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.

**Methods of Analyzing Longitudinal Datasets**

A gentle introduction to simplified forms of interrupted time series analysis.

A basic introduction to mixed linear models.

A basic introduction to fitting linear growth models.

A basic, older (2001) introduction to some frameworks for fitting logistic models to clustered data. More recent advancements have improved some estimation options:

Includes smoothing splines, interrupted times series, and associative latent growth curve models. SAS PROC GLIMMIX now offers options to simply specification of smoothing splines.

**Factor Analysis Methods**

An introduction to VARCLUS, a SAS procedure that we (mostly) prefer to exploratory factor analysis.

An overview of exploratory factor analysis.

A conceptual look at the logic underlying tests of measurement invariance within the confirmatory factor analysis (CFA) framework.

**Testing Mediators and Moderators of Outcomes**

Both of the following are introductory.

**Additional Statistical Issues**

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.

Reviews SAS options circa 2013 for 3-level logistic models. SAS now includes the FASTQUAD option in PROC GLIMMIX, which allows fitting models with larger numbers of quadrature points.

A summary of the types of inputs required to conduct statistical power analyses for common research designs.

A no-math introduction to multiple imputation.