Abstract

Score Predictor Factor Analysis (SPFA) was introduced as a method 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 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 SPFA 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 factor analysis. Moreover, the percentage of correctly identified single-item indicators was substantially larger for SPFA than for the factor model. It is proposed that SPFA is a tool that can be especially helpful when short scales or single-item indicators are to be identified.

摘要

分数预测因子分析 (SPFA) 被引入作为一种计算因子分数预测因子的方法，在某些情况下，与从因子模型计算的因子分数预测因子相比，这些因子分数预测因子与因子分析产生的公共因子的相关性更高。在本研究中，我们将 SPFA 作为自身权利的模型进行调查。为了为此提供基础，研究了 SPFA 因子得分预测因子的属性和效用以及在 SPFA 加载矩阵中识别单项指标的可能性。关于因子得分预测因子，主要结果是 SPFA 的最佳线性预测因子不仅具有完美的确定性，而且还保持相关性。关于 SPFA 载荷，在一项模拟研究中发现，与因子分析相比，通过 SPFA 可以更准确地识别仅由一个具有相当大载荷的变量表示的五个或更多人口因子。此外，SPFA 的正确识别单项指标的百分比远大于因子模型。建议将 SPFA 作为一种工具，在确定小规模或单项指标时特别有用。