Item Parceling Strategies in SEM: Investigating the Subtle Effects of Unmodeled Secondary Constructs
Item Parceling Strategies in SEM: Investigating the Subtle Effects of Unmodeled Secondary Constructs
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DOI：
10.1177/109442819923002
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发表时间：
1999-07-01
影响因子：
9.5
通讯作者：
Foust, Michelle Singer
中科院分区：
文献类型：
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作者：
Hall, Rosalie J.;Snell, Andrea F.;Foust, Michelle Singer
由于理论和经验的原因，研究人员可能会将项目层面的反应结合到总体项目包裹中，作为结构方程建模上下文中的指标。然而，具体的包装策略对参数估计和模型拟合的影响尚不清楚。在研究1中，不同的包装组合对两个组织数据集的参数估计和拟合指标有显著影响。基于外部一致性的概念，作者提出将共享未建模的次要影响的项目组合到同一包裹中（共享唯一性策略）将提高参数估计的准确性。研究2使用从已知模型生成的模拟数据支持了这一建议。当未建模的次要影响仅与一个潜在构念的指标相关时，共享唯一性打包策略导致更准确的参数估计。当两个目标潜伏构念的指标都被污染时，存在偏倚，但适当地通过恶化的拟合统计表明。
For theoretical and empirical reasons, researchers may combine item-level responses into aggregate item parcels to use as indicators in a structural equation modeling context. Yet the effects of specific parceling strategies on parameter estimation and model fit are not known. In Study 1, different parceling combinations meaningfully affected parameter estimates and fit indicators in two organizational data sets. Based on the concept of external consistency, the authors proposed that combining items that shared an unmodeled secondary influence into the same parcel (shared uniqueness strategy) would enhance the accuracy of parameter estimates. This proposal was supported in Study 2, using simulated data generated from a known model. When the unmodeled secondary influence was related to indicators of only one latent construct, the shared uniqueness parceling strategy resulted in more accurate parameter estimates. When indicators of both target latent constructs were contaminated, bias was present but appropriately signaled by worsened fit statistics.