Statistical Analysis of Textual Data for Longitudinal Analysis. A Study on Postgraduate Course Participants’ Reflections

The analysis of textual data is a complex process and requires clear thinking on the part of the analyst. However, several shortcomings can hinder the research analyst during the process, leading to inconsistencies, especially when applying qualitative and manual content analysis. This chapter reflects a quanti-qualitative approach to content analysis by Iramuteq software and its usefulness in identifying categories of analysis, automatically extracting topics, and analyzing their differences at a longitudinal level. Specifically, a study was conducted considering the reflections of 43 participants in a postgraduate course aimed at journalism professionals promoting inclusive language and increasing attention to information manipulation phenomena. The software Iramuteq was used to code, analyze, and extract the main topics considering the longitudinal level of participant’s reflection. Results show that as a quanti-qualitative analysis of textual data, the approach is a valuable means to promote the robustness of quanti-qualitative research at the longitudinal level and highlight changes in participants’ reflections during activities such as courses and interventions.