First, we will briefly recap on cross-sectional MLM and then introduce longitudinal MLMs in which individuals' observations are nested within different time points (e.g., an individual's weekly scores on a depression scale over the course of three months). In doing this we will discuss the rationale for longitudinal MLMs, the circumstances under which they are useful, and the types of research questions they can be used to answer. We will use a publically available data set to illustrate basic MLM concepts (e.g., intraclass correlation, fixed and random effects, cross-level interactions, assumptions), and we will show how much of what participants know about linear regression is applicable (e.g., intercepts, slopes). After the workshop, we will provide participants with materials that can assist them in performing longitudinal MLM with their own data (e.g., bibliography of MLM texts and articles, datasets, and other MLM resources) for those who want to learn more about longitudinal MLM.

Second, we will demonstrate the ease with which longitudinal MLMs can be estimated using R, a free and open-source computer language for performing statistical analyses and producing high-quality graphs. R provides several advantages that can benefit social work researchers, such as its packages and their adaptable resources, the flexibility it provides users when executing statistical functions, its ability to simply integrate statistical results in publishable documents (e.g. LaTex and Word documents), and its easy-to-use graphical interface. This presentation will include a brief introduction to downloading R and its available components, but will then move on to demonstrate MLM functions available in R using the “lme4” package. Scripts, datasets and additional resources referred to in the workshop will be provided to participants. Social work researchers can easily access R and the “lme4” package with no cost and use it to improve their research efforts. This workshop will provide the foundation necessary for attendees to get started with R through the introduction of longitudinal MLM functions that have relevance to outcomes of interest in social work.