Statistical Analysis of the Goodness of Classical Factor Analysis Regression Cf
Statistical Analysis of the Goodness of Classical Factor Analysis Regression Cf
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In all cases the bias for the CFAR estimators is consistent and negative, but asymptotically approaches zero as sample size increases. For the largest sample size, the bias of the CFAR estimators when four or more factors are extracted is smaller than the bias of the OLS esti- mator. Using principal components rather than statistical factor analysis gives comparable results. While the bias for factor analysis is equal to or smaller than the bias of principal components, the differences are suff...iciently small that the extra cost of statistical factor extraction rela- tive to the cost of principal components appears to be greater than the advantage gained.
The second part of Table 16 gives the bias by number of factors ex- tracted and the internal population characteristics. Here the OLS esti- mators also seem to be inconsistent. The CFAR estimators are negatively biased and in general are smallest when there is a high intercorrelation among the variables in the population. There are several cases among these data where the principal components method results in better (smaller bias) estimates than the statistical factor extraction method.
What to read after Statistical Analysis of the Goodness of Classical Factor Analysis Regression Cf?
You can find similar books in the "Read Also" column, or choose other free books by John Thackery Scott to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
In all cases the bias for the CFAR estimators is consistent and negative, but asymptotically approaches zero as sample size increases. For the largest sample size, the bias of the CFAR estimators when four or more factors are extracted is smaller than the bias of the OLS esti- mator. Using principal components rather than statistical factor analysis gives comparable results. While the bias for factor analysis is equal to or smaller than the bias of principal components, the differences are suff...iciently small that the extra cost of statistical factor extraction rela- tive to the cost of principal components appears to be greater than the advantage gained.
The second part of Table 16 gives the bias by number of factors ex- tracted and the internal population characteristics. Here the OLS esti- mators also seem to be inconsistent. The CFAR estimators are negatively biased and in general are smallest when there is a high intercorrelation among the variables in the population. There are several cases among these data where the principal components method results in better (smaller bias) estimates than the statistical factor extraction method.
What to read after Statistical Analysis of the Goodness of Classical Factor Analysis Regression Cf?
You can find similar books in the "Read Also" column, or choose other free books by John Thackery Scott to read onlineMoreLess
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