measures of health being assessed. Health-focused data collection providing a myriad of health measures should consider collecting information on all of the important components of SES, since it is possible to do so without devoting excessive amounts of interviewing time to it.

A second level of SES challenges is to appreciate and account for their dynamic nature. Family incomes are often highly volatile from one year to the next (Duncan, 1988), and education levels can increase well into adulthood (Magnuson and McGroder, 2002), while occupation and wealth typically change more slowly. Designers of health-focused longitudinal surveys should realize that it may be necessary to include SES-related questions in a number of interviewing waves.

It is more difficult to draw general conclusions about the challenges of measuring the diverse set of other family environmental influences we have considered. With regard to family structure, an important challenge is to gather needed detail on the relationships among the individuals living in the same household. Whether an adult male is the biological father or stepfather to the children is an important distinction for assessing risks to child well-being. By the same token, whether two unmarried adults of the opposite or even same sex are functioning as partners or merely roommates also appears consequential for child well-being. And yet many surveys fail to gather household composition data in a way that captures these distinctions.

Data collection efforts that aspire to understand family environmental influences on children’s health should consider including measures of parenting and the home learning and physical environments but, here again, the measures used should match the conceptual orientation of linkages between family process and health outcomes.

Addressing Gaps in Measuring Family Influences

Clinical records pose a special challenge in regard to the assessment of SES influences. Clinical facilities are reluctant to collect information on aspects of SES from patients, believing that this may be interpreted as an attempt to discriminate on the basis of such aspects in providing health care. However, most clinical facilities collect residential address data, if for no other purpose than bill collecting. When geocoded and matched to characteristics of area of residence related to social class, address information can be very useful for population or subpopulation analysis of the relationship between receipt of health services and socioeconomic characteristics (Krieger, 2000).

Methodological advances now enable researchers to estimate multilevel models of the various ecological levels of influence on health. These techniques were developed for use in the social sciences (Blalock, 1984; Di Priete and Forristal, 1994) and have increasingly been used to study the interacting influences on

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