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A straightforward approach for coping with unreliability of person means when parsing within-person and between-person effects in longitudinal studies

机译：当在纵向研究中解析人类和人物之间的效果时，对人的不可靠性应对的直接方法意味着

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摘要

Longitudinal studies enable researchers to distinguish within-person (i.e., time-varying) from between-person (i.e., time invariant) effects by using the person mean to model between-person effects and person-mean centering to model within-person effects using multilevel models (MLM). However, with some exceptions, the person mean tends to be based on a relatively small number of observations available for each participant in longitudinal studies. Unreliability inherent in person means generated with few observations results in downwardly biased between-person and cross-level interaction effect estimates. This manuscript considers a simple, easy-to-implement, post-hoc bias adjustment to correct for attenuation of between-person effects caused by unreliability of the person mean. This correction can be applied directly to estimates obtained from MLM. We illustrate this method using data from a panel study predicting adolescent alcohol involvement from perceived parental monitoring, where parental monitoring was disaggregated into within-person (i.e., person-mean-centered) and between-person (i.e., person-mean) components. We then describe results of a small simulation study that evaluated the performance of the post-hoc adjustment under data conditions that mirrored those of the empirical example. Results suggested that, under a condition in which parameter bias is known to be problematic (i.e., moderate ICCx, small n, presence of a compositional effect), it is preferable to use the bias-adjusted MLM estimates over the unadjusted MLM estimates for between-person and cross-level interaction effects.
机译：纵向研究使研究人员能够通过使用意味着在人物效应和人称效果之间模拟的人之间的人物（即，时间不变）效应来区分人（即，时间不变）效应的人（即时变）效应多级模型（MLM）。然而，在一些例外，人们的意思倾向于基于对纵向研究中每个参与者可用的相对较少的观察结果。在少数观察结果生成的人中固有的不可靠性导致人与人之间的偏差和交叉级交互效应估计。本手稿考虑了一个简单，易于实现的后偏见调整，以纠正因人均易于性造成的人类效应的衰减。该校正可以直接应用于从MLM获得的估计。我们用来自面板研究的数据来说明这种方法，该方法预测来自感知的父母监测的青少年酒精受累，父母监测被分解为人（即人为含义）和人（即人为平均值）组成部分。然后，我们描述了一个小型模拟研究的结果，评估了在反映了经验示例的数据条件下进行了HOC调整的性能。结果表明，在已知参数偏差是有问题的（即，适度的ICCC，小N，N，存在组成效果的情况下），优选使用偏置调整后的MLM估计在不调整的MLM估计之间-Person和交叉级交互效应。

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