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Record W1510536834

Preventing Data Fabrication in Telephone Survey Research

2004· article· en· W1510536834 on OpenAlexaboutno aff
Philip B. Smith, Colleen B. MacQuarrie, Rosemary Herbert, David L. Cairns, Lorraine Begley

Bibliographic record

VenueJournal of Research Administration · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionAccreditationPublic relationsPopulationBusinessQuality assuranceMarketingPolitical scienceSociologyMedical educationMedicine
DOInot available

Abstract

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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. …

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.644
metaresearch head score (Gemma)0.762
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.988
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6440.762
Meta-epidemiology (narrow)0.0030.006
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0140.013
Science and technology studies0.0100.017
Scholarly communication0.0090.010
Open science0.0100.015
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0200.011

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.821
GPT teacher head0.672
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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