Since Norman Ryder's (1965) classic essay on cohort analysis was published more than a half century ago, scores of researchers have attempted to uncover the separate effects of age, period, and cohort (APC) on a wide range of outcomes. However, rather than disentangling period effects from those attributable to age or cohort, Ryder's approach is based on distinguishing intra-cohort trends (or life-cycle change) from inter-cohort trends (or social change), which, together, constitute comparative cohort careers. Following Ryder's insights, in this article I show how to formally summarize population-level temporal variability on the Lexis table. In doing so, I present a number of parametric expressions representing intra- and inter-cohort trends, intra-period differences, and Ryderian comparative cohort careers. To aid the interpretation of results, I additionally introduce a suite of novel visualizations of these model-based summaries, including 2D and 3D Lexis heat maps. Crucially, the Ryderian approach developed in this article is fully identified, complementing (but not replacing) conventional approaches that rely on theoretical assumptions to parse out unique APC effects from unidentified models. This has the potential to provide a common base of knowledge in a literature often fraught with controversy. To illustrate, I analyze trends in social trust in the U.S. General Social Survey from 1972 to 2018.

中文翻译：

---

剖析 Lexis 表：用年龄-时期-队列数据总结人口水平的时间变异性

自从 Norman Ryder (1965) 在半个多世纪前发表关于队列分析的经典文章以来，许多研究人员一直试图揭示年龄、时期和队列 (APC) 对广泛结果的单独影响。然而，莱德的方法不是将时期效应与归因于年龄或队列的效应分开，而是基于区分队列内趋势（或生命周期变化）与队列间趋势（或社会变化），它们共同构成比较队列事业。根据 Ryder 的见解，在本文中，我展示了如何在 Lexis 表上正式总结人口水平的时间变异性。在这样做的过程中，我提出了一些代表队列内部和队列间趋势、期间内差异和 Ryderian 比较队列职业的参数表达式。为了帮助解释结果，我还介绍了一套新颖的可视化这些基于模型的摘要，包括 2D 和 3D Lexis 热图。至关重要的是，本文中开发的 Ryderian 方法得到了充分识别，补充（但不是替代）依赖理论假设的传统方法从未识别的模型中解析出独特的 APC 效应。这有可能在经常充满争议的文献中提供一个共同的知识基础。为了说明这一点，我分析了 1972 年至 2018 年美国综合社会调查中社会信任的趋势。补充（但不是替代）依赖理论假设的传统方法从未识别的模型中解析出独特的 APC 效应。这有可能在经常充满争议的文献中提供一个共同的知识基础。为了说明这一点，我分析了 1972 年至 2018 年美国综合社会调查中社会信任的趋势。补充（但不是替代）依赖理论假设的传统方法从未识别的模型中解析出独特的 APC 效应。这有可能在经常充满争议的文献中提供一个共同的知识基础。为了说明这一点，我分析了 1972 年至 2018 年美国综合社会调查中社会信任的趋势。