Nonresponse in survey research: proceedings of the Eighth International Workshop on Household Survey Nonresponse, 24-16 September 1997
Bibliographic record
Abstract
"This volume, the fourth in the ZUMA-Nachrichten Spezial series on methodological issues in empirical social science research, takes up issues of nonresponse. Nonresponse, that is, the failure to obtain measurements from all targeted members of a survey sample, is a problem which confronts many survey organizations in different parts of the world. The papers in this volume discuss nonresponse from different perspectives: they describe efforts undertaken for individual surveys and procedures employed in different countries to deal with nonresponse, analyses of the role of interviewers, the use of advance letters, incentives, etc. to reduce nonresponse rates, analyses of the correlates and consequences of nonresponse, and descriptions of post-survey statistical adjustments to compensate for nonresponse. All the contributions are based on presentations made at the '8th International Workshop on Household Survey Nonresponse'." (author's abstract). Contents: Larry Swain, David Dolson: Current issues in household survey nonresponse at Statistics Canada (1-22); Preston Jay Waite, Vicki J. Huggins, Stephen P. Mack: Assessment of efforts to reduce nonresponse bias: 1996 Survey of Income and Program Participation (SIPP) (23-44); Clyde Tucker, Brian A. Harris-Kojetin: The impact of nonresponse on the unemployment rate in the Current Population Survey (CPS) (45-54); Claudio Ceccarelli, Giuliana Coccia, Fabio Crescenzi: An evaluation of unit nonresponse bias in the Italian households budget survey (55-64); Eva Havasi and Adam Marton: Nonresponse in the 1996 income survey (supplement to the microcensus) (65-74); Metka Zaletel, Vasja Vehovar: The stability of nonresponse rates according to socio-demographie categories (75-84); John King: Understanding household survey nonresponse through geo-demographic coding schemes (85-96); Hakan L. Lindström: Response distributions when TDE is introduced (97-112); Vesa Kuusela: A survey on telephone coverage in Finland (113-120); Malka Kantorowitz: Is it true that nonresponse rates in a panel survey increase when supplement surveys are annexed? (121-138); Vasja Vehovar, Katja Lozar: How many mailings are enough? (139-150); Amanda White, Jean Martin, Nikki Bennett, Stephanie Freeth: Improving advance letters for major government surveys (151-172); Joop Hox, Edith de Leeuw, Ger Snijkers: Fighting nonresponse in telephone interviews: successful interviewer tactics (173-186); Patrick Sturgis, Pamela Campanelli: The effect of interviewer persuasion strategies on refusal rates in household surveys (187-200); Janet Harkness, Peter Mohler, Michael Schneid, Bernhard Christoph: Incentives in two German mail surveys 1996/97 and 1997 (201-218); David Cantor, Bruce Allen, Patricia Cunningham, J. Michael Brick, Renee Slobasky, Pamela Giambo, Jenny Kenny: Promised incentivcs on a random digit dial survey (219-228); Eleanor Singer; John van Hoewyk, Mary P. Maher: Does the payment of incentives create expectation effects? (229-238); Edith de Leeuw, Joop Hox, Ger Snijkers, Wim de Heer: Interviewer opinions, attitudes and strategies regarding survey participation and their effect on response (239-248); Geert Loosveldt, Ann Carton, Jan Pickery: The effect of interviewer and respondent characteristics on refusals in a panel survey (249-262); Brian A. Harris-Kojetin, Clyde Tucker: Longitudinal nonresponse in the Current Population Survey (CPS) (263-272); Ulrich Rendtel, Felix Büchel: A bootstrap strategy for the detection of a panel attrition bias in a household panel with an application to the German Socio-Economic Panel (GSOEP) (273-284); Seppo Laaksonen: Regression-based nearest neighbour hot decking (285-298); Rajendra P. Singh, Rita J. Petroni: Handling of household and item nonresponse in surveys (299-316); Susanne Raessler, Karlheinz Fleischer: Aspects concerning data fusion techniques (317-334); Siegfried Gabler, Sabine Häder: A conditional minimax estimator for treating nonresponse (335-349).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.223 | 0.278 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".