仅拟合RI-CLPM是不够的：个体内纵向研究中的诊断敏感性与报告实践
Fitting RI-CLPM Is Not Enough: Diagnostic Sensitivity and Reporting Practices in Within-Person Longitudinal Research
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中文总结 AI 辅助
该研究针对RI-CLPM在个体内纵向研究中的应用，提出需关注其适配性，通过模拟明确影响检测个体内交叉滞后效应效力的因素，并调研相关报告实践，给出解读与报告建议。
中文摘要 AI 辅助
随机截距交叉滞后面板模型（RI-CLPM）被广泛用于区分稳定的个体间差异与个体内动态变化，但仅拟合RI-CLPM并不能保证数据能支持有意义的个体内推断。我们引入了“RI-CLPM适配性”的概念，其由测量可比性和诊断敏感性定义。使用powRICLPM进行蒙特卡洛模拟后发现，检测个体内交叉滞后效应的统计效力同时取决于信度、组内相关系数（ICC）、测量波数、样本量及目标效应量；高ICC、适度信度和较少波数会大幅降低效力，即使样本量很大也是如此。随后我们对186项实证RI-CLPM应用的报告实践进行了综述：许多研究报告了样本量、波数和信度，但纵向测量不变性、ICC或个体内方差、敏感性分析的报告一致性要低得多。无显著个体内路径很常见，但解读时往往未充分关注诊断敏感性。我们认为，RI-CLPM结果的解读应基于数据适配性，并提出了实用的报告建议。
英文摘要
The random-intercept cross-lagged panel model (RI-CLPM) is widely used to separate stable between-person differences from within-person dynamics. Yet fitting an RI-CLPM does not guarantee that the data can support meaningful within-person inference. We introduce the concept of RI-CLPM readiness, defined by measurement comparability and diagnostic sensitivity. Using Monte Carlo simulations with powRICLPM, we show that power to detect within-person cross-lagged effects depends jointly on reliability, ICC, number of waves, sample size, and target effect size; high ICC, modest reliability, and few waves can substantially reduce power even in large samples. We then review reporting practices in 186 empirical RI-CLPM applications. Many studies reported sample size, wave count, and reliability, but longitudinal measurement invariance, ICC or within-person variance, and sensitivity analyses were reported much less consistently. Null within-person paths were common but often interpreted without sufficient attention to diagnostic sensitivity. We argue that RI-CLPM results should be interpreted conditionally on data readiness and offer practical reporting recommendations.
