Abstract— It is critical to the progress of developmental science that researchers make proper use of statistical models for analyzing individual change over time. Latent curve models, hierarchical linear growth models, group‐based trajectory models, and growth mixture models are increasingly important tools for longitudinal data analysis. To facilitate their understanding and use, this article clarifies similarities and differences between these models, paying particular attention to the assumptions they make about individual development. An example shows how the results and interpretation vary across model types. The discussion centers on reviewing the strengths and limitations of each approach for developmental research.

中文翻译：

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个体发展中的建模可变性：程度或种类的差异？

摘要：研究人员正确使用统计模型来分析个体随时间的变化，这对于发展科学的进步至关重要。潜在曲线模型、分层线性增长模型、基于组的轨迹模型和增长混合模型越来越成为纵向数据分析的重要工具。为便于理解和使用，本文阐明了这些模型之间的异同，特别关注它们对个体发展所做的假设。一个示例显示了结果和解释如何因模型类型而异。讨论的重点是回顾每种发展研究方法的优势和局限性。