distance Criteria
124
Managing Unequal Cases in Groups
124
Assessing Adequacy and Sufficiency of Matching
125
Summary
126
6.
PROPENSITY SCORE OPTIMIZED MATCHING
128
Full Matching
133
Optimizing Criteria
136
Optimizing Procedures
139
Optimization and Network Flow
140
Genetic Optimized Matching
142
Adequacy and Sufficiency of Optimized Solutions
145
7.
PROPENSITIES AND WEIGHTED LEAST
SQUARES REGRESSION
147
Propensities as Weights
147
Weighting Options
149
Inverse Proportional Weighting
149
Rescaled Inverse Proportional Weighting
150
Reseated Inverse Propensity Weighting
151
Augmented Inverse Propensity Weights
155
Matching Weights
157
Choosing Weights
159
Weighted Regression
163
Assessing Regression Results
165
Assessing Adequacy and Sufficiency of Weighting
167
8.
PROPENSITIES AND COVARIATE CONTROLS
169
Controlling Options
170
Adjustment Options
176
Propensities Versus Time
1
Controls
183
Propensities and Time
1
Controls
184
Assessing Covariate Results
186
Assessing Adequacy and Sufficiency of Covariates
188
9.
USE WITH GENERALIZED LINEAR MODELS
191
Generalized Linear Models
192
Logistic Regression
199
Matched Data With GZLM
207
Weighted Data With GZLM
211
Covariate Data With GZLM
212
Strata and GZLM
213
Adequacy and Sufficiency of GZLM
214
10.
PROPENSITY WITH CORRELATED SAMPLES
216
Paired Samples
216
Paired Sample
t
Test
219
Paired Sample ANOVA and ANCOVA
221
Repeated Measures
222
Pre-Post Comparisons
223
Generalized Estimation Equations
224
Panel Studies
227
Longitudinal Panels
229
Mixed Repeated Designs
229
Geographically Correlated Samples
231
Adjacent Geographic Units
231
Geographic Units Sharing Commonalities
231
Repeated Variable ANOVA
232
Traditional Repeated ANOVA
232
Propensity-Adjusted Repeated ANOVA
233
Cox Regression
233
Proportional
Hazards and Quasi-Experiments
235
Adequacy and Sufficiency With Correlated Samples
243
11.
HANDLING MISSING DATA
245
Identifying Missing Data
248
Imputing Missing Data
258
Monotone Selection
260
FCS Method
261
Propensity Imputation
263
Imbalanced Missing Data
263
Imputation of Missing Data
265
Propensity Estimation With Missing Data
266
Generalized Propensity Scores
267
Matching With Missing Data
269
Stratifying With Missing Data
270
Covariance Control With Missing Data
270
Weights With Missing Data
271
12.
REPAIRING BROKEN EXPERIMENTS
272
When Things Go Wrong
273
Incomplete Randomization
275
Differential Compliance
276
Differential Mortality
280
Differential Events
281
Differential Missing Data
282
Reactive Effects
286
Subject Communication
286
Strong Placebo Effects
287
Propensities and Breakdowns
288
Assessing the Damage
288
Presence of Impact
288
Nature of Impact
291
Strength of Impact
293
Implications of Impact
294
Developing a Strategy
295
Ex Post Facto Matching
295
Propensity Score Weighting
295
Principal Stratification
296
Instrumental Variables
296
Multiple Imputation
297
Getting Missing Data
298
Appendixes 299
Appendix
A: Stata
Commands for Propensity Use
300
Appendix
B: R
Commands for Propensity Use
304
Appendix C: SPSS Commands for Propensity Use
309
Appendix D:
SAS
Commands for Propensity Use
315
References
320
Author Index
327
Subject Index
330
|
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| id | DE-604.BV040966498 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:36:24Z |
| institution | BVB |
| isbn | 9781452205267 |
| language | English |
| lccn | 2013005294 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025944772 |
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| physical | XVII, 340 S. graph. Darst. |
| publishDate | 2014 |
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| publisher | SAGE |
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| spelling | Holmes, William M. 1931- Verfasser (DE-588)1042783349 aut Using propensity scores in quasi-experimental designs William M. Holmes Los Angeles [u.a.] SAGE 2014 XVII, 340 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Statistical decision Experimental design Statist (DE-588)4618562-8 gnd rswk-swf Experiment (DE-588)4015999-1 gnd rswk-swf Experiment (DE-588)4015999-1 s Statist (DE-588)4618562-8 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025944772&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Holmes, William M. 1931- Using propensity scores in quasi-experimental designs Statistical decision Experimental design Statist (DE-588)4618562-8 gnd Experiment (DE-588)4015999-1 gnd |
| subject_GND | (DE-588)4618562-8 (DE-588)4015999-1 |
| title | Using propensity scores in quasi-experimental designs |
| title_auth | Using propensity scores in quasi-experimental designs |
| title_exact_search | Using propensity scores in quasi-experimental designs |
| title_full | Using propensity scores in quasi-experimental designs William M. Holmes |
| title_fullStr | Using propensity scores in quasi-experimental designs William M. Holmes |
| title_full_unstemmed | Using propensity scores in quasi-experimental designs William M. Holmes |
| title_short | Using propensity scores in quasi-experimental designs |
| title_sort | using propensity scores in quasi experimental designs |
| topic | Statistical decision Experimental design Statist (DE-588)4618562-8 gnd Experiment (DE-588)4015999-1 gnd |
| topic_facet | Statistical decision Experimental design Statist Experiment |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025944772&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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