Most administrative data in the child welfare domain is composed of service event types and dates that can be configured to construct a variety of outcome indicators. The two most common configurations are descriptions of caseloads at a point in time (or several points in time) and longitudinal data analyses of individual service careers over time. In addition to program participation data, demographic data for the children and families under study (e.g., birthdate, ethnicity, home address or location) are also common elements found in these databases. When combined with these demographic data, caseload and longitudinal indicators can provide a source for estimating system performance and client status.
Caseload data provide a snapshot of welfare and child welfare at a specific point in time. They are usually used for program management purposes and can contribute to the assessment of system impacts by indicating covariation between subpopulations in welfare and child welfare. Broadly, for instance, caseload indicators of how many children of a certain age are leaving welfare and how many children of a certain age are entering foster care can provide some indication of whether the welfare exits might be contributing to increases in foster care. However, given the large size of the welfare caseload and the small numbers of children entering foster care, this relationship could not be adequately understood without individual-level data that linked welfare and child welfare histories.
Recently the ACF's Child Welfare Outcomes and Measures Project developed a set of outcome measures using point-in-time data from the Adoption and Foster Care Analysis and Reporting System (AFCARS) to assess state performance in operating child welfare programs. Outcomes include annual incidence of child maltreatment, types of exits from the child welfare system, timing of exits, and placement stability. Although point-in-time data also can be used to measure case status outcomes such as foster care length of stay; the resulting statistics are biased because they overrepresent children with longer stays in care and are not very sensitive to changes in entries to foster care because the newcomers to care are just a portion of the overall population. Thus, although point-in-time estimates are the easiest and least expensive configuration of administrative data, this inherent bias limits their usefulness until the individual records comprising these caseload data are reconfigured into longitudinal data.
Administrative data typically can be reconfigured into event-level files that record program participation histories. Depending on the scope of available data, these events may be restricted to foster care spells or placements, or may more broadly include child abuse reports, investigations, and services provided in the home. Working with entry cohorts provides the clearest evidence of changes in patterns of care that might be associated with changes in welfare programs because the interpretation of the outcomes does not have to disentangle the contributions of different service programs (e.g., AFDC and TANF). Using data that can be subset into entry cohorts captures the dynamics of both system entries and exits, and therefore provides a more accurate assessment of outcomes than caseload. Although free from the biases of point-in-time data, longitudinal data analyses often are preceded by considerable programming to reconfigure data into an even-level, longitudinal format, and to link welfare and child welfare files.
The Multistate Foster Care Data Archive provides an illustration of the complexity as well as promise that longitudinal data offer researchers trying to understand child welfare careers (and how they might be influenced by TANF). The archive is an initiative by the Children's Bureau of the U.S. Department of Health and Human Services that is designed to foster increased collaboration among states regarding administrative data collection in the child welfare services arena. Administered by the Chapin Hall Center for Children at the University of Chicago, the archive currently includes data from child welfare agencies in 11 states. The archive processes state data to make them comparable across state systems. To ensure data comparability, the project focuses on "a limited set of characteristics and events that have clear meaning in all jurisdictions" (Wulczyn et al., 1999:1). The core of the archive is two databases--one consisting of child records, including unique identifiers and demographic information, and a second event-level field that stores information on child welfare events of interest. This structure allows researchers to use the data in a longitudinal format to capture children's spells in child welfare as well as other experiences. Additionally, data can be configured to provide traditional point-in-time estimates of caseload flow over time.
A sufficiently comprehensive set of outcome indicators is shown in Box 10-1 (note that some indicators have a clearer theoretical relationship to welfare reform than others).
Depending on the purpose of the analysis, indicators can be derived from either point-in-time or longitudinal data. Indicators can be expressed as rates based on the number of people at risk in a state, county, and even zip code of the underlying populations, such as the foster care incidence (entry) rates and prevalence (caseload) rates by age and ethnicity. Benchmarks can be set for both caseload and longitudinal indicators, such as prevalence rate over time, or number and proportion of children who experience reabuse within a year of being reunified from foster care.
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