Using the 1957–1993 data from the Wisconsin Longitudinal Study, we explore reciprocal associations between socioeconomic status (SES) and body mass in this 1939 birth cohort of non-Hispanic white men and women. We integrate the fundamental cause theory, the gender relations theory, and the life-course perspective to analyze gender differences in (a) the ways that early socioeconomic disadvantage launches bidirectional associations of body mass and SES, and (b) the extent to which these mutually-reinforcing effects generate socioeconomic disparities in midlife body mass. Using structural equation modeling, we find that socioeconomic disadvantage at age 18 is related to higher body mass index and a greater risk of obesity at age 54, and that this relationship is significantly stronger for women than men. Moreover, women are more adversely affected by two mechanisms underlying the focal association: the obesogenic effect of socioeconomic disadvantage and the SES-impeding effect of obesity. These patterns were also replicated in propensity score matching models. Gender and SES act synergistically over the life course to shape reciprocal chains of two disadvantaged statuses: heavier body mass and lower SES.

Using the 1957-2004 data from the Wisconsin Longitudinal Study, we apply structural equation modeling to examine gender-specific effects of family socioeconomic status (SES) at age 18 on body weight at age 65. We further explore SES and health behaviors over the life course as mechanisms linking family background and later-life body weight. We find that early-life socioeconomic disadvantage is related to higher body weight at age 65 and a steeper weight increase between midlife and late life. These adverse effects are stronger among women than men. Significant mediators of the effect of parents' SES include adolescent body mass (especially among women) as well as exercise and SES in midlife. Yet, consistent with the critical period mechanism, the effect of early-life SES on late-life body weight persists net of all mediating variables. This study expands current understanding of life-course mechanisms that contribute to obesity and increase biological vulnerability to social disadvantage.

The present paper gives an overview of some of the modular day courses offered and, in some cases, developed by tutors at a former hospital school providing education to adults with severe learning disabilities. The aims of the courses were cognitive and personal development, as well as aesthetic and spiritual enrichment. Art history was offered as an activity within the Open University 'Learning Disability À Working as Equal People' course, and the students' responses to paintings were deep and remarkable. In a play-writing course, tutors acted as 'writing hands' and the resultant plays were given a rehearsed reading. Some poetry was also produced.

Recent years have seen a rapid growth in interest in the addition of a spatial perspective, especially in the social and health sciences, and in part this growth has been driven by the ready availability of georeferenced or geospatial data, and the tools to analyze them: geographic information science (GIS), spatial analysis, and spatial statistics. Indeed, research on race/ethnic segregation and other forms of social stratification as well as research on human health and behavior problems, such as obesity, mental health, risk-taking behaviors, and crime, depend on the collection and analysis of individual- and contextual-level (geographic area) data across a wide range of spatial and temporal scales. Given all of these considerations, researchers are continuously developing new ways to harness and analyze geo-referenced data. Indeed, a prerequisite for spatial analysis is the availability of information on locations (i.e., places) and the attributes of those locations (e.g., poverty rates, educational attainment, religious participation, or disease prevalence). This Oxford Bibliographies article has two main parts. First, following a general overview of spatial concepts and spatial thinking in sociology, we introduce the field of spatial analysis focusing on easily available textbooks (introductory, handbooks, and advanced), journals, data, and online instructional resources. The second half of this article provides an explicit focus on spatial approaches within specific areas of sociological inquiry, including crime, demography, education, health, inequality, and religion. This section is not meant to be exhaustive but rather to indicate how some concepts, measures, data, and methods have been used by sociologists, criminologists, and demographers during their research. Throughout all sections we have attempted to introduce classic articles as well as contemporary studies. Spatial analysis is a general term to describe an array of statistical techniques that utilize locational information to better understand the pattern of observed attribute values and the processes that generated the observed pattern. The best-known early example of spatial analysis is John Snow’s 1854 cholera map of London, but the origins of spatial analysis can be traced back to France during the 1820s and 1830s and the period of morale statistique, specifically the work of Guerry, d’Angeville, Duplin, and Quetelet. The foundation for current spatial statistical analysis practice is built on methodological development in both statistics and ecology during the 1950s and quantitative geography during the 1960s and 1970s and it is a field that has been greatly enhanced by improvements in computer and information technologies relevant to the collection, and visualization and analysis of geographic or geospatial data. In the early 21st century, four main methodological approaches to spatial analysis can be identified in the literature: exploratory spatial data analysis (ESDA), spatial statistics, spatial econometrics, and geostatistics. The diversity of spatial-analytical methods available to researchers is wide and growing, which is also a function of the different types of analytical units and data types used in formal spatial analysis—specifically, point data (e.g., crime events, disease cases), line data (e.g., networks, routes), spatial continuous or field data (e.g., accessibility surfaces), and area or lattice data (e.g., unemployment and mortality rates). Applications of geospatial data and/or spatial analysis are increasingly found in sociological research, especially in studies of spatial inequality, residential segregation, demography, education, religion, neighborhoods and health, and criminology.