In this section, we provide slides for presentations and lectures on a variety of statistical methods in health disparities research. Most are introductory and a few are more advanced.

**Testing Mediators and Moderators of Outcomes**

Each of the following are introductory:

- Mediation and Moderation 101: A broad overview of historical and current mediation and moderation analysis methods as of 2021.
- Testing Mediated Effects: How to conceptualize and test mediation effects (2014).
- Testing Effect Modification: Conceptualizing and testing moderated effects (2014).

**Methods of Analyzing Longitudinal Datasets**

- Interrupted Time Series Part I Introduction: A gentle introduction to simplified forms of interrupted time series (ITS) analyses (2016).
- Interrupted Time Series Part II Some Analysis Options: Detailed examples of simplified forms of ITS analyses (2016).
- Applications of SAS PROC MIXED: A basic introduction to mixed linear models (2003).
- Applications of Growth Modeling with PROC MIXED: A basic introduction to fitting linear growth models (2001).
- Growth Model Applications: Includes smoothing splines, interrupted times series, and associative latent growth curve models. SAS PROC GLIMMIX offers options to simply specification of smoothing splines (2008).
- Introduction to Mixed Logistic and GEE Models: A basic, older introduction to some frameworks for fitting logistic models to clustered data. More recent advancements have improved some estimation options (2001).

**Factor Analysis Methods**

- VARCLUS as an Alternative to EFA: An introduction to VARCLUS, a SAS procedure that we (mostly) prefer to exploratory factor analysis (2014).
- Exploratory Factory Analysis: An overview of exploratory factor analysis (2011).
- Testing Measurement Invariance via CFA: A conceptual look at the logic underlying tests of measurement invariance within the confirmatory factor analysis (CFA) framework (2003).

**Additional Statistical Issues**

- Comparing Odds Ratios Across Nested Logistic Regression Models: Care must be taken when comparing odds ratios across two logistic regression models where the X variables in one model are a subset of the X variables in the other model (2013).
- 3-level Logistic Models: Reviews SAS options circa 2013 for 3-level logistic models, including both multilevel models with random effects and alternating logistic regression (ALR) models. NOTE: SAS now includes the FASTQUAD option in PROC GLIMMIX, which allows fitting models with larger numbers of random effects and quadrature points.
- Missing Data Concepts: A no-math introduction to multiple imputation (2009).