Abstract This study presents an assessment of factors that influence how people who live in the vicinity of dams view such projects. The usefulness of the principal component analysis (PCA) method for identifying variables that determine individuals' opinion about large dam projects was reviewed. The study focuses on people affected by the construction of the Mucharski Reservoir in the Polish Carpathians. The construction took over 30 years and took place at a time when Poland transitioned from a planned economy to a free market one. We used in-depth interviews (N = 96) and a set of 18 factors classified as personal, emotional and economic. Our results indicate that the variables that significantly affect social perception of dam projects by the local population include their opinion regarding the viability of the project, sense of security, personal benefits, the extent to which they have accepted the structure, respecting the local community's interests when drafting the development plans and new opportunities. The results allow for the future optimization of research tools used to comprehensively examine social perception of hydraulic structures. Using PCA allowed us to take semi-structured data from interviews and extract meaningful relationships between the various inputs, show correlations between seemingly unrelated data, as well as explain the variances within the studied population. It also shows that PCA can be a useful tool for analyzing data that is not formally structured.

中文翻译：

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大坝水库社会认知影响因素的层次

摘要 本研究对影响住在大坝附近的人们如何看待此类项目的因素进行了评估。审查了主成分分析 (PCA) 方法在识别决定个人对大型水坝项目意见的变量方面的有用性。该研究的重点是受波兰喀尔巴阡山脉建造穆夏斯基水库影响的人们。建设历时 30 多年，发生在波兰从计划经济过渡到自由市场经济的时期。我们使用了深度访谈 (N = 96) 和一组 18 个因素，分为个人、情感和经济。我们的结果表明，显着影响当地居民对大坝项目的社会认知的变量包括他们对项目可行性的看法、安全感、个人利益，他们接受结构的程度，在起草发展计划和新机会时尊重当地社区的利益。结果允许未来优化用于全面检查水工结构的社会感知的研究工具。使用 PCA 使我们能够从访谈中获取半结构化数据并提取各种输入之间有意义的关系，显示看似不相关的数据之间的相关性，以及解释研究人群中的差异。它还表明 PCA 可以成为分析未​​正式结构化数据的有用工具。结果允许未来优化用于全面检查水工结构的社会感知的研究工具。使用 PCA 使我们能够从访谈中获取半结构化数据并提取各种输入之间有意义的关系，显示看似不相关的数据之间的相关性，以及解释研究人群中的差异。它还表明 PCA 可以成为分析未​​正式结构化数据的有用工具。结果允许未来优化用于全面检查水工结构的社会感知的研究工具。使用 PCA 使我们能够从访谈中获取半结构化数据并提取各种输入之间有意义的关系，显示看似不相关的数据之间的相关性，以及解释研究人群中的差异。它还表明 PCA 可以成为分析未​​正式结构化数据的有用工具。