Qualitative data analysis and mixed studies: Can Iramuteq software be an asset?

Mixed methods research (MMR) has been referred to psychology as a preferential trend, resulting from the current acceptance of qualitative methods (QM). However, MMR are not new in Psychology, but , in practice, it does not seem so disseminated (Creamer & Reeping, 2020). In fact, Creamer and Reeping (2020) recognized the diversity in conducting and reporting mixed studies, and the difficulty it may represent when publishing studies in some academic journals. Accordingly, they finished their article with this recommendation:

Researchers in psychology who are anxious to find publishing venues that are open to mixed methods do well to scour the aims statement of journals to find those that invite innovation, encourage interdisciplinary scholarship, and acknowledge an interest not only in quantitative approaches, but in qualitative and mixed methods research as well. (Creamer & Reeping, 2020, para. 15)

Concerning QM, the situation is not different. For instance, a literature review about qualitative research publications in School Psychology, conducted by Sabnis et al. (2023), revealed that, despite the increase of qualitative articles publications, there were only 3% of publications in that area. Currently, qualitative researchers, namely researchers in School Psychology, are encouraged to experience new forms of QM and to go beyond conventional methods (Sabnis & Wolgemuth, 2024).

Another current topic in QM and MMR is the use of Computer Assisted Qualitative Data Analysis Software (CAQDAS) (Bryda & Costa, 2023) and, recently, Artificial Intelligence (AI) (Costa, 2023). Of course, the use of CAQDAS implies  thinking and  adopting  some ethical issues and behaviors (Brandão & Costa, 2020; Marshall & Naff, 2024). Moreover, using CAQDAS demandes training by the researcher   in the software as well as in qualitative methods skills because when fulfilling these two conditions the software can be used correctly and data will analyzed properly (Freitas et al., 2017).

Several programs can be used and the researcher will have to choose the one that best suits  his studies purposes and the ability he has to manage the software (Noakes et al. 2023). In psychology, both  spoken and written speech are very important. Usually, psychology qualitative researchers use interviews to collect data and, afterwards, transcribe audios to text. Sometimes, they also ask participantes to register in a rjournal their perceptions, feelings or thoughts so this writtenmaterial can be analyzed. Consequently, text analysis is very important for psychology researchers. Therefore the importance of CAQDAS to support the data analysis in this social area is understandable , especially when QM and MMR are increasingly being adopted and reinforced in Psychology.

In this panel discussion, we will focus on qualitative data analysis and mixed studies, specifically text analysis, using the IRAMUTEQ software, which has already been studied and used by other authors (Silva Alonso et al. 2024; Sousa, 2021; Souza et al., 2018). Accordingly, the main goals of this panel are to discuss the use of the CAQDAS in qualitative data analysis and mixed studies, and to contribute to the knowledge development of participants about IRAMUTEQ and its applicability in qualitative and mixed studies.

We selected the IRAMUTEQ software because we think it presents very interesting potentialities: First and foremost different types of text analysis can be made (including statistics); second, a large amount of data can be analyzed in a short period of time (an essential feature when deadlines need to be accomplished); and third, it is a free software (which can be an asset to students or trainees that have no funding for research).

Iramueq was developed by Ratinaud and its origin language is French. However, the software has other several languages dictionaries available that can be updated (Camargo & Justo, 2013). Many studies have already been conducted with IRAMUTEQ in Brazil (Portuguese language) and we present other studies using IRAMUTEQ in Spain (Spanish language) and in Portugal (Portuguese language) in this panel, as Spanish and Portuguese are the native languages of the authours that conducted these studies and organized this panel.

We believe this software can be an asset to text analysis in mixed studies. To ilustrate that, some studies where the authors used IRAMUTEQ, as stated before, will be presented. A first study focus on the use of IRAMUTEQ in a mixed study that evaluates the training of a group of teachers of students with high abilities. Here, the contribution of the IRAMUTEQ program to the analysis and the satisfaction of the trainees will be displayed. It will also be explained its contribution to lexical analysis and creation of categories (Flores-Bravo et al., 2024).

The second study is a final undergraduate work and testifies the importance of training students in qualitative methods and its contribution to MMR. Thus, concerning the relationship between gender, interest in STEM careers, and the use of ICTs, the use of IRAMUTEQ facilitated the understanding of students' future career intentions (Díez, 2024).

A third study is also a MMR related to preferences in the type of autonomous work among university students according to their gender. Once again, the use of IRAMUTEQ helped the researchers to acknoweledge the different kind of autonomous work used by students (Flores et al., in preparation).

A fourth qualitative study, focusing on the hobbies of high-ability university students in Spain, will be mentioned and, again, the relevance of using IRAMUTEQ will be highlighted (Antunes & Borges, 2024).

A final study on the effects of attitudes towards mathematics and STEM education on high-ability students will be presented and discussed. A MMR was conducted and ALCEST was chosen because it allows text analysis and the fourth author was already familiar with this tool, since she previously made several studies using it as a textual analysis tool. In the last part of the presentation, the ALCEST and IRAMUTEQ usage will be compared.

After presenting these studies and discussing them with the panel’s participants, the advantages and limitations of the IRAMUTEQ software will be highlighted. Despite its potentialities, as listed above, the required training to manage it can be seen as a limitation, if researchers do not show availability for this purpose, leading them to avoid the use of this tool (Canuto et al. 2020). This is an important topic of this panel because it highlights key aspects of IRAMUTEQ that participants can register. Finally, some concluding remarks will be presented.