Applied survey data analysis:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
[2017]
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Chapmann & Hall/CRC Statistics in the Social and Behavioral Sciences Series
A Chapman & Hall Book |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben und Index |
| Beschreibung: | xxii, 568 Seiten Diagramme |
| ISBN: | 9781498761604 |
Internformat
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| 100 | 1 | |a Heeringa, Steven |d 1953- |0 (DE-588)14239386X |4 aut | |
| 245 | 1 | 0 | |a Applied survey data analysis |c Steven G. Heeringa, Brady T. West, and Patricia A. Berglund |
| 250 | |a Second edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press |c [2017] | |
| 300 | |a xxii, 568 Seiten |b Diagramme | ||
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| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Social sciences |x Statistics | |
| 650 | 4 | |a Social surveys |x Statistical methods | |
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Datensatz im Suchindex
| _version_ | 1804177694999445504 |
|---|---|
| adam_text | Contents
Preface......................................... ...................xv
Authors.............................................................xxi
1. Applied Survey Data Analysis: An Overview.....................1
1.1 Introduction................................................1
1.2 A Brief History of Applied Survey Data Analysis.............2
1.2.1 Key Theoretical Developments.........................2
1.2.2 Key Software Developments............................4
1.3 Example Data Sets and Exercises ............................5
1.4 Steps in Applied Survey Data Analysis..................... 9
2. Getting to Know the Complex Sample Design........................15
2.1 Introduction...............................................15
2.1.1 Technical Documentation and Supplemental
Literature Review...................................15
2.2 Classification of Sample Designs...........................16
2.2.1 Sampling Plans.................................... 17
2.2.2 Other Types of Study Designs Involving Probability
Sampling............................................18
2.2.3 Inference from Survey Data..........................19
2.3 Target Populations and Survey Populations..................20
2.4 Simple Random Sampling: A Simple Model for Design-
Based Inference............................................21
2.4.1 Relevance of SRS to Complex Sample Survey Data
Analysis............................................21
2.4.2 SRS Fundamentals: A Framework for Design-Based
Inference...........................................22
2.4.3 Example of Design-Based Inference under SRS.........24
2.5 Complex Sample Design Effects..............................26
2.5.1 Design Effect Ratio.................................26
2.5.2 Generalized Design Effects and Effective Sample Sizes.... 28
2.6 Complex Samples: Cluster Sampling and Stratification.......30
2.6.1 Cluster Sampling Plans............................. 31
2.6.2 Stratification.................................... 34
2.6.3 Joint Effects of Sample Stratification and Cluster
Sampling ......................................... 37
2.7 Weighting in Analysis of Survey Data.......................38
2.7.1 Introduction to Weighted Analysis of Survey Data....38
2.7.2 Weighting for Probabilities of Selection (wsei).....40
v
vi
Contents
2.7.3 Nonresponse Adjustment Weights (wnr).................42
2.7.3.1 Weighting Class Approach (zvnr/WC)...........42
2.7.3.2 Propensity Cell Adjustment Approach (zvnrprop).... 43
2.7.4 Poststratification Weight Factors (wps)..............45
2.7.5 Design Effects Due to Weighted Analysis..............47
2.8 Multistage Area Probability Sample Designs.................49
2.8.1 Primary Stage Sampling...............................50
2.8.2 Secondary Stage Sampling.............................51
2.8.3 Third- and Fourth-Stage Sampling of HUs
and Eligible Respondents.............................52
2.9 Special Types of Sampling Plans Encountered in Surveys......53
3* Foundations and Techniques for Design-Based Estimation
and Inference..........,.........................................55
3.1 Introduction................................................55
3.2 Finite Populations and Superpopulation Models...............56
3.3 CIs for Population Parameters...............................58
3.4 Weighted Estimation of Population Parameters................59
3.5 Probability Distributions and Design-Based Inference........62
3.5.1 Sampling Distributions of Survey Estimates...........62
3.5.2 Degrees of Freedom for t under Complex Sample
Designs..............................................65
3.6 Variance Estimation.........................................67
3.6.1 Simplifying Assumptions Employed in Complex
Sample Variance Estimation......................... 69
3.6.2 TSL Method......................................... 70
3.6.3 Replication Methods for Variance Estimation..........75
3.6.3.1 Jackknife Repeated Replication...............76
3.6.3.2 Balanced Repeated Replication................80
3.6.3.3 Fay s BRR Method.............................83
3.6.3.4 Bootstrap (Rao-Wu Rescaling Bootstrap).......84
3.6.3.5 Construction of Replicate Weights for
Replicated Variance Estimation...............85
3.6.4 Example Comparing Results from the TSL, JRR,
BRR, and Bootstrap Methods...........................86
3.7 Hypothesis Testing in Survey Data Analysis..................89
3.8 TSE and Its Impact on Survey Estimation and Inference.......91
3.8.1 Variable Errors......................................91
3.8.2 Biases in Survey Data..............................92 4
4. Preparation for Complex Sample Survey Data Analysis..............97
4.1 Introduction................................................97
4.2 Final Survey Weights: Review by the Data User...............98
4.2.1 Identification of the Correct Weight Variable(s)
for the Analysis
99
Contents
vii
4.2.2 Determining the Distribution and Scaling
of the Weight Variable(s)..........................100
4.2.3 Weighting Applications: Sensitivity of Survey
Estimates to the Weights...........................102
4.3 Understanding and Checking the Sampling Error
Calculation Model.........................................104
4.3.1 Stratum and Cluster Codes in Complex Sample
Survey Data Sets...................................105
4.3.2 Building the NCS-R Sampling Error Calculation
Model..............................................107
4.3.3 Combining Strata, Randomly Grouping PSUs,
and Collapsing Strata..............................109
4.3.4 Checking the Sampling Error Calculation Model
for the Survey Data Set............................Ill
4.4 Addressing Item Missing Data in Analysis Variables........113
4.4.1 Potential Bias due to Ignoring Missing Data........114
4.4.2 Exploring Rates and Patterns of Missing Data
Prior to Analysis..................................114
4.5 Preparing to Analyze Data for Sample Subpopulations.......116
4.5.1 Subpopulation Distributions across Sample
Design Units.......................................118
4.5.2 Unconditional Approach for Subclass Analysis.......119
4.5.3 Preparation for Subclass Analyses..................121
4.6 Final Checklist for Data Users..........................121 5
5. Descriptive Analysis for Continuous Variables..................125
5.1 Introduction..............................................125
5.2 Special Considerations in Descriptive Analysis
of Complex Sample Survey Data.............................126
5.2.1 Weighted Estimation................................126
5.2.2 Design Effects for Descriptive Statistics..........127
5.2.3 Matching the Method to the Variable Type...........128
5.3 Simple Statistics for Univariate Continuous Distributions.128
5.3.1 Graphical Tools for Descriptive Analysis
of Survey Data.................................... 129
5.3.2 Estimation of Population Totals....................131
5.3.3 Means of Continuous, Binary, or Interval
Scale Data........................................ 136
5.3.4 Standard Deviations of Continuous Variables........139
5.3.5 Estimation of Percentiles, Medians, and Measures
of Inequality in Population Distributions for
Continuous Variables................................140
5.3.5.1 Estimation of Distribution Quantiles.......140
5.3.5.2 Estimation of Measures of Inequality
in Population Distributions................142
viii
Contents
5.4 Bivariate Relationships between Two Continuous Variables..145
5.4.1 X-Y Scatter Plots..................................145
5.4.2 Product Moment Correlation Statistic (r)............146
5.4.3 Ratios of Two Continuous Variables..................148
5.5 Descriptive Statistics for Subpopulations................149
5.6 Linear Functions of Descriptive Estimates and Differences
of Means.................................................151
5.6.1 Differences of Means for Two Subpopulations.........152
5.6.2 Comparing Means over Time..........................155
6. Categorical Data Analysis .................................... 159
6.1 Introduction.............................................159
6.2 Framework for Analysis of Categorical Survey Data........160
6.2.1 Incorporating the Complex Design and Pseudo
Maximum Likelihood.................................160
6.2.2 Proportions and Percentages........................160
6.2.3 Crosstabulations, Contingency Tables, and
Weighted Frequencies...............................161
6.3 Univariate Analysis of Categorical Data..................162
6.3.1 Estimation of Proportions for Binary Variables.....162
6.3.2 Estimation of Category Proportions for Multinomial
Variables..........................................166
6.3.3 Testing Hypotheses Concerning a Vector
of Population Proportions..........................169
6.3.4 Graphical Display for a Single Categorical Variable.170
6.4 Bivariate Analysis of Categorical Data...................172
6.4.1 Response and Factor Variables......................172
6.4.2 Estimation of Total, Row, and Column Proportions
for Two-Way Tables.................................172
6.4.3 Estimating and Testing Differences in
Subpopulation Proportions..........................174
6.4.4 x2 Tests of Independence of Rows and Columns.......175
6.4.5 Odds Ratios and Relative Risks.....................181
6.4.6 Simple Logistic Regression to Estimate the Odds Ratio.... 183
6.4.7 Bivariate Graphical Analysis.......................184
6.5 Analysis of Multivariate Categorical Data............ 185
6.5.1 Cochran-Mantel-Haenszel Test.......................186
6.5.2 Log-Linear Models for Contingency Tables...........188
6.6 Summary..................................................191
7. Linear Regression Models........................................195
7.1 Introduction.............................................195
7.2 Linear Regression Model..................................197
7.2.1 Standard Linear Regression Model...................199
7.2.2 Survey Treatment of the Regression Model...........200
Contents ix
7.3 Four Steps in Linear Regression Analysis..................202
7.3.1 Step 1: Specifying and Refining the Model..........202
7.3.2 Step 2: Estimation of Model Parameters.............203
7.3.2.1 Estimation for the Standard Linear
Regression Model...........................203
73.2.2 Linear Regression Estimation for Complex
Sample Survey Data.........................205
7.3.3 Step 3: Model Evaluation...........................210
7.3.4 Step 4: Inference..................................217
7.3.4.1 Inference Concerning Model Parameters......218
7.3.4.2 Prediction Intervals.......................221
7.4 Some Practical Considerations and Tools...................223
7.4.1 Distribution of the Dependent Variable.............223
7.4.2 Parameterization and Scaling for Independent
Variables..........................................224
7.4.3 Standardization of the Dependent and Independent
Variables..........................................227
7.4.4 Specification and Interpretation of Interactions
and Nonlinear Relationships........................227
7.4.5 Model-Building Strategies..........................230
7.5 Application: Modeling Diastolic Blood Pressure with the
2011-2012 NHANES Data.................................... 231
7.5.1 Exploring the Bivariate Relationships..............232
7.5.2 Naïve Analysis: Ignoring Sample Design Features...235
7.5.3 Weighted Regression Analysis..................... 236
7.5.4 Appropriate Analysis: Incorporating All Sample
Design Features....................................238
8. Logistic Regression and Generalized Linear Models for Binary
Survey Variables................................................257
8.1 Introduction..............................................257
8.2 GLMs for Binary Survey Responses..........................258
8.2.1 Logistic Regression Model..........................260
8.2.2 Probit Regression Model............................261
8.2.3 Complementary-Log-Log Model....................... 262
8.3 Building the Logistic Regression Model: Stage 1—
Model Specification—......................................262
8.4 Building the Logistic Regression Model: Stage 2—
Estimation of Model Parameters and Standard Errors........263
8.5 Building the Logistic Regression Model: Stage 3—
Evaluation of the Fitted Model.......................... 268
8.5.1 Wald Tests of Model Parameters.....................268
8.5.2 GOF and Logistic Regression Diagnostics............270
8.6 Building the Logistic Regression Model: Stage 4—
Interpretation and Inference .................
272
X
Contents
8.7 Analysis Application................................... 283
8.7.1 Stage 1: Model Specification.......................283
8.7.2 Stage 2: Model Estimation..........................285
8.7.3 Stage 3: Model Evaluation..........................286
8.7.4 Stage 4: Model Interpretation/Inference............288
8.8 Comparing the Logistic, Probit, and C-L-L GLMs for Binary
Dependent Variables......................................293
9. Generalized Linear Models for Multinomial, Ordinal,
and Count Variables............................................299
9.1 Introduction.............................................299
9.2 Analyzing Survey Data Using Multinomial Logit
Regression Models........................................299
9.2.1 Multinomial Logit Regression Model.................299
9.2.2 Multinomial Logit Regression Model: Specification
Stage............................................ 301
9.2.3 Multinomial Logit Regression Model: Estimation
Stage..............................................302
9.2.4 Multinomial Logit Regression Model: Evaluation
Stage..............................................304
9.2.5 Multinomial Logit Regression Model: Interpretation
Stage..............................................304
9.2.6 Example: Fitting a Multinomial Logit
Regression Model to Complex Sample Survey Data.....305
9.3 Logistic Regression Models for Ordinal Survey Data.......313
9.3.1 Cumulative Logit Regression Model..................314
9.3.2 Cumulative Logit Regression Model: Specification
Stage..............................................315
9.3.3 Cumulative Logit Regression Model: Estimation
Stage..............................................315
9.3.4 Cumulative Logit Regression Model: Evaluation
Stage............................................. 316
9.3.5 Cumulative Logit Regression Model: Interpretation
Stage..............................................317
9.3.6 Example: Fitting a Cumulative Logit
Regression Model to Complex Sample Survey Data.....319
9.4 Regression Models for Count Outcomes.....................323
9.4.1 Survey Count Variables and Regression Modeling
Alternatives.......................................323
9.4.2 Generalized Linear Models for Count Variables......326
9.4.2.1 Poisson Regression Model..................326
9.4.2.2 Negative Binomial Regression Model........327
9.4.2.3 Two-Part Models: Zero-Inflated Poisson
and Negative Binomial Regression Models...327
Contents xi
9.4.3 Regression Models for Count Data: Specification
Stage...............................................329
9.4.4 Regression Models for Count Data: Estimation Stage.... 329
9.4.5 Regression Models for Count Data: Evaluation Stage.... 330
9.4.6 Regression Models for Count Data: Interpretation
Stage............................................. 330
9.4.7 Example: Fitting Poisson and Negative Binomial
Regression Models to Complex Sample Survey Data....331
10. Survival Analysis of Event History Survey Data..................339
10.1 Introduction...............................................339
10.2 Basic Theory of Survival Analysis..........................339
10.2.1 Survey Measurement of Event History Data...........339
10.2.2 Data for Event History Models.......................341
10.2.3 Important Notation and Definitions..................342
10.2.4 Models for Survival Analysis........................343
10.3 (Nonparametric) K-M Estimation of the Survivor Function...344
10.3.1 K—M Model Specification and Estimation..............345
10.3.2 K—M Estimator: Evaluation and Interpretation........347
10.3.3 K—M Survival Analysis Example.......................347
10.4 The Cox Proportional Hazards (CPH) Model...................351
10.4.1 CPH Model: Specification............................351
10.4.2 CPH Model: Estimation Stage.........................352
10.4.3 CPH Model: Evaluation and Diagnostics...............354
10.4.4 CPH Model: Interpretation and Presentation
of Results..........................................354
10.4.5 Example: Fitting a CPH Model to Complex Sample
Survey Data.........................................355
10.5 Discrete Time Survival Models............................ 359
10.5.1 Discrete Time Logistic Model........................359
10.5.2 Data Preparation for Discrete Time Survival Models ....360
10.5.3 Discrete Time Models: Estimation Stage..............363
10.5.4 Discrete Time Models: Evaluation and
Interpretation......................................364
10.5.5 Fitting a Discrete Time Model to Complex Sample
Survey Data.........................................365
11. Analysis of Longitudinal Complex Sample Survey Data..............371
11.1 Introduction............................................. 371
11.2 Alternative Analytic Objectives with Longitudinal
Survey Data............................................ 372
11.2.1 Objective 1: Descriptive Estimation at a Single
Time Point..........................................372
11.2.2 Objective 2: Estimation of Change across Two Waves ....374
xii
Contents
11.2.3 Objective 3: Trajectory Estimation Based on Three
or More Waves.....................................375
11.2.3.1 Approach 1: Weighted Multilevel Modeling.... 375
11.2.3.2 Approach 2: Covariance Structure Modeling.... 382
11.2.3.3 Approach 3: Weighted GEE Estimation......384
11.2.3.4 Approach 4: Multiple Imputation Analysis.387
11.2.3.5 Approach 5: Calibration Adjustment for
Respondents with Complete Data............388
11.3 Alternative Longitudinal Analyses of the HRS Data........389
11.3.1 Example: Descriptive Estimation at a Single Wave..390
11.3.2 Example: Change across Two Waves..................395
11.3.2.1 Accounting for Refreshment Samples
When Estimating Mean Change...............401
11.3.3 Example: Weighted Multilevel Modeling.............402
11.3.3.1 Example: Veiga et al. (2014).............410
11.3.4 Example: Weighted GEE Analysis....................416
11.4 Concluding Remarks.......................................422
12. Imputation of Missing Data: Practical Methods and
Applications for Survey Analysts...............................427
12.1 Introduction.............................................427
12.2 Important Missing Data Concepts..........................429
12.2.1 Sources and Types of Missing Data.................429
12.2.2 Patterns of Item Missing Data in Surveys..........430
12.2.3 Item Missing Data Mechanisms......................431
12.2.4 Review of Strategies to Address Item Missing Data
in Surveys........................................433
12.3 Factors to Consider in Choosing an Imputation Method.....436
12.4 Multiple Imputation......................................439
12.4.1 Overview of MI and MI Phases......................439
12.4.2 Models for Multiply Imputing Missing Data.........440
12.4.2.1 Choosing the Variables to Include in the
Imputation Model..........................441
12.4.2.2 Distributional Assumptions for the
Imputation Model..........................444
12.4.3 Creating the Mis..................................444
12.4.3.1 Transforming the Imputation Problem
to Monotonic Missing Data............... 445
12.4.3.2 Specifying an Explicit Multivariate Model
and Applying Exact Bayesian Posterior
Simulation Methods........................445
12.4.3.3 SR or Chained Regressions ..............446
12.4.4 Estimation and Inference for Multiply Imputed Data....448
12.4.4.1 Estimators for Population Parameters and
Associated Variance Estimators
448
Contents xiii
12.4.4.2 Model Evaluation and Inference...........449
12.5 Fractional Imputation....................................451
12.5.1 Background.......................................451
12.5.2 Creating the FIs.................................452
12.5.3 Estimation and Inference with Fractionally
Imputed Data......................................454
12.5.4 FI Software......................................455
12.6 Application of MI and FI Methods to the NHANES
2011-2012 Data..........................................456
12.6.1 Problem Definition...............................456
12.6.2 Imputation Models for the NHANES DBP Example.....457
12.6.3 Imputation of the Item Missing Data..............459
12.6.3.1 Multiple Imputation......................459
12.6.3.2 FEFI: Hot Deck Method....................461
12.6.4 Estimation and Inference.........................464
12.6.4.1 Multiple Imputation......................464
12.6.4.2 FI Estimation and Inference..............467
12.6.5 Comparison of Example Results from
Complete Case Analysis, MI, and FEFI..............468
13. Advanced Topics in the Analysis of Survey Data................473
13.1 Introduction......................................... 473
13.2 Bayesian Analysis of Complex Sample Survey Data..........474
13.3 GLMMs in Survey Data Analysis............................478
13.3.1 Overview of GLMMs................................478
13.3.2 GLMMs and Complex Sample Survey Data.............481
13.3.3 Alternative Approaches to Fitting GLMMs to
Survey Data: The PISA Example.....................485
13.4 Fitting Structural Equation Models to Complex Sample
Survey Data.............................................489
13.4.1 SEM Example: Analysis of ESS Data from Belgium...491
13.5 Small Area Estimation and Complex Sample Survey Data.....498
13.6 Nonparametric Methods for Complex Sample Survey Data.....499
References........................................................501
Appendix A: Software Overview.....................................519
Index.............................................................549
|
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| author | Heeringa, Steven 1953- West, Brady T. Berglund, Patricia A. |
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| edition | Second edition |
| format | Book |
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| id | DE-604.BV044408026 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:52:09Z |
| institution | BVB |
| isbn | 9781498761604 |
| language | English |
| lccn | 016050459 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029809910 |
| oclc_num | 1000507702 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-706 DE-M382 DE-473 DE-BY-UBG DE-11 DE-N2 DE-634 DE-860 |
| owner_facet | DE-355 DE-BY-UBR DE-706 DE-M382 DE-473 DE-BY-UBG DE-11 DE-N2 DE-634 DE-860 |
| physical | xxii, 568 Seiten Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapmann & Hall/CRC Statistics in the Social and Behavioral Sciences Series A Chapman & Hall Book |
| spelling | Heeringa, Steven 1953- (DE-588)14239386X aut Applied survey data analysis Steven G. Heeringa, Brady T. West, and Patricia A. Berglund Second edition Boca Raton ; London ; New York CRC Press [2017] xxii, 568 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Chapmann & Hall/CRC Statistics in the Social and Behavioral Sciences Series A Chapman & Hall Book Literaturangaben und Index Sozialwissenschaften Statistik Social sciences Statistics Social surveys Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Empirische Sozialforschung (DE-588)4014606-6 gnd rswk-swf Umfrage (DE-588)4005227-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Empirische Sozialforschung (DE-588)4014606-6 s DE-604 Umfrage (DE-588)4005227-8 s Statistik (DE-588)4056995-0 s Sozialwissenschaften (DE-588)4055916-6 s 1\p DE-604 West, Brady T. aut Berglund, Patricia A. aut Erscheint auch als Online-Ausgabe 978-1-4987-6161-1 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=029809910&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Heeringa, Steven 1953- West, Brady T. Berglund, Patricia A. Applied survey data analysis Sozialwissenschaften Statistik Social sciences Statistics Social surveys Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Umfrage (DE-588)4005227-8 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4014606-6 (DE-588)4005227-8 (DE-588)4123037-1 (DE-588)4056995-0 |
| title | Applied survey data analysis |
| title_auth | Applied survey data analysis |
| title_exact_search | Applied survey data analysis |
| title_full | Applied survey data analysis Steven G. Heeringa, Brady T. West, and Patricia A. Berglund |
| title_fullStr | Applied survey data analysis Steven G. Heeringa, Brady T. West, and Patricia A. Berglund |
| title_full_unstemmed | Applied survey data analysis Steven G. Heeringa, Brady T. West, and Patricia A. Berglund |
| title_short | Applied survey data analysis |
| title_sort | applied survey data analysis |
| topic | Sozialwissenschaften Statistik Social sciences Statistics Social surveys Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Umfrage (DE-588)4005227-8 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Sozialwissenschaften Statistik Social sciences Statistics Social surveys Statistical methods Empirische Sozialforschung Umfrage Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029809910&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT heeringasteven appliedsurveydataanalysis AT westbradyt appliedsurveydataanalysis AT berglundpatriciaa appliedsurveydataanalysis |