Sterba Sonya K, MacCallum Robert C
a The University of North Carolina at Chapel Hill.
Multivariate Behav Res. 2010 Mar 31;45(2):322-58. doi: 10.1080/00273171003680302.
Different random or purposive allocations of items to parcels within a single sample are thought not to alter structural parameter estimates as long as items are unidimensional and congeneric. If, additionally, numbers of items per parcel and parcels per factor are held fixed across allocations, different allocations of items to parcels within a single sample are thought not to meaningfully alter model fit-at least when items are normally distributed. We show analytically that, although these statements hold in the population, they do not necessarily hold in the sample. We show via a simulation that, even under these conservative conditions, the magnitude of within-sample item-to-parcel-allocation variability in structural parameter estimates and model fit can alter substantive conclusions when sampling error is high (e.g., low N, low item communalities, few items per few parcels). We supply a software tool that facilitates reporting and ameliorating the consequences of item-to-parcel-allocation variability. The tool's utility is demonstrated on an empirical example involving the Neuroticism-Extroversion-Openness (NEO) Personality Inventory and the Computer Assisted Panel Study data set.
只要项目是单维且同类的，在单个样本中将项目随机或有目的地分配到不同组块被认为不会改变结构参数估计值。此外，如果每次分配中每个组块的项目数量和每个因子的组块数量保持固定，那么在单个样本中将项目分配到不同组块被认为不会显著改变模型拟合度——至少在项目呈正态分布时是这样。我们通过分析表明，尽管这些陈述在总体中成立，但在样本中不一定成立。我们通过模拟表明，即使在这些保守条件下，当抽样误差较高时（例如，样本量小、项目共同度低、每个组块项目数少），样本中项目到组块分配的结构参数估计值和模型拟合度的变异性大小可能会改变实质性结论。我们提供了一个软件工具，有助于报告和减轻项目到组块分配变异性的后果。该工具的实用性在一个涉及神经质-外向性-开放性（NEO）人格量表和计算机辅助面板研究数据集的实证例子中得到了证明。
