Personality as a Predictor of Unit Nonresponse in an Internet Panel
Personality as a Predictor of Unit Nonresponse in an Internet Panel
复制标题
DOI：
10.1177/0049124117747305
复制
发表时间：
2020-08-01
影响因子：
6.3
通讯作者：
Orriens, Bart
中科院分区：
文献类型：
--
作者：
Cheng, Albert;Zamarro, Gema;Orriens, Bart
面板数据集中的单位无响应通常是偏差的来源。研究人员已经研究了纵向研究中为什么某些个体会出现磨损以及如何最大限度地减少这种现象。然而，这项研究通常侧重于通过电话、邮寄或面对面采访收集的数据集。此外，这项研究通常侧重于利用教育程度或收入等人口特征来解释单位无反应发生率的变化。我们对现有文献做出了两项贡献。首先，我们检查了互联网小组中单位不答复的发生率，这是一种相对较新的、因此尚未得到充分研究的收集纵向数据的方法。其次，我们假设人格特质（在许多数据集中通常未被观察和测量）会影响单位不回应的可能性。使用来自互联网小组的数据，该小组在其基线调查中包括自我报告的人格测量，我们发现，即使在控制了研究人员通常可用并用于纠正单元无反应的认知能力和人口特征之后，责任心和对经验的开放性也可以预测后续调查波中单元无反应的发生率。我们还测试了使用平行数据作为与单位无反应相关的人格特征代理的可能性。尽管我们表明这些代理与人格特质相关，并以与自我报告的人格特质测量相同的方式预测单位无反应，但它们也有可能捕捉到与未来调查完成相关的其他特质。我们的结果表明，获得对人格特质的明确测量或为它们找到更好的替代指标可能对于更全面地解决由于单位不回应而可能产生的潜在偏见是有价值的。
Unit nonresponse in panel data sets is often a source of bias. Why certain individuals attrite from longitudinal studies and how to minimize this phenomenon have been examined by researchers. However, this research has typically focused on data sets collected via telephone, postal mail, or face-to-face interviews. Moreover, this research usually focuses on using demographic characteristics such as educational attainment or income to explain variation in the incidence of unit nonresponse. We make two contributions to the existing literature. First, we examine the incidence of unit nonresponse in an Internet panel, a relatively new, and hence understudied, approach to gathering longitudinal data. Second, we hypothesize that personality traits, which typically remain unobserved and unmeasured in many data sets, affect the likelihood of unit nonresponse. Using data from an Internet panel that includes self-reported measures of personality in its baseline survey, we find that conscientiousness and openness to experience predict the incidence of unit nonresponse in subsequent survey waves, even after controlling for cognitive ability and demographic characteristics that are usually available and used by researchers to correct for unit nonresponse. We also test the potential to use paradata as proxies for personality traits related to unit nonresponse. Although we show that these proxies are correlated with personality traits and predict unit nonresponse in the same way as self-reported measures of personality traits, it is also possible that they capture other idiosyncrasies related to future survey completion. Our results suggest that obtaining explicit measures of personality traits or finding better proxies for them could be valuable for more fully addressing the potential bias that may arise as a result of unit nonresponse.