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ERIC Number: EJ1045282
Record Type: Journal
Publication Date: 2014-Dec
Pages: 22
Abstractor: As Provided
Reference Count: 50
ISSN: ISSN-0013-1644
Seeing Perfectly Fitting Factor Models That Are Causally Misspecified: Understanding That Close-Fitting Models Can Be Worse
Hayduk, Leslie
Educational and Psychological Measurement, v74 n6 p905-926 Dec 2014
Researchers using factor analysis tend to dismiss the significant ill fit of factor models by presuming that if their factor model is close-to-fitting, it is probably close to being properly causally specified. Close fit may indeed result from a model being close to properly causally specified, but close-fitting factor models can also be seriously causally misspecified. This article illustrates a variety of nonfactor causal worlds that are perfectly, but inappropriately, fit by factor models. Seeing nonfactor worlds that are perfectly yet erroneously fit via factor models should help researchers understand that close-to-fitting factor models may seriously misrepresent the world's causal structure. Statistical cautions regarding the factor model's proclivity to fit when it ought not to fit have been insufficiently publicized and are rarely heeded. A research commitment to understanding the world's causal structure, combined with clear examples of factor mismodeling should spur diagnostic assessment of significant factor model failures--including reassessment of published failing factor models.
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A