Latent Variable Models

Latent Variable Models is a class of statistical models that infer unobserved (latent) variables from observed data by specifying probabilistic relationships between the latent constructs and the observed variables, thereby accounting for hidden structure or heterogeneity in the data.

3.4K

Publications

438.9K

Citations

6.3K

Authors

1.8K

Institutions

Publications per year

2017–2026

1K

Authors

6.3K

  1. JK
    Jeroen K. Vermunt

    Tilburg University, Netherlands

    62 publications · 9.6K citations

    33

    h-index

  2. XL
    Xin Luo

    Chongqing Institute of Green and Intelligent Technology, China

    46 publications · 4.1K citations

    33

    h-index

  3. BM
    Bengt Muthén

    University of California, Los Angeles, United States

    34 publications · 28.6K citations

    32

    h-index

  4. KA
    Kenneth A. Bollen

    University of North Carolina at Chapel Hill, United States

    22 publications · 21.8K citations

    20

    h-index

  5. MS
    Mingsheng Shang

    Chongqing Institute of Green and Intelligent Technology, China

    21 publications · 2K citations

    18

    h-index

1–5 of 6.3K

Venues

  1. Structural Equation Modeling A Multidisciplinary Journal

    SEM · 193 publications · 37.4K citations

    71

    h-index

  2. Psychometrika

    118 publications · 14.8K citations

    54

    h-index

  3. arXiv (Cornell University)

    Repository · 206 publications · 14.9K citations

    49

    h-index

  4. Psychological Methods

    58 publications · 14.3K citations

    48

    h-index

  5. Journal of the American Statistical Association

    73 publications · 14.1K citations

    44

    h-index

1–5