Recruiting participants for studies of early-life longitudinal development is challenging, often resulting in practical upper bounds in sample size and missing data due to attrition. These factors pose risks for the statistical power of such studies depending on the intended analytic model. One mitigation strategy is to increase measurement precision by conducting assessments of children as close to a fixed chronological age as possible. We present analyses that illustrate how such practices are only sometimes useful, focusing on cases where temporal trajectories are analyzed using multilevel modeling approaches. Simulations were conducted using results from two studies of longitudinal development. Data were generated according to both continuous and discrete developmental processes and factorially analyzed treating time on either interval, ordinal, or categorical scales. The power to detect continuously generated developmental processes was robust to, and even benefited from, increased variability around target ages. For discrete processes, power was unaffected when modeled ordinally/categorically, but declined steadily if modeled using exact chronological age on an interval scale. Our results suggest that in many circumstances, researchers may be unnecessarily devoting resources toward minimizing age sampling variability when studying functional patterns across time. In fact, when the theoretical developmental process is continuous, increasing the age sampling variability of assessments and utilizing multilevel models in favor of latent growth curve alternatives can be associated with substantial gains rather than reductions in power. Such considerations also extend to limited equivalent formulations of other common developmental models, such as panel analysis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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纵向发展轨迹的统计功效：测量场合中年龄匹配的（非）影响。

招募参与生命早期纵向发展研究的参与者具有挑战性，通常会导致样本量达到实际上限，并因人员流失而导致数据丢失。这些因素对此类研究的统计功效构成风险，具体取决于预期的分析模型。一种缓解策略是通过对尽可能接近固定年龄的儿童进行评估来提高测量精度。我们提出的分析说明了此类实践如何仅在某些时候有用，重点关注使用多级建模方法分析时间轨迹的情况。使用两项纵向发展研究的结果进行了模拟。数据是根据连续和离散的发育过程生成的，并在间隔、序数或分类尺度上对治疗时间进行因子分析。检测持续产生的发育过程的能力对于目标年龄周围不断增加的变异性来说是强大的，甚至受益于这种变化。对于离散过程，按顺序/分类建模时功效不受影响，但如果使用间隔尺度上的精确年代年龄建模，功效会稳步下降。我们的结果表明，在许多情况下，研究人员在研究跨时间的功能模式时可能会不必要地投入资源来最大限度地减少年龄抽样变异性。事实上，当理论发展过程是连续的时，增加评估的年龄抽样变异性和利用有利于潜在生长曲线替代方案的多水平模型可能会带来实质性的收益，而不是功率的降低。这种考虑也延伸到其他常见发展模型的有限等效公式，例如面板分析。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。