A comparison of cluster algorithms as applied to unsupervised surveys

Often survey analysis collects data to try to identify response patterns leading to groupings of respondents with different characteristics as revealed by answers provided to survey questions. Without additional background information on respondents, it is often very difficult (and many times impossible) to verify the accuracy of groupings resulting from the analysis. This paper examines one such situation in which high school students in low-income neighbourhood schools in Bolivia responded to a standard periodic institutional survey and responses were analysed to better understand respondents' socioeconomic contexts. In this case study, the question to be answered was "can we identify the most impoverished students based on a 22 questions standard survey alone?". With no known dependent variable and an inability to objectively capture the socioeconomic condition of the students being surveyed, the task of coming to a conclusive answer becomes unfeasible as there is no way to validate at least some portion of the students identified as most impoverished.