Summer Internship ProgramSummer Internship ProgramAnnual Symposium 2012Annual Symposium 2012

Agenda

• Welcome

• Background

• Overall Purpose of Symposium

• Symposium Format

• Closing Remarks

• Meet and Greet the Interns

• UM Football Stadium Tour

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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

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The Effects of Incarceration and Probation on Reoffending and EmploymentNicole YadonSocial Environment & HealthSponsor: Dr. Jeffrey Morenoff

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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)

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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

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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

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

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Acknowledgements

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

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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 ProgramSponsor: Philippa Clarke, Ph.D.

Internship Goals

Analytic skills for multi-level data structureExplore the mechanisms through which

neighborhood affects older adults’ healthEngage in social environment and health

scholarships Work and family balance

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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

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

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

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Conceptual Model

Individual Factors

Socio-demographics & Health•Baseline age, race, gender, education, housing type, marital status, being alone•ADL and IADL limitation

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Social IsolationInitial Status

Social & Built Environment

• Street Conditions (% of Poor Streets in block)

• Social Disorder Index• Residential Security Sign

(% of Security Sign in block)

Social IsolationOvertime

Methodology

Analytic MethodsGeneralized Hierarchical Linear Modeling (HLM)

Data 1) Michigan Minimum Data Set (MDS) for Home Care (2000-2008) followed every 90 days2) Neighborhood Data using Systemic Social

Observation (SSO) methods

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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)

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

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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

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

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  Proportion or Mean (s.d.)

Age 55 to 64 10.56 % Age 65 to 74 22.18 % Age 75 to 84 37.42 % Age 85+ 29.84 % Female 73.89 % African American 94.31 % <HS Education 49.90 % HS Education 44.39 % College and above 5.72 %House 57.00%Apartment 38.56 %Other Residential Type 4.44 % Married 20.77 % Not Staying Alone 55.40 % ADL Limitations 1.92 (1.21) IADL Limitations 1.55 (0.43

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)

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Proportion of Residential Security Sign

Freq. Percent

0 927 91.89% 0.1~0.25 73 7.23% 0.5 9 0.89%

Total 1,006 100%

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

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Table. Multilevel Logistic Regression Coefficients for Trajectories of Social Isolation: Detroit Minimum Data Set 2000-2008), Age 55+ (Obs=4,875)

  Uncond' Growth Model Growth Model+ Socio-demographic

Controls+ Social and Built

Environment  Coef. (OR)   Coef. (OR)   Coef. (OR)   Coef. (OR)  

Individual Fixed EffectsIntercept 0.61 1.84*** 0.34(1.40) † -1.04(0.35) * -1.147 (0.32) *Age 65 to 74a 0.38(1.47) 0.27(1.30) 0.2518 (1.29)Age 75 to 84 a 0.40(1.49) † 0.18(1.19) 0.1458 (1.16)Age 85+ a 0.11(1.11) -0.15(0.86) -0.176 (0.84)Not Staying Alone -0.33(0.72) † -0.323 (0.72) †ADL Limitations 0.07(1.07) 0.0655 (1.07)IADL Limitations 0.65(1.91) ** 0.6767 (1.98) **

Neighborhood Fixed Effects

Average Street Condition -0.058 (0.94)Social Disorder 0.0161 (1.02) % Security Sign                   3.1416 (23.14) ** 

TIME (Months) -0.02 0.98 *** -0.02 0.98 *** 0.01 1.01 0.02 1.02  

Variance Components 6.69087  6.80317  7.50638 7.53534***P<.001; **P<.01, *P<.05, †p<.10

§Note 1) We fixed the effects of time (i.e., constraining the random variance in time to zero), because there was no variability to be explained between persons. Still, we tested whether the trajectory slopes vary between persons by baseline characteristics. 2) Results are controlling for gender, race/ethnicity, housing type, and marital status

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Discussions

Practical implications Generalization to urban older adults population in

povertySome limitations to be further examinedMethodological Implications Policy Implications

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Thank you

Special 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)

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Disclosure and Quality of Answers in Text and Voice Interviews on iPhones

Monique KellySurvey Methodology ProgramSponsor: Fred Conrad, Ph.D.

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

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Experiment: 4 modes on iPhone

Medium

Voice SMS Text

Interviewing Agent

Human Human voice (R speaks with I)

Human text(R texts with I)

Automated Speech IVR (R speaks with system)

Automated Text(R texts with system)

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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)

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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

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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

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Internship Project

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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.

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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?

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Rendering

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Opened in Camtasia

Then converted into an avi filePAMSS interface

Transcribing

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Coding

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Coding was done in a tool called Sequence Viewer.

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?

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•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.

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Future Analyses

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!

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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

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Understanding the Achievement Gap: Do Parent Expectations and School Climate Matter?

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Adrian Gale, MSWUniversity of MichiganJoint Program in Social Work and Developmental Psychology

Sponsor: Toni Antonucci Ph.D.Life Course Development

Background

• The achievement gap. (Ferguson, 2003; Mandara et al., 2009; Woolley & Bowen, 2004)

• The reality of differential academic performance has implications for life outcomes. (Grogan-Kaylor and Woolley, 2010)

• Physical and Mental Health

• Marital and Parental Status

• Occupation and Income

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Theoretical Framework: Bioecological Theory of Human Development

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(Bronfenbrenner, 2004)

Macro (e.g. national education policies)

Exo (e.g. neighborhood, schools)

Micro (e.g. parents, siblings)

Parent Expectations…

• Realistic beliefs about youth future achievement.

• Linked to child outcomes such as grades. (Yamamoto & Holloway, 2010)

• Differ by gender. (Wood et al. 2007; Wood et al, 2010)

• Found to be related to academic stereotypes and previous academic outcomes. (Ferguson, 2003)

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School Climate…

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• Norms and expectations defined and perceived by individuals within the school.

• Related to children’s academic achievement. (Zullig, Koopman, Patton, & Ubbes, 2010)

• Multidimensional construct, typically studied from youth’s perspective. (Zullig, Koopman, Patton, & Ubbes, 2010)

• Parents’ perception of school climate shown to be related their parent aspirations for their children. (Spera, Wentzel, & Matto, 2009)

Research Questions•RQ1: What is the impact of parent expectations

and school climate on academic achievement?

•RQ2: Do parent perceptions of school climate moderate the effect of parent expectations on academic achievement?

•RQ3: What is the impact of student gender on parent expectations and school climate?

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Data

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PI Jacque Eccles

Description of Sample

•Wave 1 collected during 7th grade (1991)

•N=1328

•51% Male; 49% Female

•66% Black; 34% White

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Measures•Parent Expectations

▫ Single item▫ “How far do you think (CHILD) will actually go in school?”▫ 9-point scale (1=8th grade or less; 9=MD, JD or PhD)

•Parent perceptions of school climate▫ 4-item scale (alpha = 0.84)▫ Ex. Children generally feel that they belong▫ 5-point scale (1=strongly disagree; 5=strongly agree)

•Academic Achievement▫ 7th grade GPA▫ 5-point scale

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Means (SD) of Variables

Mean (SD) Range

Parent Expectations 6.8 (1.7) 2-9

School Climate 3.5 (0.6) 1-5

Academic Achievement 3.6 (0.9) 1-5

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RQ1: Impact on Academic Achievement

Beta b (SE) Sig. Level

Parent Expectations .335 .175 (.013) ***

School Climate .093 .131 (.035) ***

N 1224

R2 .274***

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* p-value <.05; ** p<.01; *** p<.001

• Models control for: race and gender.

RQ2: Interaction of Parent Expectations and School Climate

Beta b (SE) Sig. Level

Parent Expectations .335 .175 (.013) ***

School Climate .093 .131 (.035) ***

Parent Expectations XSchool Climate

.234 .026 (.019) .172

N 1224

Adjusted R-Square Main Effects Model

.273***

Change in Adjusted R-Square Interaction Model

.001

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* p-value <.05; ** p<.01; *** p<.001• Models control for: race and gender.

RQ3: Impact of Gender on Parent Expectations and School Climate

Parent Expectations

Beta b (SE) Sig. Level

Gender (0=male:1=female)

.091 .319 (.096) ***

N 1321

R2 .01*

School Climate

Gender (0=male:1=female)

.014 .017 (.036) .625

N 1303

R2 .000

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* p-value <.05; ** p<.01; *** p<.001• Models control for: race.

Summary of Findings•RQ1: Parent expectations and school climate

were significant predictors of academic achievement.

•RQ2: No interaction of parent expectations and school climate on academic achievement.

•RQ3: Gender, significant predictor of parent expectations, but not related to school climate.

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Discussion•Parents role great.▫Expectations > Perceptions of School Climate

•Parent perception of school climate may not be as accurate because their interactions with school do not occur during class time.

•Parents expectations high for boys and girls.

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Future Directions•Examine relationships longitudinally to see their

affect across time.

•Examine the three way interaction between school climate, parent expectations and gender on academic achievement.  

•Examine the main and interactive effects of SES/race with gender, parent expectations, and school climate.

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Acknowledgements

Noah Webster, Ph.D.

Toni Antonucci, Ph.D.

Oksana Malachuk, Ph.D.

Jacque Eccles, Ph.D.

Stephanie Rowley, Ph.D.

George Myers, Ph.D.

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Income Inequality as a Predictor of Self-Rated Health

Beth SimmertPh.D. Student

Department of SociologyWayne State University

Sponsors: Jessica Faul, Ph.D.

Amanda Sonnega, Ph.D.Health and Retirement Study

SES/Health Gradient

SES

Rate of Chronic Diseases

High

High

Low

Low

Background

SES/Health Gradient•Individual level mechanisms▫Increased access to health care▫Afford healthier foods▫Exercise ▫Risky health behaviors

•Society level mechanisms

Background

Theory of fundamental cause•Persists across▫Time▫Age groups▫Racial groups▫Gender groups

Source: Link and Phelan. 1995. “Social Conditions as Fundamental Causes of Disease.” Journal of Health and Social Behavior 35(Extra Issue):80–94.

Research Question

•Does income/wealth inequality explain differences in the SES/Health Gradient that are not accounted for by individual level measures of behavior and access to care for older Americans?

Gini Coefficient

Independent Variables

•Gini•Gender•Education•Age•Wealth• Income•Median County Income•Race•Urbanicity

•Health and Retirement Study (HRS)▫2006 Health and Demographic data

•American Community Survey (ACS)▫2005-2010 5-year estimates for Gini and county

median income data

Data

2006 HRS DataN=16,290 Proportion

Sex59% Female41% Male

Race84% Non-Hispanic White16% Non-Hispanic Black

Above/Below Median Income60% Above40% Below

Location76% Metro24% Non-Metro

Education

19% No degree55% GED or High School degree 5% Some College 13% Bachelor Degree 9% Master or Professional Degree

2006 HRS Data

N=16,290 Mean Median

Age Group 68.3 68

Wealth $562,633 $205,750

Income $66,000 $39,200

2006 ACS Data

Mean Median Range

Gini Coefficient 0.447 0.450 0.332—0.601

Median County Income

$51,254 $47,500 $20,081—$115,574

“Would you say your health is excellent, very good, good, fair, or poor?”

1,844=Excellent4,915=Very Good5,030=Good3,133=Fair1,368=Poor

Dichotomized into:Excellent, Very Good, Good=0Fair, Poor=1

11,789=Excellent, Very Good, Good 4,501=Fair, Poor

Dependent Variables—Self rated Health

Method—Logistic Regression

•Model 1▫Gini, sex, education, age

•Model 2▫Wealth, income, county median income

•Model 3▫Race

•Model 4▫Level of urbanicity

Logistic regression for Self-Rated Health

Model 1OR 95%CI

Model 2OR 95%CI

Model 3OR 95%CI

Model 4OR 95%CI

Gini coefficient

71.5*** (25.5-200.6) 8.19*** (2.72-24.63) 5.12** (1.63-16.07) 6.17** (1.85-20.62)

Gender ns *** *** ***

Education 0.63*** (0.61-0.65) *** *** ***

Age Group

1.13*** (1.11-1.15) *** *** ***

Wealth 0.90*** (0.89-0.92) *** ***

Income 0.75*** (0.72-0.78) *** ***

Median Income

0.72*** (0.62-0.85) *** **

Race 1.173** (1.06-1.30) **

Urbanicity ns

* p<0.05, ** p<0.01, *** p<0.001

Logistic regression for Self-Rated Health: by Race

Non-Hispanic WhiteN=13,718

Model 3OR 95%CI

Gini coefficient 25.61*** (6.56-.99.99)

Gender 0.81*** (0.74-0.88)

Education 0.75*** (0.72-0.78)

Age Group 1.12*** (1.10-1.14)

Wealth 0.888*** (0.87-0.91)

Income 0.771*** (0.74-0.81)

Median Income 0.788* (0.64-0.98)

Urbanicity ns

Non-Hispanic BlackN=2,572

Model 3OR 95%CI

Gini coefficient 0.039* (0.003-0.516)

Gender ns

Education 0.80*** (0.73-0.88)

Age Group 1.11*** (1.06-1.16)

Wealth 0.94*** (0.91-0.96)

Income 0.71*** (0.65-0.78)

Median Income ns

Urbanicity ns

* p<0.05, ** p<0.01, *** p<0.001

Logistic regression for Self-Rated Health: by Gender

MalesN=6,721

Model 4OR 95%CI

Gini coefficient 14.47* (2.22-94.35)

Education 0.74*** (0.70-0.78)

Age Group 1.13*** (1.10-1.17)

Wealth 0.92*** (0.90-0.95)

Income 0.75*** (0.70-0.80)

Median Income ns

Race ns

Urbanicity ns

FemalesN=9,569

Model 4OR 95%CI

Gini coefficient ns

Education 0.78*** (0.82-1.18)

Age Group 1.10*** (0.73-0.88)

Wealth 0.90*** (1.06-1.16)

Income 0.75*** (0.91-0.96)

Median Income 0.67** (0.65-0.78)

Race 1.23** (0.52-1.25)

Urbanicity ns

* p<0.05, ** p<0.01, *** p<0.001

•A society level measurement can be predictive of individual level health.

•The Gini coefficient is predictive of self-rated health.▫Higher inequality = higher probability of having poor

self-rated health▫Gini coefficient remains significant after accounting for

individual-level measures▫Greater effect in whites than blacks▫Greater effect in males than females

Conclusions

•More sub-group analysis▫High inequality—High heterogeneity▫Low inequality—Low heterogeneity/high income▫Low inequality—Low heterogeneity/low income

•Relative influence of others on measures of self-rated health.

Implications for Future Research

•Amanda Sonnega• Jessica Faul•George Myers• ISR SAS Users Group•Nicole, Adrian, MinHee, Monique, and Mara• Janet Keller•Michigan Square HRS faculty and staff

Thank You!

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