ABSTRACT

The impact of parameter and prior specifications on Bayesian SEM estimates is examined through two simulation studies. The model of focus was a CFA. Simulation conditions for Study 1 included varying sample size, the strength of the factor loadings (also tied to issues of reliability), factor correlation strength, and estimation conditions tied to different parameter specifications. Study 2 extended these factors and included non-zero cross-loadings to highlight the flexibility that Bayesian methods afford CFAs. The main goal of these studies was to examine the impact of different parameter specifications, as crossed with different forms of prior distributions, on the accuracy of parameter estimates–examined via relative bias. We examined several parameter specification conditions focused on the latent factor covariance specification, and then crossed these conditions with different prior forms (multivariate and separation strategy priors). Findings highlight where parameter specification implemented had an overall larger impact on the accuracy of results obtained.

摘要

通过两个模拟研究检查参数和先验规范对贝叶斯 SEM 估计的影响。焦点模型是CFA。研究 1 的模拟条件包括不同的样本大小、因子载荷的强度（也与可靠性问题相关）、因子相关强度以及与不同参数规范相关的估计条件。研究 2 扩展了这些因素并包括非零交叉载荷，以突出贝叶斯方法为 CFA 提供的灵活性。这些研究的主要目标是检查不同参数规范（与不同形式的先验分布交叉）对参数估计准确性的影响——通过相对偏差进行检查。我们检查了几个专注于潜在因子协方差规范的参数规范条件，然后将这些条件与不同的先验形式（多元和分离策略先验）交叉。调查结果强调了在哪些方面实施的参数规范对获得的结果的准确性有更大的影响。