10.25384/SAGE.C.4571060
Peter Boedeker
Nathan T. Kearns
Linear Discriminant Analysis for Prediction of Group Membership: A
User-Friendly Primer
<div><p>In psychology, researchers are often interested in the predictive classification of
individuals. Various models exist for such a purpose, but which model is considered a best
practice is conditional on attributes of the data. Under certain conditions, linear
discriminant analysis (LDA) has been shown to perform better than other predictive
methods, such as logistic regression, multinomial logistic regression, random forests,
support-vector machines, and the <i>K</i>-nearest neighbor algorithm. The
purpose of this Tutorial is to provide researchers who already have a basic level of
statistical training with a general overview of LDA and an example of its implementation
and interpretation. Decisions that must be made when conducting an LDA (e.g., prior
specification, choice of cross-validation procedures) and methods of evaluating case
classification (posterior probability, typicality probability) and overall classification
(hit rate, Huberty’s <i>I</i> index) are discussed. LDA for prediction is
described from a modern Bayesian perspective, as opposed to its original derivation. A
step-by-step example of implementing and interpreting LDA results is provided. All
analyses were conducted in R, and the script is provided; the data are available
online.</p></div>
170199 Psychology not elsewhere classified
110319 Psychiatry (incl. Psychotherapy)
SAGE Journals
2019
2019-07-10
2019-09-10
Collection
10.1177/2515245919849378
CC BY 4.0