Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science
Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science
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DOI：
10.1080/17405629.2014.963549
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发表时间：
2015-01-02
影响因子：
2
通讯作者：
Willoughby, Teena
中科院分区：
文献类型：
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作者：
Adachi, Paul;Willoughby, Teena
纵向研究中的效应量通常显著小于横断面研究中的效应量。事实上，自回归模型（通常用于纵向研究，但不用于横断面研究）控制了过去的结果水平（即，稳定性效应），以便预测结果水平随时间的变化，从而可以大大降低预测因子对结果的影响的大小。然而，不幸的是，没有人试图区分纵向研究与横断面研究的效应量解释指南。因此，纵向效应量低于“小”的通用准则，可能会被错误地视为微不足道，当他们可能是有意义的。在本文中，我们首先回顾了目前解释效应量的指南。接下来，我们讨论了几个例子，如何控制稳定性的影响，可以显着衰减其他预测因子的效果大小，以支持我们的论点，目前的指导方针可能是误导解释纵向效应。最后，我们最后为研究人员就纵向自回归模型中效应大小的解释提出建议。
Effect sizes in longitudinal studies often are dramatically smaller than effect sizes in cross-sectional studies. Indeed, autoregressive models (which are often used in longitudinal studies but not in cross-sectional studies) control for past levels on the outcome (i.e., stability effects) in order to predict change in levels of the outcome over time and thus may greatly reduce the magnitude of the effect of a predictor on the outcome. Unfortunately, however, there have been no attempts to differentiate guidelines for interpreting effect sizes for longitudinal studies versus cross-sectional studies. Consequently, longitudinal effect sizes that fall below the universal guidelines for "small" may be incorrectly dismissed as trivial, when they might be meaningful. In the current paper, we first review the present guidelines for interpreting effect sizes. Next, we discuss several examples of how controlling for stability effects can dramatically attenuate effect sizes of other predictors, in order to support our argument that the current guidelines may be misleading for interpreting longitudinal effects. Finally, we conclude by making recommendations for researchers regarding the interpretation of effect sizes in longitudinal autoregressive models.