SAS Teams with AIR Connections Program
To help institutional researchers learn more about how SAS can help them better understand, analyze, and report on their institution's data, SAS and the Association for Institutional Research have teamed up to present a 2008-2009 webinar series as part of AIR's Connections Program.The webinar is free of charge, though pre-registration is required and limited to the first 200 participants.
Student Retention Analysis Using SAS: Data Analysis
| Tuesday, November 4, 2008 | 2:00-4:00 PM EDT |  |
This webinar will begin where the Data Preparation and Exploration leaves off. With the data ready to analyze, the webinar will begin with a review of statistical hypothesis testing. Two sample t-tests and chi-square tests for association will be used to explore possible relationships between the various pieces of information and student retention. If time allows, the webinar will finish with a review of logistic regression and building a multiple regression model to predict student retention.
Introduction
Rob Springer Director of Institutional Research Elon University |
Presenter
Kathy Kiraly, SAS |
Presenter
Sue Walsh, SAS |
An Analysis of Institutional Research Data Using SAS Web Seminar Series
To help institutional researchers learn more about how SAS can help them better understand, analyze, and report on their institution's data, SAS and the Association for Institutional Research teamed up to present a five-session lecture series in 2007-2008 entitled: An Analysis of Institutional Research Data Using SAS.Data Mining for Donor Modeling
There are a number of questions that arise when examining university donors, such as who is most likely to donate to my institution, how much are they likely to donate, how can I move a donor from one level of giving to a higher level, who is likely to become a major donor, who is likely to be retained as a donor. This seminar will introduce you to some of the issues and special considerations when trying to build predictive models for donations. It will include an introduction to SAS Enterprise Miner with a donor example.- Nov 9, 2007 - View recording.
Data Mining for Enrollment Management in Higher Education
Freshmen recruitment, enrollment, and retention are important issues in higher education. Researchers from across the country have used data mining techniques to develop models to predict who will enroll and why students fail to enroll for their second year. Predictive models using admissions data give enrollment management officials unique profiles of prospective students which help to estimate enrollment numbers and establish targeted recruitment initiatives. Persistence is perhaps the biggest piece of the enrollment management puzzle. University records data is used to gauge a student's intent to persist through the identification of at-risk populations using predictive modeling. In an environment with increased competition to recruit, retain, and graduate the best and brightest students, data mining is an imperative tool in higher education.In this presentation, the data mining experiences of two universities will be discussed, as well as a comparison and contrast of the private and public university perspective.
Data Mining Overview
Data mining can be defined as advanced methods for exploring and modeling relationships in large amounts of data. Data mining is being used by universities around the world in a number of ways. In particular, University Institutional researchers are using data mining for topics such as enrollment management, retention and graduation analysis, donation prediction, and faculty retention. This presentation will give you an insight into the history of data mining and an overview of the data mining process. The emphasis will be on data mining for predictive modeling in Institutional Research.- Aug 3, 2007
- Aug 24, 2007 - View recording.