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Dependent Interviewing

Explore the impact of Dependent Interviewing (DI) on reducing measurement error in longitudinal surveys. Discover how DI affects systematic and random errors and its implications on data reliability and estimates.

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Dependent Interviewing

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  1. Dependent Interviewing A Remedy or a Curse for Measurement Error in Surveys? Paulina Pankowska Vrije Universiteit Amsterdam Bart Bakker Statistics Netherlands Daniel Oberski Utrecht University Dimitris Pavlopoulos VrijeUniversiteit Amsterdam

  2. measurement error in longitudinal surveys • Measuring a state: onemistake • Measuringtransitions: twomistakes Correct Correct Wrong Correct Correct Year 4 Year 1 Year 3 Year 5 Year 2 • (Potential) solution- DI

  3. Dependent interviewing (DI) • Respondents are reminded of previous answers

  4. Di & Random Measurement error Correct Correct Wrong Correct Correct Correct Correct Correct Correct Correct Year 4 Year 4 Year 1 Year 1 Year 3 Year 3 Year 5 Year 5 Year 2 Year 2 • Expected to reduce (random) error • Assists recall less spurious change • Reduces cognitive burden

  5. Di & systematic Measurement error • Might lead to systematic error • e.g. satisficing error carry-over • Overall effect on reliability and estimates uncertain Wrong Wrong Correct Wrong Wrong Wrong Wrong Correct Wrong Wrong Year 4 Year 4 Year 1 Year 1 Year 3 Year 3 Year 2 Year 2 Year 5 Year 5

  6. Natural experiment dutchlfs: temporary contract “Do you currently have a permanent contract?” “Last time you had a temporary contract. Is this still the case?” • Until 2009: Dependent • PDI - “remind, still” • Only if no job change occurred • As from 2010: Independent • Also in 2009 if job change occurred • Permanent contract: value carried over

  7. Natural experiment: the design • A total of 3 scenarios:

  8. Method: Hidden Markov Model • Hidden Markov Models (HMMs): • Separate ‘true’ change from error • Without error-free data

  9. HMM & Dependent interviewing • DI affects also systematic error • Relax the local independence assumption • Multiple indicators per time point…

  10. Our model

  11. Data • Linked survey (LFS) – register (ER) data • Sample consists of 86,075 individuals aged 25 to 55 who • Started LFS in 2009 - DI • Started LFS in 2010 - INDI • Dataset contains quarterly information on each individual for 5 time points

  12. Results: random error • Reference: INDI would have been DI • DI reduces random error

  13. Results: systematic error • Reference: INDI would have been DI • DI does not increase systematic error

  14. Conclusions: promising for di! • DI reduces random error (spurious change) • DI is not associated with a higher incidence of systematic (autocorrelated) error • There is a high probability of repeating errors, regardless of the interviewing regime

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