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Research Methods in Practice

Dr.Pohlig
April 30, 2013

Research Methods in Practice

Applications of Research Methodology and Statistics in the Social & Health Sciences
Given Sept 14, 2012, @ the University of Scranton

Dr.Pohlig

April 30, 2013
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  1. Real world applications of Research
    Methodology & Statistics (Yes, they
    can be useful)
    Quantitative Research in the
    Social & Health Sciences
    Presentation by Ryan Pohlig, University of Scranton ’05

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  2. Research Methods & Statistics
    n  Present some of the research I have
    personally been involved in during grad
    school
    n  Going to start with the Research Question
    (RQ) and background, then cover the design,
    data, and analysis used
    ¡  RQ should always drive the design & analysis
    n  Going to cover a wide range of topics, feel
    free to interrupt to ask questions

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  3. Nursing – Pancreatic Cancer (PC)
    n  RQ: Want to find if there is a relationship
    between Quality of Life (QOL) and Cytokine
    levels in PC patients
    n  Imbalance between pro-and anti-inflammatory cytokines
    can cause chronic immune activation and inflammation
    n  Relationship between
    immune fluctuations and
    symptom burden not
    studied in PC patients
    n  Knowing these
    relationships, health care
    workers can plan for
    management of pain and
    other symptoms

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  4. Background
    n  Pancreatic Cancer (PC), 4th most common
    cancer and it’s deadly
    ¡  1 year survival rate is 24%
    ¡  5 year survival rate is 5%
    ¡  Resectable 5 year rate is 17%
    n  This cancer spreads to distant sites early
    n  Unfortunately, most patients present at
    advanced stages at their diagnosis
    n  Chemo and radiation therapy are standard
    treatment for both localized & metastatic

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  5. Cytokines
    n  Cytokines are circulating proteins produced by
    immune system
    ¡  Pro-inflammatory cytokines: IL-8 and TNF-α
    ¡  Anti-inflammatory cytokines: IL-10
    ¡  Both pro and anti: IL-6
    n  Interleukin (IL) and Tumor Necrosis Factor-alpha
    n  Previous research has shown elevated levels of
    cytokines in cancer patients relative to healthy
    people
    n  Cytokine levels are related to pain, weight loss,
    neuropathic inflammation, and fatigue

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  6. Quality of Life
    n  Unfortunately, treatment and disease affect QOL
    as both create significant toxicities
    ¡  Which can have a profound effect on patient’s
    symptom burden and immune function
    n  Stages of Cancer (I-IV) are also directly related to
    QOL
    n  (FHSI-8) The Functional Assessment of Cancer
    Therapy: Hepatobiliary Symptom Index-8
    ¡  A measure of Quality of Life
    ¡  Likert scale of pain ratings, fatigue, and symptom
    presence
    ¡  Higher scores indicate more pain & discomfort

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  7. Study Design & Analysis
    n  Design- observational study
    ¡  Examine relationship between FHSI-8 and Cytokine
    levels
    ¡  Need to worry about cancer stage as confounder
    n  Analysis- Regression
    ¡  Control for stage of cancer, sex, & age
    n  Predict FHSI with controls
    ¡  Then test if adding Cytokine levels leads to
    significantly better fit (prediction)
    n  First Block- Covariates
    n  Second Block- Cytokines

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  8. Sample
    n  n =36, newly diagnosed PC Patients
    n  Blood Samples drawn and filled out the
    FHSI-8
    n  Sample: 19 males and 17 females
    n  Stage Age
    I 4 40-59 8
    II 9 60-69 10
    III 7 70-79 9
    IV 16 80-89 9

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  9. Results
    n  Significant improvement
    seen by adding Cytokines
    ¡  R2 = .142 to .495, p = .013
    ¡  R2 can be thought of as % of
    variance that can be accounted
    for by model
    n  Slope Interpretations
    ¡  The more IL-6 the lower QOL
    ¡  The less IL-8 the lower QOL
    ¡  IL-10 not related to QOL
    ¡  The more TNFα the lower
    QOL
    Variable b p-value
    Age .237 .024
    Sex 2.548 .205
    Stage .359 .699
    IL6 4.111 .039
    IL8 -7.907 .004
    IL10 -1.755 .327
    TNFα 10.619 .002

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  10. Cytokine Plots

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  11. Bio-Engineering
    n  RQ: Test effectiveness of a Bio-feedback
    training method for proper manual wheel
    chair propulsion
    n  Ideal propulsion designed
    to decrease force and
    number of strokes
    ¡  Long smooth stroke
    during propulsive phase
    ¡  Allow the hand to drift
    down and back below the
    handrim during recovery
    phase

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  12. Background
    n  Up to 70% of Manual Wheelchair Users
    report upper extremity pain often in the
    shoulder and wrist
    n  Teaching proper propulsion is not
    standardized
    n  Majority of the studies examine performance
    & technique at fixed speeds, on treadmills or
    dynamometers
    ¡  Have not used real life propulsion scenarios,
    overground
    ¡  Have not allowed participants to use their own
    wheelchairs

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  13. Study Design
    n  Want to show improvement/growth
    ¡  Change over time, short and long term
    improvement
    n  Effectiveness of training across 2 other IVs
    ¡  Surface Type: carpet, tile, and ramp
    ¡  Speed: self-selected or targeted
    n  Design- Experiment
    ¡  Control Group
    ¡  Instruction Only Group
    n  Given a multimedia presentation showing proper form
    ¡  Bio-feedback Group
    n  Given the multimedia presentation
    n  Had a motor learning training program

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  14. Design: True-Experiment

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  15. Analysis
    n  Covariates to adjust for
    ¡  Weight – heavier you are more force used
    ¡  Time since injury – longer you have been
    injured, the more efficient pushing should be
    ¡  Level of injury – amount of function in wrists
    and hands
    n  Velocity is a confounder
    ¡  The faster you are going, the more force
    exerted and not a function of poor technique
    ¡  Velocity is not a constant like other covariates

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  16. General Linear Mixed Model (GLMM)
    n  Multi-Level Model
    ¡  Can be used for repeated measure designs
    ¡  Measurement Occasions are nested within
    Individuals
    ¡  Like a more flexible mixed design ANOVA
    n  Can specify the covariance structure of the errors
    directly
    n  If the strict assumption of Compound Symmetry is
    violated- don’t have to use a correction
    n  Compound Symmetry: assumption that the measures at
    all time periods are equally related and the same for all
    groups
    n  Velocity entered as time-varying covariate
    ¡  Its value changes for each measurement occasion

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  17. Study
    n  Sample
    ¡  n = 27
    ¡  Had to use same wheelchair throughout study
    ¡  No difference between groups in demographics
    n  Four outcome measures
    ¡  Contact Angle (larger is better)
    ¡  Stroke frequency
    ¡  Peak Force (peak force during push)
    ¡  Rate of Rise (how rapidly force is applied to
    handrim, the quicker the better)

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  18. Results, Simple Main Effects

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  19. Results cont.

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  20. Public Health
    n  RQ: Want to estimate number of Young
    Adults (18-29) in USA at risk for Type II
    Diabetes
    n  Why? Current screening
    methods for Diabetes (ADA)
    ¡  Over 45 and high BMI (≥26)
    ¡  Under 45, high BMI, and
    another other risk factor
    n  Diabetes Diagnosis
    ¡  Impaired Fasting glucose
    (IFG) ≥ 126mg/dl
    ¡  Prediabetes IFG ≥ 100mg/dl

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  21. Metabolic Syndrome (MetS)
    n  MetS is one of the most
    reliable and best
    predictors of future
    Diabetes (65% of people
    with Diabetes had MetS)
    n  Criteria for MetS is 3 of
    the following è
    n  Having both IFG & 3/4
    of other MetS risk
    factors increases chance
    of Type II Diabetes by
    twenty-fold
    n  Waist Circumference
    ¡  Men ≥ 102cm
    ¡  Women ≥ 88cm
    n  Triglyceride level ≥ 150mg/dl
    n  Cholesterol HDL
    ¡  Men ≥ 102mg/dl
    ¡  Women ≥ 88mg/dl
    n  Blood Pressure
    ¡  SBP ≥ 130mmhg
    ¡  DBP ≥ 85mmhg
    n  IFG ≥ 100mg/dl

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  22. Study - NHANES
    n  Used data from National Health and
    Nutrition Examination Survey (NHANES)
    n  NHANES is longitudinal study that
    combines survey & physical data
    n  Specifically over-sampled minorities, as
    classically they are under represented in
    national data
    n  Individuals can be “weighted” so that
    collectively they represent the population
    of the USA from census

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  23. Design & Analysis
    n  Stratified Cluster Sample, n ≈ 10,000
    ¡  1,191 young adults in NHANES
    ¡  468 were included (1/2 missing lab data, pregnancy, etc)
    n  Design: Observational Study using Secondary data analysis
    ¡  Data set available online for free
    n  Analysis: Complex survey design was used, need to account
    for that when getting estimates
    n  Specifically take into account that clusters were stratified and
    individuals were weighted
    ¡  Clustering- saves money by choosing individuals close in
    location
    ¡  Stratifying- makes sure to get representation of everyone
    ¡  Weighting- to get sample to equal population according to
    census

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  24. Results
    n  The bad news:
    ¡  Over half of adult youth are
    overweight
    n  BMI ≥ 26
    ¡  24.9% are Obese
    n  BMI ≥ 30
    ¡  IFG found in 28.2%
    ¡  16% had MetS, not with IFG
    ¡  6.4% had both IFG & MetS
    n  What about the lean?
    ¡  In our sample 12% of
    individuals had
    prediabetes (IFG
    between 100 and 125)
    but no other risk factor
    ¡  0% were
    n  Told they had pre-
    diabetes
    n  Warned by health care
    providers that they were
    at risk…

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  25. Education – School Drop Outs
    n  RQ: Want to predict who is at risk for dropping
    out of School
    ¡  Collect validity evidence showing ADSI-E is effective in
    predicting drop outs
    n  High School drop outs are at
    risk for mental health
    problems, substance abuse,
    crime, etc
    n  If we could predict drop out
    and what factors influence it,
    educators could devote
    resources to help

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  26. DUSI into ADSI-E
    n  The Drug Use Screening Inventory (DUSI)
    was adapted for school settings
    n  Converted to the Adolescent Development
    Screening Inventory for Education (ADSI-E)
    ¡  Minor changes in wording of items
    ¡  Items were grouped into different sub categories
    n  Validity evidence gathered over 15+ years for
    DUSI, since this study further evidence has
    been collected supporting use for the ADSI-E

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  27. ADSI-E
    n  ADSI-E
    ¡  20mins to complete
    ¡  154 yes or no, 8 are lie scale
    ¡  1 Total and 9 domain scores
    n  Domains
    ¡  Peer Relationships
    n  Peer pressure
    ¡  School Adjustment
    n  School climate
    ¡  Family Systems
    n  Family background
    ¡  Leisure and Recreation
    n  Physical Activity
    ¡  Physical Health
    ¡  Emotional Health
    ¡  Social Competence
    n  Social skills
    ¡  Behavior Patterns
    n  Externalization of
    negative behavior
    ¡  Substance Abuse

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  28. Study Design & Analysis
    n  Design: Quasi-experimental, naturally occurring
    groups
    ¡  Would be unethical to assign kids to drop out group
    n  Analysis: ANOVA, comparing groups on ADSI-E
    n  Sample drawn from Blue Water School district in
    Ontario
    n  Regularly attending students n = 442
    n  “Drop-outs” n = 97
    ¡  Were enrolled in one of three off-site regional
    Centers for Individual Studies
    ¡  Part-time students in alternative programs
    ¡  Conservative measure of “drop-out” as these were not
    students with 0 engagement

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  29. Results
    n  The overall score for dropouts (M = 55.66, SD =
    24.34) was significantly higher than the overall
    score for non-dropouts (M = 45.61, SD = 20.56),
    F(1, 537) = 17.73, p < .001 (p
    η2 = .03; d=.35)
    n  All domains but Emotional Health were
    significantly different
    n  Largest effect sizes:
    ¡  Substance Use p
    η2 = .08 d=.59
    ¡  School Adjustment p
    η2 = .07 d=.55
    ¡  Family Systems p
    η2 = .06 d=.51

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  30. Results Table
    Domain Group N Mean Std. Deviation
    Physical Health Dropout 93 4.280 2.013
    Non-dropout 436 3.454 1.937
    Leisure &
    Recreation
    Dropout 94 5.649 1.916
    Non-dropout 433 4.737 2.177
    School Adjustment Dropout 91 9.813 4.297
    Non-dropout 431 6.865 3.942
    Family System Dropout 88 6.227 3.183
    Non-dropout 430 3.984 3.292
    Behaviour Patterns Dropout 85 9.082 3.840
    Non-dropout 426 8.042 4.078
    Social Competence Dropout 82 4.573 3.147
    Non-dropout 416 3.726 2.718
    Peer Relationships Dropout 85 7.835 2.927
    Non-dropout 420 5.900 2.973
    Emotional Health Dropout 82 8.585 3.617
    Non-dropout 413 7.697 3.847
    Substance Use Dropout 85 6.024 4.257
    Non-dropout 406 3.192 3.491

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  31. Social Work & Justice System
    n  RQ: Examine the patterns of Child-Welfare system
    involvement among youth, who have aged out of the
    child welfare system
    ¡  Look at the impact of Race
    n  Data from Department of Human Services (DHS)
    anyone involved with Child/Youth-Welfare
    n  All kids born between 1985-1994 in Allegheny County
    with system involvement
    n  42,735 youth from 23,754 families
    ¡  51% male
    ¡  47% Black, 45% White, 6% other
    n  Aged out youth: are kids who do not have a stated goal
    of reunification with their families prior to leaving the
    custody of the child welfare system

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  32. Background
    n  Who qualifies? Any youth who
    ¡  Experienced a placement
    n  In Foster or Kinship Care
    n  In Group Home or Residential Facility
    ¡  Received any type welfare service
    n  Mental Health (MH)
    n  Drug & Alcohol (DA)
    n  Employment Training (ET)
    n  Housing & Homeless (HH)
    ¡  Involved with Justice System
    n  Juvenile Justice (JJ)
    n  Jail
    n  Special Subsample of Aging Out Youth:
    ¡  17 and older when left care with one year or more in out
    of home placement

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  33. Study 1: Design & Analysis
    n  Design: Quasi-Experimental, uses
    Administrative Data but primary data collection
    n  Part 1: Two-step Cluster Analysis
    ¡  Identifies homogeneous subgroups
    ¡  Used the diversity of DHS services to create groups
    n  Part 2: Test Cluster differences on 12 variables
    using ANOVAs- Are clusters meaningful?
    n  Part 3: Test Mediation by Race- Can race
    explain the Cluster differences?
    n  Part 4: Test Cluster Differences using ANCOVA
    to adjust for potential confounders

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  34. Study 1: Part 1 - Cluster Results
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    Low involvement
    (17%)
    MH Only (30%) MH & DA (22%) MH & JJ (15%) MH & Jail (17%)
    Mental health
    Drug & alcohol
    Juvenile justice
    Jail

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  35. Study 1: Part 2 – ANOVA Results
    n  The two low involvement groups compared to
    the high involvement groups
    ¡  Have more placements (5, 7) : (9, 11, 9)
    ¡  Spend larger % of time not in foster care
    (77%, 63) : (39%, 42, 45)
    ¡  More likely to have only foster care
    (52%, 29) : (7%, 11, 13)
    ¡  Less likely to run away (23%, 29) : (46%, 61, 50)
    n  All significant main effects, and post-hoc
    comparisons controlling for type I error inflation

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  36. Study 1: Part 3 – Race Mediation
    n  Could race mediate (explain) these cluster differences?
    n  Why look at race?
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    Allegheny County Youth CYF Involved Aged Out
    African American White
    Cluster %AA
    Low Inv. 82%
    MH only 66%
    MH & DA 60%
    MH & JJ 71%
    MH & Jail 79%

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  37. Study 1: Part 4 – ANCOVA Results
    n  Covariates
    ¡  Race, Age, Sex, ever Running Away and Age at first
    placement
    n  Ran an Exploratory Factor Analysis on MH
    services
    ¡  As a reduction technique, whether to look at all MH services
    collectively or individually
    ¡  Results indicated 1 underlying factor
    ¡  MH Factor score = sum of services received
    n  DVs
    ¡  Number of placements
    ¡  Years in Placements
    ¡  Mental Health Services
    ¡  Percent time out of home in Foster & Congregate Care

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  38. Study 1: Take Home Message
    n  Numbers with subscripts differ significantly
    n  Low Involvement and MH only groups had less
    placements, more time in foster care, less time in
    Congregate Care
    ¡  Placement type more important than length
    n  Time spent in placement not as important as placement
    stability
    (p
    η2) Low Inv. MH only MH & DA MH & JJ MH & Jail
    # Placements .058 6.17 6.83 a
    8.33 b
    10.11 a
    8.44
    Years in Placement .013 4.42 a
    5.10 4.36 4.47 4.32
    Foster Care % .068 73 a
    55 b
    40 b
    42 b
    42
    Cong. Care % .065 19 a
    36 b
    51 c
    44 c
    47
    MH Services - 3.12 a
    3.52 a
    3.59 b
    3.24

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  39. Study 2: Juvenile Justice
    n  RQ: Examine how placement experiences and
    services received effected kids’ entrance into JJ
    n  Also to examine impact of Race and Sex on the
    relationship between experiences and JJ (Race &
    Sex as Moderators)
    n  Child welfare-involved youth are more likely than
    general population to become involved with in JJ
    system
    ¡  Timing is important for looking at what may affect
    kids’ entering the Justice system
    n  Only considered child welfare involvement if it happened
    before JJ

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  40. Study 2: Analysis - GEE
    n  Our analysis has to account for the fact that
    siblings are more similar than non-siblings
    ¡  Otherwise it is a Violation of Independence
    n  Our outcome variable is dichotomous
    n  Generalized Estimating Equation used
    ¡  Provides unbiased marginal (population-average)
    regression coefficients regardless of the
    correlation structure of the errors
    ¡  Part 1: Entire sample (any interaction with DHS)
    ¡  Part 2: Just those who had placements

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  41. Study 2: Demographics
    n  Used dichotomous
    variables, to indicate ever
    having the experience
    ¡  Conservative estimate of
    impact of the variables
    n  3,712 youth from 2,918
    families
    n  AA and White only
    ¡  Other races too few
    n  20% JJ involvement
    n  55% had CC
    n  18% Ran away
    n  70% had MH
    n  25% had DA
    n  83% cases open after 13
    n  Averaged 4.8 (SD = 4.5)
    placements
    n  Out of home 2.3 years
    (SD= 2.93)

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  42. Study 2 : Part 2 –Results
    n  Placement Sample results
    ¡  Being African American increase Justice
    involvement
    ¡  Being Male increases it
    ¡  MH increases it
    ¡  DA protective
    ¡  Closing Case early protective
    ¡  Running away is protective?

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  43. Study 1: Part 2 –Results: Odds Ratios
    Main Effect
    Model
    95%CI
    Interaction
    Model
    95%CI
    African American 1.90** 1.56-2.32 2.38** 1.43-3.98
    Boys 3.35** 2.79-4.02 3.89** 2.39-6.34
    Case open after 13 2.74** 2.00-3.77 2.95** 2.12-4.10
    # of placements 1.11** 1.08-1.14 1.11** 1.08-1.14
    Years in placement .92** .89-.96 .93** .89-.96
    Congregate care 1.25* 1.01-1.55 3.60** 2.11-6.14
    Runaway .74* .57-.95 .72* .56-.92
    Mental health services 1.46** 1.17-1.81 1.47** 1.18-1.84
    Substance abuse services .73* .59-.91 .73* .59-.90
    AA & Male 1.64* 1.09-2.46
    AA & congregate care .48** .32-.73
    Male & congregate care .47* .30-.76
    n  Interaction Terms- what do they tell us?

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  44. Study 2: Part 3 – Predicted Probabilities
    n  African American boys are twice as likely as white
    boys to have JJ
    n  African American girls have only a 50% increased
    likelihood of JJ compared with white girls
    0%
    5%
    10%
    15%
    20%
    25%
    30%
    35%
    40%
    Boys Girls
    African American
    White

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  45. Study 2: Part 3 – Predicted Probabilities
    n  In general, AA more likely to have had CC
    than White Youth
    n  White Youth in CC more than 2x as likely to
    have JJ involvement
    0%
    5%
    10%
    15%
    20%
    25%
    30%
    Yes No
    Congregate Care
    African American
    White

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  46. Study 2: Part 3 – Predicted Probabilities
    n  On Average, boys more likely to have JJ
    involvement than females
    n  Girls who were in CC 2x as likely to have JJ
    involvement
    0%
    5%
    10%
    15%
    20%
    25%
    30%
    Yes No
    Congregate Care
    Boys
    Girls

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