The exploratory and confirmatory approaches of factor analysis lie on two ends of a continuum of substantive input for scale development. Recent advancements in Bayesian regularization methods enable more flexibility in covering a wide range of the substantive continuum. Based on the Bayesian Lasso (least absolute shrinkage and selection operator) methods for the regression model and covariance matrix, this research proposes a partially confirmatory approach to address the loading and residual structures at the same time. With at least one specified loading per item, a one-step procedure can be applied to figure out both structures simultaneously. With a few specified loadings per factor, a two-step procedure is preferred to capture the model configuration correctly. In both cases, the Bayesian hierarchical formulation is implemented using Markov Chain Monte Carlo estimation with different Lasso or regular priors. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed approach across different situations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

---

贝叶斯套索的规模发展的部分验证方法。

因子分析的探索性方法和确认性方法位于规模发展的大量实质性投入的两端。贝叶斯正则化方法的最新进展为覆盖范围广泛的实质连续性提供了更大的灵活性。基于贝叶斯Lasso（最小绝对收缩和选择算子）方法的回归模型和协方差矩阵，本研究提出了一种部分验证性的方法来同时处理载荷和残余结构。每个项目至少有一个指定的装载量，可以应用一步步骤同时找出两个结构。由于每个因子有几个指定的载荷，因此最好采用两步过程来正确捕获模型配置。在这两种情况下 贝叶斯分层公式是使用具有不同套索或常规先验的马尔可夫链蒙特卡罗估计来实现的。分析了模拟数据集和实际数据集，以评估所提出方法在不同情况下的有效性，鲁棒性和实用性。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。