1. Data 101: Numbers, Graphs, and More Numbers
Emily Putnam-Hornstein, MSW
Center for Social Services Research
University of California at Berkeley
2008
The Performance Indicators Project at CSSR is supported by the
California Department of Social Services and the Stuart Foundation

2. Agenda
Basic Terminology
Common Data Pitfalls
Graphics
Small Groups…

3. Data Basics… Descriptive Data Comparisons Analyses
Demographic characteristics of a population, place, office, etc.
Comparisons
Performance trends over time (one time period to another)
Differences/similarities between groups, counties, placement settings, interventions, etc.
Analyses
Exploring the relationship between two events (e.g., reunifications and re-entries to care)
Looking at the contributions of various factors to some outcome
Y=a+bX
Descriptive Data
- describe the statistical characteristics of populations (e.g., demographic data about the child welfare population – gender, race, age, etc. -
- Descriptive data

4. Computing a Percent Answers.com Dictionary: Rate
A measure of a part with respect to a whole; a proportion: the mortality rate; a foster care entry rate.
What Percentage of Children who were reunified in 2005 reunified within 12 months of entering care?
Raw Numbers (counts)
# Reunified w/in 12m
= 290
# Reunified (total)
= 440

5. Computing a Rate per 1,000 Answers.com Dictionary: Rate
A measure of a part with respect to a whole; a proportion: the mortality rate; a foster care entry rate.
What was the foster care entry rate in 2005? (i.e., how many children entered care out of all possible children?)
Raw Numbers (counts)
# Entered Care
= 1,333
Scales for a meaningful interpretation…
# Child Population
= 363,376

6. Measures of Central Tendency
Mean: the average value for a range of data
Median: the value of the middle item when the data are arranged from smallest to largest
Mode: the value that occurs most frequently within the data
= 9.7 7
= 9

7. Measures of Variability
Minimum: the smallest value within the data
Maximum: the largest value within the data
Range: the overall span of the data

8. Aggregate Permanency Outcomes
Disaggregation
One of the most powerful ways to work with data…
Disaggregation involves dismantling or separating out groups within a population to better understand the dynamics
Useful for identifying critical issues that were previously undetected
Aggregate Permanency Outcomes
Race/Ethnicity
County
Age
Placement Type

9. 2000 July-December First Entries California: Percent Exited to Permanency 72 Months From Entry
85%
Includes children first entering care between July-December 2000 who were in care for at least 8 days 9

10. 2000 First Entries California: Percent Exited to Permanency 72 Months From Entry
79%
88% 10

11. 2000 First Entries California: Percent Exited to Permanency 72 Months From Entry by Relative vs. Non-Relative Placement
=84%
=94%
=84%
=75% 11

12. 3 Key Data Samples
Data
Cohort: A common group being studied

13. How long do children stay in foster care?
January 1, 2005
July 1, 2005
January 1, 2006
Child 1
Child 2
Child 3
Child 4
Child 5
Child 6
Child 7
Child 8
Child 9
Child 10

14. California Example: Age of Children in Foster Care %
(2003 first entries, 2003 exits, July caseload)
Entries %
Updated with Q2_06 Data 12/27/06 shl, checked by eph 12/30/06

15. California Example: Age of Children in Foster Care %
(2003 first entries, 2003 exits, July caseload)
Entries
Exits %
Updated with Q2_06 Data 12/27/06 shl, checked by eph 12/30/06

16. California Example: Age of Children in Foster Care %
(2003 first entries, 2003 exits, July caseload)
Entries
Exits
Point in Time %
Updated with Q2_06 Data 12/27/06 shl, checked by eph 12/30/06

17. Continuous vs. Discrete
The average foster child has 2.6 placements while in foster care
This number makes little sense because the underlying dimension is discrete (i.e., categorical, discontinuous) 1 2 3 4 5 6
There are 260 placements for every 100 foster children
2.6 x
placements
- This and other examples seem odd because in each case the underlying dimension of interest is discontinuous (or discrete or categorical) which means that it increments from one whole number to another. An individual child in foster care can’t have 2.6 placements – they can have 2 placements or 3 placements.
- There is an all- or none- property to categorical numbers (just think of the name – you fall into one category or another – but not in between! One is either pregnant or not, one either has a 2 children or 3 children – but never 2.6.
- You just need to keep in mind the correct interpretation of the average. For instance, what does it really mean to say that the average foster child has 2.6 placements? It means that there are 260 placements for every 100 foster children.
Continuous
- A variable that is continuous can theoretically have an infinate
Continuous Data
Discrete Data
Age Days in Care Percentages / Rates
Race/Ethnicity Placement Type Referral Reason

18. Negative Correlation = Positive Correlation =
Two “events” that covary with one another…
Negative Correlation =
Positive Correlation =
% Births to Teen Moms
% Reentries
Event 1
Event 2 or
Event 1
Event 2
% Reunified within 6 months

19. Percent Change
Time Period 1
Time Period 2
10 children
11 children

20. Percent Change
Time Period 1
Time Period 2
10%
12% % %

21. Exercise: Percent Change Calculation
50.7
48.3
-4.7%
12.0
10.8
-10%
Baseline Referral Rate (time period 1):
Percent Change:
Comparison Referral Rate (time period 2):
Minor Differences due to Rounding…

22. CWS Outcomes System Summary

23. January 2004-January 2008 California CWS Outcomes System: Federal Measures, Percent IMPROVEMENT
ORIGINAL SLIDE 23

24. Federal Standard/Goal
January 2008 California CWS Outcomes System: Performance Relative to Federal Standard/Goal
Federal Standard/Goal
100%
100%
ORIGINAL SLIDE 24

25. Cross-Sectional vs. Longitudinal
Cross-Sectional (repeated)
Retrieved from:
* Figure 5.23 retrieved from:

26. There are three kinds of lies: Lies, Damned Lies and Statistics^
Misused Statistics
Slide 15:
“There are three kinds of lies: Lies, Damned Lies, and Statistics”.
A very common quote (often credited to Mark Twain, although actually coined by Benjamin Disraeli)
But perhaps this should be amended, as it is not that statistics lie, rather it is that statistics can be misused to misrepresent the underlying data, overstate the conclusions that can be drawn, and bolster an inaccurate argument.

27. Six Ways to Misuse Data (without actually lying!):
Using Raw Numbers instead of Ratios
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
Slide 16:
So, we present Six way to Misuse data. The first four are accompanied by real examples from California. The last two present examples of common logical fallacies that users must keep in mind when reviewing research.

28. 1) Numbers that conceal more than they reveal…
Challenger: “Violent crime in Anytown, CA has increased over the last year more crimes were recorded.”
Incumbent: “The violent crime rate in Anytown, CA has decreased by 2% over the last year.”
Who is telling the truth?
They both are.
Numbers that conceal more than they reveal…another example about Anytown, CA to prove the point.
Let’s say that a political incumbent makes the claim that “The crime rate in Anytown, CA has decreased by 2% over the last year.” That’s good right? Residents want to see crime decrease.
But then the political challenger counters that Crime in Anytown, CA has actually increased over the last year and that 100 more crimes were recorded.
So who is telling the truth?
They both are, since it just so happens that in Anytown, there were 100 more crimes recorded, but the population also increased. An increase in the number of crimes was accompanied by a decrease in the crime rate.

29. “There are approximately 82,000 children in the child welfare system in California – 20% of foster children in the nation, and the largest foster care population of all 50 states.” National Center for Youth Law, “Broken Promises”, 2006
These numbers are from the introduction of the National Center for Youth Law
“There are approximately 82,000 children in the child welfare system in California – 20% of foster children in the nation, and the largest foster care population of all 50 states.”
Are these statistics true?

30. Factually true? Yes Informative? Not very. Misleading?
“There are approximately 82,000 children in the child welfare system in California – 20% of foster children in the nation, and the largest foster care population of all 50 states.” NCYL, 2006
Factually true?
Yes
Informative?
Not very.
What if California has one of the largest child populations of all states?
What if California has one of the smallest child populations of all states?
Misleading?
Maybe…
What is the point being made?
Telling us that California has the largest foster care population does not shed any light on how the state is performing!
Factually True?
Every part of this statement is factually true.
Yes, California does have a foster care population of roughly 82,000 children.
Yes, this is approximately 20% of the national foster care population.
And yes, California does have the largest child welfare population of all states.
Informative?
So it’s true, but what exactly does this statement tell us about child welfare in California? Are these numbers informative?
These numbers actually tell us very little without knowing the total number of children in California (and the other 49 states)
The “20%” statistic (and the implication surrounding the mention of “largest foster care population”) mean two very different things if California has one of the smallest child populations vs. one of the largest child populations.
California has the largest child population AND (not surprisingly) the largest foster care population. So then the question becomes, how do we better assess whether California’s foster care population is larger than it should be based on the size of the underlying child population? Common sense should tell us that we should be comparing state foster care populations based on a ratio of children in care to children in the population.
So if not entirely informative, is there reason to believe that these numbers were used to mislead?
Misleading?
Maybe…
It depends in part on the point being made
If these numbers were included to make the point that since California is a large state with a large population of children in foster care, a state policy change would impact a fairly large portion of the total foster care population, then these numbers are informative.
However, if these numbers were included to suggest that since California has the largest foster care population of all fifty states, it is somehow the worst, then these numbers are misleading.
(In fact, the number of children in foster care per 1,000 children in the population is neither the best nor the worst among the 50 states (but since we hate rankings, for reasons already discussed, we won’t elaborate!)).

31. And SOMEONE will always be ranked last (and first)
2) 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
Slide 17:
The first offender: ranking data
Kansas ranks 48th in X. We come across statistics like this all of the time. Rankings are often used to elicit reactions of “Oh, no. We are third from the bottom and we must improve.”
Consider, however, that no matter what is being measured, if you have 50 states in your ranking, one of them must always be 48th (or 49th or 1st).
Also consider that if all states are performing well on a given measure, what does it mean to last? And if all states are performing poorly, should the top ranked state be content with their performance simply because they are first?
I’ll start with a simple example…
Imagine two streets in Anytown, CA. The first is Moneybags Avenue, where everyone has been ranked by wealth. We learn that Jane Doe has been ranked as the poorest person. But what does this tell us?
Her salary is $300,000 (hardly dancing about the poverty line).
And then you have Poverty Blvd. On this street, Joe Shmoe has been ranked the wealthiest person.
But even though he is the top-ranking earner on his street, his income is only $12,000.
As a society (and from a policy perspective) we should be far more concerned about how Joe Shmoe is faring than Jane Doe…but if we were only given their respective ranks on their respective streets, who knows what conclusions we would draw.

32. “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
Slide 18:
And now, a real example. This was published by the San Francisco Chronicle as part of an editorial series on foster care:
With publicly available data for all 58 counties, there is of course the temptation to rank counties against one another on various performance measures.
“San Francisco ranks 55 out of 58 counties when it comes to state and national performance measures…”
But ranking tells us very little about what is really going on in SF vs. the rest of the state…

33. Rankings mask improvement over time.
“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
Slide 19:
Here, we look at the six state measures used by the NCYL in ranking CA’s counties – but we look at improvement over time. (As mentioned earlier, in California there are additional outcome measures used by the state to track performance)
6 state and 6 federal measures were used in the actual ranking, but we will look only at the state measures as the Federal measures are fundamentally flawed. Also, this takes the data out one additional reporting period – NCYL used four quarters (averaged) up January 06, this data runs through July 2006
The first thing to notice is that on 5 out of the 6 state measures, SF has improved since AB636 was implemented. Of course, since all counties have also been working to improve on these measures, SF’s ranking relative to the rest of the state may not reflect this. This is a major problem with county rankings – they are relative and thus fail to capture improvement over time if all counties are improving.
A second problem with rankings is that they conceal very useful information. By telling SF that they are ranked 55 out of 58 counties, they are given no indication of where they should focus their improvement efforts vs. where they are already performing well.
In fact, the one state measure (stability) where no measurable improvement was observed in the above improvement chart (“Percent of children with 1-2 placements if still in care at 12 months (entry cohort)”), is the measure where San Francisco was ranked the top large county performer in the state – there wasn’t much room for improvement because they were already doing very well in providing stability for children in care. But from a composite rank of 12 measures, we have no way of knowing this…
Another shortcoming of rankings is that we have no way of knowing whether the suggested “disparity” between counties is meaningfully different…just as learning that someone on a given street has the lowest salary when everyone on the street is a millionaire, is not very helpful, telling us that SF is 55th doesn’t quantify how much it truly lags behind the top ranked county, or how close it is to the bottom ranked county.
(+) 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.

34. 3) Compare Apples and Oranges
Two doctors in Anytown, CA…
Doctor #1 Doctor #2
What if the populations served by each doctor were very different?
Doctor of the Year?
Slide 20: A second example of data misuse – comparing Apples and Oranges
Returning to Anytown, CA – imagine that you are tasked with evaluating the two doctors in the town and choosing one to receive a doctor of the year award
There is one doctor with a patient mortality rate of 2 deaths per 1,000 patients (the “Good Doctor”)
There is a second doctor with a patient mortality rate of 20 deaths per 1,000 patients treated (the “Bad Doctor”)
Can you simply conclude that one doctor is performing “better” than the other?
OF COURSE NOT!
These two rates do not (necessarily) compare the same populations – you have to learn more before drawing any conclusions
What if you then learned that the “Good Doctor” works in a dermatology clinic while the “Bad Doctor” works in a cancer clinic? Then who would you conclude was the better doctor? Would you think it meaningful to compare the mortality rates of each?
2/1000
20/1000

35. “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
Slide 21:
Here is a quote from another editorial that ran in the SF Chronicle on March 8, 2006:
“Foster Children in Fresno County are three times more likely to remain in foster care for more than a year than in Sacramento.”
So, are we to conclude that the child welfare services in Sacramento are “Good”, while those in Fresno are “Bad”?

36. Different families and children served?
“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
Slide 22:
Before any conclusions about performance should be drawn, we must (as with the doctor example) consider the populations served as well as the practices employed…here are just a few of the things that should be considered
Maybe children in Fresno are more likely to remain in care for more than a year because the problems families face are of a different nature and take longer to overcome than those in Sacramento.
If we look at a few demographic indicators, we see that Fresno has a higher percentage of children in poverty (30% vs. 18%), a higher percentage of teen births (out of total live births, 15% vs. 10%), a higher percentage of minority children (75% vs. 59%) whom we know are overrepresented in child welfare systems
Also (but not shown) Fresno lags behind Sacramento in median income by just over $10,000 ($34,850 vs. $45,351) and has a higher violent crime rate (per 100,000 in the population vs ).
These data were compiled from publicly available sources and then averaged across the years
Maybe Fresno is less likely to remove children from their homes (preferring family preservation services for their population of clients). This might result in an out of home foster care population that is less likely to be able to safely reunify.
In fact, we do see that the first entry rates in Fresno are consistently lower than those in Sacramento…
These data are from our website – first entry rates from
Maybe Sacramento follows a more liberal reunification policy. Although it may send more kids home within a year, are these reunifications successful?
In support of this possibility, we observe that the rates of re-entry in Fresno are actually half the rates in Sacramento
Again, these data are from our website – these are the % of children who were reunified within 12 months of entering care and then re-entered within 12 months of being reunified, table shows the 3 most recent first entry cohorts for whom data is available
Other things to consider…
What resources are available in the two counties?
Has Fresno allocated resources differently than Sacramento?
What are the long-term performance trends?
While it is beyond the scope of this presentation to answer all of these questions, the take-home point is that it is irresponsible and pointless to make blanket comparisons between localities without considering the myriad other factors that may be at play.

37. Number of Crimes Period 1: 76 Period 2: 51 Period 3: 91 Period 4: 76
4) 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%!!
Slide 23
Our 3rd example of a common way to misuse data: Data snapshots
Suppose we had the following crime data for Anytown, CA…
Depending upon the starting and endpoints used, three very different points could be made:
Advocates of additional police funding would be able to show that crime increased by 49% over the last two reporting periods (“Oh no, something must be done!”)
Advocates of not raising taxes would be able to show that there was no change in crime (“Look, things are fine. Crime is pretty low and hasn’t increased”)
A political incumbent would be able to show that crime has dropped by 16% (“See what a good job I’m doing? Crime has dropped by 16% since I took office”)
Finally, the average crime rate over the four periods could also be reported (73.5) – but note that because there are fairly large fluctuations from period to period, and only four periods, the average is less than three out of the four periods…It is important to keep in mind that while averages are often useful, they can also be deceptive (“The average human has one breast and one testicle.”  ~Des McHale)
So again I ask, what exactly does this tell us? It tells us that several stories can be told using data snapshots…
Discuss importance of using longitudinal data and looking for trends over time and the inappropriateness of using “snap-shots” (point-in-time) data (especially when there are large fluctuations…)
Crime dropped by 16%

38. “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
Slide 24
This editorial opening was used by the SF Chronicle in an editorial published on November 15, 2005
“A foster child living in Napa County is in greater danger of being abused in foster care than anywhere else in the Bay area…”

39. 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…”
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.
Slide 25
Here, we show the “abuse in care rate” that was used by the NCYL/SF Chronicle to create this salacious headline.
Data was drawn from four 9-month reporting periods and then averaged to 1.07 (which was the highest in the Bay Area) – Data are from CDSS Website, the source cited by NCYL
Period 1 = 1/1/04-9/30/04 = 1.80%
Period2 = 4/1/04-12/31/04 = 1.64%
Period 3 = 7/1/04-3/31/05 = .84%
Period 4 = 10/1/04-6/30/05 = 0.00%
Note that just as was the case with crime in the prior example, a VERY different story could have been told!
If only the first and last reporting periods were compared, the headline could have been “Napa county improves 100%!” (100% improvement could have been reported by comparing any of the earlier periods with the most recent period.)
Or, had only a snapshot of the most recent period been used, the headline could have been “A foster child living in Napa County is in LESS danger of being abused in foster care than anywhere else in the Bay Area…”
During these time periods, the foster care caseload was small in Napa county (it fluctuated between children) meaning that at it’s peak (when Napa would have been declared the most dangerous county in the Bay Area) approximately 2.3 children were abused. At it’s low (when Napa would have been declared the safest county in the Bay area) 0 children were abused. Of course every child matters, but responsible use of the data prevents us from making claims in either direction…the sample is too small and the time frame too limited to treat these data fluctuations as anything other than random.
Was the danger really greater in Napa County during the period when the rate was 1.8%?
Should Napa have been declared the “safest” Bay area county during the period when the rate was 0.0%
= 2/111
= 2/122
100% improvement!
= 1/119
0 Children Abused!
= 0

40. 5) 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?
Slide 26
Overgeneralize unrepresentative findings
Imagine that there was a survey of people in Anytown, CA…from which the following statistic was reported
“90% of respondents stated that they support using tax dollars to build a new football stadium”.
Of course, you would conclude that there is overwhelming support for the stadium – you would have no reason not to.
But what if you were then told that respondents had been sampled from a list of season football ticket holders?
Of course you would immediately get that the sample was biased and conclude that no real conclusion about support (or lack thereof) could be drawn based on this survey…and you also might feel misled.

41. “My Word”, Oakland Tribune, May 25, 2006
“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
Slide 27
The above quote come from a guest editorial published in The Oakland Tribune by a well-respected academic…
“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.”

42. Factually true? Misleading? Yes.
“…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?
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…
Slide 28
Factually True?
So, is this true?
Yes, in a recent (and large-scale) study, one-third of the respondents did report maltreatment while in foster care (or so we believe, although attempts were made to verify the citation/source, we did not respond)
So what’s the problem – this was not a representative sample of children in foster care!
Misleading?
I would answer with an unqualified Yes. While one-third of respondents did report maltreatment, this was a survey of youth who had emancipated out of the foster care system
Emancipated youth represent a distinct subset of the foster care population
This “accurate statistic” is being used to mislead the reader by “suggesting” that one-third of ALL foster children have been maltreated in care

43. 60% of male high school drop-outs commit violent crimes?
6) 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?
Slide 29:
Now, we will talk about the tendency to Logically Flip statistics
Lead headline in the local paper – The Anytown Chronicle – reads
“60% of violence crimes are committed by men who did not graduate from high school.”
A logical fallacy occurs when one assumes that the “flip” of that statistic is also true:
“60% of male high school drop-outs commit violent crimes”

44. “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
Slide 30:
The above quote comes from Dorothy Roberts’ book in which she cites a study that found 75% of a sample of neglect cases involved families with incomes under $10,000”

45. But the original and flipped statements have very different meanings!
“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
Families with incomes under $10,000
Put more simply, just because most neglected children are poor does not mean that most poor children are neglected
Families with open neglect cases

46. 7) 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
Slide 32:
False Causality is our final example of a common way that data are misunderstood.
- 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.
- Based on this finding, one might make conclude that hair length is somehow a factor that influences stature
- Or, that being tall or short is influences ones hair length
- OR, that some third factor determines both ones hair length and ones height – say gender?
- While this is obviously a “duh” example, there is a tendency to think that finding an association between two factors means that it is a causal association that has been uncovered.

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

48. prematurity maltreatment a third factor (Drug use?)
“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?)
Slide 34:
- Should one conclude that prematurity is a CAUSAL FACTOR in maltreatment? It is possible, I believe that there is some literature that suggest premature babies are harder to care for, more irritable, and thus more prone to maltreatment, but a far more likely explanation is that there is some third factor that is related to both prematurity and maltreatment…

49. Graphs / Charts
Keep it simple…
Use consistent color themes when possible
Think about the type of data being presented (discrete vs. continuous)
Label Clearly
Tell a story
Look at presentations on the UC site!

50. Ethnicity and Path through the Child Welfare System: California 2006
Stacked Bar Chart
Ethnicity and Path through the Child Welfare System: California 2006

51. Ethnicity of Children in Foster Care:
Pie Chart
Ethnicity of Children in Foster Care:
California 2006

52. 2006 California: Referrals per 1,000 by Age and Ethnicity
3D-Area Chart
2006 California: Referrals per 1,000 by Age and Ethnicity
*Series Total

53. California: First Entries by Race/Ethnicity
(complex) Line Chart
California: First Entries by Race/Ethnicity
TOTAL
Hispanic
White
Updated with Q2_07 Data (through June 2007)
Black
Asian/PI
Native American
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007 53

54. California: Foster Care Caseload by Race/Ethnicity
(complex) Line Chart
California: Foster Care Caseload by Race/Ethnicity
TOTAL
Black
Hispanic
White
Updated with Q2_07 Data (through July 2007)
Asian/PI
Native American
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007 54

55. Small Group Topics…
Group 1: Explore County to County variation in Group Home use in 2007
Group 2: Miscellaneous
Group 3: Describe any statewide trends in Group Home use (vs. other placements) over time
Group 4: Explore the placement stability of the Group Home population in care for 24 months or mroe
Group 5: Describe the Group Home Population in California in 2007

56. Group 1
Group 1: Explore County to County variation in Group Home use in 2007
How many children were in GH care in Sacramento County? Alameda County?
What percentage of the GH population is female in Glenn County? Del Norte County?
How does this compare with CA as a whole?
What can you conclude about Del Norte’s group home population compared with that of Glenn County?
Compare the ethnic distribution of the GH population in Los Angeles County with that of San Diego County.
What conclusions can you draw about the GH representation of Black and White children in LA versus SD?
Which counties don’t have any children in GH care on October 1, 2007?
Other observation(s)…

57. Group 2 Group 2: Miscellaneous
In 1999/2000, what percentage of children first entering foster care (“first entry”) had a first placement in a GH? What was the percentage in 2006/2007?
In 1999/2000, what percentage of children re-entering foster care (“other entry”) had a first placement in a GH? What was the percentage in 2006/2007?
Any thoughts on why more re-entries than first entries are placed in GH’s?
Any thoughts on why a greater percentage of first and re-entries are placed in GH in 2006/2007 than was the case in 1999/2000?
In 2006/2007, what was the count and percent of children in GH’s exiting to reunification and Guardianship?
How do these percentages compare with children in Kin, Foster, and FFA homes?
Other observation(s)…

58. Group 3
Group 3: Describe statewide trends in Group Home use (vs. other placements) over time
How has the size of the GH population changed over time?
What percentage of the foster care population was in GH care on October 1, 1999? And in 2007?
How do you reconcile this with the fact that the count of children in GH care has gotten smaller over time?
How has the size of the population in other placement settings changed over this same time period?
Kin? Foster? FFA? Shelter?
The overall out of home care population has decreased over time. What additional data do you need in order to assess whether this is a real change?
Other observation(s)…

59. Group 4
Group 4: Explore the placement stability of the Group Home population of children in care for 24 months or more
How has the total size of the population of children in care for 2+ years (and who are now in GH care) changed over time?
And what has been the trend over time for children in two or fewer vs. three or more placements been?
In 2006/2007, what percentage of children in GH care for 2+ years had been in two or fewer placements?
What percentage in foster homes had been in two or fewer placements?
And kinship homes?
Is it reasonable to conclude that placement in Group Home Care leads to (or causes) placement instability?
Other observation(s)…

60. Group 5
Group 5: Describe the Group Home Population in California in 2007
What was the total PIT count of children in group home care on October 1, 2007?
Which age group had the greatest number of children in GH care?
Were there any infants in GH care?
Any thoughts on why this might be?
What percentage of children in GH care were ages years?
Which racial/ethnic group had the largest percentage of children in GH care?
Of all children in care for 60 months or more, what percentage was placed in a GH setting? And what about an FFA setting?
Other observation(s)…

61. A quick look at the website…

62. Emily Putnam-Hornstein CSSR.BERKELEY.EDU/UCB_CHILDWELFARE
CSSR.BERKELEY.EDU/UCB_CHILDWELFARE
Needell, B., Webster, D., Armijo, M., Lee, S., Dawson, W., Magruder, J., Exel, M., Zimmerman, K., Simon, V., Putnam-Hornstein, E., Frerer, K., Ataie, Y., Atkinson, L., Blumberg, R., Henry, C., & Cuccaro-Alamin, S. (2007). Child Welfare Services Reports for California. Retrieved [month day, year], from University of California at Berkeley Center for Social Services Research website. URL: <