Program in Educational Psychology, City University of New York Graduate Center, 365 Fifth Avenue, New York, NY 10016, USA.
Psychol Methods. 2012 Sep;17(3):336-9; discussion 346-53. doi: 10.1037/a0027130.
Muthén and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By keeping parameters restricted to intervals (such as loadings between -.3 and .3 to produce small loadings), frequentists using standard structural equation modeling software can do something similar to what a Bayesian does by putting prior distributions on these parameters.
穆尔腾和阿斯帕鲁霍夫(2012 年)对贝叶斯方法在因子分析和结构方程模型中的优势进行了有力的论证。我展示了他们方法的更多扩展和改编,并展示了非贝叶斯主义者如何通过对参数进行区间限制来利用这些优势中的许多(尽管不是全部)。通过将参数限制在区间内(例如,将加载项限制在 -.3 和.3 之间以产生较小的加载项),使用标准结构方程建模软件的频率主义者可以通过对这些参数进行先验分布来做类似于贝叶斯主义者所做的事情。