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Both bivariate and multiple correlations can be collinear or singular. A bivariate collinear relationship has a correlation of .90 or above and is resolved by deletion of one of the two variables. With a multivariate collinear relationship, diagnosis is more dif cult because the collinearity is not necessarily apparent through examination of bivariate correlations. Instead, multivariate statistics are needed, such as squared multiple correlations (SMCs, in which each variable, in turn, serves as the DV with the others as IVs), tolerances (1 SMC), or collinearity diagnostics. SMCs are available through factor analysis and regression programs in statistical software packages. However, SMCs are not evaluated separately for each group if you are analyzing grouped data. PRELIS provides SMCs for structural equation modeling. Most modern programs automatically protect against singularity. Screening for collinearity that causes statistical instability is also routine with most programs because they have tolerance criteria for inclusion of variables. If the tolerance is too low, the variable does not enter the analysis. Default tolerance levels range between .01 and .0001, so SMCs are .99 to .9999 before variables are excluded. You may wish to take control of this process, however, by adjusting the tolerance level (an option with many programs) or deciding yourself which variables to delete instead of letting the program make the decision on purely statistical grounds. SAS, SYSTAT, and SPSS have recently incorporated collinearity diagnostics proposed by Belsley, Kuh, and Welsch (1980) in which both a condition index and variance proportions associated with each standardized variable are produced for each root (dimension, factor, principal component). Variables with large variance proportions are those with problems. Condition index is a measure of tightness or dependency of one variable on the others. The condition index is monotonic c# gs1 128 Packages matching GS1-128 - NuGet Gallery
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