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En demografisk studie om svenskans obestämda plurala artikel ena (och några ord om västsvenskt änna)
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Barnboken och klimatångesten. Ekokritiska och litteraturdidaktiska perspektiv på undergångsskildringar i Om dagen tar slut, Slutet och Kometjakten*
Full text linkVideokonferenser i brottmålsförhandlingar – Svenska och internationella perspektiv på deltagande i hybridmöten
Full text linkDas Fjäll und die Stuga. Zur Übersetzung von aktuellen Tourismusprospekten aus dem Schwedischen ins Deutsche
Full text linkA Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles
No full textMany bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is very challenging, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact Psychology journals in 2016 and 2017 using Google Scholar. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible
Replication value as a function of citation impact and sample size
No full textResearchers seeking to replicate original research often need to decide which of several relevant candidates to select for replication. Several strategies for study selection have been proposed, utilizing a variety of observed indicators as criteria for selection. However, few strategies clearly specify the goal of study selection and how that goal is related to the indicators that are utilized. We have previously formalized a decision model of replication study selection in which the goal of study selection is to maximize the expected utility gain of the replication effort. We further define the concept of replication value as a proxy for expected utility gain (Isager et al., 2023). In this article, we propose a quantitative operationalization of replication value. We first discuss how value and uncertainty - the two concepts used to determine replication value – could be estimated via information about citation count and sample size. Second, we propose an equation for combining these indicators into an overall estimate of replication value, which we denote RVCn. Third, we suggest how RVCn could be implemented as part of a broader study selection procedure. Finally, we provide preliminary data suggesting that studies that were in fact selected for replication tend to have relatively high RVCn estimates. The goal of this article is to explain how RVCn is intended to work and, in doing so, demonstrate the many assumptions that should be explicit in any replication study selection strategy
Data is not available upon request
No full textMany journals now require data sharing and require articles to include a Data Availability Statement. However, several studies over the past two decades have shown that promissory notes about data sharing are rarely abided by and that data is generally not available upon request. This has negative consequences for many essential aspects of scientific knowledge production, including independent verification of results, efficient secondary use of data, and knowledge synthesis. I assessed the prevalence of data sharing upon request in articles employing the Implicit Relational Assessment Procedure published within the last 5 years. Of 52 articles, 42% contained a Data Availability Statement, most of which stated that data was available upon request. This rose from 0% in 2018 to 100% in 2022, indicating a change in journals’ policies. However, only 27% of articles’ authors actually shared data. Among articles stating that data was available upon request, only 17% shared data upon request. The presence of Data Availability Statements was not associated with higher rates of data sharing (p = .55), indicating a lack of adherence with journals’ policies. Results replicate those found elsewhere: data is generally not available upon request, and promissory Data Availability Statements are typically not adhered to. Issues, causes, and implications are considered
Stadt lesen – Stadt schreiben: Literatur- und fremdsprachendidaktische Perspektiven auf Stadtromane aus der Weimarer Republik
Full text linkPracticing Theory Building in a Many Modelers Hackathon: A Proof of Concept
No full textScientific theories reflect some of humanity's greatest epistemic achievements. The best theories motivate us to search for discoveries, guide us towards successful interventions, and help us to explain and organize knowledge. Such theories require a high degree of specificity, which in turn requires formal modeling. Yet, in psychological science, many theories are not precise and psychological scientists often lack the technical skills to formally specify existing theories. This problem raises the question: How can we promote formal theory development in psychology, where there are many content experts but few modelers? In this paper, we discuss one strategy for addressing this issue: a Many Modelers approach. Many Modelers consists of mixed teams of modelers and non-modelers that collaborate to create a formal theory of a phenomenon. Here, we report a proof of concept of this approach, which we piloted as a three-hour hackathon at the Society for the Improvement of Psychological Science conference in 2021. After surveying the participants, results suggest that (a) psychologists who have never developed a formal model can become (more) excited about formal modeling + and theorizing; (b) a division of labor in formal theorizing is possible where only one or a few team members possess the prerequisite modeling expertise; and (c) first working prototypes of a theoretical model can be created in a short period of time. These results show some promise for the many modelers approach as a team science tool for theory development