Multivariate Techniques in Neuropsychology: III. Discriminant Function Analysis.

Franzen D. Michael; Charles J. Golden

Multivariate Techniques in Neuropsychology: III. Discriminant Function Analysis.

Franzen D. Michael
, Charles J. Golden

College of Psychology

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Discusses discriminant function analysis (DFA) as a form of multiple regression that allows for the prediction of group membership of an S on the basis of several predictors. The 2 main types of methodologies used to perform DFA are reviewed, full-model DFA and stepwise DFA. A computational example using data from the Luria-Nebraska Neuropsychological Battery is presented. (12 ref)

Original language	American English
Journal	International Journal of Clinical Neuropsychology
Volume	7
State	Published - Jan 1 1985

Disciplines

Psychology

Cite this

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title = "Multivariate Techniques in Neuropsychology: III. Discriminant Function Analysis.",

abstract = " Discusses discriminant function analysis (DFA) as a form of multiple regression that allows for the prediction of group membership of an S on the basis of several predictors. The 2 main types of methodologies used to perform DFA are reviewed, full-model DFA and stepwise DFA. A computational example using data from the Luria-Nebraska Neuropsychological Battery is presented. (12 ref)",

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T1 - Multivariate Techniques in Neuropsychology: III. Discriminant Function Analysis.

AU - Michael, Franzen D.

AU - Golden, Charles J.

PY - 1985/1/1

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N2 - Discusses discriminant function analysis (DFA) as a form of multiple regression that allows for the prediction of group membership of an S on the basis of several predictors. The 2 main types of methodologies used to perform DFA are reviewed, full-model DFA and stepwise DFA. A computational example using data from the Luria-Nebraska Neuropsychological Battery is presented. (12 ref)

AB - Discusses discriminant function analysis (DFA) as a form of multiple regression that allows for the prediction of group membership of an S on the basis of several predictors. The 2 main types of methodologies used to perform DFA are reviewed, full-model DFA and stepwise DFA. A computational example using data from the Luria-Nebraska Neuropsychological Battery is presented. (12 ref)

UR - https://nsuworks.nova.edu/cps_facarticles/752

M3 - Article

SN - 0749-8470

VL - 7

JO - International Journal of Clinical Neuropsychology

JF - International Journal of Clinical Neuropsychology

ER -

Multivariate Techniques in Neuropsychology: III. Discriminant Function Analysis.

Abstract

Disciplines

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