scholarly journals Revisiting the continuum of resistance theory in the digital age: A comparison of early and delayed respondents to the Norwegian Counties Public Health Survey

2020 ◽  
Author(s):  
Benjamin Clarsen ◽  
Jens Christoffer Skogen ◽  
Thomas Severinus Nilsen ◽  
Leif Edvard Aarø

Abstract BackgroundThe 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.MethodsRespondents 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.ResultsThe 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%).ConclusionsThere 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.

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Benjamin Clarsen ◽  
Jens Christoffer Skogen ◽  
Thomas Sevenius Nilsen ◽  
Leif Edvard Aarø

Abstract 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 only been assessed in one 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.


2020 ◽  
Author(s):  
Benjamin Clarsen ◽  
Jens Christoffer Skogen ◽  
Thomas Severinus Nilsen ◽  
Leif Edvard Aarø

Abstract BackgroundThe 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.MethodsRespondents 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.ResultsThe 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%).ConclusionsThere 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.


2012 ◽  
Vol 23 (1) ◽  
pp. 152-157 ◽  
Author(s):  
M. Linden-Bostrom ◽  
C. Persson

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107374 ◽  
Author(s):  
Joseph G. Giduthuri ◽  
Nicolas Maire ◽  
Saju Joseph ◽  
Abhay Kudale ◽  
Christian Schaetti ◽  
...  

2009 ◽  
Vol 8 (1) ◽  
Author(s):  
Theo Bodin ◽  
Maria Albin ◽  
Jonas Ardö ◽  
Emilie Stroh ◽  
Per-Olof Östergren ◽  
...  

The Lancet ◽  
1848 ◽  
Vol 52 (1297) ◽  
pp. 52-53
Author(s):  
GeoT. Cloutt

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