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Questions on earnings are counted among sensitive topics that often produce high rates of item nonresponse or measurement error. Both types of bias are well documented in the literature and are found to concentrate in the tails of the earnings distribution. In this paper, we explore whether measurement error on earnings could be explained by socially desirable
reporting and whether the error is impacted by interviewer characteristics. Using the linked dataset NEPS-SC6-ADIAB, which contains survey data from the German National Educational Panel Study, Starting Cohort "Adults", linked to administrative earnings records from the German Federal Employment Agency, we analyze the extents of over- and underreporting and the influence of respondent and interviewer characteristics on these behaviors for different quartiles of the earnings distribution. Our results show that the average level of misreporting is relatively low (approximately 6% of median earnings). Our main logistic model reveals that female and more highly educated respondents report significantly more accurately while those with higher earnings misreport to a significantly greater extent. Regarding the impact of personality traits on reporting accuracy, we find significant positive effects for more agreeable respondents and significant negative effects for extraverted respondents. When differentiating by the direction of misreporting, we find, for instance, that women are less likely to overreport across all earnings quartiles. However, the influence of interviewer characteristics is negligible.When it comes to sensitive questions, data is often affected by bias due to non-response or effects of social desirability. Several methods have been introduced to eliminate answer bias by using randomization processes and probabilistic theory to obscure the respondent’s answer and create anonymity, thus facilitating honest answers. The probably most traditional method is the Randomized Response Technique by Warner (1965). However, this method is loaded with certain disadvantages. Therefore, in the last decade, newer methods were introduced that aim at balancing the disadvantages and weaknesses of previous methods, for instance, the non-randomized models Crosswise Model and Triangular Model (Yu et al. 2008) as well as the Parallel Model (Tian 2014). Although especially the Triangular Model is easy to implement in a study, there is only little empirical evidence on its application in different survey modes and populations. Further, it is to assume that certain questions are not equally sensitive for everybody due to specific personal characteristics. Thus, indirect questioning might not be effective in general but only for certain populations. The present study extends prior work on the Triangular Model by evaluating it for different subgroups. The conducted experiment asks for sensitive characteristics in the context of mental stress among students. The Triangular Model achieves significantly higher percentages than conventional direct questioning for illegal drug use among persons that answer socially desirable according to the characteristic of Self-Deception. For the other analyzed subgroups (Impression Management, gender, and depressiveness), the Triangular Model could not achieve higher prevalence rates compared to direct questioning on a sufficient probability level. But still, hard evidence on the effectiveness of indirect questioning models is thin and further critical discussion is needed.
Segregation between ethnic or sociodemographic groups represents a longstanding key independent and dependent variable for the social sciences. However, researchers have only recently begun to take advantage of inferential rather than descriptive statistical techniques
in order to assess various aspects of segregation. Specifically, this paper shows that the multilevel binomial response approach suggested by Leckie et al. (2012) provides a particularly flexible framework for describing and explaining segregation in ways not previously possible. Taking the index of dissimilarity (D) as an example we demonstrate how the multilevel binomial response approach helps to reduce the problem of small unit bias, allows to asses segregation at different scales and enables researchers to better understand the role of individual- and contextual-level explanatory variables in shaping segregation. To this end, we employ three case studies focusing on different manifestations of ethnic and gender segregation using survey data from urban, national and cross-national settings.