Improving the Quality of Survey Data Documentation: A Total Survey Error Perspective

Jedinger, Alexander; Watteler, Oliver; Förster, André

doi:10.3390/data3040045

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…A code capturing the presence of this information is provided for most variables in the dataset. Interpretability can be assessed by comparing these codes with the social science survey reporting standards (e.g., AAPOR standards 24 ), guidelines for high-quality documentation of survey data 25 , and previous approaches to quality assessments of data documentation 26 . Accessibility: Accessibility can be assessed based on whether and when the survey data and the first results were made available to users.…”

Section: Methodsmentioning

confidence: 99%

A dataset on survey designs and quality of social and behavioral science surveys during the COVID-19 pandemic

Gummer,

Skora,

von Glasenapp

et al. 2024

Sci Data

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In the social and behavioral sciences, surveys are frequently used to collect data. During the COVID-19 pandemic, surveys provided political actors and public health professionals with timely insights on the attitudes and behaviors of the general population. These insights were key in guiding actions to fight the pandemic. However, the data quality of these surveys remains unclear because systematic knowledge about how the survey data were collected during the COVID-19 pandemic is lacking. This is unfortunate, since decades of survey research have shown that survey design impacts data. Our Survey Data Collection and the COVID-19 Pandemic (SDCCP) project deals with this research gap. We collected rich metadata on survey design for 717 social and behavioral science surveys carried out in Germany during the first two years of the COVID-19 pandemic. In this data descriptor, we present a unique resource for a systematic assessment of the survey data collection practices and quality of surveys conducted in Germany during the COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

A dataset on survey designs and quality of social and behavioral science surveys during the COVID-19 pandemic

Gummer,

Skora,

von Glasenapp

et al. 2024

Sci Data

View full text Add to dashboard Cite

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“…After data collection, processing errors can occur when preparing data for analysis. These errors, that are often underestimated (Jedinger et al 2018), can include data entry (for non-computer-assisted surveys), and cleaning and editing of data (i.e., coding open-ended answers, assigning variable and value labels, handling missing values, and implementation of survey weights) and tabulation of survey data (Biemer 2010). Computer-assisted data collection, that can be used most easily in WR, and only to a smaller extent in DOPU and PI surveys, can prevent a large number of errors, provided that routing errors and implausible values are taking into account when pretesting the software.…”

Section: Measurement: Processing Error In Wr Versus Dopu and Pimentioning

confidence: 99%

“…An important issue in surveys administered in different facilities, is that different selection probabilities in sampling due to differences in waiting room circumstances should be corrected for in sampling weights. Decisions on characteristics used as basis for the weights, their origin and the weighting method should be described in the methods section (Jedinger et al 2018).…”

Section: Measurement: Processing Error In Wr Versus Dopu and Pimentioning

confidence: 99%

Just you wait… and fill out this survey. Discussion of the methodological aspects of waiting room surveys

Ongena

Haan

2022

Health Serv Outcomes Res Method

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A method commonly used in health care research is the waiting room (WR) survey. While patients are waiting for their appointment, they are asked to complete a questionnaire measuring their attitudes, behaviors and other characteristics. In this paper we synthesize practical guidelines for WR surveys by comparing the method with two similar approaches: public intercept (PI) surveys and drop-off-pick-up (DOPU) surveys. In this comparison we use the Total Survey Error approach Groves (Survey Methodology, Wiley, New York, 2004); (Groves in Public Opinion Quarterly 74(5): 849-879, 2010) and apply it to three case examples in which one of the three surveys is used. We take into account measurement (validity, measurement- and processing error) and representation (coverage-, sampling- and nonresponse error). From our review, we conclude that waiting room surveys, though limited to patients and their caregivers, can provide useful information on patients’ perspective on health care. Response rates in waiting rooms are usually high, but often not even reported. We recommend adjustment for sampling bias by taking into account the number of visits to the hospital per respondent and sample times proportionate to the number of sample members expected on a particular time. These surveys also allow for collection of para-data; i.e., relevant information in the circumstances of a request to participate in survey research, and behavior of surveyors can easily be controlled, or investigated in an experimental design.

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“…Microsoft Excel spreadsheets will be used for data entry and organisation. Data entries will be verified by two investigators to ensure the accuracy and to minimise errors when processing data [77]. If the cases with missing data are more than 5%, multiple imputation will be performed to maximise the use of available information; and if the cases with missing data are less than 5%, listwise deletion will be performed to minimise bias [78,79].…”

Section: Quality Control Measuresmentioning

confidence: 99%

Evaluation of General Practice Pharmacists: Study Protocol to Assess Interprofessional Collaboration and Team Effectiveness

Sudeshika

Naunton

Peterson

et al. 2021

IJERPH

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The inclusion of pharmacists into general practices has expanded in Australia. However, there is a paucity of research examining interprofessional collaboration and team effectiveness after including a pharmacist into the general practice team in primary or community care. This is a protocol for a cross-national comparative mixed-methods study to (i) investigate interprofessional collaboration and team effectiveness within the general practice team after employing pharmacists in general practices in the Australian Capital Territory (ACT) and (ii) to compare interprofessional collaboration and team effectiveness of pharmacists in general practice across Australia with international sites. The first objective will be addressed through a multiphase sequential explanatory mixed-method design, using surveys and semi-structured interviews. The study will recruit general practice pharmacists, general practitioners, and other health professionals from eight general practices in the ACT. Quantitative and qualitative results will be merged during interpretation to provide complementary perspectives of interprofessional collaboration. Secondly, a quantitative descriptive design will compare findings on interprofessional collaboration (professional interactions, relationship initiation, exchange characteristics, and commitment to collaboration) and team effectiveness of general practice pharmacists in Australia with international sites from Canada and the United Kingdom. The results of the study will be used to provide recommendations on how to best implement the role of general practice pharmacists across Australia.

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Improving the Quality of Survey Data Documentation: A Total Survey Error Perspective

Cited by 7 publications

References 33 publications

A dataset on survey designs and quality of social and behavioral science surveys during the COVID-19 pandemic

A dataset on survey designs and quality of social and behavioral science surveys during the COVID-19 pandemic

Just you wait… and fill out this survey. Discussion of the methodological aspects of waiting room surveys

Evaluation of General Practice Pharmacists: Study Protocol to Assess Interprofessional Collaboration and Team Effectiveness

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