Belastbare und effiziente Wissenschaft

Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?

Zusammenfassung. Digitale Phänotypisierung stellt einen neuen, leistungsstarken Ansatz zur Realisierung psychodiagnostischer Aufgaben in vielen Bereichen der Psychologie und Medizin dar. Die Grundidee besteht aus der Nutzung digitaler Spuren aus dem Alltag, um deren Vorhersagekraft für verschiedenste Anwendungsmöglichkeiten zu überprüfen und zu nutzen. Voraussetzungen für eine erfolgreiche Umsetzung sind elaborierte Smart Sensing Ansätze sowie Big Data-basierte Extraktions- (Data Mining) und Machine Learning-basierte Analyseverfahren. Erste empirische Studien verdeutlichen das hohe Potential, aber auch die forschungsmethodischen sowie ethischen und rechtlichen Herausforderungen, um über korrelative Zufallsbefunde hinaus belastbare Befunde zu gewinnen. Hierbei müssen rechtliche und ethische Richtlinien sicherstellen, dass die Erkenntnisse in einer für Einzelne und die Gesellschaft als Ganzes wünschenswerten Weise genutzt werden. Für die Psychologie als Lehr- und Forschungsdomäne bieten sich durch Digitale Phänotypisierung vielfältige Möglichkeiten, die zum einen eine gelebte Zusammenarbeit verschiedener Fachbereiche und zum anderen auch curriculare Erweiterungen erfordern. Die vorliegende narrative Übersicht bietet eine theoretische, nicht-technische Einführung in das Forschungsfeld der Digitalen Phänotypisierung, mit ersten empirischen Befunden sowie einer Diskussion der Möglichkeiten und Grenzen sowie notwendigen Handlungsfeldern.

Der Testungseffekt beim Lernen mit Texten

Zusammenfassung. Da der Testungseffekt in der evidenzbasierten Lehr- / Lernforschung als einer der am besten gesicherten Befunde gilt (z. B. Dunlosky, Rawson, Marsh, Nathan & Willingham, 2013 ), ist er in besonderer Weise dazu geeignet, grundlegende Probleme bei der Nutzbarmachung von in der experimentellen Forschung gut gesicherten Befunden zu verdeutlichen. Im Zentrum dieses Beitrags stehen das Verhältnis zwischen Lernresultaten und metakognitiven Erwartungen, die Komplexität von Lernmaterialien als Moderator für den Testungseffekt sowie Unterschiede zwischen Feld- und Laborexperimenten.

Context Dependency as a Predictor of Replicability

We scrutinize the argument that unsuccessful replications—and heterogeneous effect sizes more generally—may reflect an underappreciated influence of context characteristics. Notably, while some of these context characteristics may be conceptually irrelevant (as they merely affect psychometric properties of the measured/manipulated variables), others are conceptually relevant as they qualify a theory. Here, we present a conceptual and analytical framework that allows researchers to empirically estimate the extent to which effect size heterogeneity is due to conceptually relevant versus irrelevant context characteristics. According to this framework, contextual characteristics are conceptually relevant when the observed heterogeneity of effect sizes cannot be attributed to psychometric properties. As an illustrative example, we demonstrate that the observed heterogeneity of the “moral typecasting” effect, which had been included in the ManyLabs 2 replication project, is more likely attributable to conceptually relevant rather than irrelevant context characteristics, which suggests that the psychological theory behind this effect may need to be specified. In general, we argue that context dependency should be taken more seriously and treated more carefully by replication research.

Young immigrants in Norway: The role of national and ethnic identity in immigrants' integration

The present work investigates the endorsement, antecedents, relationships, and consequences of young immigrants' social identities in Norway. Despite increasing numbers of refugees and immigrants entering Norway in recent years, little is known about the relationship between immigrants' different social identities and their feeling of integration into Norwegian society. The main goal of the present research is to fill this gap by investigating whether relationships found in other European countries replicate in the Norwegian context. In line with theoretical considerations and earlier international findings, results from two studies with different immigrant groups (Study 1: high school students; N = 97; Study 2: university students; N = 93) show that the more young immigrants in Norway endorse their national (i.e., Norwegian) identity, the less they endorse their ethnic identity (e.g., Kurdish). We further show that perceived conflict between the two cultures cannot explain the negative relationship between national and ethnic identity. In addition, immigrants' national identity endorsement is positively related to their dual identity endorsement (e.g., Kurdish-Norwegian). Positive contact with members of the receiving society predicts young immigrants' feeling of being integrated in Norwegian society and this relationship is mediated by national identity. Results are discussed in terms of the crucial role social identities play in immigrants' feeling of integration into European societies.

Detecting Evidential Value and p-Hacking With the p-Curve Tool

Abstract. Simonsohn, Nelson, and Simmons (2014a) proposed p-curve – the distribution of statistically significant p-values for a set of studies – as a tool to assess the evidential value of these studies. They argued that, whereas right-skewed p-curves indicate true underlying effects, left-skewed p-curves indicate selective reporting of significant results when there is no true effect (“ p-hacking”). We first review previous research showing that, in contrast to the first claim, null effects may produce right-skewed p-curves under some conditions. We then question the second claim by showing that not only selective reporting but also selective nonreporting of significant results due to a significant outcome of a more popular alternative test of the same hypothesis may produce left-skewed p-curves, even if all studies reflect true effects. Hence, just as right-skewed p-curves do not necessarily imply evidential value, left-skewed p-curves do not necessarily imply p-hacking and absence of true effects in the studies involved.