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Researchers studying person-environment fit can choose between various measurement approaches. Even though these measures are distinctly different, they often get used interchangeably, which makes interpreting the results of person-environment fit studies difficult. In the present article, we contrast the most commonly used measurement approaches for person-environment fit in higher education and compare them in terms of explained variance. We obtained data on the fit as well as subjective and objective study-related outcomes of N = 595 university students. We analyzed the fit between the demands of the study program and the abilities of the student, using the algebraic, squared and absolute difference score, response surface analysis (RSA), and direct fit as measurement approaches. Our results indicate that RSA explains the most variance for objective outcomes, and that direct fit explains the most variance for subjective outcomes. We hope that this contribution will help researchers distinguish the different measurement approaches of demands-abilities fit (and ultimately person-environment fit) and use them accordingly.

Selective exposure to online health information can be ascribed to two related defense motives: the motivation to confirm one's subjective perceptions and the motivation to protect relevant parts of the self-image, such as physical integrity. Our aim was to identify how these motives come into effect in the context of a health threat (fictitious feedback on an alleged heart disease risk). In a preregistered online study with N = 763 participants, we analyzed the impact of perceived and suggested risk on the degree of bias in selecting risk-related information on a fictitious Google search results page. Applying a 2 × 2 design with the experimental factor "risk feedback" and the quasi-experimental factor "perceived risk," we formulated six hypotheses. First, we expected a main effect of perceived risk on selective exposure to information suggesting no risk, and second, we hypothesized a main effect of perceived risk on mean quality rating of information suggesting a risk. Third, we proposed a main effect of risk feedback on selective exposure to information which suggests no risk, and fourth, we proposed a main effect of risk feedback on mean quality rating of information suggesting a risk. Fifth, we expected an interaction effect between perceived and suggested risk, and sixth, we proposed an interaction effect between perceived and suggested risk in different forms for each of the four conditions on quality ratings. Only the third hypothesis was confirmed: Receiving information which suggested a health risk increased the tendency to select information denying the risk. Additional exploratory analyses revealed moderator effects of health information literacy and participant age on the aforementioned relationships. In sum, our results underline the crucial role of defense motives in the context of a suggested health threat.

In two studies, we examined whether open science practices, such as making materials, data, and code of a study openly accessible, positively affect public trust in science. Furthermore, we investigated whether the potential trust-damaging effects of research being funded privately (e.g. by a commercial enterprise) may be buffered by such practices. After preregistering six hypotheses, we conducted a survey study (Study 1; N = 504) and an experimental study (Study 2; N = 588) in two German general population samples. In both studies, we found evidence for the positive effects of open science practices on trust, though it should be noted that in Study 2, results were more inconsistent. We did not however find evidence for the aforementioned buffering effect. We conclude that while open science practices may contribute to increasing trust in science, the importance of making use of open science practices visible should not be underestimated.

Governmental measures such as social distancing aim at curbing the spread of the novel coronavirus, SARS-COV-2. In March 2020, a large proportion of the German population supported such measures. In this paper, we analysed whether the support for social distancing would dwindle with economic worries superimposing virus-related health worries in the months to follow. We test seven pre-registered hypotheses using data from the German COVID-19 Snapshot MOnitoring (COSMO) survey, which regularly monitors behavioural and psychological factors related to the pandemic. For the present article, we analysed the German COSMO data for the period ranging from March 24, 2020 to July 7, 2020 (Ntotal = 13,094). Results revealed that health worries decreased over time, whereas economic worries remained largely constant. Furthermore, as expected, the acceptance of social distancing considerably decreased. Health worries were positively associated with acceptance of social distancing, whereas the corresponding relationship was negative with regard to economic worries (albeit smaller and less consistent). Unexpectedly, no interactions between health worries and economic worries were found. We conclude that individual differences in health and economic threat perceptions related to COVID-19 play an important role in the acceptance of social distancing. ; notReviewed ; other