Examinee Classification Accuracy

Examinee Classification Accuracy is a psychometric metric that quantifies the proportion of examinees correctly assigned to their mastery or non-mastery states in criterion-referenced testing contexts according to a predefined cut score or standard, distinguishing it from broader proficiency estimation by focusing specifically on the accuracy of binary or categorical classification decisions.

41

Publications

1.7K

Citations

126

Authors

77

Institutions

Publications per year

2017–2026

15

Authors

126

  1. LA
    László A. Erdődi

    University of Windsor, Canada

    4 publications · 138 citations

    4

    h-index

  2. LM
    Lawrence M. Rudner

    University of Maryland, College Park, United States

    3 publications · 200 citations

    3

    h-index

  3. KY
    Kelly Y. An

    University of Windsor, Canada

    2 publications · 72 citations

    2

    h-index

  4. JR
    Jason R. Soble

    University of Illinois Chicago, United States

    2 publications · 74 citations

    2

    h-index

  5. LC
    Laura Cutler

    University of Windsor, Canada

    2 publications · 69 citations

    2

    h-index

1–5 of 126

Venues

  1. Journal of Speech Language and Hearing Research

    JSLHR · 3 publications · 180 citations

    3

    h-index

  2. 2

    h-index

  3. Applied Psychological Measurement

    2 publications · 40 citations

    2

    h-index

  4. Educational and Psychological Measurement

    3 publications · 108 citations

    2

    h-index

  5. The Clinical Neuropsychologist

    TCN · 2 publications · 45 citations

    2

    h-index

1–5