Faculty of Psychology, University of Salamanca, Avda. de la Merced, 109-131, 37005, Salamanca, Spain.
Faculty of Psychology, Pontificia Universidad Católica Madre y Maestra, Santiago De Los Caballeros, Dominican Republic.
Behav Res Methods. 2020 Dec;52(6):2489-2505. doi: 10.3758/s13428-020-01401-8.
In self-report surveys, it is common that some individuals do not pay enough attention and effort to give valid responses. Our aim was to investigate the extent to which careless and insufficient effort responding contributes to the biasing of data. We performed analyses of dimensionality, internal structure, and data reliability of four personality scales (extroversion, conscientiousness, stability, and dispositional optimism) in two independent samples. In order to identify careless/insufficient effort (C/IE) respondents, we used a factor mixture model (FMM) designed to detect inconsistencies of response to items with different semantic polarity. The FMM identified between 4.4% and 10% of C/IE cases, depending on the scale and the sample examined. In the complete samples, all the theoretical models obtained an unacceptable fit, forcing the rejection of the starting hypothesis and making additional wording factors necessary. In the clean samples, all the theoretical models fitted satisfactorily, and the wording factors practically disappeared. Trait estimates in the clean samples were between 4.5% and 11.8% more accurate than in the complete samples. These results show that a limited amount of C/IE data can lead to a drastic deterioration in the fit of the theoretical model, produce large amounts of spurious variance, raise serious doubts about the dimensionality and internal structure of the data, and reduce the reliability with which the trait scores of all surveyed are estimated. Identifying and filtering C/IE responses is necessary to ensure the validity of research results.
在自我报告调查中，一些个体不够注意和努力给出有效回答的情况很常见。我们的目的是调查粗心和不充分努力作答在多大程度上导致数据产生偏差。我们对两个独立样本中的四个人格量表（外向性、尽责性、稳定性和倾向性乐观）进行了维度、内部结构和数据可靠性分析。为了识别粗心/不充分努力（C/IE）的受访者，我们使用了因子混合模型（FMM），旨在检测对具有不同语义极性的项目的反应不一致性。FMM 根据所检查的量表和样本，确定了 4.4%至 10%的 C/IE 病例。在完整样本中，所有理论模型的拟合度都不令人满意，因此必须拒绝初始假设并增加额外的措辞因素。在干净的样本中，所有理论模型的拟合度都令人满意，措辞因素几乎消失了。在干净的样本中，特质估计比完整样本中的特质估计准确 4.5%至 11.8%。这些结果表明，少量的 C/IE 数据可能会导致理论模型的拟合急剧恶化，产生大量虚假方差，对数据的维度和内部结构产生严重质疑，并降低对所有被调查对象特质得分的估计可靠性。识别和过滤 C/IE 反应是确保研究结果有效性的必要条件。
