Accessibility has become a hot topic on campus due to a flurry of recent investigations of discrimination against students with disabilities by the U.S. Department of Justice and the U.S. Department of Education. This paper provides an update on the latest improvements in SAS^® University Edition that are specifically targeted to enable students with disabilities to excel in the classroom and beyond. This paper covers the entire SAS University Edition user experience including installation, documentation, training, support, using SAS^® Studio, and the new accessibility features in the fourth maintenance release of SAS^® 9.4.

Technology plays an integral role in every aspect of daily life. As a result, educators should leverage technology-based learning to ensure that students are provided with authentic, engaging, and meaningful learning experiences (Pringle, Dawson, and Ritzhaupt, 2015).The significance and value of computer science understanding continue to increase. A major resource that can be credited with spreading support for computer science is the site Code.org. Its mission is to enable every student in every school to have the opportunity to learn computer science (https://code.org/about). Two years ago, our mentor partnered with Code.org to conduct workshops within the Charlotte, NC area to educate teachers on how to teach computer science activities and concepts in their classrooms. We had the opportunity to assist during the workshops to provide student perspectives and opinions. As we look back on the workshops, we wondered, How are the teachers who attended the workshops implementing the concepts they were taught? After each workshop, a survey was distributed to the attendees to receive workshop feedback and to follow up. We collected the data from the surveys sent to participants and analyzed it using SAS^® University Edition. The results of the survey concluded that the workshops were beneficial and that the educators had implemented a concept that they learned. We believe that computer science activity implementations will assist students across the curriculum.

It's essential that SAS^® users enhance their skills to implement best-practice programming techniques when using Base SAS^® software. This presentation illustrates core concepts with examples to ensure that code is readable, clearly written, understandable, structured, portable, and maintainable. Attendees learn how to apply good programming techniques including implementing naming conventions for data sets, variables, programs, and libraries; code appearance and structure using modular design, logic scenarios, controlled loops, subroutines and embedded control flow; code compatibility and portability across applications and operating platforms; developing readable code and program documentation; applying statements, options, and definitions to achieve the greatest advantage in the program environment; and implementing program generality into code to enable its continued operation with little or no modifications.

The discipline of data science has seen an unprecedented evolution from primordial darkness to becoming the academic equivalent of an apex predator on university campuses across the country. But, survival of the discipline is not guaranteed. This session explores the genetic makeup of programs that are likely to survive, the genetic makeup of those that are likely to become extinct, and the role that the business community plays in that evolutionary process.

The SAS^® 9.4 SGPLOT procedure is a great tool for creating all types of graphs, from business graphs to complex clinical graphs. The goal for such graphs is to convey the data in a simple and direct manner with minimal distractions. But often, you need to grab the attention of a reader in the midst of a sea of data and graphs. For such cases, you need a visual that can stand out above the rest of the noise. Such visuals insert a decorative flavor into the graph to attract the eye of the reader and to encourage them to spend more time studying the visual. This presentation discusses how you can create such attention-grabbing visuals using the SGPLOT procedure.

Longitudinal count data arise when a subject's outcomes are measured repeatedly over time. Repeated measures count data have an inherent within subject correlation that is commonly modeled with random effects in the standard Poisson regression. A Poisson regression model with random effects is easily fit in SAS^® using existing options in the NLMIXED procedure. This model allows for overdispersion via the nature of the repeated measures; however, departures from equidispersion can also exist due to the underlying count process mechanism. We present an extension of the cross-sectional COM-Poisson (CMP) regression model established by Sellers and Shmueli (2010) (a generalized regression model for count data in light of inherent data dispersion) to incorporate random effects for analysis of longitudinal count data. We detail how to fit the CMP longitudinal model via a user-defined log-likelihood function in PROC NLMIXED. We demonstrate the model flexibility of the CMP longitudinal model via simulated and real data examples.

Innovation in teaching and assessment has become critical for many reasons. This is especially true in the fields of data science and big data analytics. Reasons range from the need to significantly improve the development of soft skills (as reported in an e-skills UK and SAS^® joint report from November 2014), to the rapidly changing software standards of products used by students, to the rapidly increasing range of functionality and product set, to the need to develop lifelong learning skills to learn new software and functionality. And, this is just a few of the reasons. In some educational institutions, it is easy to be extremely innovative. However, in many institutions and countries, there are numerous constraints on the levels of innovation that can be implemented. This presentation captures the author's developing pedagogic practice at the University of Derby. He suggests fundamental changes to the classic approaches to teaching and assessing data science and big data analytics. These changes have resulted in significant improvement in student engagement and achievements and students soft skills. Improvements are illustrated by innovations in teaching SAS to first-year students and teaching IBM Bluemix and Watson Analytics to final-year students. Students have successfully developed both technical and soft skills and experienced excellent levels of achievement.

Because many SAS^® users either work for or own companies that house big data, the threat that malicious software poses becomes even more extreme. Malicious software, often abbreviated as malware, includes many different classifications, ways of infection, and methods of attack. This E-Poster highlights the types of malware, detection strategies, and removal methods. It provides guidelines to secure essential assets and prevent future malware breaches.

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SAS^® education is a mainstay across disciplines and educational levels in the United States. Along with other courses that are relevant to the jobs students want, independent SAS courses or SAS education integrated into additional courses can help a student be more interesting to a potential employer. The multitude of SAS offerings (SAS^® University Edition, Base SAS^®, SAS^® Enterprise Guide^®, SAS^® Studio, and the SAS^® OnDemand offerings) provide the tools for education, but reaching students where they are is the greatest key for making the education count. This presentation discusses several roadblocks to learning SAS^® syntax or point-and-click from the student perspective and several solutions developed jointly by students and educators in one graduate educational program.

A new ODS destination for creating Microsoft Excel workbooks is available starting in the third maintenance release for SAS^® 9.4. This destination creates native Microsoft Excel XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation, you learn step-by-step techniques for quickly and easily creating attractive multi-sheet Excel workbooks that contain your SAS^® output. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on demand and in real time using SAS server technology is discussed. Using earlier versions of SAS to create multi-sheet workbooks is also discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented.

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Chemical incidents involving irritant chemicals such as chlorine pose a significant threat to life and require rapid assessment. Data from the Validating Triage for Chemical Mass Casualty Incidents A First Step R01 grant was used to determine the most predictive signs and symptoms (S/S) for a chlorine mass casualty incident. SAS^® 9.4 was used to estimate sensitivity, specificity, positive and negative predictive values, and other statistics of irritant gas syndrome agent S/S for two exiting systems designed to assist emergency responders in hazardous material incidents (Wireless Information System for Emergency Responders (WISER) and CHEMM Intelligent Syndrome Tool (CHEMM-IST)). The results for WISER showed the sensitivity was .72 to 1.0; specificity .25 to .47; and the positive predictive value and negative predictive value were .04 to .87 and .33 to 1.0, respectively. The results for CHEMM-IST showed the sensitivity was .84 to .97; specificity .29 to .45; and the positive predictive value and negative predictive value were .18 to .42 and .86 to .97, respectively.

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It is often necessary to assess multi-rater agreement for multiple-observation categories in case-controlled studies. The Kappa statistic is one of the most common agreement measures for categorical data. The purpose of this paper is to show an approach for using SAS^® 9.4 procedures and the SAS^® Macro Language to estimate Kappa with 95% CI for pairs of nurses that used two different triage systems during a computer-simulated chemical mass casualty incident (MCI). Data from the Validating Triage for Chemical Mass Casualty Incidents A First Step R01 grant was used to assess the performance of a typical hospital triage system called the Emergency Severity Index (ESI), compared with an Irritant Gas Syndrome Agent (IGSA) triage algorithm being developed from this grant, to quickly prioritize the treatment of victims of IGSA incidents. Six different pairs of nurses used ESI triage, and seven pairs of nurses used the IGSA triage prototype to assess 25 patients exposed to an IGSA and 25 patients not exposed. Of the 13 pairs of nurses in this study, two pairs were randomly selected to illustrate the use of the SAS Macro Language for this paper. If the data was not square for two nurses, a square-form table for observers using pseudo-observations was created. A weight of 1 for real observations and a weight of .0000000001 for pseudo-observations were assigned. Several macros were used to reduce programming. In this paper, we show only the results of one pair of nurses for ESI.

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The rapidly evolving informatics capabilities of the past two decades have resulted in amazing new data-based opportunities. Large public use data sets are now available for easy download and utilization in the classroom. Days of classroom exercises based on static, clean, easily maneuverable samples of 100 or less are over. Instead, we have large and messy real-world data at our fingertips allowing for educational opportunities not available in years past. There are now hundreds of public-use data sets available for download and analysis in the classroom. Many of these sources are survey-based and require the understanding of weighting techniques. These techniques are necessary for proper variance estimation allowing for sound inferences through statistical analysis. This example uses the California Health Interview Survey to present and compare weighted and non-weighted results using the SURVEYLOGISTIC procedure.