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Complex survey data analysis with SAS by Taylor H. Lewis

By Taylor H. Lewis

Complex Survey information research with SAS® is a useful source for utilized researchers studying facts generated from a pattern layout regarding any mixture of stratification, clustering, unequal weights, or finite inhabitants correction elements. After basically explaining how the presence of those positive factors can invalidate the assumptions underlying most standard statistical concepts, this publication equips readers with the data to hopefully account for them in the course of the estimation and inference method via using the SURVEY kin of SAS/STAT® procedures.

The e-book deals complete assurance of the main crucial issues, including:

  • Drawing random samples
  • Descriptive information for non-stop and specific variables
  • Fitting and analyzing linear and logistic regression models
  • Survival analysis
  • Domain estimation
  • Replication variance estimation methods
  • Weight adjustment and imputation equipment for dealing with lacking data

The easy-to-follow examples are drawn from real-world survey facts units spanning a number of disciplines, all of which might be downloaded at no cost besides syntax documents from the author’s site: http://mason.gmu.edu/~tlewis18/.

While different books may perhaps contact on a few of the similar matters and nuances of complicated survey facts research, none beneficial properties SAS completely and as exhaustively. one other specified point of this publication is its abundance of convenient workarounds for definite suggestions no longer but supported as of SAS model 9.4, akin to the ratio estimator for a complete and the bootstrap for variance estimation.

Taylor H. Lewis is a PhD graduate of the Joint software in Survey technique on the college of Maryland, collage Park, and an accessory professor within the George Mason collage division of records. An avid SAS person for 15 years, he's a SAS qualified complex programmer and a nationally well-known SAS educator who has produced dozens of papers and workshops illustrating the way to successfully and successfully behavior statistical analyses utilizing SAS.

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Another handy feature is the MARGIN= option after the slash in the STRATA statement, which can be used to determine the sample size necessary to achieve a targeted margin of error, or confidence interval half-width. One of the practical limitations to utilizing these techniques is that they are univariate in nature. That is, they are applicable to a singlepoint estimate, whereas the typical survey effort involves estimating a wide range of population quantities. Multicriteria optimization can be tackled, however, using Microsoft Excel’s Solver add-in, or PROC NLP or PROC OPTMODEL in SAS/OR.

A few examples include sampling households in a survey measuring attitudes of individuals, sampling doctor’s offices in a Features and Examples of Complex Surveys 17 survey measuring characteristics of patient visits to doctors’ offices, or sampling classrooms in an education survey measuring the scholastic aptitude of students. Clustering is rarely ideal as it generally decreases precision, but it is often a logistical necessity or used to control data collection costs. For instance, most nationally representative, face-to-face surveys in the United States sample geographically clustered units to limit interviewer travel expenses.

There would be no variation in the sample with regard to the day of the week, which would arguably defeat the inherent purpose of sampling these units of time in the first place. To guard against these kinds of unfortunate scenarios, many practitioners advocate sorting the sampling frame beforehand using one or more control variables. You can certainly do this in a prior PROC SORT step, but a more syntactically efficient approach is to specify the control variable(s) in the CONTROL statement of PROC SURVEYSELECT.

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