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临床研究中的探索性因子分析与主成分分析:您应选用哪一种?
Author(s) -
Alavi Mousa,
Visentin Denis C.,
Thapa Deependra K.,
Hunt Glenn E.,
Watson Roger,
Cleary Michelle
Publication year - 2020
Publication title -
journal of advanced nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 155
eISSN - 1365-2648
pISSN - 0309-2402
DOI - 10.1111/jan.14377
Subject(s) - principal component analysis , exploratory factor analysis , exploratory analysis , component (thermodynamics) , factor (programming language) , component analysis , psychology , computer science , statistics , data science , clinical psychology , mathematics , psychometrics , physics , thermodynamics , programming language
Factor analysis covers a range of multivariate methods used to explain how underlying factors influence a set of observed variables. When research aims to identify these underlying factors, exploratory factor analysis (EFA) is used. In contrast, when the aim is to test whether a set of observed variables influences responses in accordance with an existing conceptual basis, confirmatory factor analysis is performed. EFA has many similarities with a commonly used data reduction technique called principal component analysis (PCA).