Preventing Data Fabrication in Telephone Survey Research
Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.644 | 0.762 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".