Advanced Methodology and Statistics Seminars (AMASS)

Presented by:

Meghan K. Cain,
Assistant Director, Educational Services, StataCorp LLC

Participants earn 4 continuing education credits

Category: Research Methods and Statistics

Keywords: Statistics

Basic to moderate level of familiarity with the material.

Mastering a statistical software package is essential for efficient, reproducible research in clinical psychology. This AMASS workshop provides a high-intensity “bootcamp” introduction to Stata, moving from fundamental navigation to advanced modeling.

The first portion of the session builds a solid foundation for navigating the Stata interface using the menu system and command syntax. We will import and clean datasets, perform basic analyses, generate publication-quality graphics, and create customizable tables. We emphasize a reproducible workflow, teaching participants how to use Do-files to ensure our work is transparent and repeatable.

The second portion of the workshop transitions into core analytical techniques. We will cover linear regression in depth, with a specific focus on interpreting coefficients and visualizing effects using predictions and marginal effects plots—tools critical for understanding clinical interactions.

Finally, the workshop introduces participants to Stata’s frameworks for handling complex data structures common in behavioral research. We will provide brief, practical demonstrations of multilevel modeling for nested or longitudinal data and structural equation modeling (SEM) for testing latent constructs and mediation.

By the end of this session, attendees will have a functional “starter kit” of Stata skills, moving from importing raw data to implementing sophisticated models. This workshop is designed for students and researchers looking to transition to Stata or formalize their existing skills into a more efficient, reproducible workflow.

Outline:

1. 1. Topic 1: Foundations and reproducible workflow
      1. Navigating the interface
      2. Data management essentials
      3. Exploratory data analysis
      4. Creating publication-quality graphics
      5. Creating customizable tables of descriptive statistics
   2. Topic 2: Regression and the power of margins
      1. Fitting linear regression models
      2. Interpreting and visualizing results through predicted means and marginal effects
      3. Creating customizable tables of regression results
   3. Topic 3: Advanced frameworks – MLM & SEM
      1. Multilevel modeling
         1. Essential commands for nested data structures
         2. Fitting random-intercept and random-slope models
         3. Small-sample inference
      2. Structural equation modeling
         1. Fitting models through the SEM Builder and command syntax
         2. Satorra-Bentler adjusted SEs and model fit
         3. Mediation models
         4. Measurement models with latent constructs

   At the end of this session, the learner will be able to:

   • Navigate the Stata interface to perform essential tasks like importing, cleaning, and managing data
   • Describe data using customizable tables, publication-quality graphs, and basic analyses
   • Fit linear regression models and interpret them using marginal means and visualizations
   • Fit multilevel models to nested data
   • Fit structural equation models by drawing path diagrams in the SEM Builder or writing syntax
   • Lead independent research projects and ensure their work meets the modern standards of reproducibility and transparency required by high-impact clinical journals.
   • Work effectively on interdisciplinary teams with biostatisticians, economists, and other social scientists in advanced research settings.

   Recommended Readings:

   Mize, T. D. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science, 6, 81-117.

   Haghish, E. F. (2016). Reproducible research with Stata. The Stata Journal, 16(4), 938-959.

   Gambino, A. J., & McCoach, D. B. (2025). r2_mlm: A command for computing R-squared measures for models fit by mixed. The Stata Journal, 25(4), 719-742.

   Zhang, Z. (2017). Structural equation modeling in the context of clinical research. Annals of Translational Medicine, 5(5), 102.