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Evaluating Mode Effects in Mixed-Mode Survey Data Using Covariate Adjustment Models
- Source :
- Journal of Official Statistics, Vol 30, Iss 1, Pp 1-21 (2014)
- Publication Year :
- 2014
- Publisher :
- Sciendo, 2014.
-
Abstract
- The confounding of selection and measurement effects between different modes is a disadvantage of mixed-mode surveys. Solutions to this problem have been suggested in several studies. Most use adjusting covariates to control selection effects. Unfortunately, these covariates must meet strong assumptions, which are generally ignored. This article discusses these assumptions in greater detail and also provides an alternative model for solving the problem. This alternative uses adjusting covariates, explaining measurement effects instead of selection effects. The application of both models is illustrated by using data from a survey on opinions about surveys, which yields mode effects in line with expectations for the latter model, and mode effects contrary to expectations for the former model. However, the validity of these results depends entirely on the (ad hoc) covariates chosen. Research into better covariates might thus be a topic for future studies.
- Subjects :
- Future studies
opinion about surveys
front-door model
Computer science
Confounding
Statistics
Mode (statistics)
Probability and statistics
measurement effects
selection effects
HA1-4737
Causal inference
Covariate
Econometrics
Mixed mode survey
causal inference
Selection (genetic algorithm)
back-door model
Subjects
Details
- Language :
- English
- ISSN :
- 20017367
- Volume :
- 30
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Journal of Official Statistics
- Accession number :
- edsair.doi.dedup.....a6ea5398be24faf7ac92c4aa73bccc63