Jackson Suzanne F
University of Toronto, Center for Health Promotion, Department of Public Health Sciences.
Prog Community Health Partnersh. 2008 Summer;2(2):161-70. doi: 10.1353/cpr.0.0010.
When conducting community-based participatory research (CBPR), community researchers are often consulted during the analysis step, but rarely participate in the entire process.
This paper describes a participatory qualitative data analysis process that was used in three projects with marginalized women in Ontario, Canada. In each project, marginalized women were trained as Inclusion Researchers (IRs) and participated in all stages of the research process. Given the emphasis of the projects on inclusion, it was important that a data analysis process be developed that was group oriented, engaging, understandable, and inclusive of the community researchers.
A five-part analysis process is described including preparation of the data, grouping and coding, consolidation, making sense of the data, and producing a report. This group analysis process took place over 2 full days with facilitation by an academic researcher, Details about the techniques used for each step are described.
The strengths of this participatory qualitative data analysis process were that it enabled participation of people with a mixture of levels of education and familiarity with analysis; it enabled community member control of the interpretation; and it could handle large volumes of data quickly. The main limitation was that additional time and procedures would be necessary for a deeper analysis or for groups of over 25 participants. The factors that contributed to the success of this participatory analysis process included accessible and clear procedures, use of visual grouping techniques, and a positive and supportive atmosphere for participation.
在开展基于社区的参与性研究（CBPR）时，社区研究人员通常在分析阶段被咨询，但很少参与整个过程。
本文描述了一种参与性定性数据分析过程，该过程用于加拿大安大略省针对边缘化女性的三个项目。在每个项目中，边缘化女性被培训成为包容性研究员（IRs），并参与研究过程的所有阶段。鉴于项目对包容性的强调，开发一种面向群体、引人入胜、易于理解且包含社区研究人员的数据分析过程非常重要。
描述了一个由五个部分组成的分析过程，包括数据准备、分组与编码、整合、理解数据以及撰写报告。这个群体分析过程在一位学术研究人员的引导下进行了整整两天，并描述了每个步骤所使用的技术细节。
这种参与性定性数据分析过程的优点在于，它能够让不同教育水平和分析熟悉程度的人参与进来；能够让社区成员控制解读过程；并且能够快速处理大量数据。主要局限性在于，进行更深入的分析或针对超过25名参与者的群体时，需要额外的时间和程序。促成这种参与性分析过程成功的因素包括可获取且清晰的程序、视觉分组技术的使用以及积极支持参与的氛围。
