Longitudinal Multilevel Modeling Using R

Social work has a longstanding interest in understanding human behavior and client outcomes over time as a function of an intervention the client receives. However, social work researchers and those in related areas have struggled with analyzing data from studies in which individuals' observations are nested within various time points; that is, in a multilevel hierarchical structure (e.g., the effectiveness of a substance abuse program on alcohol consumption over time). In recent years, statistical models and computer software have been developed to better analyze these types of hierarchically structured data. Such models are typically referred to as multilevel models (MLMs), hierarchical linear models, random-effects models, random-coefficient models, or mixed effects models. These models allow investigators to test meaningful hypotheses regarding relationships between individual- and time-level variables and the interaction between variables measured at these different levels. The purpose of this workshop is to expand on the authors' well-received workshop on cross-sectional MLM, which was presented at SSWR in 2017. This presentation has two main objectives.

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.