Department of Experimental Psychology, University of Duesseldorf, Universitaetsstrasse 1, 40225, Duesseldorf, Germany.
Behav Res Methods. 2023 Feb;55(2):716-729. doi: 10.3758/s13428-022-01819-2. Epub 2022 Apr 21.
The Randomized Response Technique (Warner, Journal of the American Statistical Association, 60, 63-69, 1965) has been developed to control for socially desirable responses in surveys on sensitive attributes. The Crosswise Model (CWM; Yu et al., Metrika, 67, 251-263, 2008) and its extension, the Extended Crosswise Model (ECWM; Heck et al., Behavior Research Methods, 50, 1895-1905, 2018), are advancements of the Randomized Response Technique that have provided promising results in terms of improved validity of the obtained prevalence estimates compared to estimates based on conventional direct questions. However, recent studies have raised the question as to whether these promising results might have been primarily driven by a methodological artifact in terms of random responses rather than a successful control of socially desirable responding. The current study was designed to disentangle the influence of successful control of socially desirable responding and random answer behavior on the validity of (E)CWM estimates. To this end, we orthogonally manipulated the direction of social desirability (undesirable vs. desirable) and the prevalence (high vs. low) of sensitive attributes. Our results generally support the notion that the ECWM successfully controls social desirability bias and is inconsistent with the alternative account that ECWM estimates are distorted by a substantial influence of random responding. The results do not rule out a small proportion of random answers, especially when socially undesirable attributes with high prevalence are studied, or when high randomization probabilities are applied. Our results however do rule out that random responding is a major factor that can account for the findings attesting to the improved validity of (E)CWM as compared with DQ estimates.
随机反应技术（Warner，《美国统计协会杂志》，60，63-69，1965）已经被开发出来，用于控制调查中对敏感属性的社会期望反应。交叉模型（CWM；Yu 等人，《计量学》，67，251-263，2008）及其扩展，即扩展交叉模型（ECWM；Heck 等人，《行为研究方法》，50，1895-1905，2018），是随机反应技术的改进，与基于传统直接问题的估计相比，它们在提高获得的流行率估计的有效性方面提供了有希望的结果。然而，最近的研究提出了一个问题，即这些有希望的结果是否主要是由于随机反应的方法artifact 而不是对社会期望反应的成功控制。本研究旨在厘清成功控制社会期望反应和随机回答行为对（E）CWM 估计有效性的影响。为此，我们正交地操纵了社会期望性（不良与良好）和敏感属性的流行率（高与低）的方向。我们的结果普遍支持这样一种观点，即 ECWM 成功地控制了社会期望偏差，并且与 ECWM 估计受到随机反应的实质性影响的替代解释不一致。结果并不排除小比例的随机回答，特别是当研究具有高流行率的不良属性时，或者当应用高随机化概率时。然而，我们的结果确实排除了随机反应是一个主要因素，可以解释与 DQ 估计相比，ECWM 的有效性得到提高的发现。
