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Cited by 441Psychometric Methodologies and TestingStatistical Methods and Bayesian InferenceStatistical Methods and Applications

Effect Size, Statistical Power, and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis

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Dziak, John J., Lanza, Stephanie T., Tan, Xianming·2014·Structural Equation Modeling: A Multidisciplinary Journal
DOI 10.1080/10705511.2014.919819PMC PMC4196274
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Abstract

Selecting the number of different classes that will be assumed to exist in the population is an important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) provides a data-driven way to evaluate the relative adequacy of a (K – 1)-class model compared to a K-class model. However, very little is known about how to predict the power or the required sample size for the BLRT in LCA. Based on extensive Monte Carlo simulations, we provide practical effect size measures and power curves that can be used to predict power for the BLRT in LCA given a proposed sample size and a set of hypothesized population parameters. Estimated power curves and tables provide guidance for researchers wishing to size a study to have sufficient power to detect hypothesized underlying latent classes.

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  • The minimum sample size required for LCA with 11 items, medium effect size w=0.3, and power 0.80 is 449.

    “when we set the medium effect size w=0.3, m80(w2) is a constant estimated at 40.4 if including 11 items with the set power as 0.80; therefore, the minimum sample size required is 449.”

    From Latent Classes of Circadian Type and Presenteeism and Work-Related Flow Differences Among Clinical Nurses: A Cross-Sectional Study (2022) · cites this paper as Dziak et al. [41]

    Simulation Experiment 1 gives the 80%-power BLRT estimate N = m80^(w²)/w², and Experiment 2 includes 11-item models. However, the passages available from our copy do not establish that m80^(w²) is 40.4 for 11 items, so they do not verify the claimed minimum of 449. The Recommendations also advise multiplying the estimated N by 1.15 as a precaution. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The sample size of 568 participants was sufficient to maintain power at 0.80 for LCA with 11 items.

    “This method determines that 568 participants is a sufficient sample size to maintain power at 0.80 if including 11 items.”

    From Latent Classes of Circadian Type and Presenteeism and Work-Related Flow Differences Among Clinical Nurses: A Cross-Sectional Study (2022) · cites this paper as Dziak et al. [41]

    Simulation Experiment 2 includes 11-item LCA models and develops benchmarks for estimating the sample size needed for 0.80 power. But the paper does not establish that 568 participants suffice merely because an analysis has 11 items: its sample-size calculation also requires an assumed effect size, and the Discussion cautions against treating it as post hoc assurance of power. Page numbers are unavailable in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cites

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Authors
Dziak, J. J., Lanza, S. T., & Tan, X.
Affiliations
  • Pennsylvania State University (US)
  • McGill University Health Centre (CA)
Corresponding author
Dziak, John J.
ORCID
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Publisher
Informa UK Limited
Published
2014-07-23
Volume
21
Issue
4
Pages
534-552
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
DOI
10.1080/10705511.2014.919819
PMC
PMC4196274
OpenAlex
W2118846274
Cited by
441
References
8
Citations by year
2026: 45 · 2025: 38 · 2024: 60 · 2023: 46 · 2022: 49 · 2021: 46 · 2020: 42 · 2019: 40 · 2018: 25 · 2017: 23 · 2016: 15 · 2015: 8 · 2014: 1 · 2013: 2
FWCI
19.79
Type
journal article
Language
en
Open access
Locations
  • Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute on Drug Abuse (P50DA010075)
  • National Cancer Institute (R01CA168676)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
latent class analysis, statistical power, sample size determination, effect size, model comparison, power curve

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