Tuesday 16th July
Wednesday 17th July
Thursday 18th July
Friday 19th July
Download the conference book
Social Desirability Bias in Sensitive Surveys: Theoretical Explanations and Data Collection Methods 2 |
|
| Convenor | Dr Ivar Krumpal (University of Leipzig) |
| Coordinator 1 | Professor Ben Jann (University of Bern) |
| Coordinator 2 | Professor Mark Trappmann (Institute for Employment Research Nürnberg) |
Survey measures of sensitive characteristics (e.g. sexual behaviour, health indicators, illicit work, voting preferences, income, or unsocial opinions) based on respondents' self-reports are often distorted by social desirability bias. More specifically, surveys tend to overestimate socially desirable behaviours or opinions and underestimate socially undesirable ones, because respondents adjust their answers in accordance with perceived public norms. Furthermore, nonresponse has a negative impact on data quality, especially when the missing data is systematically related to key variables of the survey. Besides psychological aspects (such as a respondent's inclination to engage in impression management or self-deception), cumulative empirical evidence indicates that the use of specific data collection strategies influences the extent of social desirability bias in sensitive surveys. A better data quality can be achieved by choosing appropriate data collection methodologies.
This session has three main goals: (1) discuss the theoretical foundation of the research on social desirability bias in the context of a general theory of human psychology and social behaviour. For example, a clearer understanding of the social interactions between the actors that are involved in the data collection process (respondents, interviewers, and data collection institutions) could provide empirical researchers with a substantiated basis for optimizing the survey design to achieve high quality data; (2) present experimental results evaluating conventional methods of data collection for sensitive surveys (e.g. randomized response techniques and its variants) as well as innovative and new survey designs (e.g. mixed-mode surveys, item sum techniques). This also includes advancements in the methods for statistical analysis of data generated by these techniques; (3) discuss future perspectives for tackling the problem of social desirability and present possible alternative approaches for collecting sensitive data. This may include, for example, record linkage approaches, surveys without questions (e.g. biomarkers), and non-reactive measurement.
Qualitative studies and the media coverage of elections in Eastern Europe indicate a considerable widespread of electoral misconduct compared to survey-based reports. There is a large agreement in the literature (Gonzales-Ocantos et al. 2012, Corstange 2012) that social desirability bias related to survey research accounts for most part of this discrepancy. In order to estimate this bias, we have used two appropriate strategies, the crosswise model and the item count technique. Our data were collected on a national representative panel survey concerning 2012 local elections in Romania. The results provide strong evidence that vote buying and voter intimidation are more common behaviors than expected or estimated through direct questioning technique. No matter the technique we used (crosswise or item count) or the elections we referred to (anytime or 2012 local elections), our data indicate a quite large social desirability bias regarding electoral misconduct: 8% for anytime vote buying, 15% for anytime voter intimidation, 18% vote buying in 2012 local elections, and 28% voter intimidation in 2012 local elections. Moreover, our data prove that social desirability bias varies among the categories of some relevant socio-demographic and political variables: age, education, and region, respectively political interest and partisanship.
The item count method is a way of asking sensitive survey questions which protects the anonymity of the respondents by randomization before the interview. It can be used to estimate the probability of
sensitive behaviour and to model how it depends on explanatory variables. We analyse item count survey data on the illegal behaviour of buying stolen goods. The analysis of an item count question is best formulated as an instance of modelling incomplete categorical data. We propose an efficient implementation of the estimation which also provides explicit variance estimates for the parameters. We then suggest specifications for the model for the control items, which is an auxiliary but unavoidable part of the analysis of item count data. These considerations and the results of our analysis of criminal behaviour highlight the fact that careful design of the control questions is crucial for the success of the item count method.
Numerous studies investigated the predictors of tax evasion. However, past insights must be seen with caution since tax evasion is a sensitive topic and is likely to be underreported if interviewees respond directly. There is in fact a fundamental lack of reliable data on tax evasion (Alm & Torgler, 2011).
Fortunately, new questioning techniques offer the possibility to gather more reliable information on sensitive topics by reducing socially desirable answering. We used the Crosswise Model (Yu, Tian & Tang, 2008) to determine the predictors of tax evasion in an online sample (N = 824) and compared the found effects with data from a control group (N = 298) that was asked directly. As hypothesized, prevalence estimates for tax evasion were higher when gathered via the Crosswise Model. We further found effects of the social norm, attitude towards tax evasion and a rational choice variable (computed comprising perceived risk of getting caught, perceived penalty if caught and perceived gain if not caught) on committing tax evasion in both groups. Yet, the opportunity to evade taxes and an egoistic personality were predictors only in the Crosswise Model group. Compared to the other predictors, the opportunity to evade taxes was even the strongest predictor and would have gone unrecognized in the control group. Our results show the importance of appropriate questioning techniques when studying sensitive topics like tax evasion.