e.g. mhealth
Search Results (1 to 4 of 4 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 2 Journal of Medical Internet Research
- 1 JMIR Formative Research
- 1 JMIR Public Health and Surveillance
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR Research Protocols
- 0 JMIR Human Factors
- 0 JMIR Medical Informatics
- 0 JMIR mHealth and uHealth
- 0 JMIR Serious Games
- 0 JMIR Mental Health
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Cancer
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Aging
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
In this paper, we describe the MMPFS methods designed to achieve a high response rate and minimal nonresponse bias—including the creation of the frame of facilities surveyed; survey instrument development; data collection methods including the development of multiple survey modes and phases of data collection and facility recruitment; response rates; and postsurvey data processing, including nonresponse bias analysis, weighting, and imputation.
JMIR Form Res 2025;9:e52123
Download Citation: END BibTex RIS
Weighting these samples resulted in datasets that generally well matched the national estimates. The Knowledge Panel weights (maximum weight of 2.8) brought those data completely in line with national estimates, whereas weighting the MTurk data allowing for a maximum weight of 30 brought those data within a total absolute imbalance of 0.01 across the 6 demographic variables used to construct the weights.
Table 6 shows other characteristics of the samples from each platform.
J Med Internet Res 2024;26:e63032
Download Citation: END BibTex RIS
This trade-off becomes of elevated concern since with samples obtained from opt-in panels, typical geodemographic weighting adjustments may no longer be adequate for ensuring their representativity [5].
It has been suggested that with such samples more granular weighting and calibration adjustments become necessary to ameliorate their compromised representations [6].
JMIR Public Health Surveill 2024;10:e48186
Download Citation: END BibTex RIS
Second, although we matched families with children, the number of children and their ages, which can clearly affect a household’s food purchases, were not used in weighting. Importantly, we were able to correct the differences only in the observed sociodemographic variables, and thus, unidentifiable selection bias cannot be ruled out. This may include factors that would be associated with willingness to participate, such as special dietary restrictions and socially excluded people.
J Med Internet Res 2020;22(7):e18059
Download Citation: END BibTex RIS