SMARTStats Workshop Series – School Mental Health Assessment, Research, & Training Center

To enhance research capacity and methodological rigor among faculty, postdoctoral scholars, staff, and students, the UW School Mental Health Assessment, Research, and Training (SMART) Center is hosting an ongoing Research Methodology Workshop Series, SMARTstats.

Each 1.5-hour session will provide hands-on learning opportunities, foster discussion, and promote best practices in research methodology. Given the interdisciplinary nature of the SMART Center, this series aims to equip attendees with robust quantitative-focused methodological tools applicable to school mental health, injury prevention, and related fields. Example topics include: regression analyses, mutli-level modeling, longitudinal techniques, and missing data management. Sessions will be held virtually (via Zoom), and recordings will be provided along with other resources for ongoing learning.

The goals of this series are to strengthen the SMART Center’s research infrastructure, foster collaboration across the Department of Psychiatry and Behavioral Sciences, and empower researchers with the tools to conduct rigorous, high-impact studies.

If you have questions about anything covered in the following workshop sessions, please reach out to Dr. Keith Hullenaar, khullen@uw.edu.

R Software Primer

This session provides a primer on using R Statistical Computing Software. If you do not have R/R Studio downloaded, you may do so using these installation instructions.

Instructions: Preparing for “Introduction to R for Data Analysis”
Recording: R Primer recording
Data and code files: crime.rdata and SMART Stats R Code.R

SMARTStats Workshops & Materials

Foundations of Statistical Modeling in Applied Research

This session introduces foundational principles of statistical modeling with an emphasis on real-world application across school mental health, injury prevention, and related fields. Designed for researchers with beginner to intermediate statistical experience, the session unpacks core concepts such as the goals of modeling (description, inference, prediction), common assumptions, and the difference between statistical and causal interpretations. Through a hands-on demonstration using R/STATA and a publicly available dataset, participants can explore how to fit and interpret a basic regression model, assess model assumptions, and think critically about what models can—and cannot—tell us. No prior experience with R/STATA is required, and all code is available through RStudio for optional exploration.
- Recording: SMARTStats 1: Foundations of Modeling.mp4
- Slides: SMARTStats: Foundations of Applied Statistical Modeling

Foundations of Mixed Models

In this workshop, participants will learn what a mixed model consists of, review key concepts and notations (e.g., intercepts, slopes, etc.), and have the opportunity to apply this methodology using sample sleep study data in RStudio.

Recording: SMARTStats: Foundations of Mixed Modeling Recording.mp4

Slides: SMARTStats: Foundations of Mixed Modeling Slides

R Code: mixed_modeling_SMARTSTATS.R

Exploratory Factor Analysis

In this workshop, participants will learn how to use Exploratory Factor Analysis (EFA) to uncover the latent structure underlying a set of survey or assessment items—i.e., when multiple observed measures reflect a smaller number of unobserved (i.e., latent) constructs. We’ll build an intuitive workflow: when EFA is appropriate (vs. PCA/CFA), how to choose an extraction method and rotation, how to determine the number of factors (e.g., scree/parallel analysis), and how to interpret loadings, cross-loadings, and communalities to refine a measure. The session will include a live R walkthrough, with practical guidance on common pitfalls (over factoring, Heywood cases, and “pretty” solutions that don’t replicate).

Recording: SMARTstats Exploratory Factor Analysis Recording.mp4

Slides: SMARTStats 1 Foundations of Exploratory Factor Analysis.pptx

R code: EFA_Workshop_Script_working.R

Data: ptechdata.csv

Cheat Sheet: EFA_Cheat_Sheet_Revised.docx

Foundations of Confirmatory Factor Analysis

In this workshop, participants will learn how to use Confirmatory Factor Analysis (CFA) to test whether a hypothesized factor structure adequately fits observed data—i.e., when you have a theoretical or empirically derived model (e.g., from EFA) specifying which observed indicators load onto which latent constructs, and you need to evaluate that model’s fit. We’ll build on the EFA workflow covered in the February session: when CFA is appropriate (vs. EFA/PCA), how to specify and identify a measurement model, how to evaluate model fit (e.g., chi-square, CFI, TLI, RMSEA, SRMR), and how to interpret standardized loadings, modification indices, and residuals to diagnose misfit. The session will include a live R walkthrough (using lavaan), with practical guidance on common pitfalls (model non-convergence, correlated residuals that mask misspecification, over-reliance on modification indices, and the difference between a model that “fits” and a model that’s theoretically defensible).

Find Slides & Recording here following the March 26th workshop.

SMARTstat Facilitators