ABSTRACT

We propose a general approach to detect measurement non-invariance in latent Markov models for longitudinal data. We define different notions of differential item functioning in the context of panel data. We then present a model selection approach based on the Bayesian information criterion (BIC) to choose both the number of latent states and the measurement structure. We show the practical relevance by means of an extensive simulation study, and illustrate its use on two real–data examples from the social sciences. Our results indicate that BIC is able to select the correct measurement equivalence structure more than 95% of times.

中文翻译：

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通过潜在马尔可夫模型评估纵向数据的测量不变性

摘要

我们提出了一种通用方法来检测纵向数据的潜在马尔可夫模型中的测量非不变性。我们在面板数据的背景下定义了差异项目功能的不同概念。然后，我们提出了一种基于贝叶斯信息准则（BIC）的模型选择方法来选择潜在状态的数量和测量结构。我们通过广泛的模拟研究展示了实际相关性，并说明了它在社会科学的两个真实数据示例中的应用。我们的结果表明，BIC 能够在超过 95% 的时间内选择正确的测量等价结构。