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Summer Internship Program Annual Symposium 2012 PowerPoint Presentation
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Summer Internship Program Annual Symposium 2012

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Summer Internship Program Annual Symposium 2012

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  1. Summer Internship Program Annual Symposium 2012

  2. Agenda • Welcome • Background • Overall Purpose of Symposium • Symposium Format • Closing Remarks • Meet and Greet the Interns • UM Football Stadium Tour

  3. Acknowledgements • Sponsors: • Health and Retirement Study • Life Course Development Program (2) • Survey Methodology Program • Social Environment and Health Program (2) • Partners: • Senior Staff Advisory Committee • SRC Administrators & SRC Diversity Committee • Summer Institute in Survey Research Techniques • Survey Research Operations • Inter-university Consortium for Political and Social Research • ISR and SRC Human Resources • SRC Computing • ISR and SRC Director’s Offices • ISR Director’s Diversity Advisory Committee

  4. The Effects of Incarceration and Probation on Reoffending and Employment Nicole Yadon Social Environment & Health Sponsor: Dr. Jeffrey Morenoff

  5. Issues in Studying the Effects of Mass Incarceration • Framing the research question and identifying comparison groups • Some studies use survey samples to compare people who have vs. have not been to prison • We frame the question as being about alternative ways of sanctioning convicted felons • Our comparison groups are restricted to the population of people convicted of felonies • We compare people who were sentenced to prison, jail, probation, etc. • Obtaining appropriate data • Survey samples usually don’t include institutionalized populations • Establishing causality • True experiments are not possible – judges will not randomly allocate sentences • Problem of unobserved confounders • Judges may base their decisions on factors that are not observed by researchers (e.g., temperament) • These same factors may predict future outcomes (e.g., recidivism, employment)

  6. Our Study • Question:What is the effect of being sentenced to prison vs. probation on future criminal offending and employment? • Data and sample: Administrative records on all felony convictions in Michigan from 2003-06 • Records from courts, department of corrections, police, unemployment insurance agency • Method: Quasi-experimental designs • Using random assignment of judges to cases as “instrumental variable” • Exploiting “discontinuities” in sentencing guidelines • Guidelines restrict judges’ sentencing options based on (a) offense severity and (b) prior criminal record

  7. My Role: Circuit Court Demographic Information • Background research on operation of Michigan Circuit Court system • Reading court documents • Talking to judges and court officials • Collecting data on judges (part of new project sentencing disparities) • Collecting data on judges from circuit court websites and “Judgepedia” • Obtaining records from Michigan Supreme Court Administrative Office • Biographical data on judges • Circuit-level data on court processing

  8. From 2003-2009 there were 289 judges in office • 60% (n=173) were elected • 40% (n=116) were appointed

  9. Acknowledgements • Jeffrey Morenoff, Ph.D. • David Harding, Ph.D. • MDOC & SCAO • SRC Summer Internship Program

  10. Urban Social and Built Environment and the Trajectories of Social Isolation: Findings from Detroit MI CHOICE Population Min Hee Kim (kminhee@umich.edu) Social Environment and Health Program Sponsor: Philippa Clarke, Ph.D.

  11. Internship Goals • Analytic skills for multi-level data structure • Explore the mechanisms through which neighborhood affects older adults’ health • Engage in social environment and health scholarships • Work and family balance

  12. Background • Why is social isolation important at later life? • Staying at home, instead of admission to nursing home, has benefits at both individual and societal level • Understanding social and built environment factors that affect social isolation is critical

  13. Detroit older adults experienced rapid socioeconomic • and structural decline in last decades

  14. Research Question & the Focus • How do neighborhood social and built environments explain the trajectories of social isolation, adjusting for socio-demographic and health factors? • Focus on those who have unmet needs (i.e., Medicaid Waiver Program Recipients) in Central Detroit

  15. Conceptual Model Social Isolation Initial Status Social Isolation Overtime

  16. Methodology • Analytic Methods Generalized Hierarchical Linear Modeling (HLM) • Data 1) Michigan Minimum Data Set (MDS) for Home Care (2000-2008) followed every 90 days 2) Neighborhood Data using Systemic Social Observation (SSO) methods

  17. SSO Data • Neighborhood audit of all 4 streets in each client’s residential block • Using Google Street View (2007-2009) • Indicators of built physical and social environment can be reliably assessed with a virtual audit instrument (Clarke, et al. (2010) Health and Place)

  18. Social Isolation • Social Isolation was measured as a dichotomous variable indicating whether client’s level of participation in social, religious, occupational or other preferred activities declined • As compared to the previous 180 days, as assessed by the case manager

  19. Constructed Variables • Difficulties with Activities of Daily Living (ADL) • 7 items: Transfer, Walking, Dressing, Eating, Toilet, Grooming Bathing • Individual item measured : 0 (independent) ~ 5 (activity did not occur) • Difficulties with Instrumental Activities of Daily Living (IADL) • 7 items: Meal, Housework, Money, Medications, Phone, Shop, Travel) • Individual item measured: 0 (no difficulty) ~ 2 (great difficulties) • Social Disorder Index (9 items) 1) Graffiti painted over; 2) Garbage, litter or broken glass; 3) Cigarette or cigar butts; 4) Empty beer or liquor bottles in streets, 5) Gang graffiti; 6) Other graffiti on buildings; 7) Abandoned car; 8) Condoms; 9) Drug related paraphernalia on the side walk

  20. Individual Characteristics at Baseline (2000-2008) (N=1,009) (weighted)

  21. Neighborhood Characteristics at Baseline (2000-2008) (N=1,009) (weighted) • Average % of poor street on the block 0.23 (s.d. 0.27) • Average social disorder index 1.44 (s.d. 1.25 ) • Average % of residential security sign in the block 0.02 (s.d. 0.78)

  22. Longitudinal Characteristics (2000-2008) (N=4,875) • Average number of observation per person= 5.1 • Weight generated based on the probability of retention • Individual data was truncated at 3 years • Average observations per neighborhood cluster 2.1

  23. Discussions • Practical implications • Generalization to urban older adults population in poverty • Some limitations to be further examined • Methodological Implications • Policy Implications

  24. Thank youSpecial thanks to.. Philippa Clarke Ph.D., George Myers Ph.D., and 2012 Summer SRC Interns*Funding for the geocoding/SSO part of this project was provided through Grant number K01EH000286-01 (Clarke) from the Centers for Disease Control and Prevention (CDC)

  25. Disclosure and Quality of Answers in Text and Voice Interviews on iPhones Monique Kelly Survey Methodology Program Sponsor: Fred Conrad, Ph.D.

  26. Parent Study • Examined • Data quality (satisficing, disclosure, straightlining) • Completion rates • Respondent satisfaction • Four existing or plausible survey modes that work through native apps on the iPhone

  27. Experiment: 4 modes on iPhone

  28. Items • First, safe-to-talk question • 32 Qs taken from major US social surveys and methodological studies • E.g ., Pew Internet & American Life Project • Types of QS • Yes/No • Numerical • Categorical • Battery Items (series of Qs with same response options)

  29. Respondents • n = 642 iPhone users (age > 21) • 158 to 165 randomly assigned to each mode • Recruited from: • Craigslist • Facebook • Google Ads • Amazon Mechanical Turk • Incentive • $20 iTunes gift code

  30. Summary • Voice vs. Text • Text produced higher data quality • Greater disclosure, less satisficing, high satisfaction • Human vs. Automated • Automated interviews on a smartphone (in these modes) can lead to data at least as high in quality as data from human interviews in same modes • No more satisficing than with human interviewers! • More disclosure

  31. Internship Project

  32. Goals of Project • To see how the interaction between R and the I agent differ across modes. • How this explain differences in answers to same questions across modes.   • To understand interaction around disclosure of personal/sensitive information.

  33. Example Research Questions • Does more departure from the script reduce disclosure? • automated interviewers never depart from script • Do respondents exhibit less human-like communication (e.g. disfluencies) when interacting with automated speech system?

  34. Rendering Then converted into an avi file PAMSS interface Opened in Camtasia

  35. Transcribing

  36. Coding Coding was done in a tool called Sequence Viewer.

  37. Coding (continued) • Respondent Codes • Examples • Answer question • Partial answer • Interviewer Codes • Examples • Ask question exactly as worded • Ask question with wording change • Questions Raised • Possible Additions?

  38. Future Analyses • Relationship between sciptedness and disclosure. • Whether I asks the question exactly as worded or not • Comparison of R’s speech when I is human or automated.

  39. Conclusion • Aim • Interviewing agent effect on respondent’s answers. • Project in early phases • Three other modes to be transcribed, coded, and analyzed. • Stay tuned for more!

  40. Acknowledgements • George Myers, Ph.D. • Fred Conrad, Ph.D. • Michael Schober • Andrew Hupp • Lloyd Hemingway • Chan Zhang • Mingnan Liu • Chris Antoun • The staff of Survey Methodology Program • CMT

  41. Understanding the Achievement Gap: Do Parent Expectations and School Climate Matter? Adrian Gale, MSW University of Michigan Joint Program in Social Work and Developmental Psychology Sponsor: Toni Antonucci Ph.D. Life Course Development