Transcript Slide 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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Agenda
• Basic Terminology
• Common Data Pitfalls
• Graphics
• Small Groups…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Data Basics…
• Descriptive Data
– 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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
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.
% percent (per 100) 
part
 100
total
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

part
 100
total
# reunified w/in 12m
 100
total # reunified
290

 100
440

 0.659  100
 65.9%
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.
rate per 1000 
part
 1000
total
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
part
 1000
total
# entered care

 1000
# child population


1,333
 1000
363,376
 .00366 1000
# Child Population = 363,376
 3.7
Scales for a
meaningful
interpretation…
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
124 4 15
7 963 127 15
9 417 1763
4  4  7  9  12  15  17  63
Mean 
 16.4 = 9.7
87
9  12
Median 
 10.5 = 9
2
Mode 4
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Measures of Variability
Minimum: the smallest value within the data
Maximum: the largest value within the data
Range: the overall span of the data
4
4
7
9
12
15
17
Minimum  4
Maximum 63
Range  63  4  59
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
63
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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
2000 July-December First Entries
California:
Percent Exited to Permanency 72 Months From Entry
N=11,698
100%
90%
80%
56
70%
60%
85%
50%
40%
30%
20
20%
9
10%
0%
3
In Care
6
Other
12
18
Emancipated
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
24
36
Guardianship
48
60
Adopted
72
Reunified
2000 First Entries
California:
Percent Exited to Permanency 72 Months From Entry
White (n=3,773)
Black (n=2,417)
100%
90%
80%
50
60
70%
60%
79%
88%
50%
19
40%
20
30%
20%
10
8
10%
0%
3
6
In Care
12
18
24
Other
36
48
60
72
Emancipated
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
3
6
12
Guardianship
18
24
36
Adopted
48
60
72
Reunified
2000 First Entries
California:
Percent Exited to Permanency 72 Months From Entry
by Relative vs. Non-Relative Placement
White Relative Placements (n=1,398)
Black Relative Children Placements (n=976)
43
58
=94%
19
19
22
17
12
24
36
48
60
72
12
White Non-Relative Placements (n=2,375)
24
36
48
60
72
Black Non-Relative Placements (n=1,441)
54
61
=84%
2
12
24
36
48
60
In Care
Other
=75%
21
72
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
=84%
Emancipated
2
12
24
Guardianship
36
48
Adopted
60
19
72
Reunified
3 Key Data Samples
Entry
Cohorts
Data
Point
in Time
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Exit
Cohorts
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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
California Example:
Age of Children in Foster Care
(2003 first entries, 2003 exits, July 1 2004 caseload)
50
45
Entries
40
35
31
30
%
25
22
22
20
20
15
10
5
5
0
<1 yr
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
1-5 yrs
6-10 yrs
11-15 yrs
16+ yrs
California Example:
Age of Children in Foster Care
(2003 first entries, 2003 exits, July 1 2004 caseload)
50
45
Entries
40
Exits
35
31 30
30
%
25
25
22
22
20
20
22
19
15
10
5
5
4
0
<1 yr
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
1-5 yrs
6-10 yrs
11-15 yrs
16+ yrs
California Example:
Age of Children in Foster Care
(2003 first entries, 2003 exits, July 1 2004 caseload)
50
45
Entries
40
Exits
35
30
%
25
33
31 30
23
22
22
25 24
20
20
Point in Time
22
19
15
15
10
5
4
5
5
0
<1 yr
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
1-5 yrs
6-10 yrs
11-15 yrs
16+ yrs
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
x
There are 260
placements for every
100 foster children
placements
Continuous Data
Age
Days in Care
Percentages / Rates
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Discrete Data
Race/Ethnicity
Placement Type
Referral Reason
Correlation
• Two “events” that covary with one another…
Negative
Positive
Correlation
Correlation==
% Reentries
Births to
%
Teen Moms
Event 1
Event 2
or
Event 1
Event 2
% Reunified within 6 months
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Percent Change
Time Period 1
Time Period 2
 Period 2  
% Change  
  1  100
 Period 1  
 11 kids  
 
  1  100
 10 kids  
 1.1  1 100
10 children
11 children
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
 0.1 100
 10%
Percent Change
Time Period 1
Time Period 2
10%
12%
% %
% %
% %
% %
% %
 12%  
% Change  
  1  100
 10%  
% %
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
 20%
% %
% %
% % %
% % %
Exercise: Percent Change Calculation
488,419
 .05067  1000  50.7
9,637,963
Comparison Referral Rate
(time period 2):
482,706
 .0483  1000  48.3
9,988,199
48.3
-4.7%
12.0
10.8
-10%
Percent Change:
 Comparison Rate  
   1  100

 Baseline Rate  
 48.3  
   1  100

 50.7  
(.9526 - 1)  100
 0.047  100
 4.7%
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Minor Differences due to Rounding…
Baseline Referral Rate
(time period 1):
50.7
CWS Outcomes System Summary
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
January 2004-January 2008
California CWS Outcomes System:
Federal Measures, Percent IMPROVEMENT
S1.1: No Recurrence of Maltreatment (+)
S2.1: No Maltreatment in Foster Care (+)
-0.25%
C1.1: Reunification w/in 12m (Exit Cohort) (+)
C1.2: Median Time to Reunification (-)
C1.3: Reunification w/in 12m (Entry Cohort) (+)
C1.4: Reentry Following Reunification (-) -14.2%
C2.1: Adoption w/in 24m
C2.2: Median Time to Adoption
C2.3: Adoption w/in 12m (17m In Care)
C2.4: Legally Free w/in 6m (17m In Care)
C2.5: Adoption w/in 12m (Legally Free)
(+)
(-)
(+)
(+)
(+)
C3.1: Exits to Permanency (24m In Care) (+)
C3.2: Exits to Permanency (Legally Free) (+)
C3.3: In Care 3+ Years (Emancipated/Age 18) (-)
10.6%
15.1%
5.1%
17.4%
17.4%
7.9%
-7.0%
-0.7%
C4.1: Placement Stability (8d-12m In Care) (+)
C4.2: Placement Stability (12-24m In Care) (+)
C4.3: Placement Stability (24m+ In Care) (+)-14.6%
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
2.5%
3.8%
4.0%
4.1%
33.2%
32.2%
January 2008
California CWS Outcomes System:
Performance Relative to Federal Standard/Goal
Federal Standard/Goal
S1.1: No Recurrence of M altreatment
S2.1: No M altreatment in Foster Care
100%
C1.1: Reunification w/in 12m (Exit Cohort)
C1.2: M edian Time to Reunification
C1.3: Reunification w/in 12m (Entry Cohort)
C1.4: Reentry Following Reunification
C2.1: Adoption w/in 24m
C2.2: M edian Time to Adoption
C2.3: Adoption w/in 12m (17m In Care)
C2.4: Legally Free w/in 6m (17m In Care)
C2.5: Adoption w/in 12m (Legally Free)
C3.1: Exits to Permanency (24m In Care)
C3.2: Exits to Permanency (Legally Free)
C3.3: In Care 3+ Years (Emancipated/Age 18)
C4.1: Placement Stability (8d-12m In Care)
C4.2: Placement Stability (12-24m In Care)
C4.3: Placement Stability (24m+ In Care)
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
100%
Cross-Sectional vs. Longitudinal
Longitudinal
Cross-Sectional
(repeated)
* Figure 5.23 retrieved from: http://www.mrs.umn/edu/~ratliffj/psy1051/cross.htm
There are three kinds of lies:
Lies, Damned Lies and Statistics
^
Misused Statistics
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Six Ways to Misuse Data
(without actually lying!):
1) Using Raw Numbers instead of Ratios
2) Rank Data
3) Compare Apples and Oranges
4) Use ‘snapshots’ of Small Samples
5) Rely on Unrepresentative Findings
6) Logically ‘flip’ Statistics
7) Falsely Assume an Association to be Causal
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
1) Numbers that conceal more than they reveal…
Challenger: “Violent crime in Anytown, CA has
increased over the last year. 100 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.
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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!
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
2) Rank Data
Two streets in Anytown, CA….
“Jane Doe is the poorest person
living on Moneybags Avenue.”
“Joe Shmoe is the wealthiest
person living on Poverty Blvd.”
It’s all relative…
And SOMEONE will always
be ranked last (and first)
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
Re-Entries w/in 12m (cohort) (-)
23.8%
Reunified w/in 12m (cohort) (+)
18.5%
Recurrence w/in 12m of Subst. (-)
9.8%
Adopted w/in 24m (cohort) (+)
9.6%
Recurrence w/in 12m (-)
1 or 2 Placements (at 12m, cohort) (+)
5.7%
0.1%
(+) 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.
3) Compare Apples and Oranges
Two doctors in Anytown, CA…
Doctor #1
Doctor #2
Doctor of the Year?
2/1000
20/1000
What if the populations served by each doctor were
very different?
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“Foster Children in Fresno County are three times more likely to remain in foster
care for more than a year than in Sacramento.”
1.
Different families and children served?
2.
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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
4) Data snapshots…
Crime in Anytown, CA…
Number of Crimes
Period 1: 76
Period 2: 51
Average
= 73.5
No
change.
Crime jumped by 49%!!
Period 3: 91
Crime dropped by 16%
Period 4: 76
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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:
Period 2:
Period 3:
Period 4:
1.80%
1.64%
0.84%
0.00%
= 2/111
= 2/122
100% improvement!
= 1/119
=0
0 Children Abused!
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.
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
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?
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“…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…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
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?
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Families with open
neglect cases
Families with incomes
under $10,000
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.
Hair Length
X
Height
Gender
Finding an association between two factors does
not mean that one causes the other…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
“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?)
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
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!
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Stacked Bar Chart
Ethnicity and Path through the Child Welfare System:
California 2006
100%
3.30
9.8
0.8
3.9
0.9
4.0
1.1
3.3
80%
50.2
60%
51.4
48.1
Other
42.4
26.2
28.5
Population
(9,664,747)
Native
American
Hispanic
28.2
White
30.8
7.2
1.4
Asian/PI
29.7
0%
2.3
48.1
40%
20%
1.4
15.4
14.9
19.0
Referrals
(438,666)
Substantiations
(102,365)
Entries
(39,646)
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
27.7
Black
In Care
(74,634)
Pie Chart
Ethnicity of Children in Foster Care:
California 2006
Native
American:
Black:
White:
27.7%
26.2%
1.4%
Asian/PI:
2.3%
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Hispanic:
42.4%
3D-Area Chart
2006
California:
Referrals per 1,000
by Age and Ethnicity
150
103
110 96
87
90
78
64
52
73
47
54 52
37
48
45
40
28
37
Black
(97.2*)
*Series Total
Native
American
(46.8*)
54
46
47
44
53
37
53
50 42
46
20
38
18
ALL
(50.0*)
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
15
White
(43.8*)
Hispanic
(47.4*)
Asian/PI
(17.8*)
18 14
<1 yr
1-2 yrs
3-5 yrs
6-10 yrs
11-15 yrs
16-17 yrs
21
(complex) Line Chart
California:
First Entries by Race/Ethnicity
20,000
30,000
TOTAL
25,000
20,000
Hispanic
10,000
White
15,000
Black
10,000
5,000
5,000
Asian/PI
Native American
0
1998
1999
2000
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
2001
2002 2003 2004
Entry Year
2005
2006
2007
0
TOTAL Frequency
Placement Frequency
15,000
(complex) Line Chart
California:
Foster Care Caseload by Race/Ethnicity
60,000
120,000
40,000
30,000
100,000
TOTAL
Black
Hispanic
80,000
White
60,000
20,000
40,000
10,000
20,000
0
Asian/PI
Native American
1998
1999
2000
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
2001 2002 2003
Point in Time
2004
2005 2006 2007
0
TOTAL Frequency
Placement Frequency
50,000
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
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
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)…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Group 2
Group 2: Miscellaneous
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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?
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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)…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Group 3
Group 3: Describe statewide trends in Group Home use (vs. other
placements) over time
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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)…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Group 4
Group 4: Explore the placement stability of the Group Home population
of children in care for 24 months or more
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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)…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Group 5
Group 5: Describe the Group Home Population in California
in 2007
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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 11-15 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)…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
A quick look at the website…
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley
Emily Putnam-Hornstein
[email protected]
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., & CuccaroAlamin, 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:
<http://cssr.berkeley.edu/ucb_childwelfare>
CENTER FOR SOCIAL SERVICES RESEARCH
School of Social Welfare, UC Berkeley