Quantitative Methods for Policy and Equity Analysis

The Certificate in Quantitative Methods for Policy and Equity Analysis is a highly focused, 12-credit program that prepares students with the tools necessary to investigate quantitatively the impacts of policies and programs on underrepresented and marginalized groups and on inequalities by race, ethnicity, class, immigration status, gender, sexual orientation, and other dimensions.

Students will learn how to understand deeply, and apply appropriately, rigorous quantitative methods in policy and equity analyses. The courses comprising the certificate focus on data collection, measurement and preparation for analysis, visualization and numeric description of variables and relationships, multivariate regression analysis, statistical inference, logic models, and causal analysis using observational and experimental data. The certificate is hands-on, using software packages such as Stata or R. Students will gain transferable skills by learning how to apply a wide range of quantitative methods to analyze contemporary policy and equity challenges at the local, national, and international level. The certificate will help students who need quantitative skills to advance in their careers and those who want to fill research-related positions in their government and non-profit organizations. Courses will be taught fully online, fully in-person, hybrid, or hybrid-flex (simultaneously in-person and online) with the option to complete the certificate entirely virtually.

Students in the certificate program are eligible for all graduate student services offered at the Marxe School. The credits are fully transferable to the MPA and MIA degrees. MPA and MIA degree students are eligible to apply for the certificate program.

Program Goals

At the conclusion of the certificate, students will be able to:

·         Apply quantitative methods to analyze local, regional, national, or international conditions, programs, and policies, using an equity lens

·         Design measures for and approaches to collecting quantitative data and prepare data for analysis

·         Analyze data using descriptive and inferential statistics

·         Evaluate, design, and conduct research and analyses to estimate causal effects using observational, quasi-experimental, and experimental data

·         Program (code) with statistical software to conduct analyses

·         Present and critically discuss findings from descriptive and causal analyses tailored to diverse audiences, including policymakers and the public