Evaluating and improving the quality of survey data from panel and crowd-sourced samples: A practical guide for psychological research.
Bibliographic record
Abstract
The use of crowd-sourced and panel survey data in addiction research has become widespread. However, the validity of data obtained from newer panels such as Qualtrics has not been extensively evaluated. Furthermore, few addiction researchers appear to employ previously recommended guidelines for maximizing the quality of data obtained from panel samples. The goals of the present study were as follows: (a) to evaluate the quality of survey data obtained from Qualtrics including an evaluation of the company's internal data screening process and (b) to provide a practical implementation guide for data screening practices that maximize the quality of data obtained via panel and crowd-sourced samples. To address the goals, two panel samples evaluating vaping and video gaming behaviors were recruited in Canada via Qualtrics and underwent Qualtrics's internal data screening process before being rigorously rescreened by the authors. The results demonstrate that while Qualtrics's paid internal data quality process flags and removes many low-quality participants, there is still a large portion of participants presented by Qualtrics as high-quality that are likely low-quality responses that need to be screened out. The presented methodology provides a rigorous data screening protocol, including step-by-step application, for crowd-sourced samples in addictive behavior research for maximizing data quality. Researchers should be cautious in the use of Qualtrics data for administration of addiction survey research and are encouraged to use additional data screening procedures to maximize data quality. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.456 | 0.588 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".