TY - JOUR
AU - 
AB - Poga, M. (2023) Dealing with Multicolli- Multicollinearity in factor analysis has negative effects, including unreliable nearity in Factor Analysis: The Problem, Detections, and Solutions. Open Journal of factor structure, inconsistent loadings, inflated standard errors, reduced dis- Statistics, 13, 404-424. criminant validity, and difficulties in interpreting factors. It also leads to re- https://doi.org/10.4236/ojs.2023.133020 duced stability, hindered factor replication, misinterpretation of factor im- portance, increased parameter estimation instability, reduced power to detect Received: May 28, 2023 Accepted: June 25, 2023 the true factor structure, compromised model fit indices, and biased factor Published: June 28, 2023 loadings. Multicollinearity introduces uncertainty, complexity, and limited generalizability, hampering factor analysis. To address multicollinearity, re- Copyright © 2023 by author(s) and searchers can examine the correlation matrix to identify variables with high Scientific Research Publishing Inc. This work is licensed under the Creative correlation coefficients. The Variance Inflation Factor (VIF) measures the in- Commons Attribution International flation of regression coefficients due to multicollinearity. Tolerance, the reci- License (CC BY 4.0). procal of VIF, indicates the proportion of variance in a predictor variable not http://creativecommons.org/licenses/by/4.0/ shared with others. Eigenvalues help assess multicollinearity, with values great- Open Access er than 1 suggesting the retention of factors. Principal Component 
TI - Dealing with Multicollinearity in Factor Analysis: The Problem, Detections, and Solutions
JF - Open Journal of Statistics
DO - 10.4236/ojs.2023.133020
DA - 2023-01-01
UR - https://www.deepdyve.com/lp/unpaywall/dealing-with-multicollinearity-in-factor-analysis-the-problem-BlI510zaKX
DP - DeepDyve
ER -