Sunday, March 30, 2014

Ambitious Modeling?

"As Philip Dawid once said "a causal model is just an ambitious associational model". A carefully-considered regression model, with an appropriate set of potential confounders (possibly identified using a causal diagram – see below) measured and included as covariates, is the most appropriate causal model in many simple settings."

To paraphrase Angrist and Pischke:

To the extent that the population CEF that it is estimating is causal, so is linear regression. (And that includes LPMs)

Tuesday, March 25, 2014

Institutional Research Presentations at SAS Global Forum

I'm not attending #SASGF14, but some of my colleagues in higher ed are. Here is what they are doing. If you are not attending global forum, or can't make their talks, I encourage you to check out their papers via the online proceedings once they are posted.

Tuesday March 25

Paper 1448 - From Providing Support to Driving Decisions: Improving the Value of Institutional Research For almost two decades, Western Kentucky University's Office of Institutional Research (WKU-IR) has used SAS® to help shape the future of the institution by providing faculty and administrators with information they can use to make a difference in the lives of their students. This presentation provides specific examples of how WKU-IR has shaped the policies and practices of our institution and discusses how WKU-IR moved from a support unit to a key strategic partner. In addition, the presentation covers the following topics: How the WKU Office of Institutional Research developed over time; Why WKU abandoned reactive reporting for a more accurate, convenient system using SAS® Enterprise Intelligence Suite for Education; How WKU shifted from investigating what happened to predicting outcomes using SAS® Enterprise Miner™ and SAS® Text Miner; How the office keeps the system relevant and utilized by key decision makers; What the office has accomplished and key plans for the future.

Paper 1638 - Institutional Research: Serving University Deans and Department Heads Administrators at Western Kentucky University rely on the Institutional Research department to perform detailed statistical analyses to deepen the understanding of issues associated with enrollment management, student and faculty performance, and overall program operations. This paper presents several instances of analyses performed for the university to help it identify and recruit suitable candidates, uncover root causes in grade and enrollment trends, evaluate faculty effectiveness, and assess the impact of student characteristics, programs, or student activities on retention and graduation rates. The paper briefly discusses the data infrastructure created and used by Institutional Research. For each analysis performed, it reviews the SAS® program and key components of the SAS code involved. The studies presented include the use of SAS® Enterprise Miner™ to create a retention model incorporating dozens of student background variables. It shows an examination of grade trends in the same courses taught by different faculty and subsequent student behavior and success, providing insights into the nuances and subtleties of evaluating faculty performance. Another analysis uncovers the possible influence of fraternities and sororities in freshmen algebra courses. Two investigations explore the impact of programs on student retention and graduation rates. Each example and its findings illustrate how Institutional Research can support the administration of university operations. The target audience is any SAS professional interested in learning more about Institutional Research in higher education and how SAS software is used by an Institutional Research department to serve its organization.

Monday March 24

Paper 1689 - Simple ODS Tips to Get RWI (Really Wonderful Information) SAS® continues to expand and improve its reporting capability. With new SAS® 9.4 enhancements in ODS (Output Delivery System), the opportunity to create stunning reports has expanded even further. If you are charged with creating relevant, informative, easy-to-read reports for clients or administrators, then the ODS Report Writing Interface, ODS LAYOUT enhancements, and the new ODSTEXT procedure are important tools to use. These tools allow you to create reports in a smart, eye-catching format that can be turned around quite quickly and programmed to provide optimum flexibility. How many times have you worked hours to tweak and fine-tune a report directly in Microsoft Excel, Microsoft Word, Microsoft Power Point or some other similar software only to be asked for a “quick update”, which would then take hours to recreate because you are manually transferring data? Do you ever dread receiving the compliment, “This is really wonderful information!!!!” because you know it will be followed by “Can you run this for EVERY region?” Well, dread no more, because when you harness the power of SAS® ODS, you can create first-rate, flexible, fabulous reports! Join me as I share with you two real-world examples of ODS capabilities using (1) a marketing piece I designed to help the president of our university spotlight county- and region-specific data as he recruited across the state and (2) our academic program review form, a multi-page report that outputs to Word so that program coordinators can add personalized commentary to support their program’s effectiveness.

Saturday, March 22, 2014

Quantile Regression with Count Data

I stumbled upon this paper recently:

Reforming health care: Evidence from quantile regressions for counts
Rainer Winkelmann
Journal of Health Economics 25 (2006) 131–145

"Basically, the approach transforms the discrete data problem into a continuous data problem by adding a random uniform variable to each count. The quantile regression functions of the transformed variable can then be estimated using standard quantile regression software. To interpret the results, one can compare the freely estimated quantile functions to those implied by the respective Poisson or negative binomial estimates in order to detect excess sensitivity in specific parts of the distribution, such as the lower or upper tails."

See also: 
Machado, J.A.F. and Santos Silva, J.M.C. (2005), Quantiles for Counts, Journal of the American Statistical Association, vol. 100, no. 472, pp. 1226-1237.