Evidence map›Paper›PMID 38308148›Full record

ArticleBehavior research methods2024

Statistical power analysis and sample size planning for moderated mediation models.

Ziqian Xu, Fei Gao, Anqi Fa, Wen Qu, Zhiyong Zhang

Abstract read
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In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ziqian XuUniversity of Notre Dame, South Bend, IN, USA. zxu9@nd.edu.
Fei GaoNanjing University of Posts and Telecommunications, Nanjing, China.
Anqi FaNanjing University of Posts and Telecommunications, Nanjing, China.
Wen QuFudan University, Shanghai, China.
Zhiyong ZhangUniversity of Notre Dame, South Bend, IN, USA.

Funding

U.S. Department of Education R305D140037,R305D210023
6 · The paper itself

Abstract

Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this paper, we introduce new simulation-based methods for power analysis of conditional process models with a focus on moderated mediation models. These simulation-based methods provide intuitive ways for sample-size planning based on regression coefficients in a moderated mediation model as well as selected variance and covariance components. We demonstrate how the methods can be applied to five commonly used moderated mediation models using a simulation study, and we also assess the performance of the methods through the five models. We implement our approaches in the WebPower R package and also in Web apps to ease their application.

Indexed as

Models, StatisticalBehavioral ResearchComputer SimulationData Interpretation, StatisticalHumansMediation AnalysisSample SizeSoftwareConditional process modelsModerated mediationMonte Carlo methodStatistical power analysis

Identifiers

PMID38308148

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.