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HUSSEY Ian

Recommendations:  0

Reviews:  2

Reviews:  2

11 Sep 2023
STAGE 1

Researcher Predictions of Effect Generalizability Across Global Samples

Can psychology researchers predict which effects will generalise across cultures?

Recommended by based on reviews by Michèle Nuijten, Ian Hussey, Jim Grange and Matthias Stefan
Compared to the wealth of debate surrounding replicability and transparency, relatively little attention has been paid to the issue of generalisability – the extent to which research findings hold across different samples, cultures, and other parameters. Existing research suggests that researchers in psychology are prone to generalisation bias, relying on narrow samples (e.g. drawn predominantly from US or European undergraduate samples) to draw broad conclusions about the mind and behaviour. While recent attempts to address generalisability concerns have been made – such as journals requiring explicit statements acknowledging constraints on generality – addressing this bias at root, and developing truly generalisable methods and results, requires a deeper understanding of how researchers perceive generalisability in the first place.
 
In the current study, Schmidt et al. (2023) tackle the issue of cross-cultural generalisability using four large-scale international studies that are being conducted as part of the Psychological Science Accelerator (PSA) – a globally distributed network of researchers in psychology that coordinates crowdsourced research projects across six continents. Specifically, participants (who will be PSA research members) will estimate the probability that an expected focal effect will be observed both overall and within regional subsamples of the PSA studies. They will also predict the size of these focal effects overall and by region.
 
Using this methodology, the authors plan to ask two main questions: first whether researchers can accurately predict the generalisability of psychological phenomena in upcoming studies, and second whether certain researcher characteristics (including various measures of expertise, experience, and demographics) are associated with the accuracy of generalisability predictions. Based on previous evidence that scientists can successfully predict the outcomes of research studies, the authors expect to observe a positive association between predicted and actual outcomes and effect sizes. In secondary analyses, the authors will also test if researchers can predict when variables that capture relevant cultural differences will moderate the focal effects – if so, this would suggest that at least some researchers have a deeper understanding as to why the effects generalise (or not) across cultural contexts.
 
The Stage 1 manuscript was evaluated over two rounds of in-depth review. Based on detailed responses to the reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance (IPA).
 
URL to the preregistered Stage 1 protocol: https://osf.io/vwqsa (under temporary private embargo)
 
Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA.
 
List of eligible PCI RR-friendly journals:
 
 
References
 
1. Schmidt, K., Silverstein, P. & Chartier, C. R. (2023). Registered Report: Researcher Predictions of Effect Generalizability Across Global Samples. In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/vwqsa
18 Jul 2023
STAGE 1

Test-Retest Reliability of the STRAQ-1: A Registered Report

A reliable measure of physical closeness in interpersonal relationships?

Recommended by based on reviews by Jacek Buczny and Ian Hussey
Attachment and interpersonal relationships are a major subject of research and clinical work in psychology. There are, accordingly, a proliferation of measurement instruments to tap into these broad constructs. The emphasis in these measures tends to be on the emotional dimensions of the relationships—how people feel about their partners and the support that they receive. However, that is not all there is to relationship quality. Increasing attention has been paid to the physical and physiological aspects of relationships, but there are few psychometrically sound measures available to assess these dimensions.
 
In the current study, Dujols et al. (2023) seek to assess the psychometric properties of the Social Thermoregulation and Risk Avoidance Questionnaire (STRAQ-1), a measure of physical relationships that targets social thermoregulation, or how physical proximity is used to promote warmth and closeness. The proposed project will be a thorough assessment of the measure’s reliability over time—that is, the degree to which the measure assesses the construct similarly across administrations. The authors will assess the test-retest reliability and longitudinal measurement invariance of the STRAQ-1, providing much-needed psychometric data that can build confidence in the utility of the measure.
 
The Stage 1 manuscript was evaluated over two rounds of in-depth review, the first round consisting of detailed comments from two reviewers and the second round consisting of a close read by the recommender. Based on detailed responses to the reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and was therefore awarded in-principle acceptance (IPA).
 
URL to the preregistered Stage 1 protocol: https://osf.io/pmnk2
 
Level of bias control achieved: Level 3. At least some data/evidence that will be used to the answer the research question has been previously accessed by the authors (e.g. downloaded or otherwise received), but the authors certify that they have not yet observed ANY part of the data/evidence
 
List of eligible PCI RR-friendly journals:
 
References
 
1. Dujols, O., Klein, R. A., Lindenberg, S., Van Lissa, C. J., & IJzerman, H. (2023). Test-Retest Reliability of the STRAQ-1: A Registered Report. In principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/pmnk2
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HUSSEY Ian

Recommendations:  0

Reviews:  2