{"id":31964,"date":"2022-02-10T17:24:03","date_gmt":"2022-02-10T17:24:03","guid":{"rendered":"https:\/\/caseyfamilyv2.wpengine.com\/longitudinal-data\/"},"modified":"2026-05-13T19:00:52","modified_gmt":"2026-05-13T19:00:52","slug":"datos-longitudinales","status":"publish","type":"post","link":"https:\/\/www.casey.org\/es\/longitudinal-data\/","title":{"rendered":"\u00bfC\u00f3mo pueden los datos longitudinales apoyar el bienestar de los ni\u00f1os y las familias?"},"content":{"rendered":"
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Imagine trying to get to know a child through photographs and a short update once or twice a year, compared to interacting with that same child on a daily basis. The opportunity to interact frequently provides a more clear and complete picture of the child\u2019s safety, development, and well-being. Such is the case with data as well: Collecting data over multiple points in time \u2014 or longitudinally \u2014 allows for a more thorough understanding of the complexities of a child and family\u2019s experience with the child welfare system<\/b><\/span> when compared to data collected at a single point in time that provides only a snapshot of information.\u00a0<\/span><\/p>\n

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Point-in-time data not only is limiting, it often results in an undercount of whatever is being summarized, whether that is the total number of children impacted by the child welfare system each year or the total number of children on a caseworker\u2019s caseload. For example, a caseworker may have eight children on their caseload at a single point in time (i.e., the last day of a month) but may have conducted investigations involving 20 children over the course of the full month.<\/p>\n

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Tracking data longitudinally, however, ensures that every incidence, outcome, service, family, and child is counted, making it easier to identify causal relationships and answer more impactful evaluation questions. For example, data may show that a particular intervention is linked to better reunification outcomes. Longitudinal data can be used to answer questions such as, \u201cWhat happened right before reunification?\u201d or \u201cWhich programs are most effective at preventing children from re-entering out-of-home care?\u201d<\/p>\n

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Analyzing data longitudinally does not necessarily require cutting-edge technology or teams of data scientists. To the contrary, a basic list of facts with associated dates and identifiers can be linked together to address important research and evaluation questions simply and succinctly. Federal data collection systems are making progress toward collecting, comparing, and analyzing data. State, county, and tribal child welfare systems \u2014 no matter the size \u2014 also have a capacity to use longitudinal data.\u00a0<\/span><\/p>\n

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Longitudinal data helps child welfare agencies answer questions about what approaches are most effective in keeping children safe and reducing the need for foster care. This brief shares foundational elements<\/b><\/span> and key opportunities<\/b><\/span> for collecting and using longitudinal data based on the experiences of three jurisdictions working to increase their use of data to track performance, inform interventions, and improve child and family well-being.1<\/sup><\/a><\/p>\n<\/div>\n \n \n \n <\/div>\n<\/div>\n\n\n

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Foundational elements<\/h2>\n \n \n \n

Current federal data collection requirements generally are designed for funding compliance, not performance management. As such, they represent the bare minimum of data needed to track child outcomes and are primarily focused to gauge safety and permanency \u2014 and do not tend to measure well-being indicators. Longitudinal data for specific programs should track information from inception through completion, and ideally capture evidence related to implementation, participation, and effectiveness. Longitudinal assessment of the child welfare system as a whole should begin with data about hotline calls and continue by capturing data about investigations, prevention services, out-of-home placement, and post-exit outcomes.\u00a0<\/span><\/p>\n

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User friendly, standardized data collection methods<\/b><\/h3>\n

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Data quality and accuracy are critical. Frontline staff typically are the people required to enter child and family data and \u2014 often saddled with competing demands \u2014 can view the task as an encumbrance. Data systems therefore should be set up as user-friendly as possible, with easy-to-navigate interfaces and without extraneous data fields. When possible, data entry forms and processes should be automated, standardized, and designed to decrease caseworker burden. \u00a0<\/span><\/p>\n

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When the County of Santa Clara (Calif.) Social Services Agency<\/b><\/span> (SSA) first launched its Cultural Brokers<\/span><\/a> program, data were collected in a spreadsheet with more than 20 columns, which made reliable entry difficult. After transitioning to a more user-friendly format, program staff have found that data are more complete. The county plans to further improve its process by implementing an online data entry form, which program staff expect to result in even more reliable data.<\/p>\n

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It is important to remember, however, that pre-defined, entry-form categories sometimes can be too limiting. For example, describing allegations via long-form narrative is almost always more informative and illuminating. San Francisco Human Services Agency<\/b><\/span> (HSA) is developing methods for grouping and categorizing variables (such as geographic location and time between events), which are hard coded into their data warehouse. This will make extracting and analyzing data more efficient and consistent. Ideally, data entry is inherently built into established business processes and is not a distinct task.\u00a0<\/span><\/p>\n

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Safeguarding updates and changes<\/b><\/h3>\n

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Data can and will change over time, and databases should be set up to accommodate these changes. For example, a child\u2019s race, ethnicity, or Indian Child Welfare Act status may have been reported incorrectly initially and needs to be corrected. Databases should be set up so that historical data are not overwritten when new information is entered.<\/p>\n

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Use unique identifiers for children and adults<\/b><\/h3>\n

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Creating unique child-specific identification numbers that are consistent across programs and across time is key to effectively leveraging longitudinal data. Unique identifiers also should be used for caregivers, providers, and reporters. It may be helpful to review the circumstances under which a person may be assigned multiple identifiers and the process by which duplicates and conflicts are resolved. This can be a particular issue at intake, such as when a child may receive a new identifier but is later identified as a child already in the database. Some systems assign a new identifier each time a child re-enters care, which is not a recommended practice because it inhibits a full understanding of a child\u2019s experience, resulting in a missed opportunity to understand what improvements can be made to improve outcomes in the future.\u00a0<\/span><\/p>\n

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Philadelphia Department of Human Services<\/b><\/span> (DHS) designed a study using longitudinal data to better understand why out-of-home care entry rates consistently were so high, particularly among children of color, and to inform interventions to reduce those rates. The dataset, which includes both child- and community-level data, allowed staff to track what happens to each case in terms of disparities in system involvement over time. Philadelphia DHS found that poverty was ubiquitous among families reported to its hotline and within neighborhoods where the majority of residents were families of color. Of all reports to the DHS hotline, nearly four in five were reports about neglect (which often is associated with poverty-related stressors such as housing, food insecurity, and childcare needs), and 93% of children reported to the hotline did not require a formal DHS safety service. These findings suggested that cross-system interventions outside of DHS are needed that are better equipped to address poverty, concrete resources, and other well-being concerns. When DHS was creating its longitudinal dataset, matching and joining cases across different prevention and safety services was complex and time-consuming. To avoid this issue in future analyses, the agency is working to develop a unique child identification number across the social service continuum.<\/p>\n

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Disaggregate data to monitor and address disparities<\/b><\/h3>\n

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America\u2019s history of institutionalized racism and classism has led to disparities in society that manifest themselves at all key decision points in the child welfare system. Given the entrenched nature of these biases, achieving racial equity<\/span><\/a> and ensuring anti-discriminatory practice within the child welfare system will require a variety of strategies. As a first step, child welfare systems must disaggregate data by key attributes of the child and family, including age, race, ethnicity, Indian Child Welfare Act status, tribal enrollment, gender identity, LGBTQ+<\/span><\/a>, and socioeconomic status\/income.\u00a0 <\/span>This will allow systems to monitor and address disparities as a matter of course in all data evaluations, informing the development and implementation of strategies<\/span><\/a> to achieve equity.\u00a0<\/span><\/p>\n

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Develop a common understanding of data and evaluation<\/b><\/h3>\n

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Program staff need to understand the value that longitudinal data provide, and data analysts need to understand that programs and families are more nuanced than datasets typically can capture. When both program staff and data staff understand each other\u2019s perspective, they can partner more effectively to create robust systems and protocols for data collection and analysis.<\/p>\n<\/div>\n \n \n \n <\/div>\n<\/div>\n\n\n

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It is very valuable to have a partnership where people who are managing the program understand the importance of the data. Often, it\u2019s about trying to get program staff to look at the data. Our team has gone a step further, thinking together with us about what data we need, what the program manager needs, and what do I need as an evaluator.<\/p>\n<\/blockquote>\n

— Idauli Tamarin, Senior Research and Evaluation Specialist, County of Santa Clara (Calif.) Social Services Agency<\/cite><\/figcaption>\n <\/div>\n\n <\/div>\n<\/figure>\n\n\n
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Key opportunities<\/h2>\n \n \n \n

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Connect data across systems \u2014 with appropriate safeguards<\/b><\/h3>\n

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Integration with external systems \u2014 such as vital records, healthcare, housing, public benefits, and education \u2014 can provide valuable insight and data relevant to child welfare. Concerns about confidentiality and surveillance are valid anytime data is collected, particularly when linking to datasets and systems outside of child welfare. Marginalized families experience increased surveillance<\/span><\/a> and therefore are more likely to be subject to intrusive investigations that\u00a0 <\/span>result in fear, trauma, and distancing from service providers. Child welfare agencies seeking to connect families to voluntary prevention services (such as those provided through the Family First Prevention Services Act<\/span><\/a>) will need to reassure them that receiving those services does not put them at risk of losing their children.\u00a0<\/span><\/p>\n

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As connections between datasets are created, care should be taken to securely store the data, limit the number of fields collected, and limit the number of users who can access the data. Data should be used to support children and families and, whenever possible, families should be given access to the data collected about them and be treated as partners throughout the process in deciding what data to collect, how to collect it, and how to interpret the data.<\/p>\n

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Dedicate IT staff to create longitudinal datasets<\/b><\/h3>\n

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With help from dedicated IT staff, the San Francisco HSA<\/b><\/span> is creating a data warehouse that spans numerous social services programs, including: child welfare; the Office of Early Care and Education; the Department of Disability and Aging Services; and public benefit programs such as Medi-Cal<\/span> <\/a>(California\u2019s Medicaid health care program) and CalWORKs<\/span><\/a> (California\u2019s public assistance program for eligible families). An HSA program support analyst oversees the project and serves as a \u201ctranslator\u201d between the IT staff and program staff. The goal is to set up the data warehouse in such a way that programmatic questions can be addressed. Integrating data from across social services programs will permit questions to be addressed that can result in better service delivery. For example:<\/p>\n