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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Forming bootstrap confidence intervals and examining bootstrap distributions of standardized coefficients in

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  • 1Department of Psychology, Faculty of Social Sciences, University of Macau, Macao SAR, China.

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Summary

The semboottools R package simplifies structural equation modeling (SEM) by providing easy bootstrap confidence intervals (CIs) for standardized coefficients. This tool enhances SEM analysis by offering accessible methods for assessing estimate stability and comparing CI approaches.

Keywords:
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Area of Science:

  • Psychology
  • Statistics
  • Computational Statistics

Background:

  • Standardized coefficients in structural equation modeling (SEM) are crucial for interpretation but often have skewed sampling distributions.
  • Conventional symmetric confidence intervals (CIs) fail to capture these skewed distributions, necessitating methods like bootstrap CIs.
  • Existing R packages like lavaan have limited built-in support for bootstrapping standardized coefficients, requiring advanced coding skills.

Purpose of the Study:

  • To develop a user-friendly R package, semboottools, that simplifies the process of generating bootstrap confidence intervals for SEM.
  • To provide researchers with tools to easily form bootstrap CIs for both unstandardized and standardized estimates.
  • To enable straightforward comparison of different CI methods and visualization of bootstrap distributions for assessing estimate stability.

Main Methods:

  • Development of the semboottools R package, offering a streamlined workflow for SEM bootstrapping.
  • Implementation of functions to generate percentile or bias-corrected bootstrap CIs, standard errors, and asymmetric p-values.
  • Inclusion of tools for comparing bootstrap CIs with delta method CIs and visualizing bootstrap estimate distributions.

Main Results:

  • semboottools facilitates the formation of bootstrap CIs for SEM parameters with minimal coding effort.
  • The package allows for easy generation of various bootstrap statistics and visualization of their distributions.
  • Researchers can readily compare bootstrap CIs with traditional methods, aiding in the assessment of bootstrapping necessity.

Conclusions:

  • The semboottools R package addresses limitations in existing SEM software by providing accessible bootstrap CI functionality.
  • It empowers researchers to more rigorously assess the stability of SEM estimates and compare different inferential methods.
  • This tool promotes more robust and reliable interpretation of SEM results in psychological research.