Abstract：
Wu and Browne (Psychometrika 80(3):571-600, 2015. https://doi.org/10.1007/s11336-015-9451-3 ; henceforth W &B) introduced the notion of adventitious error to explicitly take into account approximate goodness of fit of covariance structure models (CSMs). Adventitious error supposes that observed covariance matrices are not directly sampled from a theoretical population covariance matrix but from an operational population covariance matrix. This operational matrix is randomly distorted from the theoretical matrix due to differences in study implementations. W &B showed how adventitious error is linked to the root mean square error of approximation (RMSEA) and how the standard errors (SEs) of parameter estimates are augmented. Our contribution is to consider adventitious error as a general phenomenon and to illustrate its consequences. Using simulations, we illustrate that its impact on SEs can be generalized to pairwise relations between variables beyond the CSM context. Using derivations, we conjecture that heterogeneity of effect sizes across studies and overestimation of statistical power can both be interpreted as stemming from adventitious error. We also show that adventitious error, if it occurs, has an impact on the uncertainty of composite measurement outcomes such as factor scores and summed scores. The results of a simulation study show that the impact on measurement uncertainty is rather small although larger for factor scores than for summed scores. Adventitious error is an assumption about the data generating mechanism; the notion offers a statistical framework for understanding a broad range of phenomena, including approximate fit, varying research findings, heterogeneity of effects, and overestimates of power.
摘要：
吴和布朗(Psychometrika80(3)：571-600，2015。https://doi.org/10.1007/s11336-015-9451-3;此后W＆B）引入了不定误差的概念，以明确考虑协方差结构模型（CSM）的近似拟合优度。不定误差假设观察到的协方差矩阵不是直接从理论总体协方差矩阵而是从操作总体协方差矩阵采样。由于研究实现的差异，该操作矩阵从理论矩阵随机失真。W＆B显示了不定误差如何与近似的均方根误差（RMSEA）相关联，以及参数估计的标准误差（SE）如何增强。我们的贡献是将偶然错误视为一种普遍现象，并说明其后果。使用模拟，我们说明了它对SE的影响可以推广到超出CSM上下文的变量之间的成对关系。使用派生，我们推测，研究中效应大小的异质性和对统计功效的高估都可以解释为源于不定误差。我们还表明，不定错误，如果发生了,对综合测量结果的不确定性有影响，如因子得分和求和得分。模拟研究的结果表明，尽管因子得分比总和得分更大，但对测量不确定性的影响很小。不定错误是关于数据生成机制的假设；这个概念提供了一个统计框架，用于理解广泛的现象，包括近似拟合，不同的研究结果，效应的异质性，对权力的高估。
