Effects of sample size and distributional assumptions on competing models of the factor structure of the PANSS and BPRS.
Effects of sample size and distributional assumptions on competing models of the factor structure of the PANSS and BPRS.
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DOI：
10.1002/mpr.1549
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发表时间：
2017-12
影响因子：
3.1
通讯作者：
Van Dorn RA
中科院分区：
文献类型：
--
作者：
Tueller SJ;Johnson KL;Grimm KJ;Desmarais SL;Sellers BG;Van Dorn RA
对PANSS和BPRS的因子分析工作产生了不同的和相互矛盾的结果。目前的研究探讨了这些差异的潜在原因。先前的研究受限于小样本量和一个错误的假设，即项目是正态分布的，而在实践中，反应是高度倾斜的有序变量。使用模拟方法，我们检查了样本量的影响，（在）正确指定项目分布，崩溃很少认可的响应类别，和四因素分析模型。第一种是Van Dorn等人的模型，使用大型集成数据集开发，将项目分布指定为多项，并使用交叉验证。其余的模型是指定项目分布为正态分布的：White等人常用的五边形模型；Van der Gaag等人使用广泛的交叉验证方法开发的模型；Shafer的模型是通过元分析发展起来的。我们的模拟结果表明，错误地假设正态性会导致模型拟合和因子结构的偏差，特别是对于小样本量。崩溃很少使用的响应选项的影响可以忽略不计。
Factor analytic work on the PANSS and BPRS has yielded varied and conflicting results. The current study explored potential causes of these discrepancies. Prior research has been limited by small sample sizes and an incorrect assumption that the items are normally distributed when in practice responses are highly skewed ordinal variables. Using simulation methodology, we examined the effects of sample size, (in)correctly specifying item distributions, collapsing rarely endorsed response categories, and four factor analytic models. The first is the model of Van Dorn et al., developed using a large integrated data set, specified the item distributions as multinomial, and used cross-validation. The remaining models were developed specifying item distributions as normal: the commonly used pentagonal model of White et al.; the model of Van der Gaag et al. developed using extensive cross-validation methods; and the model of Shafer developed through meta-analysis. Our simulation results indicated that incorrectly assuming normality led to biases in model fit and factor structure, especially for small sample size. Collapsing rarely used response options had negligible effects.