Kocar Sebastian, Biddle Nicholas
Institute for Social Change, University of Tasmania, Hobart, Australia.
Centre for Social Research and Methods, The Australian National University, Canberra, Australia.
Qual Quant. 2023;57(2):1055-1078. doi: 10.1007/s11135-022-01385-x. Epub 2022 Apr 25.
The objective of this study is to identify factors affecting participation rates, i.e., nonresponse and voluntary attrition rates, and their predictive power in a probability-based online panel. Participation for this panel had already been investigated in the literature according to the socio-demographic and socio-psychological characteristics of respondents and different types of paradata, such as device type or questionnaire navigation, had also been explored. In this study, the predictive power of online panel participation paradata was instead evaluated, which was expected (at least in theory) to offer even more complex insight into respondents' behavior over time. This kind of paradata would also enable the derivation of longitudinal variables measuring respondents' panel activity, such as survey outcome rates and consecutive waves with a particular survey outcome prior to a wave (e.g., response, noncontact, refusal), and could also be used in models controlling for unobserved heterogeneity. Using the Life in Australia™ participation data for all recruited members for the first 30 waves, multiple linear, binary logistic and panel random-effect logit regression analyses were carried out to assess socio-demographic and online panel paradata predictors of nonresponse and attrition that were available and contributed to the accuracy of prediction and the best statistical modeling. The proposed approach with the derived paradata predictors and random-effect logistic regression proved to be reasonably accurate for predicting nonresponse-with just 15 waves of online panel paradata (even without sociodemographics) and logit random-effect modeling almost four out of five nonrespondents could be correctly identified in the subsequent wave.
本研究的目的是确定影响参与率的因素，即无回应率和自愿退出率，以及它们在基于概率的在线样本中的预测能力。根据受访者的社会人口统计学和社会心理特征，已有文献对该样本的参与情况进行了研究，同时也探讨了不同类型的辅助数据，如设备类型或问卷导航。在本研究中，取而代之的是评估在线样本参与辅助数据的预测能力，预期（至少在理论上）它能提供关于受访者随时间变化行为的更复杂见解。这类辅助数据还能推导出衡量受访者样本活动的纵向变量，如调查结果率以及在某一轮之前具有特定调查结果（如回应、无联系、拒绝）的连续轮次，并且也可用于控制未观察到的异质性的模型中。利用澳大利亚生活™ 前30轮所有招募成员的参与数据，进行了多元线性、二元逻辑和样本随机效应逻辑回归分析，以评估可用的社会人口统计学和在线样本辅助数据对无回应和退出的预测因素，这些因素有助于提高预测准确性和最佳统计建模。结果表明，所提出的带有推导辅助数据预测因素和随机效应逻辑回归的方法在预测无回应方面相当准确——仅使用15轮在线样本辅助数据（甚至不包括社会人口统计学数据），通过逻辑随机效应建模，几乎五分之四的无回应者能在后续轮次中被正确识别。
