Analyzing Multilevel Models with R

Social work has a longstanding interest in understanding human behavior in general, and client outcomes in particular, as a function of the person, environment, and interaction between person and environment. However, social work researchers and those in related areas have struggled with analyzing data from studies in which individuals are nested within environments in a multilevel hierarchical structure (e.g., children nested within families, families nested within neighborhoods). Fortunately, 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 environmental-level variables and the interaction between variables measured at these different levels.

The purpose of this workshop is twofold. First, we will provide an introduction to MLMs in which individuals are nested within environments (e.g., foster children nested within residential care settings). In doing this we will discuss the rationale for 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). We will also provide an overview of diverse applications of MLMs (e.g., analysis of longitudinal data, use with discrete dependent variables). Finally, we will give participants materials from our MLM course (e.g., bibliography of MLM texts and articles, Power Point presentations, datasets, in- and out-of-class exercises and assignments, MLM websites) for those who want to learn more about MLM.

Second, we will demonstrate the ease with which cross-sectional MLMs can be estimated using R, a free and open-source computer language for performing statistical analyses and producing high-quality graphs. R provides a number of 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 through the use of 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. Thus, this workshop will provide the foundation necessary for attendees to get started with R through the introduction of MLM functions that have relevance to outcomes of interest in social work.