The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this article we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai‘i (following McArdle, 2008; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply 3 recently available forms of longitudinal data analysis: (a) Dealing with incomplete data—We apply these methods to cohort-sequential data with relatively large blocks of data that are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (b) Ordinal measurement models (Muthén & Muthén, 2006)—We use a variety of statistical and psychometric measurement models, including ordinal measurement models, to help clarify the strongest patterns of influence. (c) Dynamic structural equation models (DSEMs; McArdle, 2008). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study.

中文翻译：

---

使用当代潜变量变化模型对夏威夷高中健康调查中的抑郁和学业成绩进行纵向动态分析


科学文献一致支持青少年抑郁症与教育成就之间存在负相关关系，但我们当然不太确定这种紧密关联的因果决定因素。在本文中，我们提供了来自夏威夷高中生的纵向队列序列研究的多变量数据（遵循 McArdle，2008 年；McArdle、Johnson、Hishinuma、Miyamoto 和 Andrade，2001 年）。我们首先描述有关学业成绩和自我报告的抑郁症的全套数据。然后，我们进行并提出一系列分析，以确定抑郁症与学业成绩之间动态关系（包括性别和种族差异）的准确性、大小和方向。我们应用 3 种最近可用的纵向数据分析形式： (a) 处理不完整数据——我们将这些方法应用于具有相对较大数据块的队列序列数据，这些数据块由于各种原因不完整（Little & Rubin，1987；McArdle） ＆滨神，1992）。 (b) 序数测量模型（Muthén & Muthén，2006）——我们使用各种统计和心理测量模型，包括序数测量模型，来帮助阐明最强的影响模式。 (c) 动态结构方程模型（DSEM；McArdle，2008）。我们发现这里采用的 DSEM 方法对于大量数据是可行的，随着时间的推移不变度量的假设对于序数估计是合理的，并且动态系统中几乎没有组间差异。我们的结论是，我们的动态证据表明抑郁会影响学业成绩，而不是相反。我们进一步讨论该研究的方法学意义。