论文标题
通过R-和Q因子组合生成的数据的R因素分析导致载荷估计值有偏差
R-factor analysis of data generated by a combination of R- and Q-factors leads to biased loading estimates
论文作者
论文摘要
研究了基于包括R-和Q因子的种群模型对观察到的变量进行R因素分析的影响。值得注意的是，估计包括R-和Q因子的模型必须面对旋转不确定性的不确定性。尽管可以根据包括R-和Q因子的人群模型对数据的R因素分析是可能的，但这可能导致模型误差。因此，即使在人群中，由此产生的R因子负载也不一定是对原始种群R因子载荷的估计。在一项模拟研究中显示，大Q因素方差会导致R-FARCTOR载荷估计值的变化增加超出机会水平。结果表明，基于包含R和Q因子的种群模型的数据进行R因子分析可能会导致巨大的负载偏差。提出了观察到的变量的多元峰度的测试，作为观察到的变量中可能的Q因素差异的指标，作为R因素分析的先决条件。
Effects of performing R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. It was noted that estimating a model comprising R- and Q-factors has to face loading indeterminacy beyond rotational indeterminacy. Although R-factor analysis of data based on a population model comprising R- and Q-factors is nevertheless possible, this may lead to model error. Accordingly, even in the population, the resulting R-factor loadings are not necessarily close estimates of the original population R-factor loadings. It was shown in a simulation study that large Q-factor variance induces an increase of the variation of R-factor loading estimates beyond chance level. The results indicate that performing R-factor analysis with data based on a population model comprising R- and Q-factors may result in substantial loading bias. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.
