Structural Equation Modeling

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.2
2024	2.50

SJR (SCImago Journal Rank)

Year	Value
2024	3.321

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	132

Journal Rank

Year	Value
2024	603

Journal Citation Indicator

Year	Value
2024	1210

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences, Economics, Econometrics and Finance, Mathematics and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

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

  Citation: 77975

  Authors: Li‐tze, Peter M.

• Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance

  Citation: 11487

  Authors: Gordon W., Roger B.

• Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

  Citation: 8418

  Authors: Karen L., Tihomir, Bengt O.

• Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance

  Citation: 8279

  Authors: Fang Fang

• To Parcel or Not to Parcel: Exploring the Question, Weighing the Merits

  Citation: 4800

  Authors: Todd D., William A., Golan, Keith F.

• In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings

  Citation: 4666

  Authors: Herbert W., Kit-Tai, Zhonglin

• The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models

  Citation: 3695

  Authors: Craig, Deborah

• Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M<i>plus</i>

  Citation: 2565

  Authors: Tihomir, Bengt

• Exploratory Structural Equation Modeling

  Citation: 1934

  Authors: Tihomir, Bengt

• How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

  Citation: 1658

  Authors: Linda K., Bengt O.

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