The world's capacity to store and analyze data has increased in ways that would have been inconceivable just a couple of years ago. Due to this development, large-scale data are collected by governments. Until recently, this was for purely administrative purposes. This study used comprehensive data files on education. The purpose of this study was to examine compulsory courses for a bachelor's degree in the economics program at the University of Copenhagen. The difficulty and use of the grading scale was compared across the courses by using the new IRT procedure, which was introduced in SAS/STAT® 13.1. Further, the latent ability traits that were estimated for all students in the sample by PROC IRT are used as predictors in a logistic regression model. The hypothesis of interest is that students who have a lower ability trait will have a greater probability of dropping out of the university program compared to successful students. Administrative data from one cohort of students in the economics program at the University of Copenhagen was used (n=236). Three unidimensional Item Response Theory models, two dichotomous and one polytomous, were introduced. It turns out that the polytomous Graded Response model does the best job of fitting data. The findings suggest that in order to receive the highest possible grade, the highest level of student ability is needed for the course exam in the first-year course Descriptive Economics A. In contrast, the third-year course Econometrics C is the easiest course in which to receive a top grade. In addition, this study found that as estimated student ability decreases, the probability of a student dropping out of the bachelor's degree program increases drastically. However, contrary to expectations, some students with high ability levels also end up dropping out.
Sara Armandi, University of Copenhagen
In the biopharmaceutical industry, biostatistics plays an important and essential role in the research and development of drugs, diagnostics, and medical devices. Familiarity with biostatistics combined with knowledge of SAS® software can lead to a challenging and rewarding career that also improves patients' lives. This paper provides a broad overview of the different types of jobs and career paths available, discusses the education and skill sets needed for each, and presents some ideas for overcoming entry barriers to careers in biostatistics and clinical SAS programming.
Justina Flavin, Independent Consultant
Thanks to advances in technologies that make data more readily available, sports analytics is an increasingly popular topic. A majority of sports analyses use advanced statistics and metrics to achieve their goal, whether it be prediction or explanation. Few studies include public opinion data. Last year's highly anticipated NCAA College Football Championship game between Ohio State and Oregon broke ESPN and cable television records with an astounding 33.4 million viewers. Given the popularity of college football, especially now with the inclusion of the new playoff system, people seem to be paying more attention than ever to the game. ESPN provides fans with College Pick'em, which gives them a way to compete with their friends and colleagues on a weekly basis, for free, to see who can correctly pick the winners of college football games. Each week, 10 close matchups are selected, and users must select which team they think will win the game and rank those picks on a scale of 1 (lowest) to 10 (highest), according to their confidence level. For each team, the percentage of users who picked that team and the national average confidence are shown. Ideally, one could use these variables in conjunction with other information to enhance one's own predictions. The analysis described in this session explores the relationship between public opinion data from College Pick'em and the corresponding game outcomes by using visualizations and statistical models implemented by various SAS® products.
Taylor Larkin, The University of Alabama
Matt Collins, University of Alabama
With advances in technology, the world of sports is now offering rich data sets that are of interest to statisticians. This talk concerns some research problems in various sports that are based on large data sets. In baseball, PITCHf/x data is used to help quantify the quality of pitches. From this, questions about pitcher evaluation and effectiveness are addressed. In cricket, match commentaries are parsed to yield ball-by-ball data in order to assist in building a match simulator. The simulator can then be used to investigate optimal lineups, player evaluation, and the assessment of fielding.
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.
Rachael Becker, University of Central Florida
Drew Doyle, University of Central Florida
This paper shows how SAS® can be used to obtain a Time Series Analysis of data regarding World War II. This analysis tests whether Truman's justification for the use of atomic weapons was valid. Truman believed that by using the atomic weapons, he would prevent unacceptable levels of U.S. casualties that would be incurred in the course of a conventional invasion of the Japanese islands.
Rachael Becker, University of Central Florida