Incorporating Measurement Nonequivalence in a Cross-Study Latent Growth Curve Analysis
Incorporating Measurement Nonequivalence in a Cross-Study Latent Growth Curve Analysis
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DOI：
10.1080/10705510802339080
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发表时间：
2008-10-01
影响因子：
6
通讯作者：
Edwards, Michael C.
中科院分区：
文献类型：
--
作者：
Flora, David B.;Curran, Patrick J.;Edwards, Michael C.
大量文献强调了在尺度上测试测量等价性的重要性，这些尺度可用作结构方程建模应用中的观测变量。当在多个发育时期测量相同的结构时，如在纵向研究中，建立跨发育时期的测量等效性或不变性尤其重要。同样，当来自多个研究的数据合并为一项分析时，评估跨数据源的测量等效性也很重要。然而，应用研究人员并没有很好地描述如何在发现非等价性时将其纳入其中。在这里，我们提出了一种项目反应理论方法，可用于根据测量创建量表分数，同时明确考虑不等价性。我们在潜在曲线分析的背景下演示了这些方法，其中来自两项独立研究的数据被组合起来以估计跨越多个发育时期的单个纵向模型。
A large literature emphasizes the importance of testing for measurement equivalence in scales that may be used as observed variables in structural equation modeling applications. When the same construct is measured across more than one developmental period, as in a longitudinal study, it can be especially critical to establish measurement equivalence, or invariance, across the developmental periods. Similarly, when data from more than one study are combined into a single analysis, it is again important to assess measurement equivalence across the data sources. Yet, how to incorporate nonequivalence when it is discovered is not well described for applied researchers. Here, we present an item response theory approach that can be used to create scale scores from measures while explicitly accounting for nonequivalence. We demonstrate these methods in the context of a latent curve analysis in which data from two separate studies are combined to estimate a single longitudinal model spanning several developmental periods.