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Data Use vs. Misuse: The challenging nature of publicly available data

Data Use vs. Misuse: The challenging nature of publicly available data. (a.k.a., “Numbers Gone Wild”). Emily Putnam-Hornstein, MSW Barbara Needell, MSW, PhD Center for Social Services Research University of California at Berkeley National Child Welfare Data and Technology Conference

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Data Use vs. Misuse: The challenging nature of publicly available data

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  1. Data Use vs. Misuse:The challenging nature of publicly available data (a.k.a., “Numbers Gone Wild”) Emily Putnam-Hornstein, MSW Barbara Needell, MSW, PhD Center for Social Services Research University of California at Berkeley National Child Welfare Data and Technology Conference July 20, 2007 The Performance Indicators Project at CSSR is supported by the California Department of Social Services and the Stuart Foundation.

  2. Overview… • Child Welfare Data in California: History • The Good Stuff: Public Website • The Bad Stuff: (Real) Examples of Data Misuse • Strategies for educating users / discussion

  3. California’s Child Welfare Data • State supervised, county administered child welfare system • 58 Diverse Counties • Longstanding Interagency Agreement • Quarterly Data Reports to State and County officials • Funding from CDSS and Stuart Foundation • Data Publicly Available…

  4. http://cssr.berkeley.edu/ucb_childwelfare/

  5. http://cssr.berkeley.edu/ucb_childwelfare/

  6. http://cssr.berkeley.edu/ucb_childwelfare/

  7. http://cssr.berkeley.edu/ucb_childwelfare/

  8. http://cssr.berkeley.edu/ucb_childwelfare/

  9. http://cssr.berkeley.edu/ucb_childwelfare/

  10. http://cssr.berkeley.edu/ucb_childwelfare/

  11. http://cssr.berkeley.edu/ucb_childwelfare/

  12. http://cssr.berkeley.edu/ucb_childwelfare/

  13. Public Data: Putting it all out there • PROS: • Greater performance accountability • Community awareness and involvement, encourages public-private partnerships • Ability to track improvement over time, identify areas where programmatic adjustments are needed • County/County and County/State collaboration • Transparency • Dialogue

  14. Public Data: Putting it all out there • CONS: • Potential for misuse, misinterpretation, and misrepresentation • Available to those with agendas or looking to create a sensational headline • Misunderstood data can lead to the wrong policy decisions • “Torture numbers, and they’ll confess to anything” (Gregg Easterbrook)

  15. There are three kinds of lies: Lies, Damned Lies and Statistics ^ Misused Statistics

  16. Six Ways to Misuse Data (without actually lying!): • Rank Data • Compare Apples and Oranges • Use ‘snapshots’ of Small Samples • Rely on Unrepresentative Findings • Logically ‘flip’ Statistics • Falsely Assume an Association to be Causal

  17. 1) Rank Data Two streets in Anytown, CA…. “Jane Doe is the poorest person living on Moneybags Avenue.” $$ Ave “Joe Shmoe is the wealthiest person living on Poverty Blvd.” It’s all relative… And SOMEONE will always be ranked last (and first) Poverty Blvd

  18. “San Francisco ranks 55 out of 58 counties when it comes to state and national performance measures…” SF Chronicle, “No refuge. For Foster youth, it’s a state of chance”, November 15, 2005

  19. “San Francisco ranks 55 out of 58 counties when it comes to state and national performance measures…” SF Chronicle San Francisco:AB636 UCB State Measures (Used in NCYL Ranking) % IMPROVEMENT Jan ‘04 compared to June ‘06 (+) indicates a measure where a % increase equals improvement. (-) indicates a measure where a % decrease equals improvement. indicates a measure where performance declined. • Rankings mask improvement over time. • However, even improvement over time and relatively high rankings can be misleading.

  20. 2) Compare Apples and Oranges Two doctors in Anytown, CA… Doctor #1Doctor #2 What if the populations served by each doctor were very different? Doctor of the Year? 2/1000 20/1000

  21. “Foster Children in Fresno County are three times more likely to remain in foster care for more than a year than in Sacramento.” SF Chronicle, “Accidents of Geography”, March 8, 2006

  22. “Foster Children in Fresno County are three times more likely to remain in foster care for more than a year than in Sacramento.” • Different families and children served? • Different related outcomes? • First entry rates in Fresno are consistently lower • Re-entries in Fresno are also lower… 3. Other considerations… • Resources available, resource allocation choices • Performance trends over time

  23. 3) Data snapshots… Crime in Anytown, CA… Number of Crimes Period 1: 76 Period 2: 51 Period 3: 91 Period 4: 76 No change. Average = 73.5 Crime jumped by 49%!! Crime dropped by 16%

  24. “A foster child living in Napa County is in greater danger of being abused in foster care than anywhere else in the Bay area...” SF Chronicle, “No refuge. For foster youth, it’s a state of chance”, November 15, 2005

  25. Abuse in Care Rate Period 1: 1.80% Period 2: 1.64% Period 3: 0.84% Period 4: 0.00% Responsible use of the data prevents us from making any of these claims (positive or negative). The sample is too small; the time frame too limited. “A foster child living in Napa County is in greater danger of being abused in foster care than anywhere else in the Bay Area…” = 2/111 = 2/122 100% improvement! = 1/119 0 Children Abused! = 0

  26. 4) Unrepresentative findings… Survey of people in Anytown, CA… 90% of respondents stated that they support using tax dollars to build a new football stadium. The implication of the above finding is that there is overwhelming support for the stadium… But what if you were then told that respondents had been sampled from a list of season football ticket holders?

  27. “Some reports indicate that maltreatment of children in foster care is a serious problem, and in one recent large-scale study, about one-third of respondents reported maltreatment at the hands of their caregivers.” “My Word”, Oakland Tribune, May 25, 2006

  28. “…in one recent large-scale study, about one-third of respondents reported maltreatment at the hands of their caregivers.” Oakland Tribune Factually true? • Yes. Misleading? • Yes. • This was a survey of emancipated foster youth • Emancipated youth represent a distinct subset of the foster care population • This “accurate” statistic misleads the reader to conclude that one-third of foster children have been maltreated in care…

  29. 5) Logical “Flipping”… Headline in The Anytown Chronicle: 60% of violent crimes are committed by men who did not graduate from high school. “Flip” 60% of male high school drop-outs commit violent crimes?

  30. “One study in Washington State found that 75 percent of a sample of neglect cases involved families with incomes under $10,000.” Bath and Haapala, 1993 as cited in “Shattered bonds: The color of child welfare” by Dorothy Roberts

  31. “One study in Washington State found that 75 percent of a sample of neglect cases involved families with incomes under $10,000.” • In reading statistics such as the above, there is a tendency to want to directionally “Flip” the interpretation • But the original and flipped statements have very different meanings! 75% of neglect cases involved families with incomes under $10,000 DOES NOT MEAN 75% of families with incomes under $10,000 have open neglect cases • Put more simply, just because most neglected children are poor does not mean that most poor children are neglected

  32. 6) False Causality… A study of Anytown residents makes the following claim: Adults with short hair are, on average, more than 3 inches taller than those with long hair. Finding an association between two factors does not mean that one causes the other… X Hair Length Height Gender

  33. “A number of child characteristics have previously been shown to be associated with risk of maltreatment. Prematurity or low birth weight is frequently reported…” As reported in Sidebotham and Heron’s 2006 article

  34. “A number of child characteristics have previously been shown to be associated with risk of maltreatment. Prematurity or low birth weight is frequently reported…” • Should one conclude that prematurity is a causal factor in maltreatment? prematurity maltreatment a third factor (Drug use?)

  35. California’s Response to Data Misuse? • CA has had the will to weather the storm(s)… • Continued efforts to frame the data, educate interested media, policymakers, and others • What do these findings mean? • How can these data be used to gain insight into where improvements are needed? • Counties have been proactive in discussing both the “good” and the “bad” (be first, but be right). • Be transparent • If not playing offense…playing defense • Data still public!! (Thank you to the CWDA for these bullets!)

  36. Barbara Needell bneedell@berkeley.edu Emily Putnam-Hornstein eputnamhornstein@berkeley.edu CSSR.BERKELEY.EDU/UCB_CHILDWELFARE Needell, B., Webster, D., Armijo, M., Lee, S., Cuccaro-Alamin, S., Shaw, T., Dawson, W., Piccus, W., Magruder, J., Exel, M., Conley, A., Smith, J. , Dunn, A., Frerer, K., & Putnam-Hornstein, E., (2007). Child Welfare Services Reports for California. Retrieved [month day, year], from University of California at Berkeley Center for Social Services Research website. URL: <http://cssr.berkeley.edu/UCB_CHILDWELFARE/>

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