Syllabus: Statistics for Social Justice

Miscellaneous Course Policies

Please download the OSU and CSW course policies that are incorporated by reference within this course.

Course Information

Course Title: Statistics II: Equity-Focused Data Analysis
Instructor: Dr. Gia Elise Barboza-Salerno
Institution: College of Social Work, The Ohio State University
Term: Spring 2025

Course Objectives

This course will push you to think creatively about statistics in the social work context, exploring regression, measurement, and latent variable models with an emphasis on real-world application. Each week will involve hands-on learning, storytelling through data, and team-based projects. By the end, you’ll not only master regression techniques but also apply them to innovative social work challenges.

Click here for the required reading and software needed for the course.

Weekly Schedule & Activities

Week 1: January 6, 2025

No Class Introduction to the course

  • Topics:
    Course structure, and role of statistics in social work.

  • Innovative Approach:
    We are using innovative data science tools to help us learn the material.

  • R Lab:

    • Introduction to R for data analysis and graphing

    • Introduction to the RStudio Interface

    • Opening files in R

    • Data wrangling

    • Skills: Use foreign, dplyr and ggplot2 to open, clean and visualize data.

  • Social Justice Policy Application:
    Begin thinking about how to use data to reclaim your story.

Week 2: January 13, 2025

Assessment

Activity:
- Complete a pre-course assessment to evaluate your understanding of statistics and R.

Week 3: January 20, 2025

No Class - MLK Day

Week 4: January 27, 2025

Simple Regression: Foundations and Application

  • Topics:
    Slope, intercept, residuals, and regression output.
Note

Note: Unfortunately, the R Cloud has stopped allowing apps to run and so I need to figure a workaround. Until then, you will have to download the R script to run it. This will require installing the learnr package.

  • Innovative Approach:
    Interactive Data Escape Room: Step-by-step example to illustrate working with data from the web. Learn more about how to use and install learnr here. Download the Historic Redlining Scores here

  • R Lab:
    Fit simple regression models using lm() and create dynamic visuals with ggplot2.

  • Social Justice Policy Application:
    Read our paper on historic redlining and child welfare investigations in Los Angeles County.

Introduction: Turning Data Into Stories

  • Topics:
    Finding Data

  • Innovative Approach:
    Data Makeover Challenge: Reimagine data using effective storytelling

  • R Lab:
    Introduction to R, JASP and SPSS

  • Social Justice Policy Application:
    Are there differences in infant mortality by race?

Week 5: February 3, 2025

Multiple Regression: Hypothesis Testing and Decomposition of Effects

  • Topics:
    F-tests, p-values, R², and decomposition of effects.

  • Innovative Approach:
    Hypothesis Auction: Students “bid” on testable hypotheses and justify their choices.

  • R Lab:
    Fit and interpret multiple regression models with lm() and conduct diagnostic checks.

  • Social Justice Policy Application:
    Explain the results of a multiple regression analysis to a argue for policy change.

  • Assignment 1 will be distributed today and due at the end of Week 9 (March 3, 2025).

Week 6: February 10, 2025

Effect Sizes & Confidence Intervals: Moving Beyond P-Values

  • Topics:
    Practical significance, confidence intervals, effect sizes.

  • Innovative Approach:
    Data Debate: Teams debate the practical and legal significance of statistical findings in a social work context.

  • R Lab:
    Calculate and interpret effect sizes with the effectsize package and create visual confidence intervals with ggdist.

  • Social Justice Policy Application:
    Write an op-ed arguing for or against the practical significance of debated findings.

Week 7: February 17, 2025

Assumptions and Violations of Assumptions

  • Topics:
    Diagnosing and addressing regression assumption violations.

  • Innovative Approach:
    Assumption Diagnosis Clinic: Diagnose and “treat” assumption violations in datasets.

  • R Lab:
    Conduct diagnostic checks with the car and performance packages.

  • Social Justice Policy Application:
    Create a “prescription card” summarizing diagnostics and fixes for your dataset.

Week 8: February 24, 2025

Hierarchical Regression & Dummy Variables

  • Topics:
    Coding categorical predictors, hierarchical models, and their applications.

  • Innovative Approach:
    Regression Battle: Teams compete to build the best hierarchical model to explain variance in a dataset.

  • R Lab:
    Code dummy variables and fit hierarchical models using lm().

  • Social Justice Policy Application:
    Write a brief explaining the ethical implications of your dummy variable coding.

Week 9: March 3, 2025

Logistic Regression: Predicting Dichotomous Outcomes

  • Topics:
    Odds ratios, ROC curves, interpreting results.

  • Innovative Approach:
    Predictive Policy Game: Prediction scenarious to tackle social work interventions.

  • R Lab:
    Fit logistic regression models with glm() and create ROC curves with pROC.

  • Social Justice Policy Application:
    Create a policy recommendation informed by logistic regression findings.

  • Assignment 1 Due Today.

Week 10: March 10, 2025

Spring Break

Week 11: March 17, 2025

Mediation & Suppressor Variables

  • Topics:
    Mediation analysis and identifying suppressor effects.

  • Innovative Approach:
    Data Detective Roleplay: Investigate intervention mechanisms using mediation analysis.

  • R Lab:
    Perform mediation analysis with the mediation package and visualize indirect effects.

  • Social Justice Policy Application:
    Examine a “case file” documenting your mediation analysis, including visualizations and key takeaways.

  • Assignment 2 will be distributed today and due at the end of Week 15 (April 14, 2025).

Week 12: March 24, 2025

Moderation (Interactions) in Regression

  • Topics:
    Interactions, visualization, and interpretation.

  • Innovative Approach:
    Build-a-Story Workshop: Create narratives around moderation analysis results using regions of significance.

  • R Lab:
    Use interactions to plot and interpret effects.

  • Social Justice Policy Application:
    Submit a 1-page illustrated story using moderation findings.

Week 13: March 31, 2025

Exploratory Factor Analysis (EFA)

  • Topics:
    Factor extraction, analysis and visualization.

  • Innovative Approach:
    Factor Scavenger Hunt: Interpret factor loadings to uncover insights using EFAShiny

  • R Lab:
    Conduct EFA with psych and visualize results with corrplot.

  • Social Justice Policy Application:
    Create an infographic summarizing EFA findings and what they mean.

Week 14: April 7, 2025

Confirmatory Factor Analysis (CFA)

  • Topics:
    Model fit and parameter estimates.

  • Innovative Approach:
    Model Critique Workshop: Propose refinements to a provided CFA model.

  • R Lab:
    Conduct CFA with lavaan and visualize results with semPlot.

  • Social Justice Policy Application:
    Evaluate the CFA model and suggesting refinements.

Week 15: April 14, 2025

Latent Class Analysis (LCA)

  • Topics:
    Identifying latent subgroups.

  • Innovative Approach:
    Latent Storytelling: Use LCA findings to understand lived experiences of a group of your choice.

  • R Lab:
    Perform LCA with poLCA and visualize latent profiles.

  • Social Justice Policy Application:
    Create a “persona deck” illustrating latent group characteristics.

  • Assignment 2 Due Today.

Week 16: April 21, 2025

Student Presentations Review

Final Exam: April 28, 2025

Create, Validate, and Apply Scales

Task:
Take the final exam, or submit a publishable manuscript.

Contact Information

Office Hours: Mondays 2–4 PM, or by appointment.
Email: barboza-salerno@osu.edu