Vivian Wong headshot
VW

Vivian C. Wong

Associate Professor
Unit: Curry School of Education
Department: Curry School of Education
Office location and address
Ruffner Hall 264
405 Emmet St S
Charlottesville, Virginia 22903
Education
B.A., University of Chicago, 1998
Ph.D., Northwestern University, 2010
Biography

Vivian C. Wong is a research methodologist in the field of Education. Currently, Dr. Wong is an Assistant Professor in Research, Statistics, and Evaluation in the Curry School of Education at the University of Virginia. Her research focuses on evaluating interventions in early childhood and K-12 systems. As a methodologist, her expertise is in improving the design, implementation and analysis of randomized experiments, regression-discontinuity, interrupted time series, and matching designs in field settings.

Dr. Wong is the lead author or co-author of numerous articles and book chapters on research methodology. Along with colleagues, she recently published a paper on regression-discontinuity designs when multiple assignment variables and cutoffs are available, as well as a paper that uses a regression-discontinuity design to evaluate five state pre-kindergarten programs. She is currently examining sorting issues in regression-discontinuity designs, as well as using within-study comparison designs to identify best methods for prospectively choosing comparison schools in education evaluation contexts.

Dr. Wong's work has appeared in the Journal of Educational and Behavioral Statistics, Journal of Policy Analysis and Management, and Psychological Methods. Dr. Wong participated in the Institute for Education Sciences (IES) Predoctoral Training Program at Northwestern University, and received the Outstanding IES Predoctoral Fellow Award in 2010 for her dissertation work on "Addressing Theoretical and Practical Challenges in the Regression-Discontinuity Design."  She is a Principal Member of IES's Statistics and Methodology review panel.

Developing Methodological Foundations for Replication Sciences
Source: U.S. Department Of Education
September 01, 2019 – August 31, 2022
Who Needs Rules? The Impact of Deregulation in Traditional Public Schools - Fellowship on Behalf of Kylie Anglin
Source: National Academy Of Education
August 01, 2020 – June 01, 2021
CU-CR Collaborative Research: Developing Methodological Foundations for Empirical Evaluations of Non-Experimental Methods in STEM Intervention Evaluations
Source: U.S. National Science Foundation (NSF)
September 15, 2015 – August 31, 2020
Pomona Invest In Success Project
Source: Pomona Unified School District
July 01, 2018 – December 31, 2019
CU-How Accountable were States under NCLB? States' Implementation Stringency under NCLB, and the Impact on Student Outcomes
Source: American Educational Research Association
July 01, 2016 – June 30, 2018
EDLF 5330: Quantitative Methods and Data Analysis I
Credits: 3
The course covers descriptive and inferential statistics. Students learn to identify the type of data, select appropriate statistic and graphical methods, analyze data, and interpret the results. Specific methods include the t-test, chi-square test, correlation, simple linear regression, one-way ANOVA, and repeated measures ANOVA. Calculations are done by hand and with statistical software.
EDLF 5500: Selected Topics
Credits: 1–6
Pilot courses to meet new program requirements and changing needs in the field. Used also to offer experimental courses, and courses under development, these courses are announced and offered on a semester-to-semester basis. May be graded or S/U, depending on the instructor, and may be repeated.
EDLF 5985: Internship
Credits: 1–6
Students apply academic experiences in professional and/or research settings; reflect and critically and constructively analyze experiences from multiple perspectives; and view the work as connecting course content authentic contexts. Students work as professionals with site supervisors and instructors to complete related assignments and relevant background research on the professional and academic resources available.
EDLF 5993: Independent Study
Credits: 1–6
Prerequisite: Instructor permission.
EDLF 8310: Generalized Linear Models
Credits: 3
Focus is on the generalized linear model (GLM) for cases when variables have specific non-normal conditional distributions, with emphasis on common data analytic challenges that arise in real world settings. Topics include nonlinear relationships, nominal and ordinal outcomes, discrepant data, and bootstrapping methods. Course materials are grounded in applied examples from the social and health sciences.
EDLF 8311: Design and Analysis of Field Experiments
Credits: 3
A rich body of literature has emerged about the design, implementation, and analysis of experiments in field settings. This course introduces students to advances in the design and analysis of field experiments; provides opportunities for students to read and discuss well-known field experiments that have had important implications for policy; and discusses methodological issues related to both experiments and non-experiments.
EDLF 8998: Masters Research Internship
Credits: 1–12
Designed to give masters students experience conducting research in professional settings appropriate to their disciplines. Prerequisites: Permission of Advisor
EDLF 8999: Masters Thesis
Credits: 1–6
For master's research, taken under the supervision of a thesis director.
EDLF 9993: Independent Study
Credits: 1–12
Under close faculty guidance, students work on an area of interest not covered by the curriculum. A plan of study must be signed by the faculty sponsor and filed in the student's permanent file in the Office of Student Affairs. Prerequisite: Instructor permission.
EDLF 9998: Doctoral Research Apprenticeship
Credits: 3–12
Designed to give doctoral students experience conducting research in professional settings appropriate to their disciplines. Prerequisites: Advisor Permission Required.
EDLF 9999: Doctoral Dissertation
Credits: 3–12
Doctoral Dissertation Research completed under the guidance of dissertation committee. 12 hours is required for graduation. Permission of instructor required.