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Structural Equation Modeling: A Multidisciplinary Journal (SEMM)

Publisher :

Taylor & Francis, Inc

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Sociology
  • Political Science
  • Modeling
  • +4

e-ISSN :

1532-8007

Issue Frequency :

Monthly

Impact Factor :

5.9

p-ISSN :

1070-5511

Est. Year :

1994

Mobile :

18003541420

Country :

United States

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

enquiries@taylorandfrancis.com

Journal Descriptions

Structural Equation Modeling: A Multidisciplinary Journal publishes refereed scholarly work from all academic disciplines interested in structural equation modeling. These disciplines include, but are not limited to, psychology, medicine, sociology, education, political science, economics, management, and business/marketing. Theoretical articles address new developments; applied articles deal with innovative structural equation modeling applications; the Teacher’s Corner provides instructional modules on aspects of structural equation modeling; book and software reviews examine new modeling information and techniques; and advertising alerts readers to new products. Comments on technical or substantive issues addressed in articles or reviews published in the journal are encouraged; comments are reviewed, and authors of the original works are invited to respond.


Structural Equation Modeling: A Multidisciplinary Journal (SEMM) is :

International, Peer-Reviewed, Open Access, Refereed, Sociology, Political Science, Modeling, Simulation, Economics, Econometrics and Finance, Decision Sciences , Online or Print, Monthly Journal

UGC Approved, ISSN Approved: P-ISSN - 1070-5511, E-ISSN - 1532-8007, Established in - 1994, Impact Factor - 5.9

Not Provide Crossref DOI

Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Publications of SEMM

  • dott image March, 1999

Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using...

  • dott image January, 2025

Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests

This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices h...

  • dott image March, 2023

RGLS and RLS in Covariance Structure Analysis

This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory ma...

40-Year Old Unbiased Distribution Free Estimator Reliably Improves SEM Statistics for Nonnormal Data

In structural equation modeling, researchers conduct goodness-of-fit tests to evaluate whether the specified model fits the data well. With nonnormal data, the standard goodness-of-fit test ...

  • dott image October, 2021

Testing Mean and Covariance Structures with Reweighted Least Squares

Chi-square tests based on maximum likelihood (ML) estimation of covariance structures often tend to over-reject the null hypothesis, Σ = Σθ, particularly when the sample size is small. Re...

  • dott image January, 2022

Distributionally-Weighted Least Squares in Growth Curve Modeling

Growth curve modeling is a widely used technique in psychological, educational, and social science research. While mainstream estimators for growth curve modeling are based on normal theory,...

  • dott image March, 2018

Number of Factors in Growth Curve Modeling

Basic growth curve models parameterize the mean and covariance structure of a set of repeated measures by latent factors that represent the polynomial influences of time. In practice it may ...

  • dott image November, 2009

Teacher's Corner: Testing Measurement Invariance of Second-Order Factor Models

We illustrate testing measurement invariance in a second-order factor model using a quality of life dataset (n = 924). Measurement invariance was tested across 2 groups at a set of hierarchi...

  • dott image November, 2009

Effects of sample size and nonnormality on the estimation of mediated effects in latent variable models

A Monte Carlo approach was used to examine bias in the estimation of indirect effects and their associated standard errors. In the simulation design, (a) sample size, (b) the level of nonnor...

  • dott image Ehri Ryu
  • dott image October, 2009

Level-Specific Evaluation of Model Fit in Multilevel Structural Equation Modeling

In multilevel structural equation modeling, the “standard” approach to evaluating the goodness of model fit has a potential limitation in detecting the lack of fit at the higher level. L...

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