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Revisiting the continuum of resistance model in the digital age: a comparison of early and delayed respondents to the Norwegian counties public health survey

机译:重新审视数字时代的抵抗模式的连续性:对挪威县公共卫生调查的早期和延迟受访者的比较

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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. 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. 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%). 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.
机译:抵抗模型的前提是,延迟受访者对调查的受访者比早期受访者更类似于非受访者。几十年来,调查研究人员已经应用了这种模型,试图评估和调整非响应偏差。尽管近期在模型的普及中重新恢复,但其价值仅在一个大型在线人口健康调查中进行了评估。挪威挪威挪威县公共卫生调查的受访者分为三组:那些在7?初始电子邮件/短信邀请的日内回复的人(Wave 1,N?6950);那些在8到14后回复的人?天和1个提醒(波2,n?= 4950);和15天或更多天后回答的人和2个提醒(波3,N?= 4045)。逻辑回归分析用于比较波浪之间的受访者的年龄,性和教育水平,以及贫困的一般健康,生活不满,精神痛苦,慢性健康问题,每周酒精消费,每月狂欢饮酒,每日吸烟,物理的普遍存在活动,低社会支持和收到残疾养老金。对调查的总体反应是41.5%。波浪1中的受访者更容易更容易更容易更容易,女性和比波浪2和3更高的教育更受教育。然而,对于任何健康结果之间的波浪之间没有大量差异,每周酒精消费的最大流行差异为2.6% (波浪1:21.3%,波3:18.7%)。似乎是人口变量的温和连续性。然而,这并没有反映在健康和相关结果中,在波浪中均匀地相似。抵抗模型的连续内容不太可能在大型在线调查的人口健康中调整非响应偏差有用。

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