我國(guó)上市銀行競(jìng)爭(zhēng)力分析(參考版)
2025-06-29 19:13本頁面
【正文】 s eigenvalue by the number of variables.Interpreting factor loadings: By one rule of thumb in confirmatory factor analysis, loadings should be .7 or higher to confirm that independent variables identified a priori are represented by a particular factor, on the rationale that the .7 level corresponds to about half of the variance in the indicator being explained by the factor. However, the .7 standard is a high one and reallife data may well not meet this criterion, which is why some researchers, particularly for exploratory purposes, will use a lower level such as .4 for the central factor and .25 for other factors call loadings above .6 high and those below .4 low. In any event, factor loadings must be interpreted in the light of theory, not by arbitrary cutoff levels.In oblique rotation, one gets both a pattern matrix and a structure matrix. The structure matrix is simply the factor loading matrix as in orthogonal rotation, representing the variance in a measured variable explained by a factor on both a unique and mon contributions basis. The pattern matrix, in contrast, contains coefficients which just represent unique contributions. The more factors, the lower the pattern coefficients as a rule since there will be more mon contributions to variance explained. For oblique rotation, the researcher looks at both the structure and pattern coefficients when attributing a label to a factor.Communality (h2): The sum of the squared factor loadings for all factors for a given variable (row) is the variance in that variable accounted for by all the factors, and this is called the munality. The munality measures the percent of variance in a given variable explained by all the factors jointly and may be interpreted as the reliability of the indicator. Spurious solutions: If the munality exceeds , there is a spurious solution, which may reflect too small a sample or the researcher has too many or too few factors.Uniqueness of a variable: 1h2. That is, uniqueness is the variability of a variable minus its munality.Eigenvalues:/Characteristic roots: The eigenvalue for a given factor measures the variance in all the variables which is accounted for by that factor. The ratio of eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue, then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important factors. Eigenvalues measure the amount of variation in the total sample accounted for by each factor.Factor scores: Also called ponent scores in PCA, factor scores are the scores of each case (row) on each factor (column). To pute the factor score for a given case for a given factor, one takes the case39。s canonical factoring, is a different method of puting the same model as PCA, which uses the principal axis method. CFA seeks factors which have the highest canonical correlation with the observed variables. CFA is unaffected by arbitrary rescaling of the data.Common factor analysis, also called principal factor analysis (PFA) or principal axis factoring (PAF), seeks the least number of factors which can account for the mon variance (correlation) of a set of variables.Image factoring: based on the correlation matrix of predicted variables rather than actual variables, where each variable is predicted from the others using multiple regressions.Alpha factoring: based on maximizing the reliability of factors, assuming variables are randomly sampled from a universe of variables. All other methods assume cases to be sampled and variables fixed.TerminologyFactor loadings: The factor loadings, also called ponent loadings in PCA, are the correlation coefficients between the variables (rows) and factors (columns). Analogous to Pearson39。s a priori assumption is that any indicator may be associated with any factor. This is the most mon form of factor analysis. There is no prior theory and one uses factor loadings to intuit the factor structure of the data.Confirmatory factor analysis (CFA) seeks to determine if the number of factors and the loadings of measured (indicator) variables on them conform to what is expected on the basis of preestablished theory. Indicator variables are selected on the basis of prior theory and factor analysis is used to see if they load as predicted on the expected number of factors. The researcher39。此外，因子得分可作為接下來模型中的變量。要計(jì)算一個(gè)給定的因素在一個(gè)給定因子的得分情況，用因素標(biāo)準(zhǔn)差乘以相應(yīng)變量的銀子載荷矩陣的方差，并將所有數(shù)據(jù)加總。特征值測(cè)量各因素對(duì)總樣品的解釋度。The ratio of eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue, then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important 。也就是說，獨(dú)特性是變量的變異1減去共同性。雜散解決方案：，有一個(gè)虛假的解決方案，可能反映太小樣品或研究者有過多或過少的因素。Communality (h2): The sum of the squared factor loadings for all factors for a given variable (row) is the variance in that variable accounted for by all the factors, and this is called the （h2）：對(duì)于所有因素，平方后的因素負(fù)荷量的總和作為一個(gè)假設(shè)變量行，稱為所有變量解釋的方差，這就是所謂的共同性。因素越多，越低的解釋模式作為一項(xiàng)規(guī)則，因?yàn)閷⒂懈嗟墓餐暙I(xiàn)。矩陣結(jié)構(gòu)是簡(jiǎn)單的正交旋轉(zhuǎn)后的因子載荷矩陣，代表變量方差通過一個(gè)因素在獨(dú)特和普遍的基礎(chǔ)上被解釋。在任何情況下，因子負(fù)荷量必須依據(jù)理論根據(jù)解釋，而不是作出任意的決定。Interpreting factor loadings: By one rule of thumb in confirmatory factor analysis, loadings should be .7 or higher to confirm that independent variables identified a priori are represented by a particular factor, on the rationale that the .7 level corresponds to about half of the variance in the indicator being explained by the ：根據(jù)一項(xiàng)經(jīng)驗(yàn)法則在驗(yàn)證性因素分析中的描述，以確認(rèn)一個(gè)先驗(yàn)獨(dú)立變量代表一個(gè)特定的確定的因素。（注意變量的數(shù)目等于該變量的總和，方差為1。類
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