论文标题
分数预测因子分析作为识别单项指标的模型
Score Predictor Factor Analysis as model for the identification of single-item indicators
论文作者
论文摘要
引入了分数预测因子因子分析（SPFA）作为一种方法，该方法允许在某些条件下与因子分析所产生的常见因素高度相关的因素得分预测因子，而不是根据公共因子模型计算的因子得分预测指标。在本研究中，我们将SPFA作为其自身权利的模型进行了研究。为了为此提供基础，研究了SPFA因子分数预测因子的属性和实用性，以及在SPFA加载矩阵中识别单个项目指标的可能性。关于因子得分预测指标，主要结果是分数预测因子分析的最佳线性预测指标不仅具有完美的确定性，而且还具有相关性保留。关于SPFA载荷，在一项模拟研究中发现，只有一个以相当大的载荷来代表的五个或更多人口因素可以通过SPFA更准确地识别出与常规因子分析相比，可以更准确地识别。此外，对于SPFA，正确识别的单项指标的百分比比共同因子模型大得多。因此，有人认为，当要识别出非常短的尺度或单项指标时，SPFA是一种工具，可以特别有用。
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.
