by providing the Stata mixed-effects modeling code and in developing the support website for the book. For all of this, I get to thank her for her professional skills and for the joy she brings to my life.
Chapter 1. The Need for Multilevel Modeling
Background and Rationale
When one considers almost any phenomenon of interest to social and health scientists, it is hard to overestimate the importance of context. For example, we know that the likelihood of developing depression is influenced by social and environmental stressors. The psychoactive effects of drugs can vary based on the social frame of the user. Early childhood development is strongly influenced by a whole host of environmental conditions: diet, amount of stimulation in the environment, presence of environmental pollutants, quality of relationship with mother, and so on. Physical activity is shaped by neighborhood environment; people who live in neighborhoods with sidewalks are much more likely to walk. The probability of teenagers engaging in risky behavior is related to being involved in structured activities with adult involvement. A child’s educational achievement is strongly affected by classroom, school, and school system characteristics.
These examples can be extended to situations beyond where individuals are being influenced by their contexts. The likelihood of couples avoiding divorce is strongly related to certain types of religious and cultural backgrounds. Group decision-making processes can be influenced by organizational climate. Hospital profitability is strongly affected by reimbursement policies set by government and insurance companies.
What all these examples have in common is that characteristics or processes occurring at a higher level of analysis are influencing characteristics or processes at a lower level. Constructs are defined at different levels, and the hypothesized relations between these constructs operate across different levels. Table 1.1 presents an example of the interdependence among levels of analysis, here with an example from the area of tobacco control. Research programs on tobacco control exist at all levels of analysis, from the genetic up to the sociocultural and political (i.e., “from cells to society”). Moreover, although research can occur strictly within any of these levels, much of the most important research will look at the links between the levels. For example, as we learn more about the genetic basis of nicotine dependence, we may be able to tailor specific preventive interventions to particular genotypes.
Table 1.1
These types of multilevel theoretical constructs require specialized analytic tools to properly evaluate. These multilevel tools are the subject of this book.
Despite the importance of context, throughout much of the history of the health and social sciences, investigators have tended to use analytic tools that could not handle these types of multilevel data and theories. In earlier years, this was due to the lack of such tools. However, even after the advent of more sophisticated multilevel modeling approaches, practitioners have continued to use more simplistic single-level techniques (Luke, 2005).
Theoretical Reasons for Multilevel Models
The simplest argument, then, for multilevel modeling techniques is this: Because so much of what we study is multilevel in nature, we should use theories and analytic techniques that are also multilevel. If we do not do this, we can run into serious problems, including making incorrect causal claims.
For example, it is very common to collect and analyze health and behavioral data at the aggregate level. Epidemiologic studies, for example, have shown that in countries where fat is a larger component of the diet, the death rate from breast cancer is also higher (Carroll, 1975). It might seem reasonable to then assume that women who eat a lot of fat would be more likely to get breast cancer. However, this interpretation is an example of the ecological fallacy, where relationships observed in groups are assumed to hold for individuals (Freedman, 1999). Recent health studies, in fact, have suggested that the link between fat intake and breast cancer is not very strong at the individual level (Holmes et al., 1999).
This type of problem can also work the other way. It is very common in the behavioral sciences to collect data from individuals and then aggregate the data to gain insight into the groups to which those individuals belong. This can lead to the atomistic fallacy, where inferences about groups are incorrectly drawn from individual-level information (Diez-Roux, 1998). It is possible to be successful assessing ecological characteristics from individual-level data; for example, see Moos’s (1996) work on social climates. However, as Shinn and Rapkin (2000) have argued, this approach is fraught with danger and a much more valid approach is to assess group and ecological characteristics using group-level measures and analytic tools.
It is useful here to consider the sociological distinction between properties of collectives and members (Lazarsfeld & Menzel, 1961). Members belong to collectives, but various properties (variables) of both collectives and their members may be measured and analyzed at the same time. Lazarsfeld and Menzel identify analytical, structural, and global properties of collectives. Analytical properties are obtained by aggregating information from the individual members of the collective (e.g., proportion of Hispanics in a city). Structural properties are based on the relational characteristics of collective members (e.g., friendship density in a classroom). Finally, global properties are characteristics of the collective itself that are not based on the properties of the individual members. Presence of an antismoking policy in a school would be a global property of the school, for example.
Using this framework, it becomes clear that fallacies are a problem of inference, not of measurement. That is, it is perfectly admissible to characterize a higher level collective using information obtained from lower level members. The types of fallacies described above come about when relationships discovered at one particular level are inappropriately assumed to occur in the same fashion at some other (higher or lower) level.
There is broad interest in social and physical context across the social sciences, and this can be seen most clearly in the ecological richness of various social science theories and conceptual frameworks. In sociology and criminology, the theory of neighborhood disorder proposes that various physical and social indicators of environmental disorder are related to a variety of individual and relational outcomes, including crime, violence, policing styles, and depression (Sampson, Morenoff, & Gannon-Rowley, 2002). Political scientists have consistently viewed political participation (e.g., voting, petitioning, contacting elected officials) as being driven by a number of contextual processes and factors, including organizational culture, media exposure, and peer influence (Uhlaner, 2015). Policy science generally views policy development and implementation as a process embedded in local, regional, and national political and geographic contexts. For example, the political stream of Kingdon’s influential multiple streams framework is defined by referring to predominately contextual structures and processes including national mood, legislative body makeup, and interest group activities (Béland & Howlett, 2016). An alternative model of the policy process, the advocacy coalition framework, more explicitly positions policy activity as an output from the policy subsystem, which is in turn made up of three types of collectives: (1) coalitions, (2) governmental bodies, and (3) institutions (Sabatier & Weible, 2007).
Finally, implementation science (sometimes called dissemination and implementation science) is a relatively new social science that focuses on how evidence-based programs, practices, and policies can be better disseminated, implemented, and maintained to benefit population health. Early frameworks used in implementation science view implementation processes and outcomes as situated within social and organizational contexts. For example, Rogers’s diffusion of innovations theory has been used extensively
