Videoconference interviews are now integral to many selection processes. Theoretical arguments and empirical findings suggest that videoconference interviews may lead to different interview performance ratings in comparison to Face-to-Face (FTF) interviews. This has led to the question of the comparability of the psychometric properties of videoconferences and FTF interviews. However, evidence from actual selection processes stems from the beginning of the century, and recent findings predominantly stem from simulated interview contexts. We present insights from an actual selection process within a large European organization where we had the unique opportunity for a quasi-experimental investigation of differences between videoconference and FTF interviews. Initially, the organization conducted FTF interviews, and after the onset of the COVID-19 pandemic, the interviews were conducted via videoconference. We examine mean differences in applicant performance ratings and evidence for response format-related validity differences. There were only small, non-significant mean differences and no evidence for response format related validity differences. We discuss possible causes for discrepancies in our findings compared to previous research. Furthermore, we conclude that downstream consequences of differences between FTF and videoconference interviews may be lower than previously expected. We end with a call for research on the interaction between technology-design and selection-tool-design features.

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.