Applied survey data analysis:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
CRC Press
2010
|
| Series: | Chapman & Hall/CRC Statistics in the social and behavioral sciences series
|
| Subjects: | |
| Online Access: | Klappentext Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIX, 467 S. graph. Darst. 25 cm |
| ISBN: | 9781420080667 |
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MARC
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| 245 | 1 | 0 | |a Applied survey data analysis |c Steven G. Heeringa ; Brady T. West ; Patricia A. Berglund |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2010 | |
| 300 | |a XIX, 467 S. |b graph. Darst. |c 25 cm | ||
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| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Social sciences |x Statistics | |
| 650 | 4 | |a Social surveys |x Statistical methods | |
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Record in the Search Index
| _version_ | 1804143580507275264 |
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| adam_text | Taking
a practicat
approach that draws on the authors extensive
teaching, consulting, and research experiences, Applied Survey Data
Analysis provides an intermediate-level statistical overview of the
analysis of complex sample survey data. It emphasizes methods and
worked examples using available software procedures while reinforcing
the principles and theory that underlie those methods.
After introducing a step-by-step process for approaching a survey
analysis problem, the book presents the fundamental features
of complex sample designs and shows how to integrate design
characteristics into the statistical methods and software for survey
estimation and inference. The authors then focus on the methods
and models used in analyzing continuous, categorical, and count-
dependent variables; event history; and missing data problems.
Some of the techniques discussed include univariate descriptive and
simple bivariate analyses, the linear regression model, generalized
linear regression modeling methods, the Cox proportional hazard
model, discrete time models, and the multiple imputation analysis
method. The final chapter covers new developments in survey
applications of advanced statistical techniques, including model-
based analysis approaches.
Designed for readers working in a wide array of disciplines who use
survey data in their work, this book also provides a useful framework
for integrating more in-depth studies of the theory and methods of
survey data analysis. A guide to the applied statistical analysis and
interpretation of survey data, it contains many examples and practical
exercises based on major real-world survey data sets. Although the
authors use
Stata®
for most examples in the text, they offer
SAS®,
SPSS®, SUDAAN®, R, WesVar®, IVEware, and Mplus software code for
replicating the examples on the book s Web site.
Titel: Applied survey data analysis
Autor: Heeringa, Steven
Jahr: 2010
Contents
Preface..
,.xv
1. Applied Survey Data Analysis: Overview................................................1
1.1 Introduction...........................................................................................1
1.2 A Brief History of Applied Survey Data Analysis...........................3
1.2.1 Key Theoretical Developments..............................................3
1.2.2 Key Software Developments..................................................5
1.3 Example Data Sets and Exercises.......................................................6
1.3.1 The National Comorbidity Survey Replication
(NCS-R)......................................................................................6
1.3.2 The Health and Retirement Study (HRS)—2006.................7
1.3.3 The National Health and Nutrition Examination
Survey (NHANES)—2005, 2006.............................................7
1.3.4 Steps in Applied Survey Data Analysis................................8
1.3.4.1 Step 1: Definition of the Problem and
Statement of the Objectives.....................................8
1.3.4.2 Step 2: Understanding the Sample Design...........9
1.3.4.3 Step 3: Understanding Design Variables,
Underlying Constructs, and Missing Data.........10
1.3.4.4 Step 4: Analyzing the Data...................................11
1.3.4.5 Step 5: Interpreting and Evaluating the
Results of the Analysis..........................................11
1.3.4.6 Step 6: Reporting of Estimates and
Inferences from the Survey Data.........................12
2. Getting to Know the Complex Sample Design......................................13
2.1 Introduction.........................................................................................13
2.1.1 Technical Documentation and Supplemental
Literature Review...................................................................13
2.2 Classification of Sample Designs......................................................14
2.2.1 Sampling Plans.......................................................................15
2.2.2 Inference from Survey Data.................................................16
2.3 Target Populations and Survey Populations...................................16
2.4 Simple Random Sampling: A Simple Model for
Design-Based Inference......................................................................18
2.4.1 Relevance of SRS to Complex Sample Survey Data
Analysis...................................................................................18
2.4.2 SRS Fundamentals: A Framework for Design-Based
Inference..................................................................................19
2.4.3 An Example of Design-Based Inference under SRS.........21
Contents
2.5 Complex Sample Design Effects.......................................................23
2.5.1 Design Effect Ratio................................................................23
2.5.2 Generalized Design Effects and Effective Sample
Sizes.........................................................................................25
2.6 Complex Samples: Clustering and Stratification............................27
2.6.1 Clustered Sampling Plans....................................................28
2.6.2 Stratification............................................................................31
2.6.3 Joint Effects of Sample Stratification and Clustering........34
2.7 Weighting in Analysis of Survey Data.............................................35
2.7.1 Introduction to Weighted Analysis of Survey Data..........35
2.7.2 Weighting for Probabilities of Selection.............................37
2.7.3 Nonresponse Adjustment Weights.....................................39
2.7.3.1 Weighting Class Approach...................................40
2.7.3.2 Propensity Cell Adjustment Approach...............40
2.7.4 Poststratification Weight Factors.........................................42
2.7.5 Design Effects Due to Weighted Analysis.........................44
2.8 Multistage Area Probability Sample Designs.................................46
2.8.1 Primary Stage Sampling.......................................................47
2.8.2 Secondary Stage Sampling...................................................48
2.8.3 Third and Fourth Stage Sampling of Housing Units
and Eligible Respondents.....................................................49
2.9 Special Types of Sampling Plans Encountered in Surveys...........50
3. Foundations and Techniques for Design-Based Estimation and
Inference.........................................................................................................53
3.1 Introduction.........................................................................................53
3.2 Finite Populations and Superpopulation Models..........................54
3.3 Confidence Intervals for Population Parameters...........................56
3.4 Weighted Estimation of Population Parameters.............................56
3.5 Probability Distributions and Design-Based Inference................60
3.5.1 Sampling Distributions of Survey Estimates.....................60
3.5.2 Degrees of Freedom for t under Complex Sample
Designs....................................................................................63
3.6 Variance Estimation............................................................................65
3.6.1 Simplifying Assumptions Employed in Complex
Sample Variance Estimation.................................................66
3.6.2 The Taylor Series Linearization Method............................68
3.6.2.1 TSL Step 1................................................................69
3.6.2.2 TSL Step 2................................................................70
3.6.2.3 TSL Step 3................................................................71
3.6.2.4 TSL Step 4................................................................71
3.6.2.5 TSL Step 5................................................................73
3.6.3 Replication Methods for Variance Estimation...................74
3.6.3.1 Jackknife Repeated Replication............................75
Contents
3.6.3.2 Balanced Repeated Replication............................78
3.6.3.3 The Bootstrap..........................................................82
3.6.4 An Example Comparing the Results from the TSL,
JRR, and BRR Methods.........................................................82
3.7 Hypothesis Testing in Survey Data Analysis.................................83
3.8 Total Survey Error and Its Impact on Survey Estimation and
Inference...............................................................................................85
3.8.1 Variable Errors........................................................................86
3.8.2 Biases in Survey Data............................................................87
4. Preparation for Complex Sample Survey Data Analysis.....................91
4.1 Introduction.........................................................................................91
4.2 Analysis Weights: Review by the Data User...................................92
4.2.1 Identification of the Correct Weight Variables for the
Analysis...................................................................................93
4.2.2 Determining the Distribution and Scaling of the
Weight Variables....................................................................94
4.2.3 Weighting Applications: Sensitivity of Survey
Estimates to the Weights.......................................................96
4.3 Understanding and Checking the Sampling Error
Calculation Model...............................................................................98
4.3.1 Stratum and Cluster Codes in Complex Sample
Survey Data Sets....................................................................99
4.3.2 Building the NCS-R Sampling Error Calculation
Model.....................................................................................100
4.3.3 Combining Strata, Randomly Grouping PSUs, and
Collapsing Strata..................................................................103
4.3.4 Checking the Sampling Error Calculation Model for
the Survey Data Set.............................................................105
4.4 Addressing Item Missing Data in Analysis Variables.................108
4.4.1 Potential Bias Due to Ignoring Missing Data..................108
4.4.2 Exploring Rates and Patterns of Missing Data Prior
to Analysis............................................................................109
4.5 Preparing to Analyze Data for Sample Subpopulations.............110
4.5.1 Subpopulation Distributions across Sample Design
Units.......................................................................................Ill
4.5.2 The Unconditional Approach for Subclass Analysis......114
4.5.3 Preparation for Subclass Analyses....................................114
4.6 A Final Checklist for Data Users........ ............................................115
5. Descriptive Analysis for Continuous Variables..................................117
5.1 Introduction.......................................................................................117
5.2 Special Considerations in Descriptive Analysis of Complex
Sample Survey Data..........................................................................118
5.2.1 Weighted Estimation...........................................................118
Contents
5.2.2 Design Effects for Descriptive Statistics...........................119
5.2.3 Matching the Method to the Variable Type.....................119
5.3 Simple Statistics for Univariate Continuous Distributions.........120
5.3.1 Graphical Tools for Descriptive Analysis of Survey
Data........................................................................................120
5.3.2 Estimation of Population Totals.........................................123
5.3.3 Means of Continuous, Binary, or Interval Scale Data.....128
5.3.4 Standard Deviations of Continuous Variables................130
5.3.5 Estimation of Percentiles and Medians of Population
Distributions.........................................................................131
5.4 Bivariate Relationships between Two Continuous Variables.....134
5.4.1 X-Y Scatterplots....................................................................134
5.4.2 Product Moment Correlation Statistic (r)..........................135
5.4.3 Ratios of Two Continuous Variables.................................136
5.5 Descriptive Statistics for Subpopulations......................................137
5.6 Linear Functions of Descriptive Estimates and Differences
of Means.............................................................................................139
5.6.1 Differences of Means for Two Subpopulations...............141
5.6.2 Comparing Means over Time............................................143
5.7 Exercises.............................................................................................144
6. Categorical Data Analysis........................................................................149
6.1 Introduction.......................................................................................149
6.2 A Framework for Analysis of Categorical Survey Data..............150
6.2.1 Incorporating the Complex Design and
Pseudo-Maximum Likelihood...........................................150
6.2.2 Proportions and Percentages..............................................150
6.2.3 Cross-Tabulations, Contingency Tables, and
Weighted Frequencies.........................................................151
6.3 Univariate Analysis of Categorical Data.......................................152
6.3.1 Estimation of Proportions for Binary Variables..............152
6.3.2 Estimation of Category Proportions for Multinomial
Variables................................................................................156
6.3.3 Testing Hypotheses Concerning a Vector of
Population Proportions.......................................................158
6.3.4 Graphical Display for a Single Categorical Variable.......159
6.4 Bivariate Analysis of Categorical Data..........................................160
6.4.1 Response and Factor Variables..........................................160
6.4.2 Estimation of Total, Row, and Column Proportions
for Two-Way Tables..............................................................162
6.4.3 Estimating and Testing Differences in
Subpopulation Proportions................................................163
6.4.4 Chi-Square Tests of Independence of Rows and
Columns................................................................................164
6.4.5 Odds Ratios and Relative Risks.........................................170
Contents
6.4.6 Simple Logistic Regression to Estimate the Odds
Ratio.......................................................................................171
6.4.7 Bivariate Graphical Analysis..............................................173
6.5 Analysis of Multivariate Categorical Data....................................174
6.5.1 The Cochran-Mantel-Haenszel Test................................174
6.5.2 Log-Linear Models for Contingency Tables.....................176
6.6 Exercises.............................................................................................177
7. Linear Regression Models........................................................................179
7.1 Introduction.......................................................................................179
7.2 The Linear Regression Model.........................................................180
7.2.1 The Standard Linear Regression Model...........................182
7.2.2 Survey Treatment of the Regression Model.....................183
7.3 Four Steps in Linear Regression Analysis.....................................185
7.3.1 Step 1: Specifying and Refining the Model......................186
7.3.2 Step 2: Estimation of Model Parameters...........................187
7.3.2.1 Estimation for the Standard Linear
Regression Model.................................................187
7.3.2.2 Linear Regression Estimation for Complex
Sample Survey Data.............................................188
7.3.3 Step 3: Model Evaluation....................................................193
7.3.3.1 Explained Variance and Goodness of Fit..........193
7.3.3.2 Residual Diagnostics............................................194
7.3.3.3 Model Specification and Homogeneity of
Variance.................................................................194
7.3.3.4 Normality of the Residual Errors.......................195
7.3.3.5 Outliers and Influence Statistics........................196
7.3.4 Step 4: Inference...................................................................196
7.3.4.1 Inference Concerning Model Parameters.........199
7.3.4.2 Prediction Intervals..............................................202
7.4 Some Practical Considerations and Tools......................................204
7.4.1 Distribution of the Dependent Variable...........................204
7.4.2 Parameterization and Scaling for Independent
Variables................................................................................205
7.4.3 Standardization of the Dependent and Independent
Variables................................................................................208
7.4.4 Specification and Interpretation of Interactions and
Nonlinear Relationships.....................................................208
7.4.5 Model-Building Strategies..................................................210
7.5 Application: Modeling Diastolic Blood Pressure with the
NHANESData..................................................................................211
7.5.1 Exploring the Bivariate Relationships..............................212
7.5.2 Naïve Analysis: Ignoring Sample Design Features........215
7.5.3 Weighted Regression Analysis..........................................216
Contents
7.5 A Appropriate Analysis: Incorporating All Sample
Design Features....................................................................218
7.6 Exercises.............................................................................................224
8. Logistic Regression and Generalized Linear Models for Binary
Survey Variables.........................................................................................229
8.1 Introduction.......................................................................................229
8.2 Generalized Linear Models for Binary Survey Responses.........230
8.2.1 The Logistic Regression Model..........................................231
8.2.2 The Probit Regression Model.............................................234
8.2.3 The Complementary Log-Log Model...............................234
8.3 Building the Logistic Regression Model: Stage 1, Model
Specification.......................................................................................235
8.4 Building the Logistic Regression Model: Stage 2, Estimation
of Model Parameters and Standard Errors....................................236
8.5 Building the Logistic Regression Model: Stage 3, Evaluation
of the Fitted Model............................................................................239
8.5.1 Wald Tests of Model Parameters.......................................239
8.5.2 Goodness of Fit and Logistic Regression
Diagnostics............................................................................243
8.6 Building the Logistic Regression Model: Stage 4,
Interpretation and Inference...........................................................245
8.7 Analysis Application........................................................................251
8.7.1 Stage 1: Model Specification...............................................252
8.7.2 Stage 2: Model Estimation..................................................253
8.7.3 Stage 3: Model Evaluation...................................................255
8.7.4 Stage 4: Model Interpretation/Inference..........................256
8.8 Comparing the Logistic, Probit, and Complementary
Log-Log GLMs for Binary Dependent Variables.........................259
8.9 Exercises.............................................................................................262
9. Generalized Linear Models for Multinomial, Ordinal, and
Count Variables...........................................................................................265
9.1 Introduction.......................................................................................265
9.2 Analyzing Survey Data Using Multinomial Logit
Regression Models............................................................................265
9.2.1 The Multinomial Logit Regression Model.......................265
9.2.2 Multinomial Logit Regression Model: Specification
Stage.......................................................................................267
9.2.3 Multinomial Logit Regression Model: Estimation
Stage.......................................................................................268
9.2.4 Multinomial Logit Regression Model: Evaluation
Stage.......................................................................................268
Contents
9.2.5 Multinomial Logit Regression Model: Interpretation
Stage.......................................................................................270
9.2.6 Example: Fitting a Multinomial Logit Regression
Model to Complex Sample Survey Data...........................271
9.3 Logistic Regression Models for Ordinal Survey Data.................277
9.3.1 Cumulative Logit Regression Model................................278
9.3.2 Cumulative Logit Regression Model: Specification
Stage.......................................................................................279
9.3.3 Cumulative Logit Regression Model: Estimation
Stage.......................................................................................279
9.3.4 Cumulative Logit Regression Model: Evaluation
Stage.......................................................................................280
9.3.5 Cumulative Logit Regression Model: Interpretation
Stage.......................................................................................281
9.3.6 Example: Fitting a Cumulative Logit Regression
Model to Complex Sample Survey Data...........................282
9.4 Regression Models for Count Outcomes.......................................286
9.4.1 Survey Count Variables and Regression Modeling
Alternatives...........................................................................286
9.4.2 Generalized Linear Models for Count Variables.............288
9.4.2.1 The Poisson Regression Model...........................288
9.4.2.2 The Negative Binomial Regression Model.......289
9.4.2.3 Two-Part Models: Zero-Inflated Poisson
and Negative Binomial Regression Models.....290
9.4.3 Regression Models for Count Data: Specification
Stage.......................................................................................291
9.4.4 Regression Models for Count Data: Estimation
Stage.......................................................................................292
9.4.5 Regression Models for Count Data: Evaluation
Stage.......................................................................................292
9.4.6 Regression Models for Count Data: Interpretation
Stage.......................................................................................293
9.4.7 Example: Fitting Poisson and Negative Binomial
Regression Models to Complex Sample Survey Data.....294
9.5 Exercises.............................................................................................298
10. Survival Analysis of Event History Survey Data................................303
10.1 Introduction.......................................................................................303
10.2 Basic Theory of Survival Analysis..................................................303
10.2.1 Survey Measurement of Event History Data...................303
10.2.2 Data for Event History Models..........................................305
10.2.3 Important Notation and Definitions.................................306
10.2.4 Models for Survival Analysis.............................................307
Contents
10.3 (Nonparametric) Kaplan-Meier Estimation of the Survivor
Function..............................................................................................308
10.3.1 K-M Model Specification and Estimation........................309
10.3.2 K-M Estimator—Evaluation and Interpretation.............310
10.3.3 K-M Survival Analysis Example.......................................311
10.4 Cox Proportional Hazards Model..................................................315
10.4.1 Cox Proportional Hazards Model: Specification.............315
10.4.2 Cox Proportional Hazards Model: Estimation Stage.....316
10.4.3 Cox Proportional Hazards Model: Evaluation and
Diagnostics............................................................................317
10.4.4 Cox Proportional Hazards Model: Interpretation and
Presentation of Results........................................................319
10.4.5 Example: Fitting a Cox Proportional Hazards Model
to Complex Sample Survey Data.......................................319
10.5 Discrete Time Survival Models.......................................................322
10.5.1 The Discrete Time Logistic Model....................................323
10.5.2 Data Preparation for Discrete Time Survival
Models...................................................................................324
10.5.3 Discrete Time Models: Estimation Stage..........................327
10.5.4 Discrete Time Models: Evaluation and
Interpretation........................................................................328
10.5.5 Fitting a Discrete Time Model to Complex Sample
Survey Data...........................................................................329
10.6 Exercises.............................................................................................333
11. Multiple Imputation: Methods and Applications for Survey
Analysts........................................................................................................335
11.1 Introduction.......................................................................................335
11.2 Important Missing Data Concepts.................................................336
11.2.1 Sources and Patterns of Item-Missing Data in
Surveys..................................................................................336
11.2.2 Item-Missing Data Mechanisms........................................338
11.2.3 Implications of Item-Missing Data for Survey Data
Analysis.................................................................................341
11.2.4 Review of Strategies to Address Item-Missing Data
in Surveys..............................................................................342
11.3 An Introduction to Imputation and the Multiple Imputation
Method................................................................................................345
11.3.1 A Brief History of Imputation Procedures.......................345
11.3.2 Why the Multiple Imputation Method?............................346
11.3.3 Overview of Multiple Imputation and MI Phases..........348
11.4 Models for Multiply Imputing Missing Data................................350
11.4.1 Choosing the Variables to Include in the Imputation
Model.....................................................................................350
Contents
9.2.5 Multinomial Logit Regression Model: Interpretation
Stage.......................................................................................270
9.2.6 Example: Fitting a Multinomial Logit Regression
Model to Complex Sample Survey Data...........................271
9.3 Logistic Regression Models for Ordinal Survey Data.................277
9.3.1 Cumulative Logit Regression Model................................278
9.3.2 Cumulative Logit Regression Model: Specification
Stage.......................................................................................279
9.3.3 Cumulative Logit Regression Model: Estimation
Stage.......................................................................................279
9.3.4 Cumulative Logit Regression Model: Evaluation
Stage.......................................................................................280
9.3.5 Cumulative Logit Regression Model: Interpretation
Stage.......................................................................................281
9.3.6 Example: Fitting a Cumulative Logit Regression
Model to Complex Sample Survey Data...........................282
9.4 Regression Models for Count Outcomes.......................................286
9.4.1 Survey Count Variables and Regression Modeling
Alternatives...........................................................................286
9.4.2 Generalized Linear Models for Count Variables.............288
9.4.2.1 The Poisson Regression Model...........................288
9.4.2.2 The Negative Binomial Regression Model.......289
9.4.2.3 Two-Part Models: Zero-Inflated Poisson
and Negative Binomial Regression Models.....290
9.4.3 Regression Models for Count Data: Specification
Stage.......................................................................................291
9.4.4 Regression Models for Count Data: Estimation
Stage.......................................................................................292
9.4.5 Regression Models for Count Data: Evaluation
Stage.......................................................................................292
9.4.6 Regression Models for Count Data: Interpretation
Stage.......................................................................................293
9.4.7 Example: Fitting Poisson and Negative Binomial
Regression Models to Complex Sample Survey Data.....294
9.5 Exercises.............................................................................................298
10. Survival Analysis of Event History Survey Data................................303
10.1 Introduction.......................................................................................303
10.2 Basic Theory of Survival Analysis..................................................303
10.2.1 Survey Measurement of Event History Data...................303
10.2.2 Data for Event History Models..........................................305
10.2.3 Important Notation and Definitions.................................306
10.2.4 Models for Survival Analysis.............................................307
Contents
A.I.I Historical Perspective..........................................................400
A.1.2 Software for Sampling Error Estimation..........................401
A.2 Overview of Stata» Version 10+......................................................407
A.3 Overview of SAS® Version 9.2.........................................................410
A.3.1 The SAS SURVEY Procedures............................................411
A.4 Overview of SUDAAN® Version 9.0...............................................414
A.4.1 The SUDAAN Procedures..................................................415
A.5 Overview of SPSS®............................................................................421
A.5.1 The SPSS Complex Samples Commands..........................422
A.6 Overview of Additional Software..................................................427
A.6.1 WesVar®.................................................................................427
A.6.2 IVEware (Imputation and Variance Estimation
Software)...............................................................................428
A.6.3 Mplus.....................................................................................429
A.6.4 The R survey Package.........................................................429
A.7 Summary............................................................................................430
References...........................................................................................................431
Index.....................................................................................................................443
|
| any_adam_object | 1 |
| author | Heeringa, Steven 1953- West, Brady T. Berglund, Patricia A. |
| author_GND | (DE-588)14239386X |
| author_facet | Heeringa, Steven 1953- West, Brady T. Berglund, Patricia A. |
| author_role | aut aut aut |
| author_sort | Heeringa, Steven 1953- |
| author_variant | s h sh b t w bt btw p a b pa pab |
| building | Verbundindex |
| bvnumber | BV036874460 |
| callnumber-first | H - Social Science |
| callnumber-label | HA29 |
| callnumber-raw | HA29 |
| callnumber-search | HA29 |
| callnumber-sort | HA 229 |
| callnumber-subject | HA - Statistics |
| classification_rvk | MR 2100 QH 244 |
| classification_tum | MAT 620f SOZ 710f |
| ctrlnum | (OCoLC)699152810 (DE-599)BVBBV036874460 |
| dewey-full | 001.4/22 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 001 - Knowledge |
| dewey-raw | 001.4/22 |
| dewey-search | 001.4/22 |
| dewey-sort | 11.4 222 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Allgemeines Soziologie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV036874460 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:49:55Z |
| institution | BVB |
| isbn | 9781420080667 |
| language | English |
| lccn | 2009051730 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020789971 |
| oclc_num | 699152810 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-384 DE-188 DE-473 DE-BY-UBG DE-11 DE-355 DE-BY-UBR DE-91 DE-BY-TUM |
| owner_facet | DE-19 DE-BY-UBM DE-384 DE-188 DE-473 DE-BY-UBG DE-11 DE-355 DE-BY-UBR DE-91 DE-BY-TUM |
| physical | XIX, 467 S. graph. Darst. 25 cm |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall/CRC Statistics in the social and behavioral sciences series |
| spelling | Heeringa, Steven 1953- Verfasser (DE-588)14239386X aut Applied survey data analysis Steven G. Heeringa ; Brady T. West ; Patricia A. Berglund Boca Raton [u.a.] CRC Press 2010 XIX, 467 S. graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC Statistics in the social and behavioral sciences series Includes bibliographical references and index Sozialwissenschaften Statistik Social sciences Statistics Social surveys Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Umfrage (DE-588)4005227-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Empirische Sozialforschung (DE-588)4014606-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Umfrage (DE-588)4005227-8 s Statistik (DE-588)4056995-0 s Sozialwissenschaften (DE-588)4055916-6 s 1\p DE-604 Datenanalyse (DE-588)4123037-1 s Empirische Sozialforschung (DE-588)4014606-6 s 2\p DE-604 West, Brady T. Verfasser aut Berglund, Patricia A. Verfasser aut Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020789971&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020789971&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\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 Umfrage (DE-588)4005227-8 gnd Datenanalyse (DE-588)4123037-1 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4005227-8 (DE-588)4123037-1 (DE-588)4014606-6 (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 ; Patricia A. Berglund |
| title_fullStr | Applied survey data analysis Steven G. Heeringa ; Brady T. West ; Patricia A. Berglund |
| title_full_unstemmed | Applied survey data analysis Steven G. Heeringa ; Brady T. West ; 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 Umfrage (DE-588)4005227-8 gnd Datenanalyse (DE-588)4123037-1 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Sozialwissenschaften Statistik Social sciences Statistics Social surveys Statistical methods Umfrage Datenanalyse Empirische Sozialforschung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020789971&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020789971&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT heeringasteven appliedsurveydataanalysis AT westbradyt appliedsurveydataanalysis AT berglundpatriciaa appliedsurveydataanalysis |