Analysis Examples Replication The analysis examples replication materials cover Chapters 512 of ASDA 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" 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 Note from Authors The most recent printing of Applied Survey Data Analysis, as of March 7, 2013, has a font issue where some symbols appear to be missing in the text. This problem is being corrected for all future printings. Please accept our apologies and this will be fixed as soon as possible.
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 is an Assistant Research Professor in the Survey Methodology Program at the University of Michigan and an Assistant Research Scientist at the Center for Statistical Consultation and Research (CSCAR) on the University of Michigan campus. He earned a PhD in Survey Methodology from the Michigan Program in Survey Methodology, and also received an MA in Applied Statistics from the University of Michigan Statistics Department. His primary research interests revolve around regression models for clustered and longitudinal data, and he has authored a book, "Linear Mixed Models: A Practical Guide Using Statistical Software" (www.umich.edu/~bwest/almmussp.html) comparing different statistical software packages in terms of their mixed modeling procedures (Chapman Hall/CRC Press, 2007). He specializes in applications of statistical software and analysis of survey data, and through CSCAR teaches several yearly short courses on statistical methodology and software. mailto:bwest@umich.edu 1. Review/Summary of ASDA from the Stata Bookstore: Stata Review of ASDA 2. Review posted on Amazon.com:
3. Review from International Statistical Review (2010), 78, 3, 445–482. (Page 463 extracted here). 2010 The Authors. International Statistical Review 2010 International Statistical Institute. To read this review click here: Review of ASDA.
4. Review from "Applied Quantitative Methods Network" Newsletter in the UK: Review of ASDA.
5. Review from Amazon.com:
6. Review posted on Amazon.com:
7. Link to Chapman Hall Bestsellers List: (see ASDA on the list!): Link to BestSellers
8. Link to ASDA review from The American Statistician: http://pubs.amstat.org/toc/tas/65/4
9. Link to review from Journal of Statistics, 2011: http://www.jos.nu/Articles/abstract.asp?article=271139
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 United States Census Bureau
Chapter Exercises Data Sets 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) Chapter Exercises Data Sets (R Format) Analysis Example Data Sets These data sets are subsets of the original data and are designed for use with the analysis examples in ASDA. 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)
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 Software Updates Stata  V13.1 is current as of April 2014 IBM/SPSS SPSS 21 is current as of April 2014 SAS  v9.4 is current as of April 2014 See websites for additional software updates and versions
This section provides key updates to software for analysis of survey data.
5. Stata v10.1Code to produce Table 8.4 and Figure 8.3: NonLinear Comparisons of Logits 6. SAS v9.2 (TS2M3)Example of PROC SURVEYPHREG (Cox Model): PROC SURVEYPHREG Example 7. Stata v11.1Example of Mediation analysis with survey data and subpopulation indicator: Stata sgmediation example 8. RExample of Quantile Regression with Bootstrap Method: R Quantile Regression Example 9. Stata 11.1Example of use of mi suite of commands: Stata 11.1 MI Example 10. SAS v9.22Example of use of NOMCAR option with PROC SURVEYMEANS: SAS NOMCAR Example 11. Stata 11.1Example of use of svy: logistic with estat gof postestimation command: Stata estat gof Example 12. Example of How to Create a Delimited Text File in SAS and Read Text File in R: Text File SAS to R Example 13. 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
14. SAS code to implement Wilcoxon rank sum test for complex sample survey data: http://www.blackwellpublishing.com/rss
15. SAS Macro for Difference Between Means (addition to PROC SURVEYMEANS): SAS Macro smsub.sas
16. 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
17. Note on How SPSS handles Strata with A Single or "Lonely" PSU: http://www01.ibm.com/support/docview.wss?uid=swg21479202
18. 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
19. Link to information about use of Stata 12.1 with the postestimation command estat gof after svy: logistic with subpopulations: http://www.stata.com/statalist/archive/201103/msg00550.html
20. SAS PROC MI  FCS imputation method with analysis of complex sample data: SAS PROC MI FCS Example. Right click here to save SAS data set: Data set for FCS example
21. SAS v9.3 PROC SURVEYMEANS with RATIO and DOMAIN statements for Example 5.9: SAS Example 5.9
22. SAS v9.4 Example of How to Obtain the 2nd Order RaoScott ChiSquare Test in PROC SURVEYFREQ: PROC SURVEYFREQ with 2nd Order RaoScott ChiSquare
23. Example of using PROC EXPORT to convert SAS data set to Stata (.dta) and SPSS (.sav): SAS PROC EXPORT Example
24. Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS, Stata, IVEware, and R: Link to Presentation
25. Analysis of Survey Data Using the SAS SURVEY Procedures: A Primer: Link to Presentation
26. 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
27. SAS Repeated Replication Macro to do DesignBased Poisson Regression (with a comparison to Stata svy: poisson command): Link to Code and Results
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 PublicationsGeneral Survey Data Analysis Topics 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. 1. Carle, A.C., Fitting multilevel models in complex survey data with design weights: Recommendations, BMC Medical Research Methodology, 14712288949, 2009. http://www.biomedcentral.com/14712288/9/49 Abstract (Background)
2. Lumley, T.S., Complex Surveys: a guide to analysis using R, John Wiley & Sons, New York, 2010. Synopsis
3. Liao, Dan., Collinearity Diagnostics for Complex Survey Data. Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, Maryland, (2010). 4. Asparouhov, T. & Muthen, B. (2006). Multilevel modeling of complex survey data. Proceedings of the Joint Statistical Meeting in Seattle, August 2006. ASA section on Survey Research Methods, 27182726. Paper can be downloaded from here. 5. Berglund, Patricia, (2010). An Introduction to Multiple Imputation of Complex Sample Data Using SAS v9.2, SAS Global Forum 2010, Paper 2652010. Paper can be downloaded from here. 6. Kolenikov, S., Resampling Variance Estimation for Complex Survey Data, Stata Journal, sj102: pp. 165–199. http://www.statajournal.com/ 7. Valliant, R., The Effect of Multiple Weighting Steps on Variance Estimation, Journal of Official Statistics, Vol. 20, No. 1, 2004, pp. 1–18. Abstract
8. Valliant, R. and Rust, K.F., Degrees of Freedom Approximations and RulesofThumb, Journal of Official Statistics, Vol. 26, No. 4, 2010, pp. 585–602.
9. Brumback, B. and He, Z., The Mantel–Haenszel estimator adapted for complex survey designs is not dually consistent, Statistics & Probability Letters Volume 81, Issue 9, September 2011, Pages 14651470. 10. Brumback, B. and He, Z., Adjusting for confounding by neighborhood using complex survey data, Statistics in Medicine, Volume 30, Issue 9, pages 965–972, 30 April 2011. 11. Liao, D. (2011). Variance Inflation Factors in the Analysis of Complex Survey Data. Paper presented at the 2011 Joint Statistical Meetings, Miami Beach, FL. Currently under review for publication in Survey Methodology. 12. Li, J. and Valliant, R.. Linear Regression Influence Diagnostics for Unclustered Survey Data, Journal of Official Statistics, Vol.27, No.1, 2011. pp. 99–119. Click here to view abstract: Link to Information about Paper
13. Wagstaff, D.A. and Harel, O., A Closer Examination of Three SmallSample Approximations to the MultipleImputation Degrees of Freedom. The Stata Journal (2011) 11, Number 3, pp. 403–419. http://www.statajournal.com/
14. Binder, D.A., ESTIMATING MODEL PARAMETERS FROM A COMPLEX SURVEY UNDER A MODELDESIGN RANDOMIZATION FRAMEWORK, Pak. J. Statist., 2011 Vol. 27(4), 371390. Link to Paper
15. Li, J. and Valliant, R., DETECTING GROUPS OF INFLUENTIAL OBSERVATIONS IN LINEAR REGRESSION USING SURVEY DATA—ADAPTING THE FORWARD SEARCH METHOD, Pak. J. Statist. 2011 Vol. 27(4), 507528. Link to Paper
16. Multiple authors, Journal of Statistical Software, Vol. 45, Issue 17, Dec 2011. Various articles on multiple imputation are included in this volume.
17. Mplus Notes area with many articles about survey data analysis: http://statmodel.com/resrchpap.shtml. 18. Kott, P. and Liao, D. Providing double protection for unit nonresponse with a nonlinear calibrationweighting routine, Survey Research Methods (2012) Vol.6, No.2, pp. 105111. Link to paper: Kott and Liao 2012
19. Sundar Natarajan, Stuart R. Lipsitz, Garrett M. Fitzmaurice, Debajyoti Sinha, Joseph G. Ibrahim, Jennifer Haas, Walid Gellad, An Extension of the Wilcoxon rank sum test for complex sample survey data. Journal of the Royal Statistical Society: Series C (Applied Statistics),Volume 61, Issue 4, pages 653664, August 2012.
20.
Czaplewski, Raymond L.
2010. Complex sample survey estimation in static statespace. Gen. Tech. Rep.
RMRSGTR239. Fort Collins, CO: U.S. Department of
Agriculture, Forest Service, Rocky Mountain Research Station. 124 p.
http://treesearch.fs.fed.us/ 21. Czaplewski, Raymond L. 2010. Recursive restriction estimation: an alternative to poststratification in surveys of land and forest cover. Res. Pap. RMRSRP81. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 32 p. http://treesearch.fs.fed.us/pubs/36116 22. Owen, A., and Eckles, D. Bootstrapping data arrays of arbitrary order. Annals of Applied Statistics, Volume 6, Number 3 (2012), 895927. Available from http://arxiv.org/abs/1106.2125.
23. A. Veiga, P. W. F. Smith and J. J. Brown, The use of sample weights in multivariate multilevel models with an application to income data collected by using a rotating panel survey. Forthcoming in the Journal of the Royal Statistical Society, 2013. Link to paper: Veiga et al
24. Newson R. Confidence intervals for rank statistics: Somers' D and extensions. The Stata Journal 2006; 6(3): 309334. Prepublication draft at: http://www.imperial.ac.uk/nhli/r.newson/papers/somdext.pdf.
25. Presentation on AIC and BIC for Survey Data by Thomas Lumley and Alastair Scott: Link to Presentation
26. T. Lumley and A.J. Scott (2013). Partial likelihoodratio tests for the Cox model under complex sampling. Statistics in Medicine, 32, 110123.
27. T. Lumley and A.J. Scott (2012). Fitting GLMs with survey data. Proceedings of the Survey Research Methods Section, Amer. Statist. Assoc, 51745181.
28. T. Lumley and A.J. Scott (2013). Twosample rank tests under complex sampling. Biometrika, 100, to appear shortly.
29. V. Landsmana*† and B. I. Graubard, Efficient analysis of casecontrol studies with sample weights: Link to Paper
30. J.N.K. Rao, F. Verret, and M.A. Hidiroglou, A WEIGHTED ESTIMATING EQUATIONS APPROACH TO INFERENCE FOR TWOLEVEL MODELS FROM SURVEY DATA: Link to Paper
31.
Pfeffermann, Danny
(2011)
Modelling of complex survey data: why is it a problem? How
should we approach it?
32. Norton, E.C., Miller, M.M., Kleinman, L.C. (2001) Computing adjusted risk ratios and risk differences in Stata, Stata Journal, Volume 13, Number 3, 492509. Link to Paper
33.
Bieler, G.S.,
Brown, G.G.,
Williams, R.L., & Brogan, D.J. (2010).
Estimating modeladjusted risks, risk differences, and risk ratios from complex
survey data. American Journal of Epidemiology, 171 (5):618623.
Link to Paper 34. Beaumont, J.F., Bocci, C. (2009) A Practical Bootstrap Method for Testing Hypotheses from Survey Data. Survey Methodology, 35, 2535. Link to Paper
35. Lumley, T., and Scott, A.J. (2014). Tests for Regression Models Fitted to Survey Data. Australian & New Zealand Journal of Statistics, 56, 114. Link to Paper
36. Berglund, P.A, and Heeringa, S.G., Multiple Imputation of Missing Data Using SAS. SAS Publishing 2014. Link to Book
37. Min Zhu, SAS Institute Inc.. Paper SAS0262014 Analyzing Multilevel Models with the GLIMMIX Procedure. Link to Paper
38. Yao, Wenliang, Ph.D., Estimation of ROC Curve with Complex Survey Data, Dissertation THE GEORGE WASHINGTON UNIVERSITY, 2013, Link to Paper
39. Lumley and Scott, AIC AND BIC FOR MODELING WITH COMPLEX SURVEY DATA, Journal of Survey Statistics and Methodology, 2015, Link to Paper 40. Thompson, Mary E., Using Longitudinal Complex Survey Data, Annual Review of Statistics.and Its Application, 2015. 2:305–20, Link to Paper 41. 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 42. 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 43. Miles, Andrew, Obtaining Predictions from Models Fit to Multiply Imputed Data, Sociological Methods & Research, pp. 111, 2015, Link to Paper 44. 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 45. 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 46. 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 47. 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. 48. 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 49. 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. 50. 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 51. 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 52. 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 53. 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 54. 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 55. 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 Survey Data Analysis PublicationsBayes Related 1. Elliott, M.R., Little, R.J.A. (2000). “Modelbased Alternatives to Trimming Survey Weights,” Journal of Official Statistics, 16, 191209. 2. Elliott, M.R., Sammel, M.D. (2002). "Discussion of 'Latent Class Analysis of Complex Sample Survey Data: Application to Dietary Data'," Journal of the American Statistical Association, 97, 732734. 3. Elliott, M.R. (2007). “Bayesian Weight Trimming for Generalized Linear Regression Models,” Survey Methodology, 33, 2334. 4. Elliott, M.R. (2008). “Model Averaging Methods for Weight Trimming,” Journal of Official Statistics, 24, 517540. 5. Elliott, M.R. (2009). “Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models,” Journal of Official Statistics, 25, 120. 6. Chen, Q., Elliott, M.R., Little, R.J.A. (2010). “Bayesian Penalized Spline ModelBased Inference for Finite Population Proportions in Unequal Probability Sampling,” Survey Methodology, 36, 2234. 7. Chen, Q., Elliott, M.R., Little, R.J.A. (2012). “Bayesian Inference for Finite Population Quantiles from Unequal Probability Samples,” Survey Methodology, 38, 203214. 8. Dong, Q., Elliott, M.R., Raghunathan, T.E. (2014). “A Nonparametric Method to Generate Synthetic Populations to Adjust for Complex Sample Designs,” Survey Methodology, 40, 2946. 9. West, B.T., Elliott, M.R. (2014). “Frequentist and Bayesian Approaches for Comparing Interviewer Variance Components in Two Groups of Survey Interviewers,” Survey Methodology, 40, 163188. 10. Dong, Q., Elliott, M.R., Raghunathan, T.E. (2014). “Combining Information from Multiple Complex Surveys,” Survey Methodology, 40, 347354.
Errata Please check this link for corrections to ASDA: ASDA Errata 
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