Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, Maryland 21211, USA.
J Am Med Inform Assoc. 2012 Jul-Aug;19(4):668-73. doi: 10.1136/amiajnl-2012-000863. Epub 2012 Apr 26.
To evaluate non-response rates to follow-up online surveys using a prospective cohort of parents raising at least one child with an autism spectrum disorder. A secondary objective was to investigate predictors of non-response over time.
Data were collected from a US-based online research database, the Interactive Autism Network (IAN). A total of 19,497 youths, aged 1.9-19 years (mean 9 years, SD 3.94), were included in the present study. Response to three follow-up surveys, solicited from parents after baseline enrollment, served as the outcome measures. Multivariate binary logistic regression models were then used to examine predictors of non-response.
31,216 survey instances were examined, of which 8772 or 28.1% were partly or completely responded to. Results from the multivariate model found non-response of baseline surveys (OR 28.0), years since enrollment in the online protocol (OR 2.06), and numerous sociodemographic characteristics were associated with non-response to follow-up surveys (all p<0.05).
Consistent with the current literature, response rates to online surveys were somewhat low. While many demographic characteristics were associated with non-response, time since registration and participation at baseline played the greatest role in predicting follow-up survey non-response.
An important hazard to the generalizability of findings from research is non-response bias; however, little is known about this problem in longitudinal internet-mediated research (IMR). This study sheds new light on important predictors of longitudinal response rates that should be considered before launching a prospective IMR study.
通过前瞻性队列研究评估至少抚养一名自闭症谱系障碍儿童的父母对在线随访调查的无应答率。次要目标是研究随时间推移的无应答预测因素。
数据来自美国在线研究数据库——互动自闭症网络(IAN)。本研究共纳入 19497 名年龄在 1.9-19 岁(平均 9 岁,标准差 3.94)的青少年。将基线入组后向父母征集的三次随访调查的应答情况作为结局指标。然后使用多变量二项逻辑回归模型来检验无应答的预测因素。
共分析了 31216 个调查实例,其中 8772 个或 28.1%被部分或完全应答。多变量模型的结果发现,基线调查的无应答(OR 28.0)、在线协议入组以来的年限(OR 2.06)以及许多社会人口学特征与随访调查的无应答相关(均 p<0.05)。
与当前文献一致,在线调查的应答率有些低。虽然许多人口统计学特征与无应答相关,但登记和参与基线时的时间长短对预测随访调查的无应答起着最大的作用。
无应答偏倚是研究结果普遍推广的一个重要危害;然而,人们对纵向互联网介导研究(IMR)中的这一问题知之甚少。本研究为重要的纵向应答率预测因素提供了新的认识,在开展前瞻性 IMR 研究之前应考虑这些因素。