It is incorrect and even harmful that many information systems researchers typically criticize their own intensive (qualitative, interpretive, critical, and case) research as lacking “generalizability.” We untangle and distinguish the numerous concepts now confounded in the single term “generalizability,” which are generality, generalization, generalize, general, and generalizing. These clarifi...

Abstract In the context of current “replication crisis” across sciences, failures to reproduce a finding are often viewed as discrediting it. This paper shows how such conclusion can be incorrect. 1981, Schuman and Presser showed that including word “freedom” in survey question significantly increased approval allowing speech against religion USA. New experiments probability sample surveys (n =...

Behavioral researchers increasingly recognize the need for more diverse samples that capture breadth of human experience. Current attempts to establish generalizability across populations focus on threats validity, constraints generalization, and accumulation large, cross-cultural data sets. But continued progress, we also require a framework lets us determine which inferences can be drawn how ...

This work investigates how generalizability, an extension of reliability, can be defined and estimated based on longitudinal data sequences resulting from, for example, clinical studies. Useful and intuitive approximate expressions are derived based on generalized linear mixed models. Data from four double-blind, randomized clinical trials into schizophrenia motivate the research and are used t...

Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The main methodological issue that influences the generalizability of clinical research findings is the sampling method. In this educational article, we are explai...

Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual representations from unlabeled data. The performance of Deep Learning models fine-tuned on pretrained SSL is par with the state-of-the-art supervised (SL) representations. Irrespective progress SSL, its generalizability has not been studied extensively. In this article, we perform a deeper analy...