Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Principles and practice in reporting structural equation analyses.

Roderick P McDonald1, Moon-Ho Ringo Ho

  • 1Department of Psychology, University of Illinois at Urbana-Champaign, 61820, USA. rmcdonal@s.psych.uiuc.edu

Psychological Methods
|April 4, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Avatar diversity perception scale (ADPS): a new multidimensional measure for perceived human avatar diversity.

Frontiers in psychology·2025
Same author

Urban Environment and Outdoor Walking Mobility Among Older Adults With and Without Cognitive Impairment in Singapore: Formative Study.

JMIR formative research·2025
Same author

What was done for youths aged 15 to 24 around the world? A systematic review of worldwide mental health interventions.

Global mental health (Cambridge, England)·2025
Same author

Mind the gap: Development and validation of an evolutionary mismatched lifestyle scale and its impact on health and wellbeing.

Heliyon·2024
Same author

Loneliness, perceived social support, and their changes predict medical adherence over 12 months among patients with coronary heart disease.

British journal of health psychology·2024
Same author

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students.

Digital health·2024
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

This study reviews reporting principles for structural equation modeling (SEM) analyses. Recommendations include detailed model justification, addressing data issues, and providing comprehensive results for critical evaluation.

Area of Science:

  • Statistics
  • Psychometrics
  • Social Sciences

Background:

  • Structural Equation Modeling (SEM) is a widely used statistical technique.
  • Consistent and transparent reporting of SEM analyses is crucial for research integrity.
  • Existing reporting practices may not always meet the standards for complete and accurate information.

Purpose of the Study:

  • To review and establish principles for reporting structural equation modeling (SEM) analyses.
  • To provide guidelines for researchers to ensure complete and accurate reporting.
  • To facilitate critical evaluation of SEM studies by readers.

Main Methods:

  • Review of established principles for statistical analysis reporting.
  • Examination of key components in structural equation modeling.

Related Experiment Videos

  • Survey of recent studies to compare current practices with recommended principles.

    • Main Results:

    • Recommended reporting includes detailed model justification and consideration of alternatives.
    • Addressing nonnormality and missing data is essential.
    • Providing a complete parameter set with standard errors, correlation matrices, and fit indices aids reader judgment.

    Conclusions:

    • Adherence to recommended reporting principles enhances the transparency and replicability of SEM studies.
    • Improved reporting practices empower readers to critically assess SEM findings.
    • The study highlights a gap between recommended practices and current reporting in SEM research.