Statistical modeling and inference for social science:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Cambridge University Press
2014
|
| Ausgabe: | First published |
| Schriftenreihe: | Analytical methods for social research
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 361-366 |
| Beschreibung: | xviii, 373 Seiten Diagramme |
| ISBN: | 9781107003149 |
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| 245 | 1 | 0 | |a Statistical modeling and inference for social science |c Sean Gailmard (University of California, Berkeley) |
| 250 | |a First published | ||
| 264 | 1 | |a New York, NY |b Cambridge University Press |c 2014 | |
| 300 | |a xviii, 373 Seiten |b Diagramme | ||
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| 500 | |a Literaturverzeichnis Seite 361-366 | ||
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Datensatz im Suchindex
| _version_ | 1804152521806053376 |
|---|---|
| adam_text | Contents
List of Figures page
xiii
List of Tables xv
Acknowledgments
1
Introduction
1
2
Descriptive Statistics: Data and Information
12
2.1
Measurement
14
2.1.1
Measurement Scales
14
2.7.2
index Construction
17
2.1.3
Measurement Validity
19
2.2
Univariate Distributions
21
2.2.1
Sample Central Tendency
22
2.2.2
Sample Dispersion
27
2.2..Î
Graphical Summaries: Histograms
30
2..:?
Bit/ariate
Distributions
33
2.3.1
Graphical Summaries: Scatterplots
34
2.
J.2 Numerical Summaries: Crosstabs
36
2.3.Л
Conditional Sample Mean
38
2.3.4
Association between Variables: Covariance
and Correlation
40
5.3.
S
Regression
43
23.6
Multiple Regression
52
2.3.7
Specifying Regression Models
56
2.4
Conclusion
61
3
Observable Data and Data-Generating Processes
69
3.1
Data and Data-Generating Processes 7()
3.2
Sampling Uncertainty
73
vii
viii Contents
33
Theoretical Uncertainty
74
3.4
Fundamental Uncertainty
77
3.5
Randomness in DGPs and Observation of Social Events
78
3.6
Stochastic DGPs and the Choice of Empirical Methodology
79
3.7
Conclusion
82
4
Probability Theory: Basic Properties of
D ata
-Generating
Processes
83
4.1
Set-Theoretic Foundations
84
4.1.1
Formal Definitions
84
4.1.2
Probability Measures and Probability Spaces
86
4.1.3
Ontological Interpretations of Probability
8 7
4.1.4
Further Properties of Probability Measures
89
4.2
Independence and Conditional Probability
90
4.2.1
Examples and Simple Combinatorics
92
4.2.2
Bayes s Theorem
96
4.3
Random Variables
98
4.4
Distribution Functions
100
4.4.1
Cumulative Distribution Functions
101
4.4.2
Probability Mass and Density Functions
102
4.5
Multiple Random Variables
106
4.6
Multiuariate Probability Distributions
107
4.6.1 ]
oint
Distributions
107
4.6.2
Marginal Distributions
109
4.6.3
Conditional Distributions
110
4.6.4
Independence of Random Variables
113
4.7
Conclusion
114
5
Expectation and Moments: Summaries of Data-Generating
Processes
116
5.2
Expectation in Univariate Distributions
116
5.1.1
Properties of Expectation
118
5.1.2
Variance
120
5.1.3
The Chebyshev and Markov Inequalities
122
5Л.4
Expectation of a Function of X
123
5.2
Expectation in Multivariate Distributions
124
5.2.1
Conditional Mean and Variance
126
5.2.2
The Law of Iterated Expectations
127
5.2.3
Covariance
128
5.2.4
Correlation
130
5.3
Conclusion
135
6
Probability and Models: Linking Positive Theories and
Data-Generating Processes
137
6.1
DGPs and Theories of Social Phenomena
138
6.1.1
Statistical Models
138
Contents
їх
6.2.2
Parametric
Families of DGPs
140
6.2
The Bernoulli and Binomial Distribution: Binary Events
142
6.2.1
Introducing
a Covar tate
144
6.2.2
Other Flavors of Logit and
Probit
149
6.3
The
Poisson
Distribution: Event Counts
151
6.4
DGPs for Durations
155
6.4.1
Exponential Distribution
155
6
A.
2
Exponential Hazard Rate Model
157
6.4.3
Weibull Distribution
158
6.5
The Uniform Distribution: Equally Likely Outcomes
159
6.6
The Normal Distribution: When All Else Fails
160
6.6.1
Normal Density
161
6.6.2
Z
Scores and the Standard Normal Distribution
163
6.6.3
Models ivith a Normal DGP
164
6.6.4
Bivariate Normal Distribution
166
6.7
Specifying Linear Models
168
6.7.1
Interaction Effects
168
6.7.2
Exponential Effects
170
6.7.3
Saturated Models
171
6.8
Beyond the Means: Comparisons of DGPs
172
6.8.1
DGP Comparisons by First-Order Stochastic
Dominance
173
6.8.2
DGP Comparisons by Variance and Second-Order
Stochastic Dominance
174
6.9
Examples
176
6.9.1
Attitudes toward High- and Low-Skilled Immigration
176
6.9.2
Protest Movements in the
Fortner
Soviet Union
181
6.10
Conclusion
184
7
Sampling Distributions: Linking Data-Generating Processes
and Observable Data
187
7.1
Random Sampling and iid Draws from a DGP
188
7.2
Sample Mean with iid Draws
189
7.2.
J
Expectation of the Sample Mean
190
7.2.2
The Standard Error of X: Standard Deviation of
the Sample Mean
192
7.2.3
The Shape of the Sampling Distribution of
li
19
A
7.2.4
Sampling Distribution of a Difference in Means
from Two Samples
196
7.3
Sums of Random Variables and the CLT
197
7.4
Sample Variance tvitb iid Draws
203
7.4.1
Expected Value of Sample Variance S1
204
7.4.2
Sampling from a Normal Distribution and the
χ2
Distribution
205
;
Contents
7.5
Sa?nple Regression Coefficients with iid Draws
207
7.5.2
Expected Value of the OLS Regression Coefficient
208
7.5.2
Exogeneity of Covariates
212
7.5.3
Omitted Variable Bias
215
7.5.4
Sample Selection Bias from Selecting on
the Dependent Variable
216
7.5.5
Standard Error of
β
under Random Sampling
217
7.5.6
The CUT and the Sampling Distribution of
β
under
Random Sampling
219
7.6
Derived Distributions: Sampling from Nortnal DGPs
When
σ2
Must Be Estimated
220
7.6.1
Student s
t
Distribution
221
7.6.2
The
F
Distribution
224
7.7
Failures of iid in Sampling
226
7.7.1
Expectation and Standard Error of X and
β
under
Nonhidependent Sampling
227
7.7.2
Heteroskedasticity and Issues for Regression Modeling
229
7.7.3
OLS ivitb Robust Standard Errors
230
7.7.4
OLS versus Generalized Least Squares
232
7.8
Conclusion
233
8
Hypothesis Testing: Assessing Claims about the Data-Generating
Process
236
8.1
A Contrived Example
237
8.2
Concepts of Hypothesis Testing
239
8.2.1
Hypotheses and Parameter Space
240
8.2.2
Test Statistics
241
8.2.3
Decision Rules and Ex Post Errors
242
8.2.4
Significance
243
8.2.5
p-Values
244
8.2.6
Test Power
246
8.2.7
False Positives in Multiple Tests
249
8.2.8
Publication Bias and the File-Drawer Problem
250
8.3
Tests about Means Based on Normal Sampling Distributions
251
8.3.1
ζ
Test for a Single DGP Mean
251
8.3.2
t
Test for a Single
D GP
Mean
255
8.3.3
z
Test for a Population Proportion
257
83.4
Difference in Means
t
Test
259
8.3.5
Difference in Proportions
z
Test
261
8.3.6
Matched Pairs
t
Test
262
8.4
Tests Based on a Normal DGP
263
8.4.1
Single Variance
χ1
Test
264
8.4.2
Difference in Variance
F
Test
264
Contents
XV
8.5 Tests
about
Regression
Coefficients
266
8.5.1
z
and t Tests for Regression Coefficients
266
8.5.2
Comparing Regression Slopes
270
8.5.3
F
Tests in Regression
272
8.6
Example: Public Debt and Gross Domestic Product Growth
274
8.7
Nonparametric Tests
278
8.7.1
Contingency Tables:
χ2
Test of Association
279
8.7.2
Mann-Whitney-Wilcoxon
U
Test
282
8.7.3
Kolmogorov-Smirnov Test for Difference
in Distribution
284
8.8
Conclusion
287
9
Estimation: Recovering Properties of the
D ata-
Generating Process
290
9.1
Interval Estimation
291
9.1.1
Confidence Intervals for Normal Sampling
Distributions
293
9.1.2
Confidence Intervals with Estimated Standard Errors
295
9.1.3
Confidence Intervals and Hypothesis Tests
296
9.1.4
Confidence intervals and Opinion Polls
297
9.2
Point Estimation and Criteria for Evaluating Point Estimators
298
9.2.1
Bias
300
9.2.2
Mean Squared Error
301
9.2.3
Variance, Precision, and Efficiency
302
9.2.4
Consistency
304
9.2.5
The
Cramér-Rao
Theorem
307
9.3
Maximum Likelihood Estimation
310
9.3.1
Maximum Likelihood Estimation of Regression Models
314
9.3.2
Likelihood Ratio Tests
318
9.3.3
Properties ofMLEs
319
9.4
Bayesian Estimation
321
9.5
Examples
325
9.5.1
Attitudes toward High- and Low-Skilled Immigration
325
9.5.2
Protest Movements in the Former Soviet Union
327
9Љ
Conclusion
331
10
Causal Inference: Inferring Causation from Correlation
335
10.1
Treatments and Counterfactuals: The Potential Outcomes
Model
336
20.2
Causal Inference in Regression: The Problem
341
10.2
J
Endogeneity Critiques in Applied Research
343
10.3
Causal Inference and Controlled Experiments
344
10.4
Solutions by Controlling for Selection Based on Observable
Covariates
347
10.4.1
Confounding Variables and Conditional Independence
in Regression
347
xii Contents
10.4.2
Matching
348
1 0.43
Regression Discontinuity
349
10.5
Solutions with Selection on Vnobservables
350
10.5.1
Difference-in-Differences and Fixed Effects Regression
Models
350
10.5.2
Instrumental Variables Regression
353
10.6
Conclusion
355
Afterword: Statistical Methods and Empirical Research
358
Bibliography
361
Index
367
|
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| id | DE-604.BV042074865 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:12:02Z |
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| isbn | 9781107003149 |
| language | English |
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| physical | xviii, 373 Seiten Diagramme |
| publishDate | 2014 |
| publishDateSearch | 2014 |
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| series2 | Analytical methods for social research |
| spelling | Gailmard, Sean Verfasser (DE-588)171922387 aut Statistical modeling and inference for social science Sean Gailmard (University of California, Berkeley) First published New York, NY Cambridge University Press 2014 xviii, 373 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Analytical methods for social research Literaturverzeichnis Seite 361-366 Methode (DE-588)4038971-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s Statistik (DE-588)4056995-0 s Methode (DE-588)4038971-6 s DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027516051&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gailmard, Sean Statistical modeling and inference for social science Methode (DE-588)4038971-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4038971-6 (DE-588)4055916-6 (DE-588)4056995-0 |
| title | Statistical modeling and inference for social science |
| title_auth | Statistical modeling and inference for social science |
| title_exact_search | Statistical modeling and inference for social science |
| title_full | Statistical modeling and inference for social science Sean Gailmard (University of California, Berkeley) |
| title_fullStr | Statistical modeling and inference for social science Sean Gailmard (University of California, Berkeley) |
| title_full_unstemmed | Statistical modeling and inference for social science Sean Gailmard (University of California, Berkeley) |
| title_short | Statistical modeling and inference for social science |
| title_sort | statistical modeling and inference for social science |
| topic | Methode (DE-588)4038971-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Methode Sozialwissenschaften Statistik |
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