Using a Structured Decision Making Protocol to Stratify Caseloads in the Child Support Program

1. Using a Structured Decision Making Protocol to Stratify Caseloads in the Child Support Program ERICSA Steven J. Golightly, Ph.D.May 23, 2011

2. Structured Decision Making Decision making in which the process and criteria that must guide decision making are formally defined (Shook & Sarri, 2007) Can be clinical or actuarial 2

3. Decision Theory In statistical theory, the process of making choices between alternatives (Berger, 1993) Can be informative or descriptive 3

4. Risk Assessment Likelihood that a harmful event will occur and such an event’s likely security (Hughes & Rycus, 2007) Study assesses child support cases in terms of risk of non payment 4

5. Risk Assessment Protocols Child Welfare Criminal Justice Health Care Credit – Risk Management 5

6. Dissertation Overachieving goal of child support Novel approach for assessing risk in order to determine the level of enforcement intervention necessary to collect child support Stratification up front Acknowledging differences Prioritization on a rational basis Knox County (TN) example (PSI, 2001) 6

7. Research Questions Can CP data be utilized to determine enforcement difficulty (e.a., risk assessment)? Can child support use structured decision making? Potential impact of case stratification using CP data? 7

8. Research Hypothesis Are there relationships between CP data and the child support agency’s ability to collect full payment from the NCP for at least 6 consecutive months? 8

9. Research Design and Method Non experimental design Non programmatic analysis Los Angeles County CSSD Archival data (FFY 2008) Secondary data use is cost effective Examine the relevance of various independent variables as determinants of case success 9

10. Dependent Variable Case Success Receipt of the ordered amount of child support for at least 6 consecutive months 10

11. Independent Variables Age Gender Residential zip code Ethnicity Marital status Welfare status Number of children Relationship to each child Ages of the children Paternity status Court Order 11

12. Purpose of Study Using CP data obtained at intake – Is it feasible to determine case success? Study Rationale • Reduced funding and staff • More cases • Increased need for efficiencies 12

13. Significance of the Study If correlation can be shown between CP data and case success, stratification could be implemented earlier in the process 13

14. Assumptions FFY 2007-08 cases were typical of cases opened in otheryears Data in applications assumed to be accurate Benchmark of 6 months of consecutive payments constitute case success 14

15. Limitations Time period was the beginning of the economic downturn Unemployment rate was only 5.1% in 2007 Given unique urban nature of Los Angeles County, may not be possible to generalize results to other Jurisdictions 15

16. Delimitations Does not intend to provide a model for using NCP data Excludes consideration of reasons why NCPs did not pay child support 16

17. Bounds May be useful only in Los Angeles Generalizing prediction tools across Jurisdictions may be “suspect” (Farrington and Tarling, 1985) 17

18. Theoretical Framework SDM is crucial in many fields SDM relies on gauging risk Child support Low Risk High Risk Decision Theory Normative Descriptive Regression Analysis Delinquency Time & money 18

19. Literature Review Problems associated with child support programs Significant change in 50 years Family structure changes Caseload composition Funding/Staffing CP data literature 19

20. Method Logistic regression Blomberg & Long (2006) Importance of “success” definition 20

21. Research Design and Approach Design is the structure that holds all elements of the research project together Two basic categories Experimental Non experimental This study utilized a non experimental research design Nonparametric design Predictive correlational study 21

22. Research Design and Approach (con’t) Archival data (FFY 2008) Los Angeles County CSSD ARS Sequel Server SQL Server Management Studio Software IBM SPSS statistics 18 software to analyze data 22

23. Scores and Calculations Correlation Coefficient Calculation No relationship (0) Strong relationship (1) 23

24. Design Justification • Quantitative approach • Non experimental • Regression analysis • Non parametric • Cramer’s V Test • Predictive Correlation 24

25. Participants and Sample Size • Custodial Parents Demographics • Caseload Composition 25

26. Study Sample • FFY 2008 – Reasons for Using • 19,000 cases - Universe • Sample size of 377 = 95% confidence level and considerable interval of 5% • Study used 1501 cases • Random selection 26

27. Ethical Considerations • Data de-identified and presented anonymously • Privacy & confidentiality • Transfer of data from SQL Software 27

28. Data Screening & Data Cleaning • 1501 randomly selected cases • Cleanup to ensure No missing valves and accurate + initiative • All cross tabulation cells had at least 5 members • Decision Points • Age (recording 14 – 41) • Zip Codes (first 3 digits) • Ethnicity (truncated) • Age of Children (parameter determination) • Paternity Status (duplicative 28

29. Descriptive Statistics • N = 1501 • 1456 Females (97%) • 856 Hispanics (57%) • 375 African Americans (25%) • 163 White (11%) • 792 Never Married (53%) • 135 Married (9%) • 940 Currently/Formerly Assisted (63%) 29

30. Descriptive Statistics (con’t) • AgeFrequency% • Gender 30

31. Descriptive Statistics (con’t) • Residential Zip Code Frequency% 31

32. Descriptive Statistics (con’t) • Ethnicity Frequency% 32

33. Descriptive Statistics (con’t) • Marital Status Frequency% 33

34. Descriptive Statistics (con’t) • Welfare Status Frequency% • Number of Children 34

35. Descriptive Statistics (con’t) • Relationship to Each Child Frequency% • Paternity Status (Child) 35

36. Descriptive Statistics (con’t) • Court Ordered Frequency% 36

37. Regression How the DV is numerically related to the IVs Correlation The relationship of the variables Variables converted into nominal data 2 types of test data & 2 types of analysis Nonparametric v parametric Data Analysis 37

38. Regression How the DV is numerically related to the IVs Correlation The relationship of the variables Variables converted into nominal data 2 types of test data & 2 types of analysis Nonparametric v parametric Data Analysis 38

39. Contingency coefficient and Cramer’s v tests utilized to test for association or strength of the relationships of the variables Strong relationship = prediction would be feasible Weak relationship = prediction not reliable Test Results 39

40. Test Results (con’t) 40

41. Strengths of Association 41

42. Results Strong Association • CP Age • Gender • Ethnicity • Welfare Status • Number of children • Relationship to each child • Ages of children 42

43. Hypothesis Testing • There are relationships between CP date and the child support agency’s ability to collect full payment for the NCP for at least 6 consecutive months. • Non experimental study • Confirmed – very strong associations between seven of the 11 independent variables and the dependent variable 43

44. Next Steps • Further Analysis • FFY 2009 data • SPSS • Deeper into sub groupings for associations • When to use? • Establishment • Enforcement 44