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Should age-period-cohort studies return to the methodologies of the 1970s?

机译:年龄组研究是否应该回到1970年代的方法论?

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Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods hierarchical APC (HAPC) modeling - to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that "solid theory" is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies. (C) 2015 Elsevier Ltd. All rights reserved.
机译:社会科学家已经认识到半衰期队列(APC)模型的重要性已有半个世纪,但这段时间中的大部分时间都沉浸在有关APC方法可行性的辩论中。最近,出现了基于现代统计知识的新型APC方法,提供了潜在的解决方案。 2009年,Hauser和Yang均使用分层APC(HAPC)建模这些新方法中的一种-研究出生队列如何可能导致美国肥胖症流行。他们发现,近来的出生队列人群患肥胖症的几率高于其前辈,但无处不在的基于时期的变化是肥胖症患病率上升的主要原因。尽管这些发现已在其他地方重复使用,但Bell和Jones的最新评论使它们与新型APC方法一起受到质疑。贝尔和琼斯特别指出,新的APC方法不能充分解决模型识别问题,并建议“坚实理论”通常足以从经验考虑中消除三个时间维度之一。他们还提出了一系列的仿真模型,据称这些模型展示了Ruth等人估计的HAPC模型如何。 (2009)可能会产生误导性的结果。但是,这些仿真模型基于以下假设:没有周期影响,并且周期与同类变量之间的关联与结果是完全线性的。在这些情况下,切勿使用APC模型。在更可靠的假设下,我们自己的模拟结果表明,HAPC方法在恢复Ruth等人提出的主要发现方面均表现良好。 (2009年),结果由Bell和Jones报道。我们还回应了对模型识别和理论上施加的约束的批评,几乎没有对这些论点的实际支持。最后,我们鼓励社会科学家们超越1970年代的辩论,而对现代APC方法论加深了解。 (C)2015 Elsevier Ltd.保留所有权利。

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