Overview

The primary goals of this Portfolio program are to:

1. Offer a cohesive course of study for graduate students seeking to enhance the statistical modeling component of their research and to prepare for successful careers upon graduation.
2. Provide a forum for graduate students from across UT to work together and exchange ideas regarding the application of statistical modeling methods to a broad range of areas.
3. Leverage the existing expertise of faculty members in departments across UT whose research focuses on statistics at foundational and applied levels.

Students must complete 12 semester hours of courses as follows in "Course Requirements". Students are expected to obtain the consent of a Portfolio Advisor (selected from the list of faculty members affiliated with SSC) soon after entering the program to advise their course selections and guide their independent study.

How to Apply

Click HERE to download the application. Applications are due October 1, 2009 to WCH 2.104/G2500. To be admitted into the program, a student must be in good standing in an approved graduate degree program. If applying before completing the first semester as a graduate student, the student must have a minimum cumulative undergraduate GPA of 3.0. If applying after completion of at least one full semester as a graduate student, the student must have a minimum cumulative graduate GPA of 3.0.

Course Requirements

PREREQUISITE KNOWLEDGE

SSC 380C Statistical Methods I
*Students who have taken one or more courses equivalent to the prerequisite course may elect to place out of it by taking an exam that will be administered by the Portfolio Committee. The next exam will be offered October 26, 2009. (Click HERE for more information).

CORE REQUIREMENTS

SSC 380C Statistical Methods II

ELECTIVES

Choose two electives: Click HERE for a current list of approved electives. To ensure courses taken for the portfolio are interdisciplinary in nature, at least one of the two elective courses chosen must meet one of the following three criteria.

1. The elective is a SSC course cross-listed with a department other than the student's home department, or
2. The elective is a non-SSC course from outside the student's home department, or
3. The elective is in the student's department but outside the student's area (e.g., Counseling Psychology and Quantitative Methods are two separate areas, both within the Educational Psychology Department).

INDEPENDENT STUDY

Under the supervision of a Portfolio Advisor, the student must successfully complete a research project (as part of a 3-credit independent study course) designed to apply advanced statistical modeling techniques to the student's research area of interest. Each student must present their final project at the Portfolio Program's end-of-semester colloquium.  Download Registering_for_Research_Course.pdf to register for the independent study course.

FAQs

Q: What are the requirements?
A: You will complete 12 semester hours, including a research project, as specified in the coursework guidelines. You must earn a letter grade of B or better in all courses required for certification and maintain an overall GPA of 3.5 or better in courses counting towards the Portfolio.

Q: How do I sign up?
A: Submit an application form to the SSC office in WCH 2.104, G2500 by the deadline specified (see the "Apply" section of this site). Students are encouraged to apply early in their course of graduate study. The SSC will help each student choose an appropriate course sequence and find an advisor for his or her independent study project.

Q: Can Certificate courses also fulfill my graduate degree requirements?
A: Depending upon the student's degree program, courses taken towards the portfolio may or may not count towards their degree.

Prerequisite Exam

The 1.5 hour long exam will be held four times per year on the first day of registration (2009 Dates: August 20 and October 26). Contact us for the exact time and location.

The following topics will be covered: Descriptive Statistics (e.g., mean, median, standard deviation, skewness), Power, Type I and Type II Errors, p-values, z-scores, Bias, Consistency, Common Sampling Distributions, Interpreting Correlations, Confidence Intervals for Means, Hypothesis Testing for Means (using one sample, independent samples, and ANOVA), Chi-square Test of Independence, Simple Linear Regression.