Showing satisfactory psychometric properties, DREEM proved suitable for assessing educational environments among dental students. Given the right circumstances, e.g., small and early clinically oriented classes, traditional curricula can generate positive environments.

Aims: The teachers' perspectives of the educational environment have as yet only been sparsely considered. This study aimed at validating the first German version of the Dundee Ready Education Environment Measure (DREEM) from the points of view of both students and teachers. Methods: Data from 1119 students and 258 teachers were available for analysis. Psychometric validation included the analysis of homogeneity and discrimination at item level as well as reliability (Cronbach's ), criterion and construct validity at test level. Effect sizes were calculated and the independent samples t-test was used for statistical inference testing of mean differences between two groups. Results: Item characteristics were satisfactory in both samples. Reliability was high with ¼ 0.92 (students) and 0.94 (teachers), respectively. Factor analyses revealed five dimensions which slightly diverged from the five subscales postulated by the DREEM authors though. The environment was evaluated significantly ( p 5 0.001) more positively by teachers (M ¼ 117.63) than by students (M ¼ 109.75). Further significant differences were observed with regard to gender, mother language, stage of studies and previous professional training among others. Conclusions: With convincing psychometric properties at item and test levels, the suitability of DREEM not only for students but also for teachers to assess the educational environment has been demonstrated.

While negative correlations have often been found between a respondent's education and his attitudes towards foreigners, the reasons for this education effect are still under debate. We examined the hypothesis that the highly educated may not be genuinely less xenophobic, but simply more prone to give socially desirable, xenophile answers in attitude questionnaires. We therefore compared the attitudes of respondents who were either questioned directly or using a cheating detection extension of the randomized-response technique (RRT). The latter is supposed to yield more honest answers to sensitive questions by experimentally offering the interviewee a higher degree of confidentiality. Under direct questioning conditions, we replicated the education effect; 75% of the highly educated expressed xenophile attitudes, as opposed to only 55% of the less educated. Under randomized-response conditions, we obtained significantly reduced estimates of 53% for the proportion of xenophiles among the highly educated, and 24% among the less educated, indicating a strong distortion of self-reported attitudes towards foreigners in both groups. However, a significant proportion of participants disobeyed the RRT instructions regardless of education. Because the education effect was found even after controlling for social desirability, it seems to be a genuine effect, rather than an artefact of a differential response bias.

Given the considerable discrepancy between the results obtained by direct questioning and by using the randomized-response technique, we propose that this technique be considered for use in epidemiologic studies of sensitive behaviors.

Randomized response techniques (RRTs) aim to reduce social desirability bias in the assessment of sensitive attributes but differ regarding privacy protection. The less protection a design offers, the more likely respondents cheat by disobeying the instructions. In asymmetric RRT designs, respondents can play safe by giving a response that is never associated with the sensitive attribute. Symmetric RRT designs avoid the incentive to cheat by not allowing such responses. We tested whether a symmetric variant of a cheating detection model (CDM) increases compliance with the instructions in a survey of academic dishonesty among 2,254 Chinese students. As we observed more noncompliance in the asymmetric than symmetric variant, we recommend the use of symmetric CDMs, which can easily be tested within multinomial models.