PCI Registered Reports

Announcements

Please note that we will be CLOSED to ALL SUBMISSIONS from 1 December 2024 through 12 January 2025 to give our recommenders and reviewers a holiday break.

We are recruiting recommenders (editors) from all research fields!

Your feedback matters! If you have authored or reviewed a Registered Report at Peer Community in Registered Reports, then please take 5 minutes to leave anonymous feedback about your experience, and view community ratings.

Back

398

Title *

How Does Model (Mis)Specification Impact Statistical Power, Type I Error Rate, and Parameter Bias in Moderated Mediation?use asterix (*) to get italics

Authors *

Jessica L. Fossum, Amanda K. Montoya, and Samantha F. AndersonPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year *

2024

Picture

Abstract *

<p>Moderated mediation models are commonly used in psychological research and other academic fields to model when and how effects occur. Researchers must choose which paths in the mediation model are moderated when specifying this type of model. While the ultimate goal is to specify the model correctly, researchers may struggle to determine whether to err on the side of including too many moderated paths (maximalist approach) or including too few moderated paths (minimalist approach). This registered report examines how the specification of moderation impacts statistical power, type I error rate, and parameter bias for the index of moderated mediation. In a systematic review, we found that six model specifications account for 85% of published moderated mediation analyses and the median sample size was 285. We ran a Monte Carlo simulation study to examine the impacts of model specification on power and type I error rate, and results were analyzed using multilevel logistic regression. In reference to the data-generating process, the data analysis model can either be correctly specified, over-specified, under-specified, or completely misspecified. Over-specified models were hypothesized to have lower statistical power to detect a significant index of moderated mediation compared to correctly specified models, and relatively low parameter bias. Under-specified models were hypothesized to have lower statistical power than correctly specified models, but unacceptably high parameter bias. Completely misspecified models were hypothesized to have inflated type I error rates and unacceptable parameter bias. Implications of results on study planning (specification and sample size) for moderated mediation will be discussed.</p>

Indicate the full web address (DOI or URL) giving public access to these data (if you have any problems with the deposit of your data, please contact contact@rr.peercommunityin.org). *

You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://

Indicate the full web address (DOI or URL) giving public access to these scripts (if you have any problems with the deposit of your scripts, please contact contact@rr.peercommunityin.org). *

You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

Indicate the full web address (DOI, SWHID or URL) giving public access to these codes (if you have any problems with the deposit of your codes, please contact contact@rr.peercommunityin.org). *

You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

Keywords (optional)

methodology; moderated mediation; statistical power; type I error rate; model misspecification

Methods that require specific expertise (optional)

Monte Carlo computer simulationPlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields *

Life Sciences, Social sciences

Suggested reviewers - Suggest up to 10 reviewers (provide names and Email addresses). (Optional)

e.g. John Doe john@doe.com

No need for them to be recommenders of PCI Registered Reports. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

Opposed reviewers - Suggest up to 5 people not to invite as reviewers. (Optional)

e.g. John Doe john@doe.com

Submission date

2023-02-04 00:13:10

Recommender

Zoltan Dienes

Reviewers

or Register
Submit a report