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Predictors of Early Termination in a University Counseling Training Clinic
Author(s) -
Lampropoulos Georgios K.,
Schneider Mercedes K.,
Spengler Paul M.
Publication year - 2009
Publication title -
journal of counseling and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.805
H-Index - 78
eISSN - 1556-6676
pISSN - 0748-9633
DOI - 10.1002/j.1556-6678.2009.tb00547.x
Subject(s) - multinomial logistic regression , logistic regression , dropout (neural networks) , psychology , training (meteorology) , linear discriminant analysis , clinical psychology , medical education , family medicine , medicine , computer science , artificial intelligence , machine learning , meteorology , physics
Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and regression trees converged on the use of a 4‐predictor model (client age, income, perceived difficulty, and functional impairment) for classifying counseling dropouts. Implications for research and practice are discussed.