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Selectivity in the German Mobility Panel

Selectivity in the German Mobility Panel. Tobias Kuhnimhof Institute for Transport Studies, University of Karlsruhe Paris, May 20th, 2005. Overview. The German Mobility Panel MOP-recruitment, non-response and assumptions about selectivity

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Selectivity in the German Mobility Panel

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  1. Selectivity in the German Mobility Panel Tobias Kuhnimhof Institute for Transport Studies, University of Karlsruhe Paris, May 20th, 2005

  2. Overview • The German Mobility Panel • MOP-recruitment, non-response and assumptions about selectivity • Additional sources of information to analyse selectivity in the MOP • Some possibilities to analyse selectivity issues • Some findings • Trustworthiness of CATI-Data • Who drops-out and who takes part • The advantages of recruiting households • Conclusions

  3. 3 years  Survey of individual developments, transitions, causes and effects Year 2 Year 1 Year 3 The German Mobility Panel The MOP-survey: 7-day mobility diary in 3 consecutive years since 1994 7 days  Longitudinal survey of individual mobility behavior

  4. Recruitment, Nonresponse and Assumptions about Selectivity The MOP recruitment: motivating relialable participants The multi-stage recruitment process: Commercial market research CATI recruitment (mailing of documents etc.) + 1-week-report + 3-year-survey = high participant burden MOP-recruitment CATI Written declaration of participation Participation: 1-Week-mobility-diary report, mailing of documents There is plenty possibility & it is understandable not to participate in the MOP

  5. Recruitment, Nonresponse and Assumptions about Selectivity High respondent burden High drop-out Strong selective bias?

  6. P Mobility in Population MOP-Recruitment, Non-response and Assumptions about Selectivity Common Assumptions about selective error in mobility surveys Stress due to job, education, children? Not interested in mobility issues High incomes ? Business trips? Not mobile due to incapacity (e.g. sick)? Large leisure activity spectrum?

  7. Documentation of entire drop-out Additional sources of information to analyse selectivity in the MOP OBJECTIVE: Get to know the drop-outs BUT: preserve the survey’s continuity Documentation of calling attempts (max. 12) Mobility interview Enquiry of reasons for declining

  8. Additional sources of information to analyse selectivity in the MOP Core-element of selectivity analysis: Mobility interview in commercial market research CATI  Data available about all CATI-interviewees (MOP-participants & drop-outs): - individual and household socio-economics - role of the person in the household - interviewee’s daily obligations (childcare, work etc.) - individual mobility on a test day (“yesterday”) - characterizing information about the person’s general mobility behavior - proxy-data on other household members

  9. Logit 4 Logit 3 Logit 2 Logit 1 (Sampling Drop-out) Global drop out logit Some possibilities to analyse the selectivity issues Analysees to understand selective bias Modeling participation: P(participation)=f(mobility, socio-demografics)

  10. CATI-Interviewees CATI and recruitment: All MOP-Participants (1.-,+ 2.-,+ 3.-time participants) Some possibilities to analyse the selectivity issues Respondent groups and data availability: Possibilities to compare mobility data of - different sources (survey methods) - and different samples First-time MOP-Participants (CATI-Interviewees + other household members) CATI-Interviewees, who participate in the MOP

  11. Findings: Trustworthiness of CATI-data Are CATI-responses useful to characterize mobility behavior? Intrapersonal comparison of general mobility information (CATI) and reported mobility (MOP)

  12. Findings: Trustworthiness of CATI-data Are CATI-responses useful to characterize mobility behavior? • - in most cases: YES • the stronger the questions relate to routines, the more reliable is the answer • active persons tend to overestimate themselves • less active persons tend to underestimate themselves • “Extrapolation of normal weekday” – Example: use of travel modes • Daily use of car = car use on 5,4 days per week • Daily use of PT = PT use on 4,5 days per week

  13. Findings: Trustworthiness of CATI-data Differences in cross-sectional mobility figures in CATI and mobility diary data Comparision of test-day cross-sectional data of CATI vs. MOP

  14. Findings: Trustworthiness of CATI-data Methodological Differences: The MOP-Report is more exact than the CATI Test day data of persons who participated in both surveys*: *No Fridays, Saturdays, exclusion of daily trips that exceed 6

  15. Findings: Who drops out and who takes part What is the impact of the sample differences due to selectivity on mobility figures? Comparison of cross-sectional data CATI – MOP: After accounting for methodological differences the selective error can be estimated

  16. Findings: Who drops out and who takes part Drop-Out of Non-trippers CATI test day data vs. MOP test day data of all persons in both surveys*: *No Fridays, Saturdays, exclusion of daily trips that exceed 6, appropriate weighting procedures applied in order to account for socio-demographic differences

  17. Significant, P<0.1 Not significant P<0.2 P<0.3 Who drops out and who takes part Socio-economic aspects of Selectivity Example: Odds ratios of 2 out of 15 variables in participation logit model “Middle class bias”

  18. Who drops out and who takes part Explanations for increasing share of mobile persons Example: Odds ratios of 2 out of 15 variables in participation logit model Significant, P<0.1 Not significant P<0.2 P<0.3 “Mobility interest bias”

  19. CATI-Interviewees CATI and recruitment: The advantages of surveying households First-time MOP-Participants (CATI-Interviewees + other household members) CATI-Interviewees, who participate in the MOP  Participation of other household member can counter-balance selectivity impacts

  20. Other Findings • CATI-data is useful but has to be interpreted • Surveying households counter-balances selectivity Recommendations • Balanced recruitment of different mobility styles is vital • Survey households not individuals • Don’t trade data quality for a high response rate Conclusions and Recommendations Findings on Selectivity • Socio-economic selective effects dominate drop-out • “Middle Class Bias” good education, good income, middle aged • “Mobility Interest Bias”  drop out of non-trippers (particularly elderly, permanent incapacity?)

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