As a part of the University of Oregon’s work with the American Institutes of Research in support of the WWC, my responsibilities included:

• Produce training materials for evidence reviewer trainings
• Update the online group design training materials
• Provide support to the development and presentation of WWC Technical Assistance Webinars
• Facilitate and participate in the statistics and methodology groups devoted to refining and revising the WWC’s Standards and Procedures handbooks
  

Prior to finishing my PhD in August of 2019, I performed these duties under a “Research Assistant” title.

• Analyze extant datasets for novel methodological problems.
• Apply advanced methods such as latent growth modeling, path analysis, and multidimensional IRT models.
• Prepare technical documentation and interpret statistical models for both technical and non-technical audiences.
  

• Provide methodological guidance to graduate students on research design, power and statistical analysis, statistical programming, and interpretation of results.
• Assist students in retrieval of datasets from a variety of sources and import them into appropriate statistical software.
• Develop and lead workshops on relevant topics such as power analysis and programming in R to students, faculty, and staff at the University of Texas at Austin.

• Collaborate on drafting manuscripts for publication in peer-reviewed methodological research journals.
• Design and run Monte Carlo Simulations.
• Assist in maintenance of a large Access database of single-case data from multiple synthetic reviews, including using SQL queries to read and write data.
• Screen and code studies for synthetic review.
• Program novel statistical methods in R and collaborate on package development to make them available to researchers.

• Introductory Statistics including levels of measurement, introductory probability, hypothesis testing, t-test, chi-square, tests of proportions, simple regression.
• Other statistical methods such as ANOVA, ANCOVA/ATI, multiple regression, SEM (e.g. path analysis, CFA, multiple group models, invariance models, and latent growth modeling), generalized linear models (e.g. logistic regression, Poisson regression).
• Use of software packages including SPSS, MPLUS, and R, especially advanced programming in R.
• Quantitative research design.