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Research article
Benjamin Clarsen
Norwegian Institute of Public Health
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3713-8938
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
The continuum of resistance model’s premise is that delayed respondents to a survey are more similar to non-respondents than early respondents are. For decades, survey researchers have applied this model in attempts to evaluate and adjust for non-response bias. Despite a recent resurgence in the model’s popularity, its value has not been assessed in a large online population health survey.
Methods
Respondents to the Norwegian Counties Public Health Survey in Hordaland, Norway, were divided into three groups: those who responded within 7 days of the initial email/SMS invitation (wave 1, n = 6950); those who responded after 8 to 14 days and 1 reminder (wave 2, n =4950); and those who responded after 15 or more days and 2 reminders (wave 3, n = 4045). Logistic regression analyses were used to compare respondents’ age, sex and educational level between waves, as well as the prevalence of poor general health, life dissatisfaction, mental distress, chronic health problems, weekly alcohol consumption, monthly binge drinking, daily smoking, physical activity, low social support and receipt of a disability pension.
Results
The overall response to the survey was 41.5%. Respondents in wave 1 were more likely to be older, female and more highly educated than those in waves 2 and 3. However, there were no substantial differences between waves for any health outcomes, with a maximal prevalence difference of 2.6% for weekly alcohol consumption (wave 1: 21.3%, wave 3: 18.7%).
Conclusions
There appeared to be a mild continuum of resistance for demographic variables. However, this was not reflected in health and related outcomes, which were uniformly similar across waves. The continuum of resistance model is unlikely to be useful to adjust for nonresponse bias in large online surveys of population health.
Keywords
Epidemiologic methods, Selection bias, Surveys and questionnaires
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CURRENT STATUS: Published
View the published article at BMC Public Health.
No community comments so far
Editorial decision: Minor revision
On 01 Mar, 2021
Review #3 received
Received 21 Feb, 2021
Review #1 received
Received 07 Feb, 2021
Review #2 received
Received 06 Feb, 2021
Reviewer #2 agreed
On 04 Feb, 2021
Reviewer #3 agreed
On 04 Feb, 2021
Reviewers invited
Invitations sent on 04 Feb, 2021
Reviewer #1 agreed
On 04 Feb, 2021
Editor assigned
On 09 Nov, 2020
Submission checks complete
On 09 Nov, 2020
Editor invited
On 09 Nov, 2020
Version 1
Posted 14 Jul, 2020
No comments provided
Editorial decision: Major revision
On 02 Sep, 2020
Review #2 received
Received 01 Sep, 2020
Review #3 received
Received 31 Aug, 2020
Reviewer #3 agreed
On 12 Aug, 2020
Reviewer #2 agreed
On 06 Aug, 2020
Review #1 received
Received 22 Jul, 2020
Reviewer #1 agreed
On 19 Jul, 2020
Reviewers invited
Invitations sent on 14 Jul, 2020
Editor assigned
On 02 Jul, 2020
First submitted
On 01 Jul, 2020
Submission checks complete
On 01 Jul, 2020
Editor invited
On 01 Jul, 2020
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Public Health.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Benjamin Clarsen
Norwegian Institute of Public Health
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3713-8938
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Public Health.
No community comments so far
Editorial decision: Minor revision
On 01 Mar, 2021
Review #3 received
Received 21 Feb, 2021
Review #1 received
Received 07 Feb, 2021
Review #2 received
Received 06 Feb, 2021
Reviewer #2 agreed
On 04 Feb, 2021
Reviewer #3 agreed
On 04 Feb, 2021
Reviewers invited
Invitations sent on 04 Feb, 2021
Reviewer #1 agreed
On 04 Feb, 2021
Editor assigned
On 09 Nov, 2020
Submission checks complete
On 09 Nov, 2020
Editor invited
On 09 Nov, 2020
Version 1
Posted 14 Jul, 2020
No comments provided
Editorial decision: Major revision
On 02 Sep, 2020
Review #2 received
Received 01 Sep, 2020
Review #3 received
Received 31 Aug, 2020
Reviewer #3 agreed
On 12 Aug, 2020
Reviewer #2 agreed
On 06 Aug, 2020
Review #1 received
Received 22 Jul, 2020
Reviewer #1 agreed
On 19 Jul, 2020
Reviewers invited
Invitations sent on 14 Jul, 2020
Editor assigned
On 02 Jul, 2020
First submitted
On 01 Jul, 2020
Submission checks complete
On 01 Jul, 2020
Editor invited
On 01 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Public Health.
Background
The continuum of resistance model’s premise is that delayed respondents to a survey are more similar to non-respondents than early respondents are. For decades, survey researchers have applied this model in attempts to evaluate and adjust for non-response bias. Despite a recent resurgence in the model’s popularity, its value has not been assessed in a large online population health survey.
Methods
Respondents to the Norwegian Counties Public Health Survey in Hordaland, Norway, were divided into three groups: those who responded within 7 days of the initial email/SMS invitation (wave 1, n = 6950); those who responded after 8 to 14 days and 1 reminder (wave 2, n =4950); and those who responded after 15 or more days and 2 reminders (wave 3, n = 4045). Logistic regression analyses were used to compare respondents’ age, sex and educational level between waves, as well as the prevalence of poor general health, life dissatisfaction, mental distress, chronic health problems, weekly alcohol consumption, monthly binge drinking, daily smoking, physical activity, low social support and receipt of a disability pension.
Results
The overall response to the survey was 41.5%. Respondents in wave 1 were more likely to be older, female and more highly educated than those in waves 2 and 3. However, there were no substantial differences between waves for any health outcomes, with a maximal prevalence difference of 2.6% for weekly alcohol consumption (wave 1: 21.3%, wave 3: 18.7%).
Conclusions
There appeared to be a mild continuum of resistance for demographic variables. However, this was not reflected in health and related outcomes, which were uniformly similar across waves. The continuum of resistance model is unlikely to be useful to adjust for nonresponse bias in large online surveys of population health.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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