A combination of computer-aided qualitative data analysis (CAQDAS) and latent class analysis (LCA) can substantially augment the qualitative analysis of textual data sources used in third-sector studies. This article explains how to employ both techniques iteratively to capture often implicit ideas and meaning-making by third-sector leaders, donors, and other stakeholders. CAQDAS facilitates the coding, organization, and quantification of qualitative data, effectively creating parallel qualitative and quantitative data structures. LCA facilities the discovery of latent concepts, document classification, and the identification of exemplary qualitative evidence to aid interpretation. For third-sector research, CAQDAS and LCA are particularly promising because diverse stakeholders usually do not share homogenous views about core issues such as organizational effectiveness, collaboration, impact measurement, or philanthropic approaches, for example. The procedure explained here provides a rigorous method for discovering and understanding diversity in perspectives and is especially useful in medium-n research settings common to third-sector scholarship.

计算机辅助定性数据分析 (CAQDAS) 和潜在类别分析 (LCA) 的组合可以大大增强对第三部门研究中使用的文本数据源的定性分析。本文解释了如何反复使用这两种技术来捕捉第三部门领导者、捐助者和其他利益相关者通常隐含的想​​法和意义。CAQDAS 促进定性数据的编码、组织和量化，有效地创建并行的定性和定量数据结构。LCA 促进潜在概念的发现、文档分类和示例性定性证据的识别以帮助解释。对于第三部门研究，CAQDAS 和 LCA 尤其有前景，因为不同的利益相关者通常不会就诸如组织有效性、协作、影响衡量或慈善方法等核心问题分享同一种观点。这里解释的程序提供了一种严格的方法来发现和理解观点的多样性，在第三部门奖学金常见的中等研究环境中尤其有用。