Category: Principles of Longitudinal Analysis

In 2013, the Data Center collaborated on a project examining patterns of healthcare needs and Medicaid expenditures for children in foster care in New York State. Read more about the findings and their implications for the state's transition from a fee-for-service health care system to managed care. ... Read more >

Applications are now being accepted for the Data Center's next Advanced Analytics course, to be held in Chicago from November 17-21, 2014. Learn more about the program and download application materials.... Read more >

Today, public child welfare agencies are taking stock of their capacity for Continuous Quality Improvement (CQI) and considering the investments they will make in order to build that capacity. How these CQI systems develop will vary from agency to agency depending on administrative structure, staffing patterns, available resources, and a host of other factors. They will all, however, be responsible for facilitating the same basic CQI process—a cycle of problem solving activities that requires the deliberate use of evidence. Given that shared responsibility, the child welfare field will benefit from a common vocabulary for describing what CQI is, the core principles on which CQI rests, and the critical role that evidence plays throughout the CQI process. In keeping with a century-long tradition of CQI that has guided improvement efforts in other fields, we put forth a common language for child welfare CQI in a new publication, Principles, Language, and Shared… Read more >

In this session, Data Center Director Fred Wulczyn provided a high-level overview of the proposed CFSR 3 measures, discussed the methods for setting national standards, baselines, and targets, and highlighted issues for states to consider as they prepare comments to the Children’s Bureau. Read a recap, view the webinar, and download the slides.... Read more >

One important thing to bear in mind about entry cohort analyses is that when we follow a cohort of children forward, it takes time for each member’s outcomes to unfold. For example, say we want to know the length of stay of children who entered foster care in 2011. We can calculate that figure for the 2011 entrants who have already exited care, but there will be other 2011 entrants for whom we can’t calculate that figure because as of the censor date–the date as of which the archive was most recently updated–they were still in foster care. These children could have exited the day after the censor date or they may not exit for another two years; we just don’t know yet. When outcomes for children cannot be determined because they were still in care as of the censor date, we say that their spells (and thus, their data)… Read more >

The best data analyses start with well-formed analytic questions. Therefore, before you set out to study your system’s administrative data—whether you are doing so with the Data Center’s web tool, your state’s complete longitudinal file, or any other dataset—it is important to spend time at the front end making sure that you’ve identified exactly what it is you want to know. What is your question? Which group of children will you need to examine in order to answer it? And what type of data will get you the answer that you are looking for? Longitudinal analyses of foster care data center around two main components—the likelihood of events and the speed with which they happen. In other words, good longitudinal inquiries are always variations on the same two-pronged theme: What happens to children who enter foster care (the likelihood of events), and how long does it take for children to… Read more >

In recent years, the child welfare community has focused attention on the role of longitudinal data analysis. But what are longitudinal data, exactly? Why is the FCDA built on a longitudinal model? And why is longitudinal data analysis the most accurate way to evaluate the experience of children moving through foster care? At its core, the driving concept behind longitudinal analysis is a simple one: Longitudinal analysis examines change in particular individuals or entities over time. In a child welfare context, this means tracking the experiences of children as they move through the foster care system and interpreting the patterns that are found. Longitudinal analysis provides a way to talk about what happens to children in foster care (e.g., How long do children stay in care? How likely is it that they will re-enter after they exit?) and a way to talk about the extent to which foster care systems… Read more >

The FCDA contains case record information on over 2 million children who have spent time in foster care. It includes dates of events, placement types, demographic data, county characteristics—just a huge amount of information. Because all states manage their electronic data differently, when we at the Data Center receive data from a member state, we have to organize that material systematically so that information from all member states can be analyzed in the same way, according to the same rules. Therefore, to make maximum use of the FCDA web tool, you’ll need to get familiar with the way in which the FCDA organizes data. Here at the Data Center, we call this “thinking inside the box”—the FCDA has a certain structure, and once you’re inside it, you can use the elements of that structure to conduct powerful analyses. The structural components of the FCDA are explained in detail in the… Read more >