Hierarchical Factor Models

Hierarchical Factor Models is a latent variable modeling framework that structures observed variables under multiple levels of latent factors, distinguishing itself by organizing these latent factors in a hierarchical arrangement where higher-order factors influence lower-order ones, thereby capturing both general and specific dimensions of the underlying construct.

426

Publications

60.8K

Citations

1.1K

Authors

575

Institutions

Publications per year

2017–2026

92

Authors

1.1K

  1. FH
    Fred H. Wallbrown

    Kent State University, United States

    11 publications · 331 citations

    10

    h-index

  2. GL
    Gary L. Canivez

    Eastern Illinois University, United States

    9 publications · 499 citations

    9

    h-index

  3. JB
    John Blaha

    West Virginia University, United States

    9 publications · 286 citations

    9

    h-index

  4. HW
    Herbert W. Marsh

    Western Sydney University, Australia

    7 publications · 1.3K citations

    7

    h-index

  5. TZ
    Timothy Z. Keith

    The University of Texas at Austin, United States

    5 publications · 247 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Psychological Assessment

    17 publications · 2.9K citations

    17

    h-index

  2. Educational and Psychological Measurement

    15 publications · 500 citations

    13

    h-index

  3. Assessment

    ASM · 10 publications · 8.1K citations

    11

    h-index

  4. Psychometrika

    10 publications · 1.9K citations

    10

    h-index

  5. Personality and Individual Differences

    10 publications · 5.2K citations

    10

    h-index

1–5