Uanhoro James Ohisei, Soyoye Olushola O
University of North Texas, Denton, USA.
University of Delaware, Newark, USA.
Educ Psychol Meas. 2025 Jun 13:00131644251330851. doi: 10.1177/00131644251330851.
This study investigates the incorporation of historical measurement information into structural equation models (SEM) with small samples to enhance the estimation of structural parameters. Given the availability of published factor analysis results with loading estimates and standard errors for popular scales, researchers may use this historical information as informative priors in Bayesian SEM (BSEM). We focus on estimating the correlation between two constructs using BSEM after generating data with significant bias in the Pearson correlation of their sum scores due to measurement error. Our findings indicate that incorporating historical information on measurement parameters as priors can improve the accuracy of correlation estimates, mainly when the true correlation is small-a common scenario in psychological research. Priors derived from meta-analytic estimates were especially effective, providing high accuracy and acceptable coverage. However, when the true correlation is large, weakly informative priors on all parameters yield the best results. These results suggest leveraging historical measurement information in BSEM can enhance structural parameter estimation.
本研究探讨了如何将历史测量信息纳入小样本结构方程模型（SEM）中，以提高结构参数的估计。鉴于已发表的因子分析结果中包含了常用量表的载荷估计值和标准误差，研究人员可以在贝叶斯结构方程模型（BSEM）中将这些历史信息用作信息先验。我们专注于在由于测量误差导致其总分的皮尔逊相关存在显著偏差的数据生成后，使用BSEM估计两个构念之间的相关性。我们的研究结果表明，将测量参数的历史信息作为先验纳入可以提高相关性估计的准确性，主要是在真实相关性较小的情况下——这在心理学研究中是常见的情况。来自元分析估计的先验尤其有效，提供了高精度和可接受的覆盖率。然而，当真实相关性较大时，对所有参数使用弱信息先验会产生最佳结果。这些结果表明，在BSEM中利用历史测量信息可以增强结构参数估计。
