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CQIdeas Blog

In the previous Recipe, you learned how to calculate the number of children entering care each year who are at risk of aging out within five years. This Recipe will show you how to calculate the proportion of children in this group that actually do age out. This Recipe will take you about 20 minutes to complete. In addition to the web tool, you will need a spreadsheet program such as Microsoft Excel.   Question: Of children who are at risk of aging out of foster care within 5 years of entry, how many actually do age out? [Note: This Recipe presumes that children will age out of foster care upon turning 18. The steps can be altered for jurisdictions that extend foster care benefits to children through age 19, 20, or 21.] On the All spells page, scroll down to the Spell overview section. Under Spell started age, enter… Read more >

Among other changes, the Fostering Connections law introduced new requirements for casework with older foster youth. It also established new reporting requirements regarding youth aging out of care. In light of these changes, monitoring the number of children who are at risk of aging out and providing them with the services they need are pressing issues for foster care systems today. In this Recipe I explain how to use the web tool to determine the number of youth entering care in your jurisdiction who are at risk of aging out within five years. This Recipe will take you about 10 minutes to complete. In addition to the web tool, it requires a spreadsheet program such as Microsoft Excel.   Question: In recent years, how many children entering care in my jurisdiction were at risk of aging out within five years of entry? Has that figure changed over time? [Note: This… Read more >

In the previous Recipe, we found that children entering foster care for the first time stayed in care longer than children who re-entered care after a previous spell. Why might that be? This Recipe shows you how to explore demographic and case-related differences between the comparison groups in your length of stay analysis to help you get closer to the answer.   Question: In my length of stay analysis, I found that first-time entrants stayed in care longer than re-entrants. Are there differences between these two groups that might account for that? Follow the steps for the length of stay analysis in the previous Recipe. In the upper right hand corner of the output page, click the button labeled Go to Demographic Comparison. On the next screen you’ll see a summary of your comparison groups. Under Report Options, select the variables that you’re interested in. To get a sense of… Read more >

As you know, different populations of children don’t always experience foster care in the same way. That’s why it’s so important to stratify your population—to take a measure of the whole group, and then break the results down by those child- and case-related characteristics that might make a difference in the outcome. One of those characteristics is admission type—whether the spell in question is the child’s first spell in foster care, or whether that spell is a re-entry after a prior discharge. Springboarding off my earlier post on length of stay, this Recipe will show you how to break length of stay results down by admission type in order to see whether children entering care for the first time have similar lengths of stay as children returning to care.   Question: Do children entering foster care for the first time stay in care as long as children re-entering care? On… Read more >

One of the main indicators of permanency for children in foster care is length of stay—the amount of time children spend in care. As child welfare agencies strive to keep children in foster care as briefly as possible, tracking length of stay trends is essential. In this first Recipe, I use the web tool to conduct a simple analysis of length of stay. In this Recipe, I walk through a simple length of stay analysis. Note that this Recipe uses the FCDA’s Multistate site—the part of the site that allows you to compare counties within your state and compare your state to others. Working with your state- or agency-specific site will be discussed in later Recipes. For now, know that these different sites share the same basic functions; you will be able to apply what you learn here to the other interfaces.   Question: How long do children in my… 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 >

January 7, 2012
Key FCDA concepts

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 >