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Applied Survey Data Analysis 2nd edition [Pehme köide]

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Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software.



Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website.

Arvustused

"Anyone analyzing survey data, even once, should have a copy of this book. The book has something for everyone. It is a solid, yet accessible introduction to analyzing data from complex sample surveys (i.e., those with stratification and clustering), a statistical text of the highest caliber, and a reference for experienced analysts and statisticians. The authors are masterful instructors on the topic, and leaders in the field of survey methodology at the University of Michigan's world-renowned Institute for Social Research and Survey Research Center. Their profound understanding of the topic, and talent for describing it shines through vividly in the text. One of my favorite parts remains section 1.2 "A Brief History of Applied Survey Data Analysis", which is split into "Key Theoretical Developments" and "Key Software Developments". The historical context provided in those sections helps motivate the technical material that follows. My other favorite parts of this book are the presentations of analysis code and output from various programs, and their "Theory Boxes", which tie specific analysis steps and code to the statistical theory behind them. Among the numerous updates to this edition, I think readers will find the new content on model diagnostics and testing goodness-of-fit (GOF) to be extremely helpful, as this is an area of complex sample survey analysis that can be difficult to translate from standard regression analysis. Throughout, the authors make it a point to describe analyses in discrete steps that can help direct even the most complex analyses." Matt Jans, Senior Associate/Scientist, Abt Associates

"This is an excellent book to use for a graduate level applied statistics course teaching public health students how to analyze complex survey data. Each chapter is clearly written with a nice balance of theoretical background and practical guidance on survey data analytical issues as illustrated by many relevant real-data examples. All the chapters are nicely structured and coherently connected. The second edition includes a new chapter on the current approaches for analyzing longitudinal survey data, which will be instrumental for many practitioners trying to analyze this type of data. Several other new additions such as bootstrap methods of variance estimate, log-linear models, and fractional imputation methods for item missing data, are all important tools for an applied survey data analyst to learn and thus further enhance the scope and usability of this book. I would highly recommend this book to any instructor of an applied survey data analysis course and anyone who needs to analyze complex survey data to conduct their research." Luohua Jiang, Assistant Professor, Department of Epidemiology, University of California, Irvine

"The first edition of Applied Survey Data Analysis was my go-to reference and textbook for all types of analysis of complex survey data. This second edition is even better. The book maintains its practical analytic approach, understanding that all users of survey data want to know about methods that can be easily and appropriately implemented with major statistical analysis software, and that more sophisticated users of survey data want insights into why the methods work. In this edition, Heeringa, West, and Berglund have added an entire chapter about longitudinal data analyses (including multilevel models) using complex survey data, expanded the chapter on multiple imputation (including a discussion of fractional imputation), provided detailed examples for common complex survey data analyses that did not have them in the previous edition (such as the Pearson product-moment correlation coefficient and structural equation models), significantly and usefully expanded the discussion of model interpretation and evaluation, updated and expanded the examples to more recent data sets including the European Social Survey, and address the important perpetual question of the use of weights in regression models. All of the examples reflect the latest version of Stata (14+), including user-generated commands and other software programs where Stata falls short. The exercises in this edition also have been updated. This edition is hands-down the best book out there for an applied graduate-level course on complex survey data analysis or any user who wants to know how to obtain the correct design-based analyses." Kristen Olson, University of Nebraska-Lincoln

"I have been using Applied Survey Data Analysis in my sampling methods class for years. This is an ideal text for the course it covers design, analysis and a host of extra topics that make it a one-stop shop for a survey methods curriculum. The new version really adds to the material without skimping or detracting from the integrity of the original. What I like best is that the authors try to cover the widest array of methods so they include survey data analysis using STATA, SAS, SUDAAN and WesVar PC all in one text. There are good books for designing samples and good books for analyzing public use datasets, but this book is the best of both worlds. Unlike most statistical texts, Heeringa and his co-authors write in a style that is very easy to read and avoids copious formulas and symbolic notation. This makes it a great option for someone who might want to delve into survey design and analysis where a full semester course is not available and a self-learning text would be desirable.

I think that the best measure of the quality of a text in a classroom setting is the prevalence of used copies I observe from semester to semester. While I see an abundance of used textbooks in the other classes I teach throughout the year I may only see one or two used copies of this one each term. This tells me that the students find the book just as helpful and useful a resource as I do." Paul Weiss, Senior Associate at the Rollins School of Public Health, Emory University

"This second edition is well written, organized, and also adds a new chapter on longitudinal complex sample survey data. This book is full of real data examples from several different analysis programs. It will be an excellent reference with 13 structured chapters giving an overview, basic structures and ideas, and real applications. Faculty, researchers, and graduate students working in survey data research and consulting could reasonably adopt this book as an introduction to survey knowledge and practical analysis skills from beginner to expert." Yoonsung Jung, Research Scientist/Statistician, Prairie View A&M University

Praise for the first edition:

"...the authors do an admirable job of striking a balance between statistical theory and practical advice and analysis. The authors provide excellent coverage of each aspect of the survey analysis process This book is an excellent general resource and if the reader is left wanting on a topic the authors never fail to provide an ample set of citations and references to a wide variety of notable texts on the topic in question. an excellent and helpful addition to the desk of any analyst, researcher, or student with a general background in statistics who is dealing with the special challenges and demands of complex survey data." Gregory Holyk, Journal of Official Statistics, Vol. 27, 2011

"Overall, the book is clearly written and easy to follow, and well equipped with real data examples and a book website. The program codes used in the example are also available, mostly written in Stata. I like the presentations with real survey examples and, in particular, the unified four-step approach to the regression analysis in different models. Anyone working on survey data analysis would find the book very helpful and instructive. The book website seems to be a good complement, with additional resources on this book." Jae-Kwang Kim, The American Statistician, November 2011

"The book is well-written by authors who have over 60 years of combined teaching and consultation experience in survey methodology and research techniques. It is excellent for reference, with 12 structured chapters coherently organised, providing intermediate-level statistical overview of techniques used in analysing complex survey data. It provides analysts with a framework of how to plan and conduct analysis of survey data, familiarise with terminologies used and understand common complex sample design features of clustering, stratification and weighting. it is an excellent reference book for Stata users and the accompanying website provides useful resources and updated information. I feel that the book seamlessly links theory with practical applications of the statistical methods and helps the reader to develop an understanding of the framework of thinking required to effectively analyse complex survey data sets. " E.C. Abraham, AQMeNtion Newsletter, April 2011

" there is a wealth of instruction here. The writing style is expansive, keeping mathematics in check, and the material is well organized clearly into appropriate sections. I think that the book would serve any budding survey practitioner well: armed with the knowledge and practical skills covered herein, plus some real-life experience of course, one could reasonably claim to be well qualified in the subject." International Statistical Review (2010), 78, 3

"This book covers applied survey analysis, from survey design and weighting, to a complete list of techniques for analyzing survey data. As discussed in the preface, the motivation for the 2nd edition is to provide effective methods for teaching a complex subject as survey analysis. The book is appropriate for a graduate student with at least one applied statistics course and one statistical theory course, as well as any research scientist or survey practitioner desiring a complete and reliable survey reference. It is an excellent intermediate book, providing the opportunity to learn about complex survey design, weighting, and sampling error calculation models, as well as more advanced techniques, such as Poisson, negative binomial, multinomial logistic, survival analysis, longitudinal, and structural equation modelling, to name but a few. Overall, the text is an easy read containing a great balance of theory and applied explanations. ...The book and the corresponding resources impart years worth of experience, from both an industry perspective, as well as from teaching statistics and survey analysis. The wealth of knowledge is priceless to a graduate student, who may not cover half of the material in such detail until actually in the field." -Stephanie A. Besser, ISCB June 2018 "Anyone analyzing survey data, even once, should have a copy of this book. The book has something for everyone. It is a solid, yet accessible introduction to analyzing data from complex sample surveys (i.e., those with stratification and clustering), a statistical text of the highest caliber, and a reference for experienced analysts and statisticians. The authors are masterful instructors on the topic, and leaders in the field of survey methodology at the University of Michigan's world-renowned Institute for Social Research and Survey Research Center. Their profound understanding of the topic, and talent for describing it shines through vividly in the text. One of my favorite parts remains section 1.2 "A Brief History of Applied Survey Data Analysis", which is split into "Key Theoretical Developments" and "Key Software Developments". The historical context provided in those sections helps motivate the technical material that follows. My other favorite parts of this book are the presentations of analysis code and output from various programs, and their "Theory Boxes", which tie specific analysis steps and code to the statistical theory behind them. Among the numerous updates to this edition, I think readers will find the new content on model diagnostics and testing goodness-of-fit (GOF) to be extremely helpful, as this is an area of complex sample survey analysis that can be difficult to translate from standard regression analysis. Throughout, the authors make it a point to describe analyses in discrete steps that can help direct even the most complex analyses." Matt Jans, Senior Associate/Scientist, Abt Associates

"This is an excellent book to use for a graduate level applied statistics course teaching public health students how to analyze complex survey data. Each chapter is clearly written with a nice balance of theoretical background and practical guidance on survey data analytical issues as illustrated by many relevant real-data examples. All the chapters are nicely structured and coherently connected. The second edition includes a new chapter on the current approaches for analyzing longitudinal survey data, which will be instrumental for many practitioners trying to analyze this type of data. Several other new additions such as bootstrap methods of variance estimate, log-linear models, and fractional imputation methods for item missing data, are all important tools for an applied survey data analyst to learn and thus further enhance the scope and usability of this book. I would highly recommend this book to any instructor of an applied survey data analysis course and anyone who needs to analyze complex survey data to conduct their research." Luohua Jiang, Assistant Professor, Department of Epidemiology, University of California, Irvine

"The first edition of Applied Survey Data Analysis was my go-to reference and textbook for all types of analysis of complex survey data. This second edition is even better. The book maintains its practical analytic approach, understanding that all users of survey data want to know about methods that can be easily and appropriately implemented with major statistical analysis software, and that more sophisticated users of survey data want insights into why the methods work. In this edition, Heeringa, West, and Berglund have added an entire chapter about longitudinal data analyses (including multilevel models) using complex survey data, expanded the chapter on multiple imputation (including a discussion of fractional imputation), provided detailed examples for common complex survey data analyses that did not have them in the previous edition (such as the Pearson product-moment correlation coefficient and structural equation models), significantly and usefully expanded the discussion of model interpretation and evaluation, updated and expanded the examples to more recent data sets including the European Social Survey, and address the important perpetual question of the use of weights in regression models. All of the examples reflect the latest version of Stata (14+), including user-generated commands and other software programs where Stata falls short. The exercises in this edition also have been updated. This edition is hands-down the best book out there for an applied graduate-level course on complex survey data analysis or any user who wants to know how to obtain the correct design-based analyses." Kristen Olson, University of Nebraska-Lincoln

"I have been using Applied Survey Data Analysis in my sampling methods class for years. This is an ideal text for the course it covers design, analysis and a host of extra topics that make it a one-stop shop for a survey methods curriculum. The new version really adds to the material without skimping or detracting from the integrity of the original. What I like best is that the authors try to cover the widest array of methods so they include survey data analysis using STATA, SAS, SUDAAN and WesVar PC all in one text. There are good books for designing samples and good books for analyzing public use datasets, but this book is the best of both worlds. Unlike most statistical texts, Heeringa and his co-authors write in a style that is very easy to read and avoids copious formulas and symbolic notation. This makes it a great option for someone who might want to delve into survey design and analysis where a full semester course is not available and a self-learning text would be desirable.

I think that the best measure of the quality of a text in a classroom setting is the prevalence of used copies I observe from semester to semester. While I see an abundance of used textbooks in the other classes I teach throughout the year I may only see one or two used copies of this one each term. This tells me that the students find the book just as helpful and useful a resource as I do." Paul Weiss, Senior Associate at the Rollins School of Public Health, Emory University

"This second edition is well written, organized, and also adds a new chapter on longitudinal complex sample survey data. This book is full of real data examples from several different analysis programs. It will be an excellent reference with 13 structured chapters giving an overview, basic structures and ideas, and real applications. Faculty, researchers, and graduate students working in survey data research and consulting could reasonably adopt this book as an introduction to survey knowledge and practical analysis skills from beginner to expert." Yoonsung Jung, Research Scientist/Statistician, Prairie View A&M University

Praise for the first edition:

"...the authors do an admirable job of striking a balance between statistical theory and practical advice and analysis. The authors provide excellent coverage of each aspect of the survey analysis process This book is an excellent general resource and if the reader is left wanting on a topic the authors never fail to provide an ample set of citations and references to a wide variety of notable texts on the topic in question. an excellent and helpful addition to the desk of any analyst, researcher, or student with a general background in statistics who is dealing with the special challenges and demands of complex survey data." Gregory Holyk, Journal of Official Statistics, Vol. 27, 2011

"Overall, the book is clearly written and easy to follow, and well equipped with real data examples and a book website. The program codes used in the example are also available, mostly written in Stata. I like the presentations with real survey examples and, in particular, the unified four-step approach to the regression analysis in different models. Anyone working on survey data analysis would find the book very helpful and instructive. The book website seems to be a good complement, with additional resources on this book." Jae-Kwang Kim, The American Statistician, November 2011

"The book is well-written by authors who have over 60 years of combined teaching and consultation experience in survey methodology and research techniques. It is excellent for reference, with 12 structured chapters coherently organised, providing intermediate-level statistical overview of techniques used in analysing complex survey data. It provides analysts with a framework of how to plan and conduct analysis of survey data, familiarise with terminologies used and understand common complex sample design features of clustering, stratification and weighting. it is an excellent reference book for Stata users and the accompanying website provides useful resources and updated information. I feel that the book seamlessly links theory with practical applications of the statistical methods and helps the reader to develop an understanding of the framework of thinking required to effectively analyse complex survey data sets. " E.C. Abraham, AQMeNtion Newsletter, April 2011

" there is a wealth of instruction here. The writing style is expansive, keeping mathematics in check, and the material is well organized clearly into appropriate sections. I think that the book would serve any budding survey practitioner well: armed with the knowledge and practical skills covered herein, plus some real-life experience of course, one could reasonably claim to be well qualified in the subject." International Statistical Review (2010), 78, 3





"This book covers applied survey analysis, from survey design and weighting, to a complete list of techniques for analyzing survey data. As discussed in the preface, the motivation for the 2nd edition is to provide effective methods for teaching a complex subject as survey analysis. The book is appropriate for a graduate student with at least one applied statistics course and one statistical theory course, as well as any research scientist or survey practitioner desiring a complete and reliable survey reference. It is an excellent intermediate book, providing the opportunity to learn about complex survey design, weighting, and sampling error calculation models, as well as more advanced techniques, such as Poisson, negative binomial, multinomial logistic, survival analysis, longitudinal, and structural equation modelling, to name but a few. Overall, the text is an easy read containing a great balance of theory and applied explanations. ...The book and the corresponding resources impart years worth of experience, from both an industry perspective, as well as from teaching statistics and survey analysis. The wealth of knowledge is priceless to a graduate student, who may not cover half of the material in such detail until actually in the field." -Stephanie A. Besser, ISCB June 2018

Preface xv
Authors xxi
1 Applied Survey Data Analysis: An Overview
1(14)
1.1 Introduction
1(1)
1.2 A Brief History of Applied Survey Data Analysis
2(3)
1.2.1 Key Theoretical Developments
2(2)
1.2.2 Key Software Developments
4(1)
1.3 Example Data Sets and Exercises
5(4)
1.4 Steps in Applied Survey Data Analysis
9(6)
2 Getting to Know the Complex Sample Design
15(40)
2.1 Introduction
15(1)
2.1.1 Technical Documentation and Supplemental Literature Review
15(1)
2.2 Classification of Sample Designs
16(4)
2.2.1 Sampling Plans
17(1)
2.2.2 Other Types of Study Designs Involving Probability Sampling
18(1)
2.2.3 Inference from Survey Data
19(1)
2.3 Target Populations and Survey Populations
20(1)
2.4 Simple Random Sampling: A Simple Model for Design-Based Inference
21(5)
2.4.1 Relevance of SRS to Complex Sample Survey Data Analysis
21(1)
2.4.2 SRS Fundamentals: A Framework for Design-Based Inference
22(2)
2.4.3 Example of Design-Based Inference under SRS
24(2)
2.5 Complex Sample Design Effects
26(4)
2.5.1 Design Effect Ratio
26(2)
2.5.2 Generalized Design Effects and Effective Sample Sizes
28(2)
2.6 Complex Samples: Cluster Sampling and Stratification
30(8)
2.6.1 Cluster Sampling Plans
31(3)
2.6.2 Stratification
34(3)
2.6.3 Joint Effects of Sample Stratification and Cluster Sampling
37(1)
2.7 Weighting in Analysis of Survey Data
38(11)
2.7.1 Introduction to Weighted Analysis of Survey Data
38(2)
2.7.2 Weighting for Probabilities of Selection (West)
40(2)
2.7.3 Nonresponse Adjustment Weights (Wnr)
42(1)
2.7.3.1 Weighting Class Approach (Wnr,wc)
42(1)
2.7.3.2 Propensity Cell Adjustment Approach (Wnr,prop)
43(2)
2.7.4 Poststratification Weight Factors (Wps)
45(2)
2.7.5 Design Effects Due to Weighted Analysis
47(2)
2.8 Multistage Area Probability Sample Designs
49(4)
2.8.1 Primary Stage Sampling
50(1)
2.8.2 Secondary Stage Sampling
51(1)
2.8.3 Third- and Fourth-Stage Sampling of HUs and Eligible Respondents
52(1)
2.9 Special Types of Sampling Plans Encountered in Surveys
53(2)
3 Foundations and Techniques for Design-Based Estimation and Inference
55(42)
3.1 Introduction
55(1)
3.2 Finite Populations and Superpopulation Models
56(2)
3.3 CIs for Population Parameters
58(1)
3.4 Weighted Estimation of Population Parameters
59(3)
3.5 Probability Distributions and Design-Based Inference
62(5)
3.5.1 Sampling Distributions of Survey Estimates
62(3)
3.5.2 Degrees of Freedom for t under Complex Sample Designs
65(2)
3.6 Variance Estimation
67(22)
3.6.1 Simplifying Assumptions Employed in Complex Sample Variance Estimation
69(1)
3.6.2 TSL Method
70(5)
3.6.3 Replication Methods for Variance Estimation
75(1)
3.6.3.1 Jackknife Repeated Replication
76(4)
3.6.3.2 Balanced Repeated Replication
80(3)
3.6.3.3 Fay's BRR Method
83(1)
3.6.3.4 Bootstrap (Rao--Wu Rescaling Bootstrap)
84(1)
3.6.3.5 Construction of Replicate Weights for Replicated Variance Estimation
85(1)
3.6.4 Example Comparing Results from the TSL, JRR, BRR, and Bootstrap Methods
86(3)
3.7 Hypothesis Testing in Survey Data Analysis
89(2)
3.8 TSE and Its Impact on Survey Estimation and Inference
91(6)
3.8.1 Variable Errors
91(1)
3.8.2 Biases in Survey Data
92(5)
4 Preparation for Complex Sample Survey Data Analysis
97(28)
4.1 Introduction
97(1)
4.2 Final Survey Weights: Review by the Data User
98(6)
4.2.1 Identification of the Correct Weight Variable(s) for the Analysis
99(1)
4.2.2 Determining the Distribution and Scaling of the Weight Variable(s)
100(2)
4.2.3 Weighting Applications: Sensitivity of Survey Estimates to the Weights
102(2)
4.3 Understanding and Checking the Sampling Error Calculation Model
104(9)
4.3.1 Stratum and Cluster Codes in Complex Sample Survey Data Sets
105(2)
4.3.2 Building the NCS-R Sampling Error Calculation Model
107(2)
4.3.3 Combining Strata, Randomly Grouping PSUs, and Collapsing Strata
109(2)
4.3.4 Checking the Sampling Error Calculation Model for the Survey Data Set
111(2)
4.4 Addressing Item Missing Data in Analysis Variables
113(3)
4.4.1 Potential Bias due to Ignoring Missing Data
114(1)
4.4.2 Exploring Rates and Patterns of Missing Data Prior to Analysis
114(2)
4.5 Preparing to Analyze Data for Sample Subpopulations
116(5)
4.5.1 Subpopulation Distributions across Sample Design Units
118(1)
4.5.2 Unconditional Approach for Subclass Analysis
119(2)
4.5.3 Preparation for Subclass Analyses
121(1)
4.6 Final Checklist for Data Users
121(4)
5 Descriptive Analysis for Continuous Variables
125(34)
5.1 Introduction
125(1)
5.2 Special Considerations in Descriptive Analysis of Complex Sample Survey Data
126(2)
5.2.1 Weighted Estimation
126(1)
5.2.2 Design Effects for Descriptive Statistics
127(1)
5.2.3 Matching the Method to the Variable Type
128(1)
5.3 Simple Statistics for Univariate Continuous Distributions
128(17)
5.3.1 Graphical Tools for Descriptive Analysis of Survey Data
129(2)
5.3.2 Estimation of Population Totals
131(5)
5.3.3 Means of Continuous, Binary, or Interval Scale Data
136(3)
5.3.4 Standard Deviations of Continuous Variables
139(1)
5.3.5 Estimation of Percentiles, Medians, and Measures of Inequality in Population Distributions for Continuous Variables
140(1)
5.3.5.1 Estimation of Distribution Quantiles
140(2)
5.3.5.2 Estimation of Measures of Inequality in Population Distributions
142(3)
5.4 Bivariate Relationships between Two Continuous Variables
145(4)
5.4.1 X--Y Scatter Plots
145(1)
5.4.2 Product Moment Correlation Statistic (r)
146(2)
5.4.3 Ratios of Two Continuous Variables
148(1)
5.5 Descriptive Statistics for Subpopulations
149(2)
5.6 Linear Functions of Descriptive Estimates and Differences of Means
151(8)
5.6.1 Differences of Means for Two Subpopulations
152(3)
5.6.2 Comparing Means over Time
155(4)
6 Categorical Data Analysis
159(36)
6.1 Introduction
159(1)
6.2 Framework for Analysis of Categorical Survey Data
160(2)
6.2.1 Incorporating the Complex Design and Pseudo Maximum Likelihood
160(1)
6.2.2 Proportions and Percentages
160(1)
6.2.3 Crosstabulations, Contingency Tables, and Weighted Frequencies
161(1)
6.3 Univariate Analysis of Categorical Data
162(10)
6.3.1 Estimation of Proportions for Binary Variables
162(4)
6.3.2 Estimation of Category Proportions for Multinomial Variables
166(3)
6.3.3 Testing Hypotheses Concerning a Vector of Population Proportions
169(1)
6.3.4 Graphical Display for a Single Categorical Variable
170(2)
6.4 Bivariate Analysis of Categorical Data
172(13)
6.4.1 Response and Factor Variables
172(1)
6.4.2 Estimation of Total, Row, and Column Proportions for Two-Way Tables
172(2)
6.4.3 Estimating and Testing Differences in Subpopulation Proportions
174(1)
6.4.4 Χ2 Tests of Independence of Rows and Columns
175(6)
6.4.5 Odds Ratios and Relative Risks
181(2)
6.4.6 Simple Logistic Regression to Estimate the Odds Ratio
183(1)
6.4.7 Bivariate Graphical Analysis
184(1)
6.5 Analysis of Multivariate Categorical Data
185(6)
6.5.1 Cochran--Mantel--Haenszel Test
186(2)
6.5.2 Log-Linear Models for Contingency Tables
188(3)
6.6 Summary
191(4)
7 Linear Regression Models
195(62)
7.1 Introduction
195(2)
7.2 Linear Regression Model
197(5)
7.2.1 Standard Linear Regression Model
199(1)
7.2.2 Survey Treatment of the Regression Model
200(2)
7.3 Four Steps in Linear Regression Analysis
202(21)
7.3.1 Step 1: Specifying and Refining the Model
202(1)
7.3.2 Step 2: Estimation of Model Parameters
203(1)
7.3.2.1 Estimation for the Standard Linear Regression Model
203(2)
7.3.2.2 Linear Regression Estimation for Complex Sample Survey Data
205(5)
7.3.3 Step 3: Model Evaluation
210(7)
7.3.4 Step 4: Inference
217(1)
7.3.4.1 Inference Concerning Model Parameters
218(3)
7.3.4.2 Prediction Intervals
221(2)
7.4 Some Practical Considerations and Tools
223(8)
7.4.1 Distribution of the Dependent Variable
223(1)
7.4.2 Parameterization and Scaling for Independent Variables
224(3)
7.4.3 Standardization of the Dependent and Independent Variables
227(1)
7.4.4 Specification and Interpretation of Interactions and Nonlinear Relationships
227(3)
7.4.5 Model-Building Strategies
230(1)
7.5 Application: Modeling Diastolic Blood Pressure with the 2011--2012 NHANES Data
231(26)
7.5.1 Exploring the Bivariate Relationships
232(3)
7.5.2 Naive Analysis: Ignoring Sample Design Features
235(1)
7.5.3 Weighted Regression Analysis
236(2)
7.5.4 Appropriate Analysis: Incorporating All Sample Design Features
238(19)
8 Logistic Regression and Generalized Linear Models for Binary Survey Variables
257(42)
8.1 Introduction
257(1)
8.2 GLMs for Binary Survey Responses
258(4)
8.2.1 Logistic Regression Model
260(1)
8.2.2 Probit Regression Model
261(1)
8.2.3 Complementary-Log-Log Model
262(1)
8.3 Building the Logistic Regression Model: Stage 1---Model Specification
262(1)
8.4 Building the Logistic Regression Model: Stage 2---Estimation of Model Parameters and Standard Errors
263(5)
8.5 Building the Logistic Regression Model: Stage 3---Evaluation of the Fitted Model
268(4)
8.5.1 Wald Tests of Model Parameters
268(2)
8.5.2 GOF and Logistic Regression Diagnostics
270(2)
8.6 Building the Logistic Regression Model: Stage 4---Interpretation and Inference
272(11)
8.7 Analysis Application
283(10)
8.7.1 Stage 1: Model Specification
283(2)
8.7.2 Stage 2: Model Estimation
285(1)
8.7.3 Stage 3: Model Evaluation
286(2)
8.7.4 Stage 4: Model Interpretation/Inference
288(5)
8.8 Comparing the Logistic, Probit, and C-L-L GLMs for Binary Dependent Variables
293(6)
9 Generalized Linear Models for Multinomial, Ordinal, and Count Variables
299(40)
9.1 Introduction
299(1)
9.2 Analyzing Survey Data Using Multinomial Logit Regression Models
299(14)
9.2.1 Multinomial Logit Regression Model
299(2)
9.2.2 Multinomial Logit Regression Model: Specification Stage
301(1)
9.2.3 Multinomial Logit Regression Model: Estimation Stage
302(2)
9.2.4 Multinomial Logit Regression Model: Evaluation Stage
304(1)
9.2.5 Multinomial Logit Regression Model: Interpretation Stage
304(1)
9.2.6 Example: Fitting a Multinomial Logit Regression Model to Complex Sample Survey Data
305(8)
9.3 Logistic Regression Models for Ordinal Survey Data
313(10)
9.3.1 Cumulative Logit Regression Model
314(1)
9.3.2 Cumulative Logit Regression Model: Specification Stage
315(1)
9.3.3 Cumulative Logit Regression Model: Estimation Stage
315(1)
9.3.4 Cumulative Logit Regression Model: Evaluation Stage
316(1)
9.3.5 Cumulative Logit Regression Model: Interpretation Stage
317(2)
9.3.6 Example: Fitting a Cumulative Logit Regression Model to Complex Sample Survey Data
319(4)
9.4 Regression Models for Count Outcomes
323(16)
9.4.1 Survey Count Variables and Regression Modeling Alternatives
323(3)
9.4.2 Generalized Linear Models for Count Variables
326(1)
9.4.2.1 Poisson Regression Model
326(1)
9.4.2.2 Negative Binomial Regression Model
327(1)
9.4.2.3 Two-Part Models: Zero-Inflated Poisson and Negative Binomial Regression Models
327(2)
9.4.3 Regression Models for Count Data: Specification Stage
329(1)
9.4.4 Regression Models for Count Data: Estimation Stage
329(1)
9.4.5 Regression Models for Count Data: Evaluation Stage
330(1)
9.4.6 Regression Models for Count Data: Interpretation Stage
330(1)
9.4.7 Example: Fitting Poisson and Negative Binomial Regression Models to Complex Sample Survey Data
331(8)
10 Survival Analysis of Event History Survey Data
339(32)
10.1 Introduction
339(1)
10.2 Basic Theory of Survival Analysis
339(5)
10.2.1 Survey Measurement of Event History Data
339(2)
10.2.2 Data for Event History Models
341(1)
10.2.3 Important Notation and Definitions
342(1)
10.2.4 Models for Survival Analysis
343(1)
10.3 (Nonparametric) --M Estimation of the Survivor Function
344(7)
10.3.1 K--M Model Specification and Estimation
345(2)
10.3.2 K--M Estimator: Evaluation and Interpretation
347(1)
10.3.3 K--M Survival Analysis Example
347(4)
10.4 The Cox Proportional Hazards (CPH) Model
351(8)
10.4.1 CPH Model: Specification
351(1)
10.4.2 CPH Model: Estimation Stage
352(2)
10.4.3 CPH Model: Evaluation and Diagnostics
354(1)
10.4.4 CPH Model: Interpretation and Presentation of Results
354(1)
10.4.5 Example: Fitting a CPH Model to Complex Sample Survey Data
355(4)
10.5 Discrete Time Survival Models
359(12)
10.5.1 Discrete Time Logistic Model
359(1)
10.5.2 Data Preparation for Discrete Time Survival Models
360(3)
10.5.3 Discrete Time Models: Estimation Stage
363(1)
10.5.4 Discrete Time Models: Evaluation and Interpretation
364(1)
10.5.5 Fitting a Discrete Time Model to Complex Sample Survey Data
365(6)
11 Analysis of Longitudinal Complex Sample Survey Data
371(56)
11.1 Introduction
371(1)
11.2 Alternative Analytic Objectives with Longitudinal Survey Data
372(17)
11.2.1 Objective 1: Descriptive Estimation at a Single Time Point
372(2)
11.2.2 Objective 2: Estimation of Change across Two Waves
374(1)
11.2.3 Objective 3: Trajectory Estimation Based on Three or More Waves
375(1)
11.2.3.1 Approach 1: Weighted Multilevel Modeling
375(7)
11.2.3.2 Approach 2: Covariance Structure Modeling
382(2)
11.2.3.3 Approach 3: Weighted GEE Estimation
384(3)
11.2.3.4 Approach 4: Multiple Imputation Analysis
387(1)
11.2.3.5 Approach 5: Calibration Adjustment for Respondents with Complete Data
388(1)
11.3 Alternative Longitudinal Analyses of the HRS Data
389(33)
11.3.1 Example: Descriptive Estimation at a Single Wave
390(5)
11.3.2 Example: Change across Two Waves
395(6)
11.3.2.1 Accounting for Refreshment Samples When Estimating Mean Change
401(1)
11.3.3 Example: Weighted Multilevel Modeling
402(8)
11.3.3.1 Example: Veiga et al. (2014)
410(6)
11.3.4 Example: Weighted GEE Analysis
416(6)
11.4 Concluding Remarks
422(5)
12 Imputation of Missing Data: Practical Methods and Applications for Survey Analysts
427(46)
12.1 Introduction
427(2)
12.2 Important Missing Data Concepts
429(7)
12.2.1 Sources and Types of Missing Data
429(1)
12.2.2 Patterns of Item Missing Data in Surveys
430(1)
12.2.3 Item Missing Data Mechanisms
431(2)
12.2.4 Review of Strategies to Address Item Missing Data in Surveys
433(3)
12.3 Factors to Consider in Choosing an Imputation Method
436(3)
12.4 Multiple Imputation
439(12)
12.4.1 Overview of MI and MI Phases
439(1)
12.4.2 Models for Multiply Imputing Missing Data
440(1)
12.4.2.1 Choosing the Variables to Include in the Imputation Model
441(3)
12.4.2.2 Distributional Assumptions for the Imputation Model
444(1)
12.4.3 Creating the Mis
444(1)
12.4.3.1 Transforming the Imputation Problem to Monotonic Missing Data
445(1)
12.4.3.2 Specifying an Explicit Multivariate Model and Applying Exact Bayesian Posterior Simulation Methods
445(1)
12.4.3.3 SR or "Chained Regressions"
446(2)
12.4.4 Estimation and Inference for Multiply Imputed Data
448(1)
12.4.4.1 Estimators for Population Parameters and Associated Variance Estimators
448(1)
12.4.4.2 Model Evaluation and Inference
449(2)
12.5 Fractional Imputation
451(5)
12.5.1 Background
451(1)
12.5.2 Creating the FIs
452(2)
12.5.3 Estimation and Inference with Fractionally Imputed Data
454(1)
12.5.4 FI Software
455(1)
12.6 Application of MI and FI Methods to the NHANES 2011--2012 Data
456(17)
12.6.1 Problem Definition
456(1)
12.6.2 Imputation Models for the NHANES DBP Example
457(2)
12.6.3 Imputation of the Item Missing Data
459(1)
12.6.3.1 Multiple Imputation
459(2)
12.6.3.2 FEFI: Hot Deck Method
461(3)
12.6.4 Estimation and Inference
464(1)
12.6.4.1 Multiple Imputation
464(3)
12.6.4.2 FI Estimation and Inference
467(1)
12.6.5 Comparison of Example Results from Complete Case Analysis, MI, and FEFI
468(5)
13 Advanced Topics in the Analysis of Survey Data
473(28)
13.1 Introduction
473(1)
13.2 Bayesian Analysis of Complex Sample Survey Data
474(4)
13.3 GLMMs in Survey Data Analysis
478(11)
13.3.1 Overview of GLMMs
478(3)
13.3.2 GLMMs and Complex Sample Survey Data
481(4)
13.3.3 Alternative Approaches to Fitting GLMMs to Survey Data: The PISA Example
485(4)
13.4 Fitting Structural Equation Models to Complex Sample Survey Data
489(9)
13.4.1 SEM Example: Analysis of ESS Data from Belgium
491(7)
13.5 Small Area Estimation and Complex Sample Survey Data
498(1)
13.6 Nonparametric Methods for Complex Sample Survey Data
499(2)
References 501(18)
Appendix A Software Overview 519(30)
Index 549
Steve G. Heeringa is a research scientist in the Survey Methodology Program, the director of the Statistical and Research Design Group in the Survey Research Center, and the director of the Summer Institute in Survey Research Techniques at the University of Michigans Institute for Social Research.





Brady T. West is a Research Associate Professor in the Survey Research Center at the University of Michigans Institute for Social Research. He is also a statistical consultant on the Consulting for Statistics, Computing, and Analytics Research (CSCAR) team at the University of Michigan.





Patricia A. Berglund is a senior research associate in the Youth and Social Indicators Program and Survey Methodology Program in the Survey Research Center at the University of Michigans Institute for Social Research.