Construindo formas para análise de conteúdo: o plano plurianual participativo e o software Iramuteq em questão

Abstract

This dissertation investigates the application of content analysis techniques using the IRAMUTEQ software for interpreting large volumes of textual data. The central objective is to analyze the use of the software in the categorization and interpretation of proposals submitted to the Participatory Multi-Year Plan (Plano Plurianual Participativo – PPA), as well as to systematically review its application in academic literature. The rationale for the research stems from the growing need for systematic and replicable methods for analyzing large qualitative data sets, especially in the context of public administration and social participation. To achieve the proposed objectives, two complementary studies were conducted. The first consisted of a systematic literature review, analyzing academic works that used the software for content analysis, allowing the identification of its main uses, advantages, and limitations. The second study involved the empirical application of the methodology to a real dataset composed of proposals submitted to the Brasil Participativo platform, enabling the identification of thematic patterns. The results showed that the software allows for efficient organization of textual data, enabling the automated categorization of information and ensuring transparency in data processing. Furthermore, limitations were observed related to the need to complement the automated analysis with qualitative interpretation by the researcher. The contributions of this study include the presentation of a systematized technique for qualitative analysis, applicable to both academic research and public management. Additionally, the study offers guidelines for future research, suggesting the combination of the software with other analytical approaches to enhance data interpretation. The findings reinforce the importance of using computational tools to optimize content analysis and broaden access to rigorous methods for large-scale text interpretation.