The Office of Institutional Analysis - a unit of Planning, Budget, and Analysis - produces both descriptive reports and inferential studies to support evaluation of university operations for senior management, campus departments, and university constituents. The office is the institutional source for tracking annual changes in census enrollment, enrollment by academic unit and student demographics, collects and coordinates information for submission to the Integrated Postsecondary Education Data System (IPEDS), produces the Common Data Set (CDS) to support higher education surveys, conducts in-depth research on student success, and collaborates with Information Technology in the dissemination and online access to data via the Power BI Cloud.
TRANSITION TO MS POWER BI (OFFICE 365) DATA REPORTS AND DASHBOARDS
Our office is transitioning to MS Power BI (PBI) for online interactive dashboard data reporting. You may download the following PDF document with a range of sample dashboards that cover student admission data, class section enrollment tracking, census enrollment data, student retention and graduation rate data, and institutional benchmarking data: Power BI Sample Dashboards. Here is the most recent list of data elements included in these dashboards. If you have an active MS Power BI account, you may access these online dashboards with your NetID here. A quick introduction on how to access Power BI and navigate reports and dashboards is available here. To apply for a Power BI account, please go here. Since our PBI reports require an account login, you may experience the functionality of PBI with this public domain example.
Demonstration Sessions will be held in the Scrugham Engineering/Mines Building, Lab Room SEM231 (no rsvp needed):
Sep 26 @ Lab B 12pm - 12:50pm
Oct 16 @ Lab B 12pm - 12:50pm
Oct 25 @ Lab D 2pm - 2:50pm
UPCOMING POWER BI FULL-DAY WORKSHOP: Thursday, October 18, 2018 , 8:30 am - 4:30 pm, Kellogg West Conference Center, Pomona, CA: Putting Analytics to Work: Enhancing Institutional Productivity in the Age of Big Data
- Enrollment projections are based on dynamic estimates and are thus updated regularly as new information becomes available. Estimates are derived from a statistical student class level flow model that calculates the 'conversion rate' for students progressing through the enrollment pipeline, while taking into account average credit load, attrition rate, transfer-in rate, student academic preparation, and socio-demographics at each progression stage.
- Common Data Set: The CDS annual survey includes the following major sections: General college information, Enrollment & Persistence, First-Time, First-Year Admissions, Transfer Admissions, Academic Offerings & Policies, Student Life, Annual Expenses, Financial Aid, Instructional Faculty & Class Size, and Degrees Conferred
Applying a counterfactual analytical framework, the study estimates the influence of subsidized and unsubsidized loans on academic success of first-year students. The estimated effect of loan aid controls for first-year academic experience and takes into account 26 factors related to loan selection and persistence in order to match students with loan aid to a counterfactual case in covariate adjusted regression. Comparison with results from non-matched-sample analysis suggests selection bias may mask the negative effect of loans detected with matched-sample estimation. Validity of covariates determining the loan selection process and criteria for acceptable balance in the matched data are discussed, and implications for future research are addressed. The full study can be downloaded here.
How accurate are students’ self-assessment of their learning in college? Are student survey responses to assess their cognitive development valid measures of college success? Should we use such data for program review, professional accreditation, institutional self-studies, or to support grant proposals?
The above link takes you to the latest research volume on the validity and limitations of student self-report data, demonstrates how to identify biases in self-report data and how to measure student learning using longitudinal direct measures of academic development. For questions, contact Serge Herzog, the volume’s co-editor.
- Northwest Commission on Colleges and Universities
- Accreditation Resources
- Links to Education Research Resources
- Office of University Assessment
- Nevada Center for Surveys, Evaluation, and Statistics
- Association for Institutional Research
- Rocky Mountain Association for Institutional Research
- Voluntary System of Accountability - College Portrait