Factorial Invariance and the Specification of Second-Order Latent Growth Models
Factorial Invariance and the Specification of Second-Order Latent Growth Models
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DOI：
10.1027/1614-2241.4.1.22
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发表时间：
2008-01-01
影响因子：
3.1
通讯作者：
Widaman, Keith F.
中科院分区：
文献类型：
--
作者：
Ferrer, Emilio;Balluerka, Nekane;Widaman, Keith F.
在过去的二十年里，潜在增长模型一直是人们非常感兴趣的话题。大多数理论和应用工作都采用一阶增长模型，其中一个单一的显变量作为指标的性状水平在每次测量。在目前的文件中，我们集中在二阶增长模型的问题，它有多个指标在每次测量。对于多个指标，可以检验参数在不同测量时间的阶乘不变性。我们进行这样的测试，使用两组数据，这在何种程度上不同的因子不变性持有，并评估纵向验证性因素，潜在的增长曲线，和潜在的差异得分模型。我们证明，如果阶乘不变性未能举行，选择用于识别潜在变量的指标可以有实质性的影响增长模式的特征，强大到足以改变有关增长的结论。我们还讨论了与生长因子的缩放有关的问题，并为实践和未来的研究提出了建议。
Latent growth modeling has been a topic of intense interest during the past two decades. Most theoretical and applied work has employed first-order growth models, in which a single manifest variable serves as indicator of trait level at each time of measurement. In the current paper, we concentrate on issues regarding second-order growth models, which have multiple indicators at each time of measurement. With multiple indicators, tests of factorial invariance of parameters across times of measurement can be tested. We conduct such tests using two sets of data, which differ in the extent to which factorial invariance holds, and evaluate longitudinal confirmatory factor, latent growth curve, and latent difference score models. We demonstrate that, if factorial invariance fails to hold, choice of indicator used to identify the latent variable can have substantial influences on the characterization of patterns of growth, strong enough to alter conclusions about growth. We also discuss matters related to the scaling of growth factors and conclude with recommendations for practice and for future research.