Schnell mal zum Buchladen um die Ecke - das wird immer schwieriger. Obwohl die Branche mehr Umsatz macht, gibt es immer weniger Geschäfte. Das Statistikamt nennt auch mögliche Gründe für den Rückgang.

Artificial intelligence (AI) has profoundly transformed numerous facets of both private and professional life. Understanding how people evaluate AI is…

Information and communication technology (ICT) literacy encompasses a range of cognitive abilities that facilitate the effective use of digital techno…

Interval scales are frequently assumed in educational and psychological research involving latent variables, but are rarely verified. This paper outlines methods for investigating the interval scale assumption when fitting the Rasch model to item response data. We study a Bayesian method for evaluating an item response dataset’s adherence to the cancellation axioms of additive conjoint measurement under the Rasch model, and compare the extent to which the axiom of double cancellation holds in the data at sample sizes of 250 and 1000 with varying test lengths, difficulty spreads, and levels of adherence to the Rasch model in the data-generating process. Because the statistic produced by the procedure is not directly interpretable as an indicator of whether an interval scale can be established, we develop and evaluate procedures for bootstrapping a null distribution of violation rates against which to compare results. At a sample size of 250, the method under investigation is not well powered to detect the violations of interval scaling that we simulate, but the procedure works quite consistently at N = 1000. That is, at moderate but achievable sample sizes, empirical tests for interval scaling are indeed possible.

A growing number of self-report measures aim to define interactions with social media in a pathological behavior framework, often using terminology focused on identifying those who are ‘addicted’ to engaging with others online. Specifically, measures of ‘social media addiction’ focus on motivations for online social information seeking, which could relate to motivations for offline social information seeking. However, it could be the case that these same measures could reveal a pattern of friend addiction in general. This study develops the Offline-Friend Addiction Questionnaire (O-FAQ) by re-wording items from highly cited pathological social media use scales to reflect “spending time with friends”. Our methodology for validation follows the current literature precedent in the development of social media ‘addiction’ scales. The O-FAQ had a three-factor solution in an exploratory sample of N = 807 and these factors were stable in a 4-week retest (r = .72 to .86) and was validated against personality traits, and risk-taking behavior, in conceptually plausible directions. Using the same polythetic classification techniques as pathological social media use studies, we were able to classify 69% of our sample as addicted to spending time with their friends. The discussion of our satirical research is a critical reflection on the role of measurement and human sociality in social media research. We question the extent to which connecting with others can be considered an ‘addiction’ and discuss issues concerning the validation of new ‘addiction’ measures without relevant medical constructs. Readers should approach our measure with a level of skepticism that should be afforded to current social media addiction measures.

Machine learning and automation remain underutilized and inconsistently reported in evidence syntheses. Our large-scale review of 2,271 studies highlights limited adoption beyond screening, with sign...