A complex survey data set is one characterized by any combination of the following four features: stratification, clustering, unequal weights, or finite population correction factors. In this paper, we provide context for why these features might appear in data sets produced from surveys, highlight some of the formulaic modifications they introduce, and outline the syntax needed to properly account for them. Specifically, we explain why you should use the SURVEY family of SAS/STAT^® procedures, such as PROC SURVEYMEANS or PROC SURVEYREG, to analyze data of this type. Although many of the syntax examples are drawn from a fictitious expenditure survey, we also discuss the origins of complex survey features in three real-world survey efforts sponsored by statistical agencies of the United States government--namely, the National Ambulatory Medical Care Survey, the National Survey of Family of Growth, and the Consumer Building Energy Consumption Survey.

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This paper provides an overview of analysis of data derived from complex sample designs. General discussion of how and why analysis of complex sample data differs from standard analysis is included. In addition, a variety of applications are presented using PROC SURVEYMEANS, PROC SURVEYFREQ, PROC SURVEYREG, PROC SURVEYLOGISTIC, and PROC SURVEYPHREG, with an emphasis on correct usage and interpretation of results.

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Survey research can provide a straightforward and effective means of collecting input on a range of topics. Survey researchers often like to group similar survey items into construct domains in order to make generalizations about a particular area of interest. Confirmatory Factor Analysis is used to test whether this pre-existing theoretical model underlies a particular set of responses to survey questions. Based on Structural Equation Modeling (SEM), Confirmatory Factor Analysis provides the survey researcher with a means to evaluate how well the actual survey response data fits within the a priori model specified by subject matter experts. PROC CALIS now provides survey researchers the ability to perform Confirmatory Factor Analysis using SAS^®. This paper provides a survey researcher with the steps needed to complete Confirmatory Factor Analysis using SAS. We discuss and demonstrate the options available to survey researchers in the handling of missing and not applicable survey responses using an ARRAY statement within a DATA step and imputation of item non-response. A simple demonstration of PROC CALIS is then provided with interpretation of key portions of the SAS output. Using recommendations provided by SAS from the PROC CALIS output, the analysis is then modified to provide a better fit of survey items into survey domains.

Texas is one of about 30 states that has recently passed laws requiring voters to produce valid IDs in an effort to avoid illegal voters. This new regulation, however, might negatively affect voting opportunities for students, low-income people, and minorities. To determine the actual effects of the regulation in Dallas County, voters were surveyed when exiting the polling offices during the November midterm election about difficulties that they might have encountered in the voting process. The database of the voting history of each registered voter in the county was examined, and the data set was converted into an analyzable structure prior to stratification. All of the polling offices were stratified by the residents' degrees of involvement in the past three general elections, namely, the proportion of people who have used early election and who have at least voted once. A two-phase sampling design was adopted for stratification. On election day, pollsters were sent to select polling offices and interviewed 20 voters at a selected time period. The number of people having difficulties was estimated when data was collected.

Respondent Driven Sampling (RDS) is both a sampling method and a data analysis technique. As a sampling method, RDS is a chain referral technique with strategic recruitment quotas and specific data gathering requirements. Like other chain referral techniques (for example, snowball sampling), the chains and waves are the start point to conduct analysis. But building the chains and waves still would be a daunting task because it involves too many transpositions and merges. This paper provides an efficient method of using Base SAS^® to build up chains and waves.

This presentation emphasizes use of SAS^® 9.4 to perform multiple imputation of missing data using the PROC MI Fully Conditional Specification (FCS) method with subsequent analysis using PROC SURVEYLOGISTIC and PROC MIANALYZE. The data set used is based on a complex sample design. Therefore, the examples correctly incorporate the complex sample features and weights. The demonstration is then repeated in Stata, IVEware, and R for a comparison of major software applications that are capable of multiple imputation using FCS or equivalent methods and subsequent analysis of imputed data sets based on complex sample design data.

Replication techniques such as the jackknife and the bootstrap have become increasingly popular in recent years, particularly within the field of complex survey data analysis. The premise of these techniques is to treat the data set as if it were the population and repeatedly sample from it in some systematic fashion. From each sample, or replicate, the estimate of interest is computed, and the variability of the estimate from the full data set is approximated by a simple function of the variability among the replicate-specific estimates. An appealing feature is that there is generally only one variance formula per method, regardless of the underlying quantity being estimated. The entire process can be efficiently implemented after appending a series of replicate weights to the analysis data set. As will be shown, the SURVEY family of SAS/STAT^® procedures can be exploited to facilitate both the task of appending the replicate weights and approximating variances.

This paper presents an application of the SURVEYSELECT procedure. The objective is to draw a systematic random sample from financial data for review. Topics covered in this paper include a brief review of systematic sampling, variable definitions, serpentine sorting, and an interpretation of the output.

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Sampling for audits and forensics presents special challenges: Each survey/sample item requires examination by a team of professionals, so sample size must be contained. Surveys involve estimating--not hypothesis testing. So power is not a helpful concept. Stratification and modeling is often required to keep sampling distributions from being skewed. A precision of alpha is not required to create a confidence interval of 1-alpha, but how small a sample is supportable? Many times replicated sampling is required to prove the applicability of the design. Given the robust, programming-oriented approach of SAS^®, the random selection, stratification, and optimizing techniques built into SAS can be used to bring transparency and reliability to the sample design process. While a sample that is used in a published audit or as a measure of financial damages must endure a special scrutiny, it can be a rewarding process to design a sample whose performance you truly understand and which will stand up under a challenge.

Surveys are designed to elicit information about population characteristics. A survey design typically combines stratification and multistage sampling of intact clusters, sub-clusters, and individual units with specified probabilities of selection. A survey sample can produce valid and reliable estimates of population parameters at a fraction of the cost of carrying out a census of the entire population, with clear logistical efficiencies. For analyses of survey data, SAS^®software provides a suite of procedures from SURVEYMEANS and SURVEYFREQ for generating descriptive statistics and conducting inference on means and proportions to regression-based analysis through SURVEYREG and SURVEYLOGISTIC. For longitudinal surveys and follow-up studies, SURVEYPHREG is designed to incorporate aspects of the survey design for analysis of time-to-event outcomes based on the Cox proportional hazards model, allowing for time-varying explanatory variables.We review the salient features of the SURVEYPHREG procedure with application to survey data from the National Health and Nutrition Examination Survey (NHANES III) Linked Mortality File.

The Behavioral Risk Factor Surveillance System (BRFSS) collects data on health practices and risk behaviors via telephone survey. This study focuses on the question, On average, how many hours of sleep do you get in a 24-hour period? Recall bias is a potential concern in interviews and questionnaires, such as BRFSS. The 2013 BRFSS data is used to illustrate the proper methods for implementing PROC SURVEYREG and PROC SURVEYLOGISTIC, using the complex weighting scheme that BRFSS provides.