Posts Tagged by data sharing
|June 11, 2015||Posted by M. P. under Children and Family, Health, Juvenile Delinquency, Policy, Research, Youth Development|
According to 2011 data, 12.5 percent of children under the age of 18 are abused or neglected in the United States each year. A Facts on Youth brief from the Center for Health and Justice at TASC cites a study published in JAMA Pediatrics that found confirmed maltreatment for 1 in 8 youth, with nearly 6 percent of cases (just less than half of confirmed reports) involving children ages 5 and under. The brief also notes that studies of child abuse and maltreatment that rely on self-reports rather than substantiated reports indicate a rate of up to 40 percent.
The Child Trends brief Preventing Violence: Understanding and addressing determinants of youth violence in the United States reviewed relevant research on interventions and policy approaches to reducing youth violence, with an emphasis on individual, family and school/community factors. This review identified several predictors of violence, including domestic violence, dysfunctional parenting, gun availability, low self-control, and lack of connectedness to school. Child maltreatment, however, was a strong predictor of nearly every type of violence. The prevention of child abuse and provision of interventions to address the impact of such trauma appear to be critical actions in reducing the potential of future violence. That said, although child maltreatment is a risk factor for criminal behavior, the longer term negative effects of that experience may be offset or amplified by other life events. Completing high school/getting a GED and getting married were two factors identified by a research team at the Social Development Research Group at the University of Washington as having a positive impact on a person’s life, thus reducing the power of the relationship between the maltreatment and future high risk behaviors. A history of maltreatment combined with additional risk factors, such as poverty, increases the likelihood of criminal behavior.
As safety and health are essential factors in optimal child development, and may affect a multitude of life outcomes, new strategies have emerged to better identify and “triage” high-risk situations. States are turning to the big data playbook to assist in investigations of abuse and maltreatment, using predictive analysis to help prioritize reports and better provide preventive services. Information such as family history, school reports and other administrative data, plus case officer knowledge, gives child welfare decision-makers more (if not necessarily better) data to guide the use of resources for the protection of children. Along with Connecticut, Florida, and Los Angeles County, Allegheny County here in western Pennsylvania is utilizing predictive analytics in an effort to reduce child maltreatment, abuse, and fatalities. For more information on how predictive analysis is being used in child welfare, see Who will Seize the Child Abuse Prediction Market by Darian Woods and Checklists, Big Data and the Virtues of Human Judgement by Holden Slattery, both in The Chronicle of Social Change.
|June 19, 2013||Posted by M. P. under Federal Government, News, Policy, Research||
An April 2013 briefing to Congress on surveys and statistics focused on the problematic trend of declining response rates for federal surveys, including the American Community Survey and the National Survey of Child Health. The briefing, Policy Makers & Businesses Need Reliable Information and Data: The Impact of Falling Response Rates to Social Surveys and What Can Be Done, organized by The American Academy of Political and Social Science (AAPSS), outlined the risks to research and the impact on policy-making if response rates to surveys on health, employment and household continue to subside. The largest risk is that of biased results. Other issues:
- Nonresponse rates currently range from 30 to more than 60 percent. This is an all-time high.
- Over 60 percent of nonresponses were refusals, while approximately another 1/4 were due to the inability to contact the intended recipient.
- Young single-person households, minorities, renters and the poor were less likely to respond.
- One-time surveys have higher nonresponse rates than more complex longitudinal studies that follow the same group of respondents over period of time.
While incentives (such as a gift card or a small amount of money) for completing and returning a survey would boost response rates, it would also increase costs – a risky proposition in an atmosphere of austerity. The authors of a related paper, Where Do We Go from Here? Nonresponse and Social Measurement, published in the January 2013 volume of AAPSS’s The Annals, suggest that a solution to this growing problem is a strategic outreach plan to inform both politicians and the public of the purpose of national surveys. Clear explanation of what the data is used for, as well as the regulations and protocols in place to protect it from being presented other than in aggregate form could have a favorable impact on perception. Unfortunately for these and other large-scale surveys, the recent news of metadata collected absent suspicion may have even the most tech-savvy survey-loving among us rethinking issues of privacy, transparency and information storage and retrieval.
Perhaps in the future these surveys that, by the way, inform funding decisions on infrastructure, education, and transportation to name a few, will be deemed too intrusive and/or obsolete and left behind. Funding and other governing decisions can then be made based on variables extracted from all that we have uploaded onto the digital data heap. So, will big data replace big surveys? Will traditional statistical methods be successful in tracking, analyzing and accurately reporting big data to inform policies at the federal, state and local level?
|November 13, 2012||Posted by M. P. under Education, Policy, Research||
Got the comparative analysis blues? Need more or better data? Well, difficult-to-find data on pre-K programs just got easier to access thanks to a combined effort from the Early Education Initiative and the Federal Education Budget Project (FEBP) of the New America Foundation. An expansion of the FEBP database added 2007 through 2011 enrollment and funding information on public early education programs at both the state and local levels – including Head Start and federally mandated special education services to young children.
Alex Holt gives an overview of this valuable resource at the Foundation’s website, and discusses the serious deficit in reliable pre-K data reporting in the brief (with Lisa Guernsey) Counting Kids and Tracking Funds: Falling Short at the Local Level.
|September 21, 2012||Posted by M. P. under Behavorial Health, Elderly, Health, Research||
An international study out of Australia found that happiness peaks (on average) during a person’s 60’s, then begins to decline, before dropping off considerably. Earlier this year, Dr Tony Beatton of Queensland University of Technology and Professor Paul Frijters of The University of Queensland reported findings from their analysis of data from approximately 60,000 people from Australia, Britain and Germany. Highlights include:
- Persons entering middle/retirement age (55 to 75 years) reported the highest levels of happiness
- The data from Germany showed a decrease in happiness as persons entered adulthood, then a peak at age 65 – a pattern different from the other data
- Happiness dropped significantly after age 75 across cases
This research adds to the discussion of the ‘U bend of happiness” (see a great write-up on it in The Economist), the concept that happiness ultimately culminates in late middle age; but Beatton and Frijters also address the drop in happiness after age 75, suggesting that it is related to the onset or worsening of health problems. This aligns with prior research on the relationship between the presentation of depression symptoms and medical issues/illnesses among the elderly population.
Study Citation: Frijters, Paul & Beatton, Tony, 2012. “The mystery of the U-shaped relationship between happiness and age,” Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 525-542