On the role of variation in measures, the worth of underpowered studies, and the need for tolerance among researchers

Zitzmann, Steffen; Wagner, Wolfgang; Lavelle-Hill, Rosa; Jung, Alexander J.; Jach, Hayley; Loreth, Lukas; Lindner, Christoph; Schmidt, Fabian T. C.; Edelsbrunner, Peter A.; Schaefer, Christoph D.; Deutschländer, Robert; Schauber, Stefan K.; Krammer, Georg; Wolff, Fabian; Hui, Bronson; Fischer, Christian; Bardach, Lisa; Nagengast, Benjamin; Hecht, Martin

doi:10.1177/27000710241257413

On the role of variation in measures, the worth of underpowered studies, and the need for tolerance among researchers

Some more reflections on Leising et al. from a methodological, statistical, and social-psychological perspective

Publication date

2024-11-01

Document type

Sonstiger Artikeltyp

Author

Zitzmann, Steffen

Wagner, Wolfgang

Lavelle-Hill, Rosa

Jung, Alexander J.

Jach, Hayley

Loreth, Lukas

Lindner, Christoph

Schmidt, Fabian T. C.

Edelsbrunner, Peter A.

Schaefer, Christoph D.

Deutschländer, Robert

Schauber, Stefan K.

Krammer, Georg

Wolff, Fabian

Hui, Bronson

Fischer, Christian

Bardach, Lisa

Nagengast, Benjamin

Hecht, Martin

Organisational unit

Psychologische Methodenlehre

DOI

10.1177/27000710241257413

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/20726

Publisher

Sage Publications

Series or journal

Personality Science

ISSN

2700-0710

Periodical volume

5

Peer-reviewed

✅

Part of the university bibliography

✅

Language

English

Abstract

We point out potential drawbacks of some of Leising et al.’s (2022a) proposed ways how personality science can be improved. We argue that it is ill-advised to use only one measure for a concept. Also, we argue that researchers should not refrain from conducting a study when a high level of statistical power is precluded. Then, we go one step further and formulate additional ideas of how to improve research. Specifically, we argue that it is a good thing to use different methods rather than only one when attempting to generalize across these methods. Moreover, we argue for a more theory-driven strategy for specifying factor analytic models, and we emphasize that high-quality research is often interdisciplinary. Finally, we point to a particular risk associated with any formal reward system.

Description

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons
Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/).

Version

Published version

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