Robust Invariance Testing

Robust Invariance Testing is a statistical methodology within latent variable modeling that, as a specialized form of measurement invariance assessment, employs robust estimation techniques to evaluate the stability of factor structures across groups under conditions of non-normality or model misspecification.

14

Publications

742

Citations

28

Authors

20

Institutions

Publications per year

2017–2026

4

Authors

28

  1. BA
    Benjamin A. Kite

    University of Kansas, United States

    2 publications · 79 citations

    2

    h-index

  2. PC
    Po‐Yi Chen

    University of Kansas, United States

    2 publications · 79 citations

    2

    h-index

  3. JH
    James H. Stock

    Harvard University, United States

    1 publications · 49 citations

    1

    h-index

  4. WL
    Warody Lombardi

    Centre National de la Recherche Scientifique, France

    1 publications · 11 citations

    1

    h-index

  5. JI
    John I. Marden

    University of Illinois Urbana-Champaign, United States

    1 publications · 32 citations

    1

    h-index

1–5 of 28

Venues

  1. The Annals of Statistics

    2 publications · 118 citations

    2

    h-index

  2. Journal of Econometrics

    2 publications · 399 citations

    2

    h-index

  3. IFAC Proceedings Volumes

    1 publications · 11 citations

    1

    h-index

  4. Journal of Statistical Planning and Inference

    1 publications · 14 citations

    1

    h-index

  5. Psychometrika

    1 publications · 15 citations

    1

    h-index

1–5