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 the highly fragmented landscape of central Europe, dispersal is of particular importance as it determines the long-term survival of animal populations. Dispersal not only secures the recolonization of patches where...In the highly fragmented landscape of central Europe, dispersal is of particular importance as it determines the long-term survival of animal populations. Dispersal not only secures the recolonization of patches where populations went extinct, it may also rescue small populations and thus prevent local extinction events. As dispersal involves different individual fitness costs, the decision to disperse should not be random but context- dependent and often will be biased toward a certain group of individuals （e.g., sex- and wing morph-biased dispersal）. Although biased dispersal has far-reaching consequences for animal populations, immediate studies of sex- and wing morph-biased dispersal in orthopterans are very rare. Here, we used a combined approach of morphological and genetic analyses to investigate biased dispersal of Metrioptera bicolor, a wing dimorphic bush-cricket. Our results clearly show wing morph-biased dispersal for both sexes of M. bicolor. In addition, we found sex-biased dispersal for macropterous individuals, but not for micropters. Both, morphological and genetic data, favor macropterous males as dispersal unit of this bush-cricket species. To get an idea of the flight ability ofM. bicolor, we compared our morphological data with that of Locusta migratoria and Schistocerca gregaria, which are very good flyers. Based on our morphological data, we suggest a good flight ability for macropters of M. bicolor, although flying individuals of this species are seldom observed.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.
文摘In the highly fragmented landscape of central Europe, dispersal is of particular importance as it determines the long-term survival of animal populations. Dispersal not only secures the recolonization of patches where populations went extinct, it may also rescue small populations and thus prevent local extinction events. As dispersal involves different individual fitness costs, the decision to disperse should not be random but context- dependent and often will be biased toward a certain group of individuals （e.g., sex- and wing morph-biased dispersal）. Although biased dispersal has far-reaching consequences for animal populations, immediate studies of sex- and wing morph-biased dispersal in orthopterans are very rare. Here, we used a combined approach of morphological and genetic analyses to investigate biased dispersal of Metrioptera bicolor, a wing dimorphic bush-cricket. Our results clearly show wing morph-biased dispersal for both sexes of M. bicolor. In addition, we found sex-biased dispersal for macropterous individuals, but not for micropters. Both, morphological and genetic data, favor macropterous males as dispersal unit of this bush-cricket species. To get an idea of the flight ability ofM. bicolor, we compared our morphological data with that of Locusta migratoria and Schistocerca gregaria, which are very good flyers. Based on our morphological data, we suggest a good flight ability for macropters of M. bicolor, although flying individuals of this species are seldom observed.
