Survey Data Quality. Andreadis, I. & Andreadis, A. In JSM Proceedings, Survey Research Methods Section, pages 1999–2006, Alexandria, VA, 2022. American Statistical Association. ![Survey Data Quality [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex In this paper, we introduce the Survey Data Quality R Package, a library of functions that can be used for the assessment of the quality of survey data. Surveys offering rewards may attract careless respondents or even bots (automatic survey-takers) resulting in meaningless, careless, or fraudulent responses, (i.e. responses of lower quality) that we need to identify and probably remove in order to get a final cleaned dataset of high quality. Survey methodology scholars have used various methods to measure the attentiveness of the respondents and the quality of the collected data: item-nonresponse, mid-point responses in Likert-type scale items, straight-lining, the time spent on questionnaire items (speeding), etc. Using the aforementioned response quality indicators we can create an innovative multidimensional estimation of response quality for each completed questionnaire. Using this estimation, we can identify questionnaires that have been submitted by less attentive web survey respondents and we can decide to remove them or not depending on their quality score.
@inproceedings{AndreadisAndreadis2022,
address = {Alexandria, VA},
title = {Survey {Data} {Quality}},
url = {http://www.asasrms.org/Proceedings/y2022/files/323439_163724.pdf},
abstract = {In this paper, we introduce the Survey Data Quality R Package, a library of functions that can be used for the assessment of the quality of survey data. Surveys offering rewards may attract careless respondents or even bots (automatic survey-takers) resulting in meaningless, careless, or fraudulent responses, (i.e. responses of lower quality) that we need to identify and probably remove in order to get a final cleaned dataset of high quality. Survey methodology scholars have used various methods to measure the attentiveness of the respondents and the quality of the collected data: item-nonresponse, mid-point responses in Likert-type scale items, straight-lining, the time spent on questionnaire items (speeding), etc. Using the aforementioned response quality indicators we can create an innovative multidimensional estimation of response quality for each completed questionnaire. Using this estimation, we can identify questionnaires that have been submitted by less attentive web survey respondents and we can decide to remove them or not depending on their quality score.},
booktitle = {{JSM} {Proceedings}, {Survey} {Research} {Methods} {Section}},
publisher = {American Statistical Association},
author = {Andreadis, Ioannis and Andreadis, Andreas},
year = {2022},
pages = {1999--2006},
}
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