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New Developments in Nonresponse Adjustment Methods

New Developments in Nonresponse Adjustment Methods. Fannie Cobben Statistics Netherlands Department of Methodology and Quality. In this presentation…. Response selection model Use of response propensities Application to POLS 2002 Discussion. The response selection model.

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New Developments in Nonresponse Adjustment Methods

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  1. New Developments in Nonresponse Adjustment Methods Fannie Cobben Statistics Netherlands Department of Methodology and Quality

  2. In this presentation… • Response selection model • Use of response propensities • Application to POLS 2002 • Discussion

  3. The response selection model • Consists of two equations: • Response equation (dichotomous) • Survey item equation (continuous) • The error terms are allowed to be correlated • The outcome is adjusted for a possible selection bias

  4. Extensions to response selection model • Multiple selection equations, i.e. different response types • Contact equation • Participation equation • Survey item equation • Contact and participation are dependent and can both introduce a bias for the survey item • If desirable, equations for other response types • Categorical survey items

  5. Yi* missing Yi* missing Yi* = Yi Sample Ci = 0 Ci = 1 γi* Non-contact Contact Pi = 0 Pi = 1 ςi* Refusal Participation Response selection model – contact and participation

  6. Advantages • Model relationship with both R and Y • Efficient use of auxiliary variables; paradata • Closely follow fieldwork process Disadvantages • Model based; dependent on distributional assumptions • Issues of identification; exclusion restriction

  7. Response propensities (1) • Assume: Sample is selected from a sampling frame by some random selection procedure • Two groups: • R=1 response • R=0 non-response • X auxiliary information, available for all elements. For instance from the sampling frame. • ρ(X) = P(R=1| X) is the propensity score, for instance determined by a logistic regression, i.e.

  8. Response propensities (2) • We can use the response propensities to adjust for nonresponse bias: • Directly • Response propensity weighting • Response propensity stratification Indirectly • In combination with linear weighting

  9. Direct use of response propensities • Response propensity weighting, Särndal (1981) • Response propensity stratification

  10. Indirect use of response propensities • GREG – estimator with adopted inclusion probabilities

  11. POLS 2002 • Integrated Survey on Household Living Conditions (in Dutch: Permanent Onderzoek LeefSituatie) • Monthly 3.000 persons are selected • Questions on living conditions, safety, health • Basic module for persons > 12 years • Datafile aggregated over 2002: n = 35.594 and nr = 20.168 (57%) • Survey variables: Employment, Education and Religion • Numerous auxiliary variables, such as age, region, house value, social insurance, ethnicity, etc.

  12. Analysis POLS 2002 Aim: • Compare different response propensity methods • Compare regular GREG-estimator and other methods • Two models: • Weighting model (relation Y and R; Schouten, 2004) • Age15 + Houseval14 + % Non-natives8+ Ethnicity7 + Region15 • + Type hh4 + Telephone2 • (1) • Response model (relation R; psuedo R2 = 2,2%) • Age15 + Houseval14 + Urbanicity5+ Mar_staat4+ Ethnicity7 + Region15 • + Type hh4 + Telephone2 • (2)

  13. Results - Employment • No significant differences between method; clear difference with response average • GREG and propensity weighting with same model: same results • Propensity weighting and propensity GREG highest estimate employed labour force

  14. Results - Education

  15. Results - Religion • More differences between methods • Propensity weighting and propensity GREG highest estimate no religion

  16. Conclusions Remarks: • Almost the same variables in weighting model and response model • Low pseudo R2 response model • Conclusions: • Small difference between methods • No reduction of variance direct response propensity methods; reduction of variance GREG-estimator • Propensity weighting and GREG-estimator with same model gives same results for employed and education

  17. Thank you for your attention!

  18. Total error Sampling error Estimation error Selection error Non-sampling error Observation error Over-coverage error Measurement error Processing error Non-observation error Under-coverage error Nonresponse error Total Survey Error

  19. Probability based surveys (2) • Under-coverage in telephone surveys • Two groups: • with telephone (C=1) • without telephone (C=0) Non-response • Two groups: • Respondents (C=1) • Non-respondents (C=0)

  20. Discussion • Which variables should be inserted in the different equations, and how should these variables be selected? • How to construct the hierarchical structure of the model. For instance, which response types should be distinguished? • Is it profitable to construct a model for every survey item? If so, how should we deal with this in practice? • The response selection assumes a correlation structure between response types and survey items. This correlation has a model-based interpretation, but how should it be interpreted in practice?

  21. Example relationship R and Y in the LFS • Relation between the number of contact attempts and estimated size of the labour force all attempts 4 attempts size of the labour force 2 attempts 1 attempt Quarter

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