Scale Length Optimization

Scale Length Optimization is a methodological process in psychometric scale development that systematically determines the optimal number of items or response categories in a rating scale to maximize reliability, validity, and efficiency while minimizing respondent burden and measurement error.

19

Publications

1.1K

Citations

84

Authors

34

Institutions

Publications per year

2017–2026

9

Authors

84

  1. AM
    A. M. Karo

    Lawrence Livermore National Laboratory, United States

    2 publications · 146 citations

    2

    h-index

  2. JR
    J. R. Hiskes

    Lawrence Livermore National Laboratory, United States

    2 publications · 146 citations

    2

    h-index

  3. GG
    Gil Garnier

    Monash University, Australia

    1 publications · 22 citations

    1

    h-index

  4. PC
    Paul Cummings

    Optical Sciences (United States), United States

    1 publications · 87 citations

    1

    h-index

  5. AS
    Atsushi Sunahara

    The University of Osaka, Japan

    1 publications · 108 citations

    1

    h-index

1–5 of 84

Venues

  1. 2

    h-index

  2. Structural and Multidisciplinary Optimization

    2 publications · 31 citations

    2

    h-index

  3. Journal of Applied Physics

    2 publications · 155 citations

    2

    h-index

  4. Applied Physics Letters

    2 publications · 119 citations

    2

    h-index

  5. Soft Computing

    1 publications · 25 citations

    1

    h-index

1–5