Evidence map›Paper›PMID 30291072›Full record

ArticleJMIR diabetes2017

Assessing Diabetes-Relevant Data Provided by Undergraduate and Crowdsourced Web-Based Survey Participants for Honesty and Accuracy.

Mary Turner DePalma, Michael C Rizzotti, Matthew Branneman

Open access · goldAbstract read
In one paragraph

Article in JMIR diabetes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018
    Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Mary Turner DePalmaIthaca College, Department of Psychology, Ithaca, NY, United States.ORCID http://orcid.org/0000-0002-4656-5053
Michael C RizzottiIthaca College, Department of Psychology, Ithaca, NY, United States.ORCID http://orcid.org/0000-0003-0660-7463
Matthew BrannemanIthaca College, Department of Psychology, Ithaca, NY, United States.ORCID http://orcid.org/0000-0002-7135-1508
Ithaca College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo eliminate health disparities, research will depend on our ability to reach select groups of people (eg, samples of a particular racial or ethnic group with a particular disease); unfortunately, researchers often experience difficulty obtaining high-quality data from samples of sufficient size.

objectivePast studies utilizing MTurk applaud its diversity, so our initial objective was to capitalize on MTurk's diversity to investigate psychosocial factors related to diabetes self-care.

methodsIn Study 1, a "Health Survey" was posted on MTurk to examine diabetes-relevant psychosocial factors. The survey was restricted to individuals who were 18 years of age or older with diabetes. Detection of irregularities in the data, however, prompted an evaluation of the quality of MTurk health-relevant data. This ultimately led to Study 2, which utilized an alert statement to improve conscientious behavior, or the likelihood that participants would be thorough and diligent in their responses. Trap questions were also embedded to assess conscientious behavior.

resultsIn Study 1, of 4165 responses, 1246 were generated from 533 unique IP addresses completing the survey multiple times within close temporal proximity. Ultimately, only 252 responses were found to be acceptable. Further analyses indicated additional quality concerns with this subsample. In Study 2, as compared with the MTurk sample (N=316), the undergraduate sample (N=300) included more females, and fewer individuals who were married. The samples did not differ with respect to race. Although the presence of an alert resulted in fewer trap failures (mean=0.07) than when no alert was present (mean=0.11), this difference failed to reach significance: F

conclusionsPublished studies that utilize MTurk participants are rapidly appearing in the health domain. While MTurk may have the potential to be more diverse than an undergraduate sample, our efforts did not meet the criteria for what would constitute a diverse sample in and of itself. Because some researchers have experienced successful data collection on MTurk, while others report disastrous results, Kees et al recently identified that one essential area of research is of the types and magnitude of cheating behavior occurring on Web-based platforms. The present studies can contribute to this dialogue, and alternately provide evidence of disaster and success. Moving forward, it is recommended that researchers employ best practices in survey design and deliberately embed trap questions to assess participant behavior. We would strongly suggest that standards be in place for publishing the results of Web-based surveys-standards that protect against publication unless there are suitable quality assurance tests built into the survey design, distribution, and analysis.

Indexed as

crowdsourcingdata accuracydiabetes mellitusmechanical turksMTurksurvey designsurvey methodologysurvey quality

Identifiers

PMID30291072
PMCPMC6238844
OpenAlexW2735296243

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.