Hochheimer Camille J, Sabo Roy T, Perera Robert A, Mukhopadhyay Nitai, Krist Alex H
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States.
Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States.
J Med Internet Res. 2019 Aug 23;21(8):e12811. doi: 10.2196/12811.
Although Web-based questionnaires are an efficient, increasingly popular mode of data collection, their utility is often challenged by high participant dropout. Researchers can gain insight into potential causes of high participant dropout by analyzing the dropout patterns.
This study proposed the application of and assessed the use of user-specified and existing hypothesis testing methods in a novel setting-survey dropout data-to identify phases of higher or lower survey dropout.
First, we proposed the application of user-specified thresholds to identify abrupt differences in the dropout rate. Second, we proposed the application of 2 existing hypothesis testing methods to detect significant differences in participant dropout. We assessed these methods through a simulation study and through application to a case study, featuring a questionnaire addressing decision-making surrounding cancer screening.
The user-specified method set to a low threshold performed best at accurately detecting phases of high attrition in both the simulation study and test case application, although all proposed methods were too sensitive.
The user-specified method set to a low threshold correctly identified the attrition phases. Hypothesis testing methods, although sensitive at times, were unable to accurately identify the attrition phases. These results strengthen the case for further development of and research surrounding the science of attrition.
尽管基于网络的调查问卷是一种高效且越来越受欢迎的数据收集方式，但其效用常常受到高参与者退出率的挑战。研究人员可以通过分析退出模式来深入了解高参与者退出率的潜在原因。
本研究提出在一种新的环境（调查退出数据）中应用并评估用户指定的和现有的假设检验方法，以识别调查退出率较高或较低的阶段。
首先，我们提出应用用户指定的阈值来识别退出率的突然差异。其次，我们提出应用两种现有的假设检验方法来检测参与者退出的显著差异。我们通过模拟研究和应用于一个案例研究来评估这些方法，该案例研究涉及一份关于癌症筛查决策的调查问卷。
在模拟研究和测试案例应用中，设置为低阈值的用户指定方法在准确检测高损耗阶段方面表现最佳，尽管所有提出的方法都过于敏感。
设置为低阈值的用户指定方法正确识别了损耗阶段。假设检验方法虽然有时很敏感，但无法准确识别损耗阶段。这些结果为进一步发展和研究损耗科学提供了有力依据。
