Adjusting samples for nonresponse bias: pros and cons of surveying nonrespondents compared with other approaches in ESS

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Outline. Very short introductionShort overview: Approaches to the assessment of bias applied in ESS (Billiet, Matsuo, Beullens
Adjusting samples for nonresponse bias: pros and cons of sur...

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1. Adjusting samples for nonresponse bias: pros and cons of surveying nonrespondents compared with other approaches in ESS Jaak Billiet: CeSO - K.U. Leuven Hideko Matsuo: CeSO ? K.U. Leuven The European Social Survey Round 4 launching conference ?Poland and Europe: continuation and change?. Institute of Philosophy and Sociology Polish Academy of Sciences, Warsaw 13 Jan 2010

2. Outline Very short introduction Short overview: Approaches to the assessment of bias applied in ESS (Billiet, Matsuo, Beullens & Vehovar, Research & Methods. ASK, vol 18 (1, 2009), pp. 3-43). The surveys among nonrespondents (post R3): what, how, when, with what?? Main results of NRS in PL and NO (comparison of results and focus on adjusting samples) Pros and cons of NRS compared with other 3 approaches

3. 1. Introduction Analysis of nr bias still needed: WHY? Still large differences in NR rates based on CF R4

4. Short overview of approaches to the assessment of bias applied in ESS (Billiet, Matsuo, Beullens & Vehovar, Research & Methods. ASK. vol 18 (1, 2009), pp. 3-43). In all rounds (R1, R2, R3, R4?..) Bias as deviation between obtained sample and population (or ?Golden standard? survey) = post-stratification and evaluations of samples before and after weighting Bias as difference between cooperative and converted refusals collected via refusal conversion = comparison of cooperative with reluctant respondents (converted refusals) Bias as difference in ?observable? data among all sampling units (collected in contact forms)= sample based comparison between all respondents and all nonrespondents

5. In context of R3 4. Bias as difference between respondents and non-respondents collected via post hoc nonresponse survey = surveys among nonrespondents after R3 in PL, NO and CH (real NRS) in BE (at moment of refusal only among refusals = Doorstep Questions Survey)

6. 3. Survey among nonrespondents (1) New survey among refusals with very small & easy questionnaire (some crucial variables) (Voogt, 2004; Saris) Implemented in ESS Round 3 : 4 participating countries - Full mail survey (15 questions) months after main survey in NO (medium rr), CH (low rr) & PL (high rr) - At moment of refusal 7 crucial questions in BE (7 questions) Response rates BE (44.7% = 303) response among refusals NO (30.3% = 342) response among noncontacts & refusals PL (23.2% = 192) CH (52.9% = 771) (cooperative much higher response)

7. Survey among nonrespondents (2) 1. The questions asked Key questions procedure (Pedaksi approach) Short 7 question module (+ at door): work situation, highest level of education, # of members in household, frequency of social activities, feeling (un)safe, interest in politics, attitude towards surveys Normal 16 questions module: same as short + gender, year of birth, TV watching, voluntary work, trust in people, satisfied with democracy, trust in politics, immigration good/worse for country, (+ reasons for refusal (closed) in one subgroup)

8.

9.

10. Kinds of respondents in data analyses [NO, CH & PL]

11. Method used for adjusting the sample for nonresponse bias 1. Identify survey response differences on key explanatory variables between types of respondent (?nonrespondent vs. cooperative respondent?). 2. Study net effects of key explanatory variables on response probabilities via logistic regression model (dependent variable: prob ratio?s ?nonrespondent/cooperative?). 3. Obtain propensity scores on all cases on non-response probabilities via logistic regression model (dependent variable: prob ratio?s ?cooperative/nonrespondent?).

12. Transform propensity scores into weights via stratification method (Rosenbaum & Rubin 1984; Little 1986; Lee & Vaillant 2008): Form 10 strata with equal number of cases after sorting on ps; Assign each sample unit into correct corresponding sub-strata Weight = expected probability/observed probability of the coop. respondent (or nonrespondent) in the corresponding sub-strata. 5.

13. 5. Evaluate effects of propensity weighting via two main criteria: Tests between unweighted & weighted sample on cooperative respondents (NRS3_Rco & ESS3_Rco). 1b. In case of significant differences: test differences between parameters of relevant substantive explanatory models Study differences in distributions on key questions between types of respondents (NRS3_Rco vs NRS3_NR or ESS3_Rco vs. NRS3_NR).

14. 4. Main results in NO and PL (1) Differences between ESS cooperative and NRS !nonrespondents* * Only single ESS cooperative respondents (not ?double? respondents). All tests: ESS resp = expected freq

15. Main results in NO and PL (2)

16. Main results in NO and PL (3)

17. Main results in NO and PL (4)

18. Main results in NO and PL (5) ? Main net effects on probability ratio coop resp / nonresp (inversed parameters!) In Norway: probability of response INCREASES if Higher educated Participate more in social activities then most (subjective?) More satisfied with democracy Positive attitude towards ?consequences? of immigration In Poland: probability of nonresponse INCREASES if Higher educated!!! Unemployed Feel safe Participate less in social activities than most! (political interested?!!)

19. Main results in NO and PL (6) 3. Evaluation of the propensity weights First approach A: is the adjusted sample (weighted) of cooperative ESS respondents significant different from the original sample? if yes: we may conclude that the adjustment had effect on the sample estimates conclusion: no significant differences at all example: variable with largest differences = education

20. Main results in NO and PL (7) Differences between original sample and adjusted sample even smaller in PL Not necessary to test a substantive regression model since the univariate distributions do not differ (first approach B) This is nonetheless checked for model with ?consequences of immigration? as relevant dependent variable? and number of predictor variables: age, TV watching, involvement in charity org, trust in politics, social trust, and two value orientations (conservation, self-transcendence) R? = 0,26 in both models (not weighted & weighted) all predictors contribute significantly to variance of dept. var BUT: no differences at all between the two models Conclusion = was ps weighting useless? Let us see the second approach

21. Main results in NO and PL (8) Second approach: do the initial significant differences of belonging to a response category of all key questions between ESS respondents and nonrespondents (NRS res) in first table disappear after adjusting the sample of ESS cooperative respondents? in other words, did we move from NMAR to MAR let us see:

22. Main results in NO and PL (9) Norway sample (Chisq values or t-values; p-values) largely successful: all differences disappeared except political interest

23. Main results in NO and PL (10) Poland: sample (Chisq values and p-values) Not completely successful since still sign differences between NRS and ESS for two variables (political interest and social participation)

24. 5. Pros and cons of NRS compared with 3 other approaches

25. Conclusions Future: Other methods (contacting sequences using contact forms data) = expect low effect (result of some studies, see Blom) More model based method: crucial is what additional information can be used Combining different methods Info in all methods = view on sensitive variables Finally: low effect may mean LOW BIAS


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