In repeated cross-sections of organizations, different individuals are sampled from the same set of organizations at each time point of measurement. As a result, common longitudinal data analysis methods (e.g., latent growth curve models) cannot be applied in the usual way. In this contribution, a multilevel structural equation modeling approach to analyze data from repeated cross-sections is presented. Results from a simulation study are reported which aimed at obtaining guidelines on appropriate sample sizes. We focused on a situation where linear growth occurs at the organizational level, and organizational growth is predicted by a single organizational level variable. The power to identify an effect of this organizational level variable was moderately to strongly positively related to number of measurement occasions, number of groups, group size, intraclass correlation, effect size, and growth curve reliability. The Type I error rate was close to the nominal alpha level under all conditions.

中文翻译：

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使用多层结构方程模型分析重复截面设计中的组织增长

在组织的重复截面中，在每个测量时间点从同一组组织中采样不同的个体。结果，不能以通常的方式来应用常见的纵向数据分析方法（例如，潜伏增长曲线模型）。在此贡献中，提出了一种用于分析来自重复横截面的数据的多级结构方程建模方法。报告了模拟研究的结果，旨在获得有关适当样本量的指导。我们关注的是线性增长发生在组织级别，而组织增长是由单个组织级别变量预测的情况。识别此组织级别变量的影响的能力与测量场合的数量，组的数量，组大小，组内相关性，效应大小和增长曲线可靠性。在所有条件下，I型错误率均接近标称alpha水平。