Barbara Entwisle

Contextual analysis through the multilevel linear model.

A general linear multilevel model and its estimation are described and illustrated empirically. The specification of a multilevel linear model within the covariance component framework was rendered. This model is appropriate for a wide range of applications in contextual analysis because it allows for macro as well as micro errors. Then an estimation procedure the restricted maximum likelihood/Bayes (REML/Bayes) was proposed and described for this multilevel model. Finally an extended empirical example was presented with the primary purpose of illustrating the use of the statistical methodology in conjunction with a meaningful substantive problem not to carry out a critical test of the underlying substantive theory and not to demonstrate the general superiority of the proposed estimation procedure. The example suggests that the REML/Bayes estimation procedure may be appropriate inasmuch as the results extracted with REML/Bayes are more consistent with theoretical anticipations than those extracted with ordinary least squares or estimated generalized least squares. The methodology presented by no means exhausts the subject of multilevel estimation. There is a need for estimation procedures to handle discrete micro response variables systems of micro-structural equations and other generalizations. Work is currently proceeding on some of these extensions of the multilevel linear model as the goal of sound estimation procedures for generalized multilevel models is worthwhile and by no means esoteric. It may be hard for researchers to find contextual effects unless they use efficient and appropriate estimation techniques.

Residential segregation and interracial friendship in schools

This article uses social network and spatial data from the National Longitudinal Study of Adolescent Health (Add Health) to examine the effect of racial residential segregation on school friendship segregation in the United States. The use of hierarchical models allows the authors to simultaneously estimate the effects of race, within‐school residential segregation, and school diversity on friendship choice using the Add Health data. The authors use these results to predict the decline in friendship segregation that would occur if across‐ and within‐school residential segregation were eliminated in U.S. metropolitan areas. The results suggest that about a third of the level of racial friendship segregation in schools is attributable to residential segregation. Most of this effect is the result of residential segregation across schools rather than within them.

Putting People Into Place

Over the past two decades, there has been an explosion of empirical research on neighborhoods and health. However, although the data and approaches owe much to the early contributions of demographers and population scientists, this debt is largely unrecognized. Likewise, challenges posed in the early literature remain largely unanswered. I argue that just as demographers and population scientists were pioneers in the study of neighborhoods and health, they are uniquely poised to lead the field again. Putting people into place means explaining behavior and outcomes in relation to a potentially changing local context. A more dynamic conceptualization is needed that fully incorporates human agency, integrates multiple dimensions of local social and spatial context, develops the necessary longitudinal data, and implements appropriate tools. Diverse approaches with complementary strengths will help surmount the many analytic challenges to studying the dynamics of neighborhoods and health, including agent-based microsimulation models.

Community and contraceptive choice in rural Thailand: a case study of Nang Rong.

This paper blends quantitative with qualitative data in an investigation of community and contraceptive choice in Nang Rong, Thailand. Specifically, it develops an explanation of 1) method dominance within villages, coupled with 2) marked differences between villages in the popularity of particular methods. The quantitative analysis demonstrates the importance of village location and placement of family planning services for patterns of contraceptive choice. The qualitative data provide a complementary perspective, emphasizing the importance of social as well as physical space and giving particular attention to the structure of conversational networks.

Networks and Contexts: Variation in the Structure of Social Ties1

A core axiom of sociology is that social structure affects and is affected by human behavior. The term “social structure” conveys two quite different meanings. One meaning is relational, involving networks of ties between individuals or groups of individuals. A second meaning refers to the contexts containing these individuals. Studies of neighborhood and community effects depend on variability in both types of social structure. Using data from multiple villages in Nang Rong, Thailand, this article documents substantial variability in network structure and shows that network structure covaries with context in meaningful ways, suggesting reciprocal effects of changes in both. Finally, it considers implications of variability in network structure, showing that social cohesion affects the likelihood of finding and interviewing former village residents.

GENDER AND FAMILY BUSINESSES IN RURAL CHINA

The authors investigate the roles played by women and men in the emerging private sector in rural China. Specifically the authors explore gender and the allocation of labor in household-run businesses in the rural areas of eight provinces. Data collected in the China Health and Nutrition Survey (1989) indicate that households with a large pool of female labor are at no advantage in starting and running a small business; rather business involvement depends on the male labor pool especially the presence of older men. Furthermore if a household runs a business men are more likely than women to work in it. Men apparently have led the development and expansion of household business in rural China; while women increasingly specialize in agricultural activities. Possible reasons for these findings are discussed. (authors)

Confidentiality and spatially explicit data: Concerns and challenges

Recent theoretical, methodological, and technological advances in the spatial sciences create an opportunity for social scientists to address questions about the reciprocal relationship between context (spatial organization, environment, etc.) and individual behavior. This emerging research community has yet to adequately address the new threats to the confidentiality of respondent data in spatially explicit social survey or census data files, however. This paper presents four sometimes conflicting principles for the conduct of ethical and high-quality science using such data: protection of confidentiality, the social–spatial linkage, data sharing, and data preservation. The conflict among these four principles is particularly evident in the display of spatially explicit data through maps combined with the sharing of tabular data files. This paper reviews these two research activities and shows how current practices favor one of the principles over the others and do not satisfactorily resolve the conflict among them. Maps are indispensable for the display of results but also reveal information on the location of respondents and sampling clusters that can then be used in combination with shared data files to identify respondents. The current practice of sharing modified or incomplete data sets or using data enclaves is not ideal for either the advancement of science or the protection of confidentiality. Further basic research and open debate are needed to advance both understanding of and solutions to this dilemma.

Villages as contexts for contraceptive behavior in rural Egypt.

This research joins sociological and demographic traditions in a study of villages as contexts for contraceptive behavior in rural Egypt. Using survey data collected in the early 1980s the authors explore the effects of village household and individual characteristics on contraceptive use and expectations about future use. Primary interest centers on the effects of the village variables including the structure of the village economy modernization of agriculture level of school participation and family planning service environment. The analysis demonstrates clearly that contraceptive behavior in rural Egypt varies systematically with these features of the village setting. In addition village effects appear to vary according to characteristics of individual respondents: women respond differently depending on the stage in their reproductive career and their motivation to regulate fertility. (authors)

Multilevel Effects of Socioeconomic Development and Family Planning Programs on Children Ever Born

This article describes an approach to an explanation of fertility that is sensitive to the dependence of the behavior of individuals or couples on social context and sets forth hypotheses about micro and macro determinants of children ever born (CEB). Data from 15 World Fertility Survey countries are used in a multilevel test of these hypotheses. The findings are that per capita GNP and family planning program effort affect not only country-specific average levels of CEB, but also the direction and magnitudes of the within country effects of two micro socioeconomic variables on CEB. These findings, which are largely consistent with the hypotheses, illustrate the utility of a multilevel approach.

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.