HONR 46400: Evidence-Driven Research

Honors College, Purdue University (Fall 2026 — Mon/Wed/Fri)


IMPORTANT

This document does not replace the official material in the course Brightspace page


Course Description and Objectives

In this course, Honors students learn how to turn curiosity into credible, evidence-based insight without requiring a strong background in quantitative methods or computing. Students will learn to identify meaningful gaps in a chosen field, translate those gaps into well-scoped research problems, and formulate research questions that can be answered with data. Through a structured, supported workflow, students learn how to select and justify an appropriate quantitative method approach (description, statistical inference, predictive modeling, and/or causal reasoning) and how to use computational tools and AI to locate sources, operationalize concepts, analyze evidence, verify results, and document decisions responsibly. By the end of the course, students will be enabled to design and defend a rigorous research approach, interpret findings with appropriate uncertainty and limitations, and communicate results effectively in written and oral formats.

Course Website: https://davi-moreira.github.io/2026F_evidence_driven_research_purdue_HONR464/

Instructor: Professor Davi Moreira

  • Email: dcordeir@purdue.edu
  • Class meetings: Monday / Wednesday / Friday — in person (time and room in the course syllabus)
  • Office Hours: See the course syllabus, or by appointment.

Learning Outcomes

By the conclusion of this course, you will be able to:

  1. Identify meaningful gaps in a chosen field and translate them into well-scoped research problems.

  2. Formulate research questions that can be answered with data.

  3. Select and justify an appropriate quantitative method approach: description, statistical inference, predictive modeling, and/or causal reasoning.

  4. Use computational tools and AI to locate sources, operationalize concepts, analyze evidence, verify results, and document decisions responsibly.

  5. Design and defend a rigorous research approach.

  6. Interpret findings with appropriate uncertainty and limitations.

  7. Communicate results effectively in written and oral formats.

Course Materials

  • Course book (free to read, required): EDR|AI — Evidence-Driven Research in the Age of AI: How to Design, Analyze, Verify, and Defend. It walks through the material at an undergraduate reading level, and every chapter has its own companion Colab notebook where you run the chapter’s code and complete its closing “It is your turn” section. EDR|AI is a work in progress: it is still under development and will keep growing and improving during the semester.

  • Recommended (free): Blair, Coppock & Humphreys (2023), Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign (RDSS, Princeton University Press). Read free online at book.declaredesign.org.

  • Computing (Required): A laptop or desktop with internet access and a modern web browser. Course activities run in the browser through Google Colab.

  • AI Tools:

    • Google Gemini is the primary research assistant, used in and alongside Colab under the course’s Ask → Verify → Document policy: draft with AI assistance, verify every claim and source against real, retrievable evidence, and document your decisions. You own every claim you submit.
    • Purdue GenAI Studio supplies custom reviewer roles (for example, the Causal Identification Skeptic, the Poster Critic, and the Reproducibility Auditor) that you are encouraged to use when attacking your own work.
    • Microsoft Copilot (Purdue-provided, with data protection) and other general-purpose tools are permitted with disclosure.

Course Infra-structure

Brightspace: The Course Brightspace Page https://purdue.brightspace.com/ should be checked on a regular basis for announcements and course material.