用于定性和定量研究结果合成的贝叶斯数据增强方法。
Bayesian data augmentation methods for the synthesis of qualitative and quantitative research findings.
作者信息
Crandell Jamie L, Voils Corrine I, Chang Yunkyung, Sandelowski Margarete
机构信息
Department of Biostatistics, University of North Carolina at Chapel Hill, #7460 Carrington Hall, Chapel Hill, NC 27599, USA; School of Nursing, University of North Carolina at Chapel Hill, #7460 Carrington Hall, Chapel Hill, NC 27599, USA.
出版信息
Qual Quant. 2011 Apr;45(3):653-669. doi: 10.1007/s11135-010-9375-z.
Abstract
The possible utility of Bayesian methods for the synthesis of qualitative and quantitative research has been repeatedly suggested but insufficiently investigated. In this project, we developed and used a Bayesian method for synthesis, with the goal of identifying factors that influence adherence to HIV medication regimens. We investigated the effect of 10 factors on adherence. Recognizing that not all factors were examined in all studies, we considered standard methods for dealing with missing data and chose a Bayesian data augmentation method. We were able to summarize, rank, and compare the effects of each of the 10 factors on medication adherence. This is a promising methodological development in the synthesis of qualitative and quantitative research.
摘要
贝叶斯方法在定性和定量研究综合中的潜在效用已被多次提及，但研究尚不充分。在本项目中，我们开发并使用了一种贝叶斯综合方法，旨在确定影响艾滋病毒药物治疗方案依从性的因素。我们研究了10个因素对依从性的影响。由于认识到并非所有研究都考察了所有因素，我们考虑了处理缺失数据的标准方法，并选择了一种贝叶斯数据增强方法。我们能够总结、排序并比较这10个因素中每个因素对药物依从性的影响。这是定性和定量研究综合方面一项很有前景的方法学进展。
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