Neighborhood Collective Efficacy and Participation in Household Surveys Carolina Casas-Cordero PhD Candidate, JPSM University of Maryland ITSEW 2009 (Tällberg, Sweden)

1. Motivation Increased focus on Nonrespose Bias Interest on “potential” of Paradata – Observational data collected by interviewers is a special case – Focus of my research: Neighborhood characteristics Already existing frameworks linking – Neighborhood Characteristics and Survey Participation (P) (Groves and Couper 1998; Johnson et al 2006) – Neighborhood Characteristics and Individual Outcomes (Y) (Sampson et al 2002; Kawachi and Berkman 2003) 2

Conceptual Framework of Survey Participation Social Environment Survey Design Household(er) Interviewer Decision to Participate Groves and Couper (1998) 3

2. Research Papers Paper 1: Neighborhood Collective Efficacy and Participation in Household Surveys (Data: LA FANS) Paper 2: Quality of Observational Data Collected by Survey Interviewers (Data: LA FANS) Paper 3: Impact of Different Types of Auxiliary Data on Estimates of Nonresponse Bias (Data: NRB) 4

3. Datasets Los Angeles Family and Neighborhood Study (LA FANS) – ~2600 interviews (parents + children) – 65 census tracts, 422 census blocks, 2029 blockfaces – 2+ interviewer ratings of neighborhood characteristics (per blockface) – 64 neighborhood items (physical disorder, social disorder, physical decay, security measures, land use, presence of institutions, etc) Nielsen Nonresponse Bias Study (NRB) – Follow up study of respondents and nonrespondents (n=9,000) – Multiple sources of auxiliary data for different sets of cases: Frame, Claritas (~Census data), Call Record, Interviewer Observations, Follow Up Survey, Original Diary Survey 5

4. Planned Analysis 6

Analysis Paper 1 - Neighborhood Collective Efficacy and Participation in HH Surveys 1.Estimate Survey Response Models (SRM) using predictors at different levels – Neighborhood level, Household level, Person level 2.Estimate SRM using different indicators of Neighborhood Collective Efficacy – Respondent Reports, Interviewer Observations, Census Data 3.Estimate SRM for different disposition outcomes – Ineligible HU, Unable to Contact, Unable to Communicate, Refused, Screened, Rostered, Interviewed 7

Analysis Paper 2 - Quality of Observational Data Collected by Survey Interviewers 1.Descriptive Stats – % missing data, same response pattern, kappa 2. Multilevel Cross-Classified Models – Structure: Tracts > Interviewers X Blockfaces > Items – Random effects: Tract, Interviewers, Blockfaces – Fixed effects: Interviewers, Blockfaces, Items 3.Item Response Model 4.Meta Analysis across 64 Items 8

Analysis Paper 3 - Impact of Different Types of Auxiliary Data on Estimates of NR Bias 1.Potential for NR Bias Analysis – Corr(NOBS, P) – Corr(NOBS, Y) 2.Comparison of estimates of NR Bias using different types of auxiliary data – Apply “typical” estimation methods for each type of data (frame, follow up, census, etc) – Standardize estimates – Compare relative size effects 9

This is work in progress … Feedback is really welcomed! 10