Eigenvalue-based Factor Retention

Eigenvalue-Based Factor Retention is a dimensionality assessment method that determines the number of factors to retain in factor analysis by selecting components with eigenvalues exceeding a predetermined threshold, typically unity, to distinguish meaningful variance from noise.

53

Publications

18.3K

Citations

97

Authors

69

Institutions

Publications per year

2017–2026

7

Variants

Kaiser CriterionKaiser-guttman Rule

Authors

97

  1. VH
    Vivek H. Patil

    Gonzaga University, United States

    2 publications · 377 citations

    2

    h-index

  2. RT
    Russell T. Warne

    Utah Valley University, United States

    2 publications · 178 citations

    2

    h-index

  3. RL
    Ross Larsen

    Brigham Young University, United States

    2 publications · 178 citations

    2

    h-index

  4. AL
    Andrew L. Comrey

    University of California, Los Angeles, United States

    2 publications · 170 citations

    2

    h-index

  5. SJ
    Sunho Jung

    Kyung Hee University, South Korea

    2 publications · 271 citations

    2

    h-index

1–5 of 97

Venues

  1. Educational and Psychological Measurement

    4 publications · 1.2K citations

    4

    h-index

  2. Psychological Bulletin

    3 publications · 1.6K citations

    3

    h-index

  3. Multivariate Behavioral Research

    MBR · 3 publications · 858 citations

    3

    h-index

  4. Scholarworks (University of Massachusetts Amherst)

    Repository · 2 publications · 938 citations

    2

    h-index

  5. Psychological Methods

    2 publications · 768 citations

    2

    h-index

1–5