Covariance Structure Analysis

Covariance Structure Analysis is a statistical modeling technique in multivariate trait analysis that specifies and tests the pattern of covariances among observed variables to infer the underlying structure of latent traits or factors.

1.4K

Publications

334K

Citations

2.9K

Authors

1.2K

Institutions

Publications per year

2017–2026

197

Authors

2.9K

  1. PM
    Peter M. Bentler

    University of California, Los Angeles, United States

    42 publications · 137.9K citations

    32

    h-index

  2. KY
    Ke‐Hai Yuan

    University of Notre Dame, United States

    24 publications · 3.5K citations

    22

    h-index

  3. RC
    Robert C. MacCallum

    The Ohio State University, United States

    17 publications · 16.4K citations

    16

    h-index

  4. TR
    Tenko Raykov

    Michigan State University, United States

    16 publications · 1.4K citations

    13

    h-index

  5. MW
    Michael W. Browne

    The Ohio State University, United States

    12 publications · 16.3K citations

    12

    h-index

1–5 of 2.9K

Venues

  1. Multivariate Behavioral Research

    MBR · 75 publications · 9.2K citations

    39

    h-index

  2. Structural Equation Modeling A Multidisciplinary Journal

    SEM · 46 publications · 109K citations

    30

    h-index

  3. Journal of the American Statistical Association

    32 publications · 5.3K citations

    24

    h-index

  4. Psychological Bulletin

    24 publications · 31.3K citations

    23

    h-index

  5. 22

    h-index

1–5