得分预测因子分析作为单项指标识别模型
Score Predictor Factor Analysis as model for the identification of single-item indicators
摘要：
得分预测因子分析（SPFA）被引入作为一种方法，允许计算因子得分预测因子，在某些条件下，这些因子得分预测因子与因子分析产生的公共因子的相关性比从公共因子模型计算的因子得分预测因子更高。在本研究中，我们调查SPFA作为一个模型在其自身的权利。为了提供一个基础，SPFA因子得分预测的属性和效用和可能性，以确定单项目指标SPFA负载矩阵进行了研究。关于因子得分预测因子，主要结果是得分预测因子分析的最佳线性预测因子不仅具有完美的确定性，而且是相关保持的。关于SPFA负荷，它被发现在模拟研究中，五个或更多的人口因素，仅代表一个变量，相当大的负荷可以更准确地识别SPFA比传统的因素分析。此外，战略规划和财务分析正确确定的单项指标的百分比大大高于共同因素模式。因此，有人认为，战略规划和财务分析是一种工具，在确定非常短的尺度或单项指标时特别有用。
英文摘要：
Score Predictor Factor Analysis (SPFA) was introduced as a method that allows to compute factor score predictors that are -- under some conditions -- more highly correlated with the common factors resulting from factor analysis than the factor score predictors computed from the common factor model. In the present study, we investigate SPFA as a model in its own rights. In order to provide a basis for this, the properties and the utility of SPFA factor score predictors and the possibility to identify single-item indicators in SPFA loading matrices were investigated. Regarding the factor score predictors, the main result is that the best linear predictor of the score predictor factor analysis has not only perfect determinacy but is also correlation preserving. Regarding the SPFA loadings it was found in a simulation study that five or more population factors that are represented by only one variable with a rather substantial loading can more accurately be identified by means of SPFA than with conventional factor analysis. Moreover, the percentage of correctly identified single-item indicators was substantially larger for SPFA than for the common factor model. It is therefore argued that SPFA is a tool that can be especially helpful when very short scales or single-item indicators are to be identified.
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