Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties in interpreting factor...Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties in interpreting factors. It also leads to reduced stability, hindered factor replication, misinterpretation of factor importance, increased parameter estimation instability, reduced power to detect the true factor structure, compromised model fit indices, and biased factor loadings. Multicollinearity introduces uncertainty, complexity, and limited generalizability, hampering factor analysis. To address multicollinearity, researchers can examine the correlation matrix to identify variables with high correlation coefficients. The Variance Inflation Factor (VIF) measures the inflation of regression coefficients due to multicollinearity. Tolerance, the reciprocal of VIF, indicates the proportion of variance in a predictor variable not shared with others. Eigenvalues help assess multicollinearity, with values greater than 1 suggesting the retention of factors. Principal Component Analysis (PCA) reduces dimensionality and identifies highly correlated variables. Other diagnostic measures include the condition number and Cook’s distance. Researchers can center or standardize data, perform variable filtering, use PCA instead of factor analysis, employ factor scores, merge correlated variables, or apply clustering techniques for the solution of the multicollinearity problem. Further research is needed to explore different types of multicollinearity, assess method effectiveness, and investigate the relationship with other factor analysis issues.展开更多
In early life, the immune system plays an essential role in brain development. In our study, the immunopotentiator thymosin alpha-1（Ta1） was peripherally administered to neonatal mice to explore whether the peripher...In early life, the immune system plays an essential role in brain development. In our study, the immunopotentiator thymosin alpha-1（Ta1） was peripherally administered to neonatal mice to explore whether the peripheral immunopotentiator affects neurodevelopment and cognition, and to further investigate the relevant mechanism. Compared with the control group, the Ta1 mice displayed better cognitive abilities in early life. The numbers of 5-bromodeoxyuridine（Brd U）＋, nestin＋,T-box transcription factor 2（Tbr2）＋, Brd U＋/doublecortin（DCX）＋, Brd U＋/ionized calcium-binding adaptor molecule 1（Iba1）＋, and Brd U＋/neuronal nuclei（Neu N）＋ cells in the hippocampus were increased in the Ta1 group,accompanied by increased interleukin-4（IL-4）, interferon-gamma, brain-derived neurotrophic factor, nerve growth factor, and insulin-like growth factor-1 as well as decreased IL-6 and tumor necrosis factor-a. Furthermore, the Ta1-group showed a Th1-polarized immune response, and the neurotrophic factors were positively associated with the Th1/Th2 ratio. More importantly, administration of Ta1 blocked lipopolysaccharide-induced impairment of hippocampal neurogenesis in early life. These findings suggest that peripheral Ta1 contributes to neurogenesis and cognition probably through a systemic Th1 bias, as well as neuroprotection against LPS infection by Ta1.展开更多
文摘Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties in interpreting factors. It also leads to reduced stability, hindered factor replication, misinterpretation of factor importance, increased parameter estimation instability, reduced power to detect the true factor structure, compromised model fit indices, and biased factor loadings. Multicollinearity introduces uncertainty, complexity, and limited generalizability, hampering factor analysis. To address multicollinearity, researchers can examine the correlation matrix to identify variables with high correlation coefficients. The Variance Inflation Factor (VIF) measures the inflation of regression coefficients due to multicollinearity. Tolerance, the reciprocal of VIF, indicates the proportion of variance in a predictor variable not shared with others. Eigenvalues help assess multicollinearity, with values greater than 1 suggesting the retention of factors. Principal Component Analysis (PCA) reduces dimensionality and identifies highly correlated variables. Other diagnostic measures include the condition number and Cook’s distance. Researchers can center or standardize data, perform variable filtering, use PCA instead of factor analysis, employ factor scores, merge correlated variables, or apply clustering techniques for the solution of the multicollinearity problem. Further research is needed to explore different types of multicollinearity, assess method effectiveness, and investigate the relationship with other factor analysis issues.
基金supported by the Natural Science Foundation of Guangdong Province, China (2014A030310343, 2015A030313153, and 2016A030313253)the Medical Scientific Research Foundation of Guangdong Province, China (A2015382)the Doctoral Program of Guangzhou Medical University, China (2014C19)
文摘In early life, the immune system plays an essential role in brain development. In our study, the immunopotentiator thymosin alpha-1（Ta1） was peripherally administered to neonatal mice to explore whether the peripheral immunopotentiator affects neurodevelopment and cognition, and to further investigate the relevant mechanism. Compared with the control group, the Ta1 mice displayed better cognitive abilities in early life. The numbers of 5-bromodeoxyuridine（Brd U）＋, nestin＋,T-box transcription factor 2（Tbr2）＋, Brd U＋/doublecortin（DCX）＋, Brd U＋/ionized calcium-binding adaptor molecule 1（Iba1）＋, and Brd U＋/neuronal nuclei（Neu N）＋ cells in the hippocampus were increased in the Ta1 group,accompanied by increased interleukin-4（IL-4）, interferon-gamma, brain-derived neurotrophic factor, nerve growth factor, and insulin-like growth factor-1 as well as decreased IL-6 and tumor necrosis factor-a. Furthermore, the Ta1-group showed a Th1-polarized immune response, and the neurotrophic factors were positively associated with the Th1/Th2 ratio. More importantly, administration of Ta1 blocked lipopolysaccharide-induced impairment of hippocampal neurogenesis in early life. These findings suggest that peripheral Ta1 contributes to neurogenesis and cognition probably through a systemic Th1 bias, as well as neuroprotection against LPS infection by Ta1.
