A SIMPLICIAL DESIGN FOR THE ANALYSIS OF CORRELATIONAL LEARNING DATA

Autor:	Edwin A. Fleishman, Benjamin Fruchter
Rok vydání:	2016
Předmět:	Statistics and Probability business.industry fungi Experimental and Cognitive Psychology General Medicine Machine learning computer.software_genre Factor structure Learning data Arts and Humanities (miscellaneous) Artificial intelligence business Psychology computer
Zdroj:	Multivariate behavioral research. 2(1)
ISSN:	0027-3171
Popis:	The usual matrix of intercorrelations among trials in a study of the learning process produces a superdiagonal matrix with resulting factors which are uninteresting: viz, a factor for the early trials and a factor for the later trials; with perhaps also a factor for the middle trials. By introducing several independent measures of learning for each trial a more meaningful factor structure can be obtained.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::729161f79da61521759fa3d7af18cf8e https://pubmed.ncbi.nlm.nih.gov/26753681 Zobrazit plný text záznamu