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The Multi-Level, Multi-Source (ML-MS) Approach to Improving Survey Research

The Multi-Level, Multi-Source (ML-MS) Approach to Improving Survey Research. Tom W. Smith, NORC/University of Chicago Jibum Kim, Sungkyunkwan University. ML-MS Approach. Extract all case-level & aggregate data from sample frame Link all sample cases to auxiliary data (AD) sources

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The Multi-Level, Multi-Source (ML-MS) Approach to Improving Survey Research

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  1. The Multi-Level, Multi-Source (ML-MS) Approach to Improving Survey Research Tom W. Smith, NORC/University of Chicago Jibum Kim, Sungkyunkwan University

  2. ML-MS Approach • Extract all case-level & aggregate data from sample frame • Link all sample cases to auxiliary data (AD) sources • Case-level • Aggregate-level • Iterative searches of AD • Paradata

  3. Sampling Frames • Available data highly variable across sampling frames and these vary across countries • Population registers; Address-based samples; Random walk • Case-level • Aggregate-levels (multi-levels)

  4. Auxiliary Data (AD) - I • Case-level links by address/other identifiers - Found/not found - If found, add additional information • Aggregate-level links - Specific address (city-style) - GPS readings - Postal code - Community and other geocodes

  5. Auxiliary Data (AD) - II • Types of AD - Household/individual-based, general databases (e.g. Accurint, Equifax, Peoplefinders) - Administrative records – single purpose databases (e.g. voting records, government benefit lists, association membership rolls)

  6. Auxiliary Data (AD) - I • Increased computerization has made more AD available and eased linkages to surveys

  7. Iterative Searches • Information gained from initial searches can facilitate additional searches • Using address-based reversed directory can find out both phone number and/or resident’s name • Name/phone number can then be used in searches of additional databases to make new links and add new information

  8. Paradata • Process • Interviewer characteristics (All) • Record of attempts (A) • Interactions with household members (A) • Keystroke (Respondents) • CARI (R) • Observational • Neighborhoods (A) • Residence, Exterior (A) • Residence, Interior (R)

  9. ML-MS Methodological Uses • Detect and adjust for non-response bias • Fill in item non-response for some variables • Validate information supplied by respondents • AD sometimes superior sources • AD other times alternative source for cross-checks • Validation interviews

  10. ML-MS Substantive Uses - I • Multi-level contextual effects • Individuals nested within households, neighborhood, communities, regions, nations • Examples of contextual, aggregate-level geographic effects from General Social Survey - racial composition of racial prejudice - crime rates on law enforcement attitudes - poverty levels and support for welfare

  11. ML-MS Substantive Uses -II • Making theory-driven, contextual, aggregate-level, geographic data a routine, automatic part of all surveys, not an special, extra (and costly) add-on

  12. Confidentially • Deductive disclosure risk increased • Need to have public file edited to insure that deductive disclosure is not possible • Use of only publically-accessible information from AD sources • Confidentiality rules vary across countries and target populations

  13. ML-MS Cross-nationally • Can be applied cross-nationally • But approach can not be standardized or rigid • Sample frames vary, available AD sources vary, and rules governing access vary • Only paradata suitable to standardization • ML-MS must adapt to those variations

  14. ML-MS Summary • Augment survey data with data from 1) sample frame, 2) AD, and 3) paradata. • Include multi-level measures from case-level and aggregate-levels (e.g. neighborhood, community) • Use for both methodological and substantive purposes • Make a routine part of doing regular surveys

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