The current study looks at several ways to investigate latent variables in longitudinal surveys and their use in regression models. Three different analyses for latent variable discovery are briefly reviewed and explored. The latent analysis procedures explored in this paper are PROC LCA, PROC LTA, PROC TRAJ, and PROC CALIS. The latent variables are then included in separate regression models. The effect of the latent variables on the fit and use of the regression model compared to a similar model using observed data is briefly reviewed. The data used for this study was obtained via the National Longitudinal Study of Adolescent Health, a study distributed and collected by Add Health. Data was analyzed using SAS^® 9.4. This paper is intended for any level of SAS^® user. This paper is also written to an audience with a background in behavioral science and/or statistics.

In recent years, many companies are trying to understand the rare events that are very critical in the current business environment. But a data set with rare events is always imbalanced and the models developed using this data set cannot predict the rare events precisely. Therefore, to overcome this issue, a data set needs to be sampled using specialized sampling techniques like over-sampling, under-sampling, or the synthetic minority over-sampling technique (SMOTE). The over-sampling technique deals with randomly duplicating minority class observations, but this technique might bias the results. The under-sampling technique deals with randomly deleting majority class observations, but this technique might lose information. SMOTE sampling deals with creating new synthetic minority observations instead of duplicating minority class observations or deleting the majority class observations. Therefore, this technique can overcome the problems, like biased results and lost information, found in other sampling techniques. In our research, we used an imbalanced data set containing results from a thyroid test with 3,163 observations, out of which only 4.7 percent of the observations had positive test results. Using SAS^® procedures like PROC SURVERYSELECT and PROC MODECLUS, we created over-sampled, under-sampled, and the SMOTE sampled data set in SAS^® Enterprise Guide^®. Then we built decision tree, gradient boosting, and rule induction models using four different data sets (non-sampled, majority under-sampled, minority over-sampled with majority under-sampled, and minority SMOTE sampled with majority under-sampled) in SAS^® Enterprise Miner™. Finally, based on the receiver operating characteristic (ROC) index, Kolmogorov-Smirnov statistics, and the misclassification rate, we found that the models built using minority SMOTE sampled with the majority under-sampled data yields better output for this data set.

The success of any marketing promotion is measured by the incremental response and revenue generated by the targeted population known as Test in comparison with the holdout sample known as Control. An unbiased random Test and Control sampling ensures that the incremental revenue is in fact driven by the marketing intervention. However, isolating the true incremental effect of any particular marketing intervention becomes increasingly challenging in the face of overlapping marketing solicitations. This paper demonstrates how a look-alike model can be applied using the GMATCH algorithm on a SAS^® platform to design a truly comparable control group to accurately measure and isolate the impact of a specific marketing intervention.

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This paper examines the various sampling options that are available in SAS^® through PROC SURVEYSELECT. We do not cover all of the possible sampling methods or options that PROC SURVEYSELECT features. Instead, we look at Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Sampling, and Sequential Random Sampling.

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Sampling, whether with or without replacement, is an important component of the hypothesis testing process. In this paper, we demonstrate the mechanics and outcome of sampling without replacement, where sample values are not independent. In other words, what we get in the first sample affects what we can get for the second sample, and so on. We use the popular variant of poker known as No Limit Texas Hold'em to illustrate.

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Big data, small data--but what about when you have no data? Survey data commonly include missing values due to nonresponse. Adjusting for nonresponse in the analysis stage might lead different analysts to use different, and inconsistent, adjustment methods. To provide the same complete data to all the analysts, you can impute the missing values by replacing them with reasonable nonmissing values. Hot-deck imputation, the most commonly used survey imputation method, replaces the missing values of a nonrespondent unit by the observed values of a respondent unit. In addition to performing traditional cell-based hot-deck imputation, the SURVEYIMPUTE procedure, new in SAS/STAT^® 14.1, also performs more modern fully efficient fractional imputation (FEFI). FEFI is a variation of hot-deck imputation in which all potential donors in a cell contribute their values. Filling in missing values is only a part of PROC SURVEYIMPUTE. The real trick is to perform analyses of the filled-in data that appropriately account for the imputation. PROC SURVEYIMPUTE also creates a set of replicate weights that are adjusted for FEFI. Thus, if you use the imputed data from PROC SURVEYIMPUTE along with the replicate methods in any of the survey analysis procedures--SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, or SURVEYPHREG--you can be confident that inferences account not only for the survey design, but also for the imputation. This paper discusses different approaches for handling nonresponse in surveys, introduces PROC SURVEYIMPUTE, and demonstrates its use with real-world applications. It also discusses connections with other ways of handling missing values in SAS/STAT.

This paper is a primer on the practice of designing, selecting, and making inferences on a statistical sample, where the goal is to estimate the magnitude of error in a book value total. Although the concepts and syntax are presented through the lens of an audit of health-care insurance claim payments, they generalize to other contexts. After presenting the fundamental measures of uncertainty that are associated with sample-based estimates, we outline a few methods to estimate the sample size necessary to achieve a targeted precision threshold. The benefits of stratification are also explained. Finally, we compare several viable estimators to quantify the book value discrepancy, making note of the scenarios where one might be preferred over the others.

The SURVEYSELECT procedure is useful for sample selection for a wide variety of applications. This paper presents the application of PROC SURVEYSELECT to a complex scenario involving a state-wide educational testing program, namely, drawing interdependent stratified cluster samples of schools for the field-testing of test questions, which we call items. These stand-alone field tests are given to only small portions of the testing population and as such, a stratified procedure was used to ensure representativeness of the field-test samples. As the field test statistics for these items are evaluated for use in future operational tests, an efficient procedure is needed to sample schools, while satisfying pre-defined sampling criteria and targets. This paper provides an adaptive sampling application and then generalizes the methodology, as much as possible, for potential use in more industries.

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