Tuesday July 28, 2009 Noon-2:00 p.m. ISR Building, Room 6050

1. SRC Summer Internship ProgramResearch Symposium • Tuesday • July 28, 2009 • Noon-2:00 p.m. • ISR Building, Room 6050 The Survey Research Center is an equal opportunity employer that values diversity in the workplace.

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

3. Acknowledgements • Sponsors: • Health and Retirement Study/School of Public Health • Life Course Development Program • Survey Methodology Program • Quantitative Methodology Program (2) • Social Environment and Health Program (2) • 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. The Michigan Study of Life After Prison Andrea Garber University of Michigan-Flint Jeffrey Morenoff, PhD & Dave Harding, PhD Social Environment and Health

5. Overview 1. The Michigan Study of Life After Prison • Analysis of Michigan Department of Corrections Administrative Records • Pilot Interview Study • Comprehensive Evaluation of MPRI at Ionia Bellamy Creek 2. Drug Tests, Drug Screens, & Our Data

6. Parolee Characteristics for Population and Sample

7. Parolee Characteristics for Population and Sample Continued

8. Parolees’ First Addresses (2003) Many parolees return to a small number of neighborhoods- 12% of tracts receive 50% of parolees But parolees are spread throughout the State- Of Michigan’s 2,707 census tracts, 78% received at least one parolee

9. Qualitative Interviews Half of respondents received cell phone incentives and half received cash To enroll 24 subjects, we approached 28  4 refusals

10. Drug Screen v. Drug Test Drug Screen Drug Test Gas Chromatography/Mass Spectrometry (GC/MS): Gas chromatography separates the substances found in the urine then mass spectrometry identifies the substances found Extremely accurate Sensitive to small doses • Immunoassay: Based on competitive binding, and it uses antibodies to detect the presence of a particular drug or metabolite in a urine sample • Less accurate • Less sensitive to small doses

11. Preliminary Results on Drug Screening 94.1% of our 3,689 parolees have been drug screened As of May 2008, each parolee in our sample has been screened an average of 17.6 times, but some have had as many as 209 screens Of those screened, 59.12% have had a positive test

12. A false positive is when someone has a positive result but was not using; This may occur with numerous different cross-reactions including poppy seeds for opiates and ibuprofen with THC. • A false negative is when someone has been using a drug but received a negative result.

13. A false positive is when someone has a positive result but was not using; This may occur with numerous different cross-reactions including poppy seeds for opiates and ibuprofen with THC. • A false negative is when someone has been using a drug but received a negative result.

14. Reasons for a Drug Screen or Test May be a condition of parole Agent may suspect substance use Family may suspect use May be screened if they abscond They may request a GC/MS (confirmatory test) In most cases, parolees usually admit to using

15. Effects of a Positive Test In most instances, a parolee will be referred to a substance abuse treatment center. There are four levels of care: Outpatient, Intensive Outpatient, Domiciliary Outpatient, Residential. A return to prison rarely happens, but if it is being considered then a portion of the positive sample will be sent to the laboratory for confirmation testing using GC/MS.

16. Should We Analyze Drug Screens? False positives are relatively rare False negatives are more widespread Parole agents may have discretion in how they react to screens Are there extra legal indicators that change parole agent responses?

17. Thank you Jeff Morenoff and Dave Harding George Myers and Anita Johnson Barb Strane, Bianca Espinoza, Paulette Hatchett, Amy Cooter, Claire Herbet, Liz Johnston, Elena Kaltsas, Ash Siegel, Jay Borchert SRC Interns

18. Matching and Randomization In A Prisoner Reentry Program Gabriel Moreno University of Arizona David Childers and Ben Hansen PhD SRC-Quantitative Methodology Program

19. MPRI The goal of the Michigan Prisoner Reentry Initiative (MPRI) is to reduce recidivism among prisoners. The pilot program provides various classes and services to promote successful reentry into the community. If the pilot program is deemed successful it will be expanded to more sites across the state.

20. Replenishment • The pilot site has a fixed number of spaces and a predetermined ratio of prisoners with low, medium and high recidivism risks. • When a space becomes available, an eligible prisoner list is compiled and two of these prisoners are selected. • One of these two prisoner is randomly assigned to the empty space while the other is sent to receive the standard prisoner reprogramming services.

21. Tracking The outcomes of the prisoners entering the program (treatment group) and the eligible prisoners who end up receiving the standard programming (control group) are tracked. The recidivism of these groups will eventually be used to assess the program’s effectiveness.

22. Current Flow of Prisoners Randomization

23. Considerations In order to better measure the effectiveness of the program, the treatment and control groups should have a similar makeup. The current randomization scheme for keeping the overall treatment and control populations similar can be improved upon.

24. Objective Our task was to create a randomization and matching scheme that could be used by the Michigan Department of Correction (MDOC) to better sustain the overall similarity of the treatment and control groups’ compositions.

25. Data We began with the MDOC’s 2003 parolee demographics data. We then examined indicators of recidivism and prisoner demographic information.

26. Indicators of Recidivism Absconding Technical Parole Violation Recommitment

27. Demographic Data Mental Health Status Age Sex Offenses Income Dependents • Race • Drug Dependence • Assault Risk • Prior Offenses • Education Level • Offense Category

28. Analysis • Ran logistic regressions for each recidivism indicator using the demographic data. • Using the regression coefficients, we created our own risk scores. • We created our own risk scores because the MDOC’s risk scores were recently introduced. So outcome measures for these scores are not available.

29. Function in R We created a function in the R statistical programming environment that takes a list of eligible prisoners and matches them based on their risk scores. It then randomizes the pairs into the treatment and control groups. Matching and randomizing in this way will achieve better balance between the treatment and control groups.

30. A Problem and a Solution • The MDOC employees who will use our R function are not familiar with the R programming environment. • To solve this we used the RExcel package. • This allowed us to design an Excel spreadsheet that would compute and present the results of our function in a familiar environment.

31. Eligibles

32. The Final Product

33. Acknowledgements Ben Hansen Dave Childers Dave Harding Jeff Morenoff George Myers Anita Johnson

34. Introducing the Telephone Survey: Verbal Interaction and the Decision to Participate Colleen McClain Survey Methodology Program Sponsor: Dr. Fred Conrad

35. Assessing the Introduction Why are response rates so low in telephone surveys? Why do phone answerers decline to participate? In telephone conversations, all information must be conveyed through the audio channel Variation in interviewers’ delivery-- and answerer’s responses-- in the introduction may matter “Introduction” defined: From “hello” to the first question of the interview-- or the hang up, refusal, etc.

36. Background • Work in linguistics and the psychology of interaction goes beyond content of speech • Use of disfluencies “to manage ongoing speech production” (Clark & Fox Tree, 2002) and to serve as “uncertainty cues” (Schober & Bloom, 2004) • Linguistic analyses focusing on speech frequency or intonational patterns in the introduction

37. The Interviewer Voices Project • Designed to extract speech and voice variables from the recorded interaction between interviewer and telephone answerer that may predict the participation decision • Levels of analysis: • Interviewer • Contact • Turn • Move

38. The Interviewer Voices Project • MSU team • Transcribing conversations • Measuring acoustic variables • U-M team • Coding speech, including content and paralinguistic features such as disfluencies and overspeech • Global ratings of speaker attributes (such as gender and whether or not they are native speakers) • Maryland team • Conducting multivariate modeling

39. My Role • Code speech in the recorded telephone invitations • Propose and conduct preliminary analyses • Specific hypotheses focus on the relationship between answerer’s participation decision and: • The proportion of overspeech in a contact • The proportion of backchannel moves in a contact • The proportion of interviewer moves containing fillers in a contact • Interviewers’ phrasing of indirect invitations

40. Methods • Coders listened to recordings and read corresponding transcripts in order to assign codes to speech segments • Transcripts organized by turns and moves • Sequence Viewer used to code conversations, link audio files, run pattern analyses • Some variables autocoded • Onset and offset times of moves recorded • Analysis of variance run using dataset exported to SPSS • Currently, 589 contacts (conversations) in database • Preliminary analyses: N = 491 • SRO introductions from five different studies, with corresponding audio recordings

41. Sequence Viewer Breaking a turn into moves: I 2 : [P=.40] This is Marilyn Figreal and I'm calling from the University of Michigan./ I 2 : [P=.54] We are doing a nation wide study about the economy I 2 : and we did send you something in the mail *about our study*.

42. Measures • Participation decision, or outcome: (1) Hang up, (2) agree, (3) refusal, (4) other, or (5) scheduled call back • Coder-determined • Proportion of interviewer moves in a contact that are backchannels • Proportion of overspeech in a contact • Proportion of interviewer moves containing fillers in a contact • Proportion of indirect invitations that are yes/no questions

43. Results • Hypothesis 1: The proportion of all moves that are backchannels will be highest in contacts resulting in agreements. .086 .044 .029 .003 ANOVA and paired comparisons reveal that the mean proportion of moves with backchannels is significantly higher for agreements than for any other outcome (p<.001); the same, significant pattern is observed when only answerer moves are examined.

44. Results .028 p=.059 .020 .012 .002 However: when only interviewer backchannels are examined, the highest mean proportion of moves with backchannels occurs for contacts resulting in refusals.

45. Results • Hypothesis 2: Proportion of overspeech in a contact will be higher for refusals than for more desirable outcomes. .223 .147 .116 .042 Analysis of variance and post-hoc tests reveal that mean proportion of overspeech in contacts resulting in refusals is significantly higher (p<.001) than for contacts resulting in any other outcome.

46. Results • Hypothesis 3: The proportion of interviewer moves containing fillers will be related to the answerer’s participation decision. p=.022 .200 .168 Analysis of variance reveals that a higher mean proportion of interviewer moves contain fillers in contacts with desirable outcomes than in those with undesirable outcomes, F(1, 479)=5.295, p=.022.

47. Results • Hypothesis 4: Yes/no questions provide an opportunity for answerers to opt out; contacts with an indirect invitation phrased as a question will lead to less desirable outcomes than those phrased as statements. • The mean proportion of indirect invitations phrased as Y/N questions is higher for desirable than undesirable outcomes, F(1, 479)=19.377, p<.001. .271 p<.001 .131 .070 .042

48. Still to Come • More transcripts to code • Additional development of the coding system and consistency checks • Acoustic data from the MSU team • Ratings from the U-M team: masculinity/femininity, animation, nativeness, accent, and coherence • Multivariate modeling by the Maryland team

49. Conclusions • Analyses of backchannels, overspeech, fillers, and the phrasing of indirect invitations all reveal intriguing potential relationships with participation decision. • While analyses were conducted on a preliminary dataset and using a coding system that is still evolving, the questions that I examined address several key variables and will allow the team to explore paths for more in-depth analyses.

50. Thank you to: • Dr. Fred Conrad • Jessica Broome • Dr. George Myers III • Anita Johnson • The Survey Methodology staff • The coding team- Gabe Moss, Daniel Nielsen, Dave Vannette, and Dylan Vollans • The SRC interns