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## Sample of SASGF2020 Proceedings for Statistics: Getting Started

I'll be sharing the sample agendas that the conference team built for different topic areas to help you get started regarding which SAS Global Forum 2020 proceedings might interest you.  Here are some Statistics papers, good for those that are getting started using SAS for Statistics.

Statistics:  Getting Started

Post-9/11 GI Bill: Teasing Out Insights about Veterans’:  Walter Ochinko, Research Director, Veterans Education Success, examines the challenges researchers face in using publicly available databases to track veterans use of GI Bill benefits and their success in earning postsecondary credentials, and presents the methodology and findings of several Veterans Education Success reports that, despite these challenges, teased out important insights on veteran outcomes.

Examining the Impact of Discussion Activities on Student Understanding in Introductory Statistics:  Rachael N. Becker of Southern Methodist University, explains how her study aims to examine the impact that voluntary participation in online discussion activities has on students’ understanding of statistical concepts in an undergraduate statistics course. A study of 90 undergraduate students enrolled in an introductory statistics course was conducted. The Levels of Conceptual Understanding in Statistics (LOCUS) assessment was utilized to measure students’ conceptual understanding in statistics. Read her e-Poster to learn how a statistical analysis of the difference between pre- and post-test data was completed in SAS® using propensity score matching techniques.

A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education:  Deanna Schreiber-Gregory, from the Henry M Jackson Foundation for the Advancement of Military Medicine, explores an example of how to use propensity score analyses. To demonstrate this technique, she seeks to explore whether clerkship order influences National Board of Medical Examiners (NBME) and United States Medical Licensing Examination (USMLE) exam scores for 3rd year military medical students. In order to conduct this analysis, a selection bias was identified, and adjustment was sought through three common forms of propensity scoring: stratification, matching, and regression adjustment.

Dealing with Missing Data in Epidemiological and Clinical Research:  Andrew T. Kuligowski, Independent Consultant, and Lida Gharibvand, from Loma Linda University, presents an introduction to categories of missing data and demonstrates some techniques that researchers can use to deal with missing data.  Missing values can have a surprising impact on the way data is analyzed and processed. Epidemiological and clinical research typically involve complex data and large databases that frequently contain missing data. The impact of missing data on data analysis and research findings can be significant, so it is important to develop a sound methodology to deal with it. Fortunately, there are powerful tools to represent and reference the missing data in SAS® analytics. There are several SAS functions and procedures that enable differentiated approaches for handling missing data. However, dealing with missing data can still be a bit of a minefield.

Introducing the GAMSELECT Procedure for Generalized Additive Model Selection:  Michael Lamm and Weijie Cai from SAS Institute Inc. explains how model selection is an important area in statistical learning. Both SAS/STAT® software and SAS® Visual Statistics software provide a rich set of tools for performing model selection over linear models, generalized linear models, and Cox proportional hazards models. With these tools, you can build parsimonious predictive models by constructing linear or fixed nonlinear effects to describe the dependency structure. But what if the dependency structure is nonlinear and the nonlinearity is unknown? And what if covariates are nonlinearly correlated? The new GAMSELECT procedure, available in SAS Visual Statistics, addresses these questions by using spline terms to approximate the nonlinear dependency, then selecting important variables in appropriate nonlinear transformations by using the boosting method or the shrinkage method. The procedure builds models for response variables in the exponential family, so that you can use it for continuous, count, or binary responses. This paper introduces the GAMSELECT procedure and provides a brief comparison to related SAS® procedures.

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