322,877 research outputs found
Qualità e informazione statistico-economica territoriale. Aspetti del processo di formazione dei dati e delle metodologie di analisi
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IASS Satellite Conference on Small Area Estimation Pisa, Italy, 3 – 5 September 2007
No full textThe Scientific Committee proposes 6 Invited Speakers and 6 Specialized Sessions.
The Invited Speakers are: R. Chambers, J. Jiang, W. Fuller, D. Cocchi, M. Ghosh and D. Morales. J.N.K. Rao could be asked to give the keynote talk on current research and future developments in SAE.
The Specialized sessions are organized by a researcher who is in charge of finding 3 or 4 speakers (and a discussant) for the session. A list of possible themes for specialized sessions is: SAE and informative sampling/nonresponse, SAE in mapping (poverty, disease, marketing), SAE and environmetrics, SAE in establishment surveys. Possible organizers of specialized sessions are C. Skinner, D. Pfeffermann, G. E. Montanari, P. Lanjouw, C. Elbers, J. Opsomer, R. Lehtonen, S. Biffignandi.
The call for paper (abstracts are due by the end of march) is intended only for contributed papers and the contributed papers sessions are set in parallel with the specialized sessions (the contributed paper session will have a chairman/discussant)
List based Web surveys: quality, timeliness and nonresponse in the steps of the participation flow
No full textThe debate on quality issues in web surveys is open and lively (see , i.e., the Web Survey Methodology site). Data quality is required to satisfy the user's needs. Improving the survey process quality is a precondition for obtaining product quality at acceptable cost. This article contributes to the debate focusing on the timeliness of web surveys. From our perspective the timeliness of data collection is mainly due to the timeliness of response from the members of the eligible population. We identify several steps in the web survey process and divide the final response rate into different components, one for each step. We model the survival of eligible respondents, finding out which participants come farthest in the process of a web survey. The analysis contributes to the efforts to explore the nonresponse process and to shorten the individual survey period length
Improving Web survey quality: potentials and constraints of propensity score weighting
No full textThe chapter aims to explore and evaluate in more detail the efficiency of the propensity score adjustment (PSA) and the power of webographic (i.e. behavioral and attitudinal) variables in adjusting biases arising from non-randomized sample selection. In this context, it is to be considered that evidence for the applicability of PSA in the field of surveys in the scientific community is very limited. The empirical application is based on the Dutch sample of the WageIndicator Survey for 2009 - a multi-country, continuous volunteer web survey devoted to the collection of labor-related variables. In the analysis, the target variable is the monthly gross wage. The sample is compared with a probability-based web sample from the LISS panel (Longitudinal Internet Studies for the Social Sciences) which is also used as a reference survey in the PSA application. The findings indicate that with the availability of an accurate probability-based reference survey the application of PSA can help reducing biases in volunteer samples. With respect to the inclusion of webographic variables, at least for the target variable wages, the computed propensity weights did not lead to the expected improvements. This was also due to the fact that those propensity weights which effectively reduced the bias between the samples showed a much higher variability impacting on the validity of estimates. Nevertheless, and considering the advantages of volunteer web surveys (like reduced costs, flexibility, worldwide coverage, etc.) and their more and more extensive use, it is important to be aware of how far can weights improve the representativeness of nonprobability panels. The presented adjustment approaches seem to offer improvements with respect to bias correction which also allow for better generalizations of estimates from volunteer samples
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