Quarto Workshop Series:
Transitioning from SPSS to R

Center for Customer Insights and Digital Marketing

Cal Poly Pomona

4/15/2025

Greetings and Introduction of the Team

  • Jarrod Griffin, Master’s Student, Moderator
  • Jae Jung, Ph.D., Professor of Marketing, Presenter

Announcement

Overall Agenda

  1. Greetings and Introduction to Presenters (Moderator)
  2. Announcement (Moderator)
  3. Expected Learning Outcomes & Agenda (Moderator)
  4. Overview (15 min)
    • Introduction to R, RStudio, and Quarto
    • Key differences and advantages of R over SPSS
    • Introduction to Case 31
  5. Data Preparation (25 min)
  6. Hypothesis Testing (25 min)
  7. Q&A (5 min)

Expected Learning outcomes:

After taking this workshop, participants should be able to:

  1. Explain the data cleaning and preparation process.
  2. Explain the benefits of using R for teaching and reproducible research
  3. Perform Exploratory Data Analysis.
  4. Examine psychometric properties of multi-item scales (i.e., reliability, exploratory factor analysis, PCA).
  5. Create a composite scale/index.
  6. Explain how to recode and change labels for variables and levels.
  7. Save cleaned data back to SPSS data.
  8. Describe data analysis and report creation process in Quarto.
  9. Generate descriptive statistics and visualize the data.
  10. Perform typical hypothesis tests (i.e., t-test, Chi-square test, one-way ANOVA, multiple Regression)

Overview (15 min)

Introduction to R, RStudio, and Quarto

Key differences between SPSS and R

Category SPSS R
Accessibility & Cost Proprietary software; requires a paid license Open-source and free-to-use
Ease of Use & Learning Curve GUI-based; beginner-friendly with point-and-click workflows Script-based; steeper learning curve but more flexible and powerful
Statistical & Analytical Power Strong for standard statistical tests; limited custom analysis Supports basic to cutting-edge analysis; highly extensible with packages
Customization & Reproducibility Limited scripting; harder to reproduce analyses without detailed logs Fully scriptable; supports reproducible workflows via R scripts or Quarto/Rmd
Community & Ecosystem Smaller, vendor-supported community; fewer user-developed extensions Large, active global community; thousands of user-contributed packages (CRAN)

Introduction to Case 31

Data Preparation (25 min)

  1. Import SPSS data into RStudio
  2. Run EDA to examine data and decide if a modification is needed
  3. Generate descriptive statistics for categorical and continuous data
  4. Prepare data (e.g., recode, change variable labels, and value levels).
  5. Test psychometric properties of multi-item scale (i.e., reliability, factor analysis, PCA) and create a composite scale/index.
  6. Create a cleaned dataset and export it back to SPSS data.

Data Analysis and Results Presentation (25 min)

  1. Reading Data
  2. Writing a report while doing an analysis
  3. Running hypothesis tests
    • Descriptive stats
    • One sample t-test
    • Ch-square Goodness of fit test
    • Two Samples t-test
    • One-way ANOVA
    • Multiple regression
  4. Generating insights

Q&A

  • Open the floor for any questions.

Thank You!