Analysis Examples ReplicationSecond Edition The analysis examples replication materials cover Chapters 513 of ASDA Second Edition but not every software package contains all chapters. Lack of a link for a given chapter indicates that this software package does not include the ability to perform this type of analysis technique. SAS v9.4 Code and Results Stata v14 Code (Results Are Presented Throughout the Book) SPSS V22 Code and Results IVEware V0.3 Code and Results Sudaan 11.0 Code and Results R Survey Package v3.315 Code and Results WesVar 5.1 Code and Results MPlus V7.4 Code and Results
ASDA First Edition Analysis Examples ReplicationFirst Edition The analysis examples replication materials cover Chapters 512 of ASDA First Edition but not every software package contains all 8 chapters. Lack of a link for a given chapter indicates that this software package does not include the ability to perform this type of analysis technique. SAS v9.2 Code and Results
Sudaan 10.0 Code and Results
SPSS/PASW V18.0 Code and Results
IVEware Code and Results
WesVar 4.3 Code and Results
R Survey 3.2 Code and Results
Mplus 5.2 Code and Results
Stata v10.1 Code and Results

Site Overview This site contains information about the text "Applied Survey Data Analysis", (first and second editions) including author biographies,links to public release data sets and related sites, code and output for analysis examples replicated in current software packages, and information about new publications of interest to survey data analysts. Other features include a FAQ log and links to other software and statistical sites. We plan to intermittently update this site with news about ongoing statistical and software advances in the field of analysis of survey data.
Special Notes from Authors ASDASecond Edition is Available as of June 28, 2017! Applied Survey Data Analysis is the product born of many years of teaching applied survey data analysis classes and practical experience analyzing survey data. We have taught various versions of this course in the ISR/SRC Summer Institute Program, as part of University of Michigan/CSCAR, and within the Survey Methodology Program at University of Michigan and University of Maryland. Our goal has been to integrate teaching materials and practical analysis knowledge into a textbook geared to a level accessible for graduate students and working analysts who may have varying levels of statistical and analytic expertise. We intend to update the materials on this website as statistical and software improvements emerge with the goal of assisting analyst and researchers performing survey data analysis.
Patricia A. Berglund is a Senior Research Associate in the Survey Methodology Program at the Institute for Social Research. She has extensive experience in the use of computing systems for data management and complex sample survey data analysis. She works on research projects in youth substance abuse, adult mental health, and survey methodology using data from Army STARRS, Monitoring the Future, the National Comorbidity Surveys, World Mental Health Surveys, Collaborative Psychiatric Epidemiology Surveys, and various other national and international surveys. In addition, she is involved in development, implementation, and teaching of analysis courses and computer training programs at the Survey Research CenterInstitute for Social Research. She also lectures in the SAS® InstituteBusiness Knowledge Series. mailto:pberg@umich.edu Steven 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 Institute for Social Research. He has over 25 years of statistical sampling experience directing the development of the SRC National Sample design, as well as sample designs for SRC's major longitudinal and crosssectional survey programs. During this period he has been actively involved in research and publication on sample design methods and procedures such as weighting, variance estimation, and the imputation of missing data that are required in the analysis of sample survey data. He has been a teacher of survey sampling methods to U.S. and international students and has served as a sample design consultant to a wide variety of international research programs based in countries such as Russia, the Ukraine, Uzbekistan, Kazakhstan, India, Nepal, China, Egypt, Iran, and Chile. mailto:sheering@umich.edu Brady T. West Brady T. West is a Research Associate Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of MichiganAnn Arbor (UM) campus. He also serves as a Statistical Consultant on the UM Consulting for Statistics, Computing, and Analytics Research (CSCAR) team. He earned his PhD from the Michigan Program in Survey Methodology in 2011. Before that, he received an MA in Applied Statistics from the UM Statistics Department in 2002, being recognized as an Outstanding Firstyear Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the UM Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixedeffects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software,Second Edition, Chapman Hall/CRC Press, 2014), and he is a coauthor of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), which was published by Chapman Hall in April 2010 and has a second edition in press that will be available in mid2017. Brady lives in Dexter, MI with his wife Laura, his son Carter, his daughter Everleigh, and his American Cocker Spaniel Bailey. mailto:bwest@umich.edu
Professional Reviews of ASDASecond Edition Review/Summary from Chapman Hall Website
Links to Data Sets for First and Second Editions National Comorbidity SurveyReplication (Collaborative Psychiatric Epidemiology Surveys) http://www.icpsr.umich.edu/cpes (for online documentation tools and data download) http://www.hcp.med.harvard.edu/ncs (for NCSR specific information) National Health and Nutrition Examination Survey (National Center for Health Statistics) Health and Retirement Survey (Institute for Social ResearchUniversity of Michigan) http://hrsonline.isr.umich.edu European Social Survey (ESS) http://www.europeansocialsurvey.org/ United States Census Bureau
Chapter Exercises Data Sets  Second Edition These data sets are subsets of the original data and are designed for use with the chapter exercises in ASDA Second Edition. We provide SAS and Stata format data sets here but for other software, please use a data transfer software or import/export tools within software of choice to translate to needed format. Chapter Exercises Data Sets (SAS Format)  Second Edition Chapter Exercises Data Sets (Stata Format)  Second Edition Chapter Exercises Data Sets  First Edition These data sets are subsets of the original data and are designed for use with the chapter exercises in ASDA. Chapter Exercises Data Sets (Stata and SAS Format)  First Edition Chapter Exercises Data Sets (R Format)  First Edition Analysis Example Data Sets  First Edition These data sets are subsets of the original data and are designed for use with the analysis examples in ASDA  First Edition. We have included the raw variables used in the variable recodes and constructed variables used in the analysis examples. Analysis Examples Data Sets (Stata and SAS Format)  First Edition
This document contains frequently asked questions and brief answers. Click here: FAQ Document This working paper addresses Accounting for Multistage Sample Designs in Complex Sample Variance Estimation by Brady West. Click here to download: MultiStage Sample Designs
Data Archive University of Michigan (ICPSR) Data Archive http://www.icpsr.umich.edu Software for Survey Data Analysis SAS® software http://www.sas.com STATA® software http://www.stata.com Sudaan® software http://www.rti.org SPSS® software http://www.spss.com Mplus® software http://statmodel.com R software http://www.rproject.org/ WesVar software http://www.westat.com/westat/statistical_software/wesvar IVEware http://www.isr.umich.edu/src/smp/ive SDA from ICPSR http://www.icpsr.umich.edu (online analysis system with survey correction capabilities) Manual for Package ‘svydiags’ from R, Linear Regression Model Diagnostics for Survey Data Link to Manual IVEware Version 0.3 Software and Examples (Windows) Link to Software Zip File (Windows) Link to Examples Zip File (Windows) Software Updates Stata  V14 is current as of May 2017 IBM/SPSSSPSS 22 is current as of May 2017 SAS  v9.4 is current as of May 2017 See software websites for additional software updates and versions
This section provides key updates to software for analysis of survey data. SASExample of how to use replicate weights using NHANES data: SAS Replicate Weights Example StataExample of Mediation analysis with survey data and subpopulation indicator: Stata sgmediation example RExample of Quantile Regression with Bootstrap Method: R Quantile Regression Example SASExample of use of NOMCAR option with PROC SURVEYMEANS: SAS NOMCAR Example Example of How to Create a Delimited Text File in SAS and Read Text File in R: Text File SAS to R Example An Example of Fuller’s (1984) Method for Testing the Bias of Unweighted Estimates of Regression Parameters in a Linear Regression Model: Fuller's Method
SAS code to implement Wilcoxon rank sum test for complex sample survey data: http://www.blackwellpublishing.com/rss
SAS Paper with Examples of ODS Graphics and SG Procedures with Examples of Weighted Frequency Plots: SAS Paper with ODS Graphics and SG Procedures Examples
Note on How SPSS handles Strata with A Single or "Lonely" PSU: http://www01.ibm.com/support/docview.wss?uid=swg21479202
Link to Stata command for calculation of Population Attributable Risk proportions (user written "punaf" command): http://www.imperial.ac.uk/nhli/r.newson/usergp/uk2012/newson_ohp1.pdf
Example of using PROC EXPORT to convert SAS data set to Stata (.dta) and SPSS (.sav): SAS PROC EXPORT Example
Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS, Stata, IVEware, and R: Link to Presentation
Analysis of Survey Data Using the SAS SURVEY Procedures: A Primer: Link to Presentation
Link to Web Site with Information about Free Tools for Survey Data Analysis and Map Production: http://www.asdfree.com/2014/12/mapsandartofsurveyweighted.htm Link to full code for Map Examples: https://github.com/davidbrae/swmap
SAS Repeated Replication Macro to do DesignBased Poisson Regression (with a comparison to Stata svy: poisson command): Link to Code and Results
New Stata V14+ Features: 1.The "survwgt" contributed package for creating replicate weights: Link to Package. 2.The "bs4rw" modifier for performing quantile regression. Install using http://www.stata.com/users/jpitblado/bs4rw. Implement a command referring to replicate weights that have already been generated: "survwgt: bs4rw, rw(brrrwt*): qreg $depvar $demo if subpop==1 [pw=perwt5], q(.5)". R package for fractional hot deck imputation (FHDI) is now available from CRAN (Primary Author, Dr. Jae Kim). Link to Code and Information Modified Stata file, pwigls_genlin_adcv_modAV1.do for C11 for Viega Method (Author is Dr. A. Viega). Link to File
Example of SAS 9.4 PROC SURVEYMEANS with DOMAIN Statement and DIFF Option for Difference of Means Test. Link to File
Example of Use of R "Convey" Package for Svy GINI Coefficient. Link to File
Examples of R Survey Package RegTermTest Command Syntax For Tests of Interactions Only and Main Effects Plus Interactions. Link to File
Information and Link to R svydiags package for Survey Regression Diagnostics by Dr. Valliant. This work contains functions for computing diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors. Link to CRAN
Example of Stata v16 Lincom Command. The latest syntax is included in this example. Note that this is different than previous Stata versions. Link to Example
Slides and R/STAN code from Presentation "PseudoBayesian Inference for Complex Survey Data", April 2020, Matt Williams and Terrance Savitsky. Link to Slides Link to R/STAN Code
Statistical Resources for Analysis of Survey Data University of Michigan Institute for Social ResearchSummer Institute www.isr.umich.edu/src/si IVEware (Imputation and Variance Estimation software) www.isr.umich.edu/src/smp/ive ICPSR summer institute http://www.icpsr.umich.edu/icpsrweb/sumprog/ Center for Statistical Consulting and Research www.umich.edu/~cscar/ University of CaliforniaLos Angeles Statistical and Survey Data Analysis http://www.ats.ucla.edu/stat/ University of North CarolinaChapel Hill Population Center http://www.cpc.unc.edu/ American Statistical Association Home Page http://www.amstat.org/
Survey Data Analysis Publications  General Survey Data Analysis Topics (since 2015) This section is designed to provide information about key updates in publications regarding Survey Data analysis. We will add to the list as new publications emerge.
Mplus Notes area with many articles about survey data analysis: http://statmodel.com/resrchpap.shtml. Presentation on AIC and BIC for Survey Data by Thomas Lumley and Alastair Scott: Link to Presentation Lumley and Scott, AIC AND BIC FOR MODELING WITH COMPLEX SURVEY DATA, Journal of Survey Statistics and Methodology,2015, Link to Paper Thompson, Mary E., Using Longitudinal Complex Survey Data, Annual Review of Statistics.and Its Application,2015. 2:305–20, Link to Paper Bridget L. Ryan, John Koval, Bradley Corbett, Amardeep Thind, M. Karen Campbell, and Moira Stewart, Assessing the impact of potentially influential observations in weighted logistic regression, The Research Data Centres Information and Technical Bulletin, Catalogue no. 12002‑X —No. 2015001, Link to Paper Jianzhu Li and Richard Valliant, Linear Regression Diagnostics in Cluster Samples,Journal of Official Statistics, Vol. 31, No. 1, 2015, pp. 61–75, Link to Paper Miles, Andrew, Obtaining Predictions from Models Fit to Multiply Imputed Data, Sociological Methods & Research, pp. 111, 2015, Link to Paper Luchman, J.N., Determining Subgroup Difference Importance with Complex Survey Designs An Application of Weighted Dominance Analysis, Survey Practice, Vol. 8, no 4, 2015, Link to Paper Oya Kalaycioglu,Andrew Copas, Michael King and Rumana Z. Omar, A comparison of multipleimputation methods for handling missing data in repeated measurements observational studies, Journal of the Royal Statistical Society, June 2015, Link to Paper Natalie Dean, Marcello Pagano, EVALUATING CONFIDENCE INTERVAL METHODS FOR BINOMIAL PROPORTIONS IN CLUSTERED SURVEYS, Journal of Survey Statistics and Methodology, October 2015, Link to Paper Zhou, H., Elliott, M.R., Raghunathan, T.E. (2015). "Synthetic Multiple Imputation Procedure For MultiStage Complex Samples," to appear in Journal of Official Statistics soon. Zhou, H., Elliott, M.R., Raghunathan, T.E. (2015). "A TwoStep Semiparametric Method to Accommodate Sampling Weights in Multiple Imputation," in Biometrics 2015 Sep 22. Link to Paper Zhou, H., Elliott, M.R., Raghunathan, T.E. (2015). "Multiple Imputation In TwoStage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap," to appear in Journal of Survey Statistics and Methodology soon. Stapleton, L. and Kang, Y. (2016). "Design Effects of Multilevel Estimates From National Probability Samples", Sociological Methods & Research 0049124116630563, first published on February 11, 2016 as doi:10.1177/0049124116630563, Link to Paper Daoying Lin, Lingxiao Wang, and Yan Li, "HAPLOTYPEBASED STATISTICAL INFERENCE FOR POPULATIONBASED CASE–CONTROL AND CROSSSECTIONAL STUDIES WITH COMPLEX SAMPLE DESIGNS", J Surv Stat Methodol published 25 April 2016, 10.1093/jssam/smv040. Link to Paper Bollen,K., Biemer,P., Karr,A., Tueller,S., Berzofsky,M.,"Are Survey Weights Needed? A Review of Diagnostic Tests in Regression Analysis", Annual Review of Statistics and Its Application Vol. 3: 375392 (Volume publication date June 2016). Link to Paper Hanzhi Zhou, Michael R. Elliott, and Trivellore E. Raghunathan,"Multiple Imputation in Twostage Cluster Samples Using the Weighted Finite Population Bayesian Bootstrap", J Surv Stat Methodol 2016 4: 139170. Link to Paper Minsun Kim Riddles, Jae Kwang Kim, and Jongho Im, "A Propensityscoreadjustment Method for Nonignorable Nonresponse", J Surv Stat Methodol 2016 4: 215245. . Link to Paper Brady T. West, Joseph W. Sakshaug, Guy Alain S. Aurelien, "How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data?", Published: June 29,http://dx.doi.org/10.1371/journal.pone.0158120. Link to Paper Ismael Flores Cervantes and J. Michael Brick, "Nonresponse adjustments with misspecified models in stratified designs", Survey Methodology, Catalogue no. 12001X, Release date: June 22, 2016. Link to Paper Xiaying Zheng and Ji Seung Yang, "Using Sample Weights in Item Response Data Analysis Under Complex Sample Designs", L.A. van der Ark et al. (eds.), Quantitative Psychology Research, Springer, Proceedings in Mathematics & Statistics 167, DOI 10.1007/9783319387598_10. Link to Paper Xing Lui, "Fitting Proportional Odds Models for Complex Sample Survey Data with SAS, IBM SPSS, Stata, and R", General Linear Model Journal, 2016, Vol. 42(2). Link to Paper Toth, Daniel, Bureau of Labor Statistics, "An R Package for Modeling Survey Data with Regression Trees", WSS Seminar, 2017. Link to Presentation Hsu HY1, Lin JJH2, Skidmore ST3, "Analyzing individual growth with clustered longitudinal data: A comparison between modelbased and designbased multilevel approaches", Behav Res Methods. 2017 Jun 20. doi: 10.3758/s1342801709057. [Epub ahead of print]. Link to Paper Qixuan Chen, Michael R. Elliott, David Haziza, Ye Yang, Malay Ghosh, Roderick J. A. Little, Joseph Sedransk, and Mary Thompson, "Approaches to Improving SurveyWeighted Estimates", Statist. Sci.Volume 32, Number 2 (2017), 227248. Link to Paper Kott, Phillip S. A designsensitive approach to fitting regression models with complex survey data. Statist. Surv. 12 (2018), 117. doi:10.1214/17SS118. Link to Paper von Hippel, Paul T. How Many Imputations Do You Need? A Twostage Calculation Using a Quadratic Rule. Sociological Methods & Research, Article first published online: January 18, 2018. Link to Paper Brady T. West PhD, Linda Beer PhD, Garrett W. Gremel BS, John Weiser MD, MPH, Christopher H. Johnson MS, Shikha Garg MD, MPH, and Jacek Skarbinski MD., "Weighted Multilevel Models: A Case Study", American Journal of Public Health (AJPH), Article first published online: October 9, 2015. Link to Paper Ashley L. Buchanan, Michael G. Hudgens, Stephen R. Cole, Katie R. Mollan, Paul E. Sax, Eric S. Daar, Adaora A. Adimora, Joseph J. Eron and Michael J. Mugavero, "Generalizing evidence from randomized trials using inverse probability of sampling weights", Version of Record online: 26 FEB 2018  DOI: 10.1111/rssa.12357. Link to Paper Lumley, Thomas, Description and Link to R package for mixed models under complex sampling. Link to Paper J.N.K. Rao, François Verret and Mike A. Hidiroglou. "A weighted composite likelihood approach to inference for twolevel models from survey data", Survey Methodology, December 2013, 263 Vol. 39, No. 2, pp. 263282. Statistics Canada, Catalogue No. 12001X. Link to Paper Giovanni Nattino and Bo Lu, "Estimating Causal Effects with Propensity Score in Cluster Sample Surveys", JSM 2018 Online Program. . Link to Paper Abstract Sixia Chen and Yan Daniel Zhao, "Quantile Regression Analysis of Survey Data Under Informative Sampling", Journal of Survey Statistics and Methodology, Published: 29 October 2018. . Link to Paper Abstract Carolina Franco, Rodericak J A Little, Thomas A Louis, Eric V Slud, "Comparative Study of Confidence Intervals for Proportions in Complex Sample Surveys", Journal of Survey Statistics and Methodology, smy019, Published: 07 January 2019. Link to Paper Xu Qin, Guanglei Hong, Jonah Deutsch, and Edward Bein, "Multisite causal mediation analysis in the presence of complex sample and survey designs and nonrandom nonresponse", Journal of the Royal Statistics Society, First published: 14 April 2019. Link to Paper Natalie A. Koziol,"Weighted Multilevel Versus Robust SingleLevel Methods for Analyzing Subpopulation Data", Methodology (2019),15,pp. 6776, © 2019 Hogrefe Publishing. Link to Paper Carolina Franco, Roderick J A Little, Thomas A Louis, Eric V Slud, "Comparative Study of Confidence Intervals for Proportions in Complex Sample Surveys", Journal of Survey Statistics and Methodology, Volume 7, Issue 3, September 2019, Pages 334–364. Link to Paper Toth, Daniell, "A Permutation Test on Complex Sample Data", Journal of Survey Statistics and Methodology, smz018, Published:13 August 2019. Link to Paper Jacques Muthusi, Samuel Mwalili, Peter Young, "%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publicationready tables using survey or nonsurvey data", Plos One, Published: September 3, 2019. Link to Paper M Quartagno, J R Carpenter, H Goldstein, "Multiple Imputation with Survey Weights: A Multilevel Approach", Journal of Survey Statistics and Methodology, smz036, https://doi.org/10.1093/jssam/smz036, Published: 13 September 2019. Link to Paper Jihnhee Yu, Ziqiang Chen, Kan Wang and Mine Tezal, "Suggestion of confidence interval methods for the Cronbach alpha in application to complex survey data", Statistics Canada,Survey Methodology Journal, https://www150.statcan.gc.ca/n1/pub/12001x/12001x2019003eng.htm. Link to Paper Gösta Andersson, "Optimal" calibration weights under unit nonresponse in survey sampling, Statistics Canada,Survey Methodology Journal, https://www150.statcan.gc.ca/n1/pub/12001x/12001x2019003eng.htm. Link to Paper Jing Wang, "The Pseudo Maximum Likelihood Estimator for Quantiles of Survey Variables", Journal of Statistics and Methodolgy, https://academic.oup.com/jssam, December 17, 2019. Link to Paper Paul T. von Hippel, "How Many Imputations Do You Need? A Twostage Calculation Using a Quadratic Rule", First Published January 18, 2018 Research Article. Link to Paper Kott, Phillip S., "The degrees of freedom of a variance estimator in a probability sample", August 2020, DOI: 10.3768/rtipress.2020.mr.0043.2008. Link to Paper Lumley, Thomas, "svy2lme: Linear mixed models by pairwise likelihood, in tslumley/svylme: Linear Mixed Models for Complex Survey Data", Posted on Github February 2020. Link to GitHub Documentation Lumley, Thomas, "svyVGAM: DesignBased Inference in Vector Generalised Linear Models. Provides designbased inference for the wide range of parametric models in the 'VGAM' package", Posted to Cran September 30, 2020. Link to Package Survey Data Analysis Publications  Bayes Related (since 2015) Si, Y., Pillai, N.S., and Gelman, A., "Bayesian nonparametric weighted sampling inference" Bayesian Analysis, 2015, 10(3) 605625. Link to Paper Link to STAN Codes for Binary Outcome Link to STAN Codes for Continuous Outcome Goldstein, Harvey; Carpenter, James; Kenward, Michael. "Bayesian models for weighted data with missing values: a bootstrap approach." In: Journal of the Royal Statistical Society: Series C, 18.01.2018. Link to Paper Terrance D. Savitsky, Matthew R. Williams. "Bayesian Mixed Effects Model Estimation under Informative Sampling". arXiv:1904.07680 [stat.ME] (Submitted on 16 Apr 2019). Link to Paper Terrance D. Savitsky, Matthew R. Williams. "Bayesian Uncertainty Estimation Under Complex Sampling". arXiv.org/abs/1807.11796v1 [stat.ME], Submitted on 31JUL2018. Link to Paper Luis G. LeonNovelo and Terrance D. Savitsky, "Fully Bayesian estimation under informative sampling". Electronic Journal of Statistics Volume 13, Number 1 (2019), 16081645. Link to Paper Matthew R. Williams and Terrance D. Savitsky, "Bayesian Estimation Under Informative Sampling with Unattenuated Dependence", Bayesian Analysis, 2018. Link to Paper Yutao Liu, Qixuan Chen, "Bayesian Inference of Finite Population Quantiles for Skewed Survey Data Using SkewNormal Penalized Spline Regression", Journal of Survey Statistics and Methodology, Published: 3 September 2019. Link to Paper Matthew R. Williams and Terrance D. Savitsky, "Uncertainty Estimation for PseudoBayesian Inference Under Complex Sampling", International Statistics Review, First published:08 June 2020. Link to Paper
Errata Second Edition Please check this link for corrections to ASDA Second Edition: ASDA Second Edition Errata Errata First Edition Please check this link for corrections to ASDA First Edition : ASDA Errata 
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