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SRC Summer Internship Program 3rd Annual Symposium

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SRC Summer Internship Program 3rd Annual Symposium

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  1. SRC Summer Internship Program3rd Annual Symposium • Tuesday, July 25, 2006 • Noon-2:00 p.m. • ISR Building, Room 6050 The Survey Research Center is an equal opportunity employer who values diversity in the workplace.

  2. Agenda • Welcome • Coordinators • Background • Overall Purpose of Symposium • 10 Minute Presentations (wide spectrum of topics) • Symposium Format • General Q/A

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

  4. Joslyn M. GainesAir Force Study Penny Pierce, PhD, RN, Col, USAFR Amiram Vinokur, PhD Social Environment and Health

  5. Ohio welcomes back the 211th Maintenance Company upon their return from Iraq - Google Video

  6. Women’s Veterans Project • Sample consists solely of Air Force women who were deployedto various military operations since March of 2003. • Purpose of the study is to determine the effects of deployment experiences as well as civilian work and family on the women’s physical and mental health and their likelihood to remain in the Air Force. • Commonly referred to as OIF for Operation Iraqi Freedom. About half of the sample was deployed to OIF and half were deployed elsewhere.

  7. Work, Family, and Stress: Deployment, Resilience, and Retention • Very similar to OIF with the exception that men are included in the sample. Also this sample tracks Air Force personnel who have been deployed since October of 2001. • Half of the sample are personnel deployed to the theatre of war and half are deployed elsewhere. • Commonly referred to as READI. • Similar UK study being done, but there are significant differences.

  8. Current Procedure

  9. Problems • Military personnel are very mobile. • Contact info can be outdated very quickly. • Deployments increase response problems. • Respondents simply won’t answer the phone. • Families may be wary of releasing information or even speaking about enlisted individuals.

  10. Solutions • Various Tracking Methods: • Insight Collect. • Internet Searches. • Base Locators. • Determined Callers: • Nag Calling. • Calling Methods. • There’s always someone at ISR. • Dana, Isabella, Jessica, and Mona.

  11. “The trouble with research is that it tells you what people were thinking about yesterday, not tomorrow. It’s like driving a car using a rear-view mirror.” ~ Bernard Loomis

  12. Real-Time Research • “Anthrax Shot - end it! People have gotten really ill. People have not said anything out of fear of confidentiality.” • “Mandatory anthrax shots were very controversial… studies should be sure to look into the issue because a lot of people got out of the military to protest it… huge health issue of this time period."

  13. Real-Time Research

  14. Real-Time Research • “The phrase, ‘If the Air Force wanted you to have a family, it would issue you one!’ has been shown increasingly to be true, where it is clear that if you have a family and give them priority in your life, you can’t continue to serve.” • “Reservists are not getting the help they need and deserve, not the same treatment as Active Duty members.”

  15. Real-Time Research

  16. Real-Time Research • “ I have trouble reconnecting with my children (ages 3 and 7) especially the 3 year old. I left when she was 1 year old.” • “I have major concerns about how my deployment will affect my kids later in life. I hope that they will always see the positive side of it – mom defended our country in war.”

  17. Real-Time Research

  18. The True Value of Survey Research

  19. Thank You! • SRC Summer Internship Program George Myers, Anita Johnson, Fellow Interns • SEH Staff Amiram Vinokur, Penny Pierce, Susan Clemmer, Lisa Lewandowski-Romps, Lillian Berlin, Elli Georgal, Brianne Ott, Jumoke Johnson, Courtney Baarman

  20. Jionglin Wu • Tailoring Treatments to Individual Patients Quantitative Methodology Program Advisor: Susan Murphy, PhD

  21. Main Points Motivation: heterogeneity of treatment effects to individual patients. Statistical learning and Q-learning algorithm. Computer simulations.

  22. Motivation: Heterogeneity of Treatment Effects • Dimensions: • Difference in baseline risk/ risk without treatment. • Treatment may only be worthwhile for patients with poor prognosis. • Responsiveness to the treatment. • Absorb a drug rapidly, metabolize it slowly , or have a high concentration of functional drug receptor. • Vulnerability to adverse side effects. • Related to intrinsic biological characteristics of different patients. • Utilities for different outcomes. • Patients vary on how they trade off side effects vs. reduction in symptoms.

  23. Our Practical Goal • Individualized treatment to patient is a process involving multiple time points with a combination of various medications. • The data we use are from randomized trials that randomly assign people to treatments whenever they are altered. • Develop an estimation method that use those data to determine tailoring of individualized treatments.

  24. Statistical Learning • Q-learning is a statistical algorithm that is frequently used to tackle maximization problems involving multiple decision points whenever treatments are altered. • We use Q learning to construct individualized treatments with the goal of maximizing the expected patient benefit. • Q learning use regression for every decision points. • Statistical learning tools: Test sample, Cross-validation, Training sample, Bootstrap are used to produce results that are reproducible.

  25. Simulation Goal • Each regression in Q-learning requires a model. • We want to find models that maximize expected patient benefit. • Find some reliable statistics to help determine whether any of our models are maximizing the expected patient benefit.

  26. Computer Simulation • We test statistics using simulated data. • Generative model: We generate 3,000 set of patient response and characteristics data with each of size 300. • Fitted model: We engineered 2 false models and the correct model for contrast.

  27. True Expected Benefit Under 3 different models using 100000 Test sample ExpectedBenefit

  28. Estimated Expected Benefit Under 3 different models using Cross-validation Expected Patient Benefit

  29. Using mean and variance to choose the best model

  30. Future Work • Multiple Time Points. • Integration of information such as mean and variance.

  31. Acknowledgements • Susan Murphy, Daniel Almirall, and Hasan Cheema • George Myers and Anita Johnson • All the ISR interns

  32. Jennifer Swayne Jail Recidivism Social Environment and Health Program

  33. Introduction The goal of this research project is to answer the following overarching questions about how jail recidivism operates within the penal system: • Does jail overcrowding affect sentence length? • Do longer sentences make recidivism more or less likely?

  34. History of Criminology Deterrence Theory – influenced by two theoretical frameworks: Classical Criminology: • Role of legislatures. • Role of judges. • Seriousness of crime and subsequent punishment. • Punishment should be prompt and certain. • Laws should be structured to prevent crime from happening.

  35. History of Criminology Positivist Criminology: - Emerged as a response to classical criminology. - Changes in punishment policies alone would not change crime. - Classical Code does not allow for individual differences such as mental condition, age, repeats in offenses, and other extenuating circumstances.

  36. Criminal Justice Process Preliminary Exam Pretrial Plea Bargain Arraignment Trial 1 night jail Sentencing Crime committed Review Hearings

  37. Jail Data Collection • The sample consists of data that either correspond to five overcrowding emergencies or five comparison time periods in 2002. 1) Jail Database – collection of information from the Washtenaw County Jail. 2) Prosecutor’s Data- collection of information on the same individuals listed in the jail database, as well as offenders not included in the jail database.

  38. Jail Project Measures • Selection of Offenders: - Domestic Violence Assault and Battery (misdemeanor and felony offenses). - Drug Use, Possession, Delivering, Manufacturing, etc. (misdemeanor and felony drug crimes). - Non-support (Child support payments).

  39. Jail Data Method Instrumental Variables (IV) Method: - quasi-experimental method where variable Z (overcrowding) is an IV for the causal effect of X (sentence length) on Y (recidivism). X (sentence length) Y (recidivism) Z (Overcrowding)

  40. Next Steps Learned that we have to examine more than short vs. long sentences because: • Short vs. long is not random. • Those who have longer sentences are more likely to recidivate. • What differences are there between people who commit more severe crimes and those who commit less severe crimes?

  41. Implications • Causes of recidivism • Effect of jail on various people • Provide better research

  42. Conclusion - Further study is needed to determine the relationship between overcrowding and sentence length on recidivism. - The researchers on this project will continue assessing the various associations between overcrowding, sentence length, and recidivism.

  43. Closing and Thanks SRC Summer Internship Program - George Myers - Anita Johnson Jail Data Project - Jeffrey Morenoff - Ben Hansen - Sarah Jirek -Washtenaw County Jail and Prosecutor’s Office

  44. Corina Mommaerts Health Insurance Trends Among the Near Elderly: 1998-2004 Health and Retirement Study

  45. Presentation Overview • Background on the Health and Retirement Study • Classwork and Officework • Health Insurance Trends

  46. Health and Retirement Study http://hrsonline.isr.umich.edu/ • The leading resource for data on health and economic circumstances for Americans over 50. • Sponsored by National Institute on Aging. • Longitudinal survey of over 20,000 every two years. • Data since 1992. • Recently awarded $70 million, the largest single research award in UM history!

  47. Officework • ADAMS database • The Aging, Demographics, and Memory Study. • First population-based study of dementia: 856 HRS participants aged 70+. • Helped creation of codebook. • 2005 Prescription Drug Study and Participant Lifestyle Questionnaire • Mail Questionnaires. • PLQ first to use scanning techniques.

  48. Classwork • Introduction to Survey Research Techniques • 8 weeks, created a survey over the course of it. • Health and Retirement Study Workshop • Weeklong. • Morning lecture, afternoon lab. • Used this knowledge to experiment with data and come up with health insurance project.

  49. Health Insurance Trends • What are the current trends in health insurance coverage among the near elderly? • Important to examine due to aging population, rising costs of healthcare, and the worry of decreasing employer-based coverage. • Complex question, much more research must be done. • Data from 1998, 2000, 2002, 2004 HRS waves • Used ages 55-64 in each wave.

  50. Health Insurance Trends New cohorts added to existing sample in 1998 and 2004.