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Enregistrement W1510536834

Preventing Data Fabrication in Telephone Survey Research

2004· article· en· W1510536834 sur OpenAlex

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Notice bibliographique

RevueJournal of Research Administration · 2004
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSurvey Methodology and Nonresponse
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésData collectionAccreditationPublic relationsPopulationBusinessQuality assuranceMarketingPolitical scienceSociologyMedical educationMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract Not all data fabrication originates with, or is known by, the researcher. Fabrication of survey data can occur at multiple sites in the data collection hierarchy. To prevent such survey fraud, each research program must give attention to increasing operational safeguards. Funding agencies should be prepared to allocate sufficient monies to cover the costs of increased oversight. National and international standards of professionalism and accreditation should be established. Introduction A research team at the University of Prince Edward Island, Canada, discovered that the data collection company hired to conduct telephone interviews fabricated 23% of the data sets. To prevent a recurrence, the team developed a menu of options which research administrators and others concerned with research ethics can disseminate to program directors and researchers within their organizations for application pre, during, and post data collection. Menu items relate to (i) the organizational structure of data collection companies; (ii) strategies in developing contracts with companies; (iii) operational procedures; (iv) data/record review; (v) budget; and (vi) national or international standards. Research planners are encouraged to incorporate suggestions from the menu of options. Funders are encouraged both to require and to fund quality assurance initiatives. A call for professionalization and accreditation of data collection companies aims to address quality assurance issues in survey research. In 2003 at the University of Prince Edward Island, Canada, the Smoke-Free Homes Research Project was jeopardized by a survey firm hired to conduct telephone interviews. The research featured a baseline population survey of 1,410 households in the first year, a subsequent social marketing intervention, and, in the second year, a post-test of another 1,410 household interviews. Challenges to data collection included strict inclusion criteria, a small population base in both intervention and control sites, and requirements to complete data collection within a one-month period each year. The survey firm contracted to conduct the interviews delivered the stipulated number of data sets on time. During an examination of the data, in year two, inexplicable consistencies in text portions of a number of surveys raised questions about their veracity. It became evident that many interviews were manuiactured by copying and pasting the whole or parts of genuine interviews to create the number specified in the contract. A re-examination of year-one data uncovered similar, but more cunningly concealed, fabricated data. In all, 23% of surveys were found to be fabricated. Fortunately, an unexpectedly large effect size meant that, even with the loss of these data, this particular study was not underpowered. There is an emerging literature within professional research organizations (Johnson, Parker & Clements, 2001; Methods of Interviewer Fraud Detection, 2003), in conference presentations (Caspar, 2003; Qi, 2002), and academic publications (Marshall, 2000) that concentrates on the interviewer as the site of fraudulent activity. This narrow focus can blind researchers to the possibility that fraud can be perpetrated by others in the data handling hierarchy. This paper draws the attention of the whole research community to the reality of survey fraud originating beyond the interviewer level. Researchers need to incorporate, in survey planning, adequate procedures for the prevention and detection of telephone survey fraud. We present a menu of options citing advantages and disadvantages under six themes: (i) organizational structure of data collection companies, (ii) strategies in developing contracts with companies, (iii) operational procedures, (iv) data/record review, (v) budget, and (vi) national or international standards. Results I. Organizational Structure of Data Collection Organizations Data quality assurance can be enhanced if researchers avoid contracting with companies where managers and staff are also engaged in for-profit work such as telephone promotions, sales, and customer service. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,435
score de la tête « metaresearch » (Gemma)0,226
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,227
Score d'incertitude au seuil0,982

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,4350,226
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,821
Tête enseignante GPT0,672
Écart entre enseignants0,149 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle