Comparative research is how evidence is generated to support or refute broad hypotheses (e.g., Pagel 1999). However, the foundations of such research must be solid if one is to arrive at the correct conclusions. Determining the external validity (the generalizability across situations/individuals/populations) of the building blocks of comparative data sets allows one to place appropriate caveats around the robustness of their conclusions (Steckler & McLeroy 2008).

In the current study, Alley and colleagues (2023) tackled the external validity of comparative research that relies on subjects who are either university students or participating in experiments via an online platform. They determined whether data from these two types of subjects have measurement equivalence - whether the same trait is measured in the same way across groups.

Although they use data from studies involved in the Many Labs replication project to evaluate this question, their results are of crucial importance to other comparative researchers whose data are generated from these two sources (students and online crowdsourcing). The authors show that these two types of subjects do not often have measurement equivalence, which is a warning to others to evaluate their experimental design to improve validity. They provide useful recommendations for researchers on how to to implement equivalence testing in their studies, and they facilitate the process by providing well annotated code that is openly available for others to use.

After one round of review and revision, the recommender judged that the manuscript met the Stage 2 criteria and awarded a positive recommendation.

 

Level of bias control achieved: Level 2. At least some data/evidence that was used to answer the research question had been accessed and partially observed by the authors prior to Stage 1 IPA, but the authors certify that they had not yet observed the key variables within the data that were used to answer the research question AND they took additional steps to maximise bias control and rigour.

 

 

• Advances in Methods and Practices in Psychological Science
• Collabra: Psychology
• F1000Research
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Studia Psychologica
• Swiss Psychology Open

 

 

1. Pagel, M. (1999). Inferring the historical patterns of biological evolution. Nature, 401, 877-884. https://doi.org/10.1038/44766

 

2. Steckler, A. & McLeroy, K. R. (2008). The importance of external validity. American Journal of Public Health 98, 9-10. https://doi.org/10.2105/AJPH.2007.126847

 

3. Alley L. J., Axt, J., & Flake J. K. (2023). Convenience Samples and Measurement Equivalence in Replication Research [Stage 2 Registered Report] Acceptance of Version 2 by Peer Community in Registered Reports​. https://osf.io/s5t3v

Comparative research is how evidence is generated to support or refute broad hypotheses (e.g., Pagel 1999). However, the foundations of such research must be solid if one is to arrive at the correct conclusions. Determining the external validity (the generalizability across situations/individuals/populations) of the building blocks of comparative data sets allows one to place appropriate caveats around the robustness of their conclusions (Steckler & McLeroy 2008).

 

In this registered report, Alley and colleagues plan to tackle the external validity of comparative research that relies on subjects who are either university students or participating in experiments via an online platform (Alley et al. 2023). They will determine whether data from these two types of subjects have measurement equivalence - whether the same trait is measured in the same way across groups. Although they use data from studies involved in the Many Labs replication project to evaluate this question, their results will be of crucial importance to other comparative researchers whose data are generated from these two sources (students and online crowdsourcing). If Alley and colleagues show that these two types of subjects have measurement equivalence, then this indicates that it is more likely that equivalence could hold for other studies relying on these type of subjects as well. If measurement equivalence is not found, then it is a warning to others to evaluate their experimental design to improve validity. In either case, it gives researchers a way to test measurement equivalence for themselves because the code is well annotated and openly available for others to use.

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).

 

Level of bias control achieved: Level 2. At least some data/evidence that will be used to answer the research question has been accessed and partially observed by the authors, but the authors certify that they have not yet observed the key variables within the data that will be used to answer the research question AND they have taken additional steps to maximise bias control and rigour (e.g. conservative statistical threshold; recruitment of a blinded analyst; robustness testing, multiverse/specification analysis, or other approach) 

 

 

• Advances in Methods and Practices in Psychological Science
• F1000Research
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Swiss Psychology Open

 

 

Alley L. J., Axt, J., & Flake J. K. (2023). Convenience Samples and Measurement Equivalence in Replication Research, in principle acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/7gtvf

 

Steckler, A. & McLeroy, K. R. (2008). The importance of external validity. American Journal of Public Health 98, 9-10. https://doi.org/10.2105/AJPH.2007.126847

 

Pagel, M. (1999). Inferring the historical patterns of biological evolution. Nature, 401, 877-884. https://doi.org/10.1038/44766