Accuracy of estimates and statistical power for testing meditation in latent growth curve modeling

Cheong, JeeWon

doi:10.1080/10705511.2011.557334

Cited by 55Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingAdvanced Causal Inference Techniques

Accuracy of estimates and statistical power for testing meditation in latent growth curve modeling

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Cheong, JeeWon·2011·Structural Equation Modeling: A Multidisciplinary Journal

DOI 10.1080/10705511.2011.557334PMC PMC4990218

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Abstract

The latent growth curve modeling (LGCM) approach has been increasingly utilized to investigate longitudinal mediation. However, little is known about the accuracy of the estimates and statistical power when mediation is evaluated in the LGCM framework. A simulation study was conducted to address these issues under various conditions including sample size, effect size of mediated effect, number of measurement occasions, and R 2 of measured variables. In general, the results showed that relatively large samples were needed to accurately estimate the mediated effects and to have adequate statistical power, when testing mediation in the LGCM framework. Guidelines for designing studies to examine longitudinal mediation and ways to improve the accuracy of the estimates and statistical power were discussed.

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A sample size of at least 150 was necessary for the more complex models.
“Based on previous studies with a similar design, it was determined that a sample size of at least 150 was necessary for the more complex models (Cheong, 2011; Sunnhed et al., 2022).”
From Distress tolerance as a mediator of mindfulness-based intervention for anxiety and depression: Evidence from two randomized controlled trials (2024) · cites this paper as Cheong, 2011
The Discussion (page unavailable) says that more complex latent-factor models might require larger samples. But the Method tests sample sizes of 100 and 200, not 150, and the Results give power requirements that vary by condition. The paper does not establish 150 as a necessary minimum. (LLM, conf=0.88)
Experimental — accuracy not yet measured
A minimum of 150 participants was required for Study 2 based on sample size calculation.
“The sample size calculation was identical to Study 1; a minimum of 150 participants was required (Cheong, 2011; Sunnhed et al., 2022).”
From Distress tolerance as a mediator of mindfulness-based intervention for anxiety and depression: Evidence from two randomized controlled trials (2024) · cites this paper as Cheong, 2011
The Method section lists simulated sample sizes of 100, 200, 500, 1,000, 2,000, and 5,000; it does not calculate a 150-participant minimum. The Results discuss power requirements for the paper’s mediation models, but our copy does not address Study 2 or its sample-size calculation (searched for “150 participants” and “Study 2”). (LLM, conf=0.73)
Experimental — accuracy not yet measured

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Authors: Cheong, J.
Affiliations: University of Pittsburgh (US)
Corresponding author: Cheong, JeeWon
ORCID: Cheong, JeeWon
Venue: Structural Equation Modeling: A Multidisciplinary Journal
Publisher: Informa UK Limited
Published: 2011-04-06
Volume: 18
Issue: 2
Pages: 195-211
ISSN: 1070-5511 (ISSN-L)
1532-8007
DOI: 10.1080/10705511.2011.557334
PMC: PMC4990218
OpenAlex: W2034989530
Cited by: 55
References: 52
Citations by year: 2026: 1 · 2025: 2 · 2024: 6 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 4 · 2016: 6 · 2015: 2 · 2014: 3 · 2013: 4 · 2012: 2
FWCI: 2.52
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute on Alcohol Abuse and Alcoholism (R01AA013117)
National Institute on Drug Abuse (R01DA009757, R01DA007356, R37DA009757)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: latent growth curve models, longitudinal mediation, statistical power, estimation accuracy, sample size, indirect effects

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Accuracy of estimates and statistical power for testing meditation in latent growth curve modeling

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