General Cognitive Ability vs. General and Specific Aptitudes in the Prediction of Training Performance: Some Preliminary Findings.

Autor/inn/en	Schmidt, Frank L.; und weitere
Titel	General Cognitive Ability vs. General and Specific Aptitudes in the Prediction of Training Performance: Some Preliminary Findings.
Quelle	(1988), (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Ability; Aptitude Tests; Cognitive Ability; Military Personnel; Military Training; Performance; Predictive Validity; Armed Services Vocational Aptitude Battery + Suchen Sie Ihr Suchwort? Fähigkeit; Fertigkeit; Aptitude test; Eignungsprüfung; Eignungstest; Denkfähigkeit; Militärausbildung; Achievement; Leistung
Abstract	Recently there appears to have been an increase in interest in the relative power of general ability and narrower cognitive aptitudes to predict real world performance in training programs and on the job. This area has important practical implications for personnel selection and classification, particularly for large organizations such as the United States military which assigns people differentially to jobs based on patterns of measured abilities. This study examined the ability of Armed Services Vocational Aptitude Battery (ASVAB), forms 8, 9, and 10 to predict training performance, using 10 technical jobs in the U.S. Navy with at least 900 people in each job. Performance was the measured criterion. For one job, Mess Management Specialist, performance was measured as final school grade. The other nine technical jobs were self-paced; the criterion was the number of hours required to complete training. General mental ability alone did about as well as differential weighting at the level of the three general aptitudes (quantitative, verbal, technical). Using all 10 specific aptitude tests as separate predictors increased validity by about eight percent. Future analysis will examine the specific values of beta weights and will be testing Hunter's (1983) path models for fit to the data from individual jobs. (ABL)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC