Statistics with a Purpose

Preparing Research Leaders

Whether driving policy reforms or advancing academic scholarship, visionary ideas require robust statistical evidence. The Statistics with a Purpose seminar series develops scholarly expertise and technical fluency with invaluable quantitative methods.

Over an intensive term, doctoral students master industry-standard statistical techniques to address both real-world research questions and policy needs. From detecting outcome disparities based on race and class to modeling community-based supports that mitigate recidivism rates, justice data is put through its paces.

Experts support student growth as statistics-savvy research leaders prepared to uncover injustices through academic scholarship and fight them by informing reforms.

The series culminates in a capstone analysis project, with students applying new statistical skills to advance equality and empirically validate interventions in self-selected domains.

Below you will find the 8 course modules included in the Statistics with a Purpose seminar series:

Basic Statistics

Build fundamental quantitative skills to descriptively and inferentially analyze justice data for policy decisions and reforms.

Data Preparation

Manage and prepare real justice datasets. Provides hands-on experience to reinforce understanding of key statistical concepts.

Descriptive Statistics

Develop skills in summarizing large justice datasets into compelling reports – critical for clear communication to policymakers.

Student t-tests

Learn to evaluate pre/post diffs and compare groups. Assess effectiveness of justice interventions and quantify potential biases.

Analyses of Variance and Post-Hoc Tests

Efficiently compare three+ groups such as effects of policy changes over time. Assess systematic variances in treatment or outcomes.

Chi-Square Analyses

Analyze complex multivariate categorical relationships in justice data. Uncover dependencies between two variables.

Regressions Part 1

Model quantitative data to predict key outcomes like recidivism rates. Identify strongest predictive risk and protective factors.

Regressions Part 2

Take predictive modeling skills to the next level. Quantify accuracy of predictions to guide sentencing policy reforms.

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