Contributions of IRAMUTEQ software for Discursive Textual Analysis

  • Maurivan Güntzel Ramos
  • Valderez Marina do Rosário Lima
  • Marcelo Prado Amaral-Rosa
Keywords: Textual Discursive Analysis; IRAMUTEQ Software; Qualitative Analysis.

Abstract

The research had as a guiding question: How can IRAMUTEQ software contribute to the procedures of Discursive Textual Analysis? In this sense, the aim was to carry out exploratory analysis with the software IRAMUTEQ to identify its potential of aid with the method of TDA - Textual Discursive Analysis. It is pointed out that the purpose of the TDA (Moraes & Galiazzi, 2014) is to produce new understandings about phenomena and speeches by worrying about the context of the speaker. The relevance of this work is based on the use of the software IRAMUTEQ (Ratinaud, 2014; Camargo & Justo, 2013) in the partial treatment of the data in front of the ATD stages. It declares, the unprecedented aspect for the area of teaching in Sciences and Mathematics. The IRAMUTEQ software is the acronym for Interface de R pour les Analyses Multidimensionnalles de Textes et de Questionnaires. Statistical textual analysis allow the retrieval of the context of the words. In Brazil, IRAMUTEQ, began to be used in 2013. There are five possibilities for analyzing textual data, namely: i) textual statistics; ii) search of group specificities; iii) Descending Hierarchical Classification (consequently Factorial Correspondence Analysis); iv) similarity analysis; and v) cloud of words. It was only the Descending Hierarchical Classification (CHD) and the Factorial Analysis of Correspondence (AFC). The subjects were newcomers to the Strict sense (n=40), of that 30% (12) males and 70% (28) females, with a mean age of 32 years. Of these, 37.5% (15) are professors of Sciences (Chemistry, Physics and Biology), 55% (22) are Mathematics and 7.5% (03) are from other areas. All volunteered to fill in the information and consented to the use of the data. The anonymity was guaranteed through the designation of subject with sequential numbering (Subject1, Subject2, ..., Subject40). The global corpus of analysis consisted of 446 text segments (ST), with use by the software of 77.8% (347 STs). The textual segments present, on average, 3.25 lines, with approximately 36 words per ST. The total lexical set has 16,119 occurrences (average approximate per text of 400 words), being 1,691 distinct and 761 of single occurrence. The instrument of data collection was the open questionnaire. The software identified five textual classes based on the corpus of analysis, which in turn gave rise to three definitive categories: i) technologies for approaching teachers and students; ii) technologies to modify teaching practice; and iii) technology for school change. The main conclusion is that the IRAMUTEQ software contributes to TDA, offering agility, new perspectives and rigor to the qualitative textual data, since it "illuminates the black box" that is generally the data constructions, as are the categories in the TDA, deriving from instruments such as questionnaires and interviews. However, it is confirmed that it is limited to generating information aimed at the confirmations of intermediate categories, since the final categories require texts described in a gradual dialogue between the tacit knowledge of the researcher, theory and empirical data.

Published
2018-10-01