| 1. |
- Schouten, Barry, et al.
(author)
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A Bayesian analysis of design parameters in survey data collection
- 2018
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In: Journal of Survey Statistics and Methodology. - : Oxford University Press. - 2325-0984 .- 2325-0992. ; 6:4, s. 431-464
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Journal article (peer-reviewed)abstract
- In the design of surveys, a number of input parameters such as contact propensities, participation propensities, and costs per sample unit play a decisive role. In ongoing surveys, these survey design parameters are usually estimated from previous experience and updated gradually with new experience. In new surveys, these parameters are estimated from expert opinion and experience with similar surveys. Although survey institutes have fair expertise and experience, the postulation, estimation, and updating of survey design parameters is rarely done in a systematic way. This article presents a Bayesian framework to include and update prior knowledge and expert opinion about the parameters. This framework is set in the context of adaptive survey designs in which different population units may receive different treatment given quality and cost objectives. For this type of survey, the accuracy of design parameters becomes even more crucial to effective design decisions. The framework allows for a Bayesian analysis of the performance of a survey during data collection and in between waves of a survey. We demonstrate the utility of the Bayesian analysis using a simulation study based on the Dutch Health Survey.
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| 2. |
- Silber, H., et al.
(author)
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Lack of Replication or Generalization? Cultural Values Explain a Question Wording Effect
- 2022
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In: Journal of Survey Statistics and Methodology. - : Oxford University Press (OUP). - 2325-0984 .- 2325-0992. ; 10:5, s. 1121-1147
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Journal article (peer-reviewed)abstract
- In the context of the current "replication crisis" across the sciences, failures to reproduce a finding are often viewed as discrediting it. This paper shows how such a conclusion can be incorrect. In 1981, Schuman and Presser showed that including the word "freedom" in a survey question significantly increased approval of allowing a speech against religion in the USA. New experiments in probability sample surveys (n = 23,370) in the USA and 10 other countries showed that the wording effect replicated in the USA and appeared in four other countries (Canada, Germany, Taiwan, and the Netherlands) but not in the remaining countries. The effect appeared only in countries in which the value of freedom is especially salient and endorsed. Thus, public support for a proposition was enhanced by portraying it as embodying a salient principle of a nation's culture. Instead of questioning initial findings, inconsistent results across countries signal limits on generalizability and identify an important moderator.
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| 3. |
- Särndal, Carl-Erik, et al.
(author)
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Accuracy in estimation with nonresponse : A function of degree of imbalance and degree of explanation
- 2014
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In: Journal of Survey Statistics and Methodology. - : Oxford University Press. - 2325-0984 .- 2325-0992. ; 2:4, s. 361-387
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Journal article (peer-reviewed)abstract
- Responsive Design is a trend in recent survey literature concerned notably with managing data collection, through planning and appropriate intervention, so as to realize a well-balanced final set of respondents. In this effort, auxiliary variables, including paradata, are central. But regardless of what is done in the data collection, accurate estimation despite nonresponse is the ultimate goal. The auxiliary variables are important at the estimation stage as well, as when calibrated weights are used in the nonresponse adjustment. For accuracy, two factors intervene: (1) in the data collection, the level of imbalance achieved with the auxiliary information; and (2) in the estimation, the degree to which the auxiliaries explain the study variable. In practice, both objectives are less than completely satisfied. Reduced imbalance in data collection does not by itself guarantee low bias in the estimates. We ask: Is balancing worth a perhaps costly and demanding effort in data collection? Could one have done equally well by saving the use of the auxiliary information until the estimation stage? Complete bias elimination is not achieved at either stage. We outline a theory for a two-factor explanation of accuracy, and apply it to two important surveys at Statistics Sweden. The factors-thedegreeof imbalance and the degree of explanation-are systematically varied, and their joint effect on the accuracy of the estimates is evaluated empirically. The results show that reduced imbalance makes the adjustment of the simple estimate lose some of its importance. More importantly, the calibration-adjusted estimate realizes some accuracy improvement by having been preceded in data collection by a reduced imbalance. The explanation of why this happens is not simple, but a theoretical justification is outlined.
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