Optimizing the use of Iramuteq in research using qualitative and mixed methods

In Behavioral Sciences and Education, methodologies are divided into four approaches: Qualitative, Observational, Surveys, and Experimental. However, from a more synthetic point of view, we can talk about qualitative and quantitative methodology. While the first emerged in the 1950s from a positivist framework, the second is based on constructivism and emerged in the 1970s with the idea of exploring and collecting information from life experiences. The samples are small and non-random, and there is no attempt to generalize the results obtained (Stahl, et al., 2019). In the last decade of the 20th century, a new methodology emerged, the Mixed Method Research or MMR, which has come to be called the third paradigm (Denscombe, 2008; Johnson and Onwuegbuzie, 2004), which integrates both perspectives, but from an integrative perspective and under the assumption of the compatibility of both methods (Cook and Reichardt, 1979), including both quantitative and qualitative methodology in the same research, although it is necessary to identify the weight and sequencing of each part in the design, with an explicit explanation of how to relate both approaches (Creswell and Plano Clark, 2011). The determination and naming of the different designs have not yet taken definitive form, although various categorizations have been made (Sandelowski et al., 2006; Teddlie and Tashakkori, 2006). In this third paradigm, the research process is not entirely straightforward and requires some steps to be established in an organized manner, as pointed out by Collins et al. (2006). In the first phase, which is where the research is formulated, the mixed objectives of the study must be defined, the logic of the study determined, the reasons for using a mixed methodology established, and the research questions posed from a multiple perspective. In the second phase, research planning, the sampling procedure and the specific design to be used are determined, while in the third phase, the two types of data are collected, the data are analyzed according to their focus, the multiple data are validated, the results are interpreted, and the report is prepared. In recent years, the development of software has made it possible to carry out MMR in a more sophisticated manner. As mentioned above, one of the problems faced when researching within this third paradigm is the integration of qualitative and quantitative data, which is usually done in the interpretation. However, the software can help when integration takes place in data analysis (Bazeley, 2006). This task is facilitated using software for qualitative data analysis, ranging from simple Excel (Niglas, 2007) to more complex integrative programs designed for qualitative analysis, known as Computer Assisted Qualitative Data Analysis (CAQDAS), for example, NVivo (http://www.qsrinternational.com; Bazeley and Richards, 2000) or MAXQDA (http://www.maxqda.co; Bazeley, 2006). For several decades, qualitative research has used Computer Assisted Qualitative Data Analysis Software (CAQDAS) to facilitate both the analysis and interpretation of results. The most common software involves the transcription of oral interviews, after which the researcher, either inductively or based on a theoretical foundation, formalizes a set of categories that allow the results obtained to be synthesized, hypotheses to be confirmed, and results to be interpreted. However, over the last few decades, programs have emerged that allow texts to be analyzed and content to be organized, grouping them by similarity in a descending hierarchical order. Two such programs, ALCESTE and IRAMUTEQ, are based on a procedure called Lexicometry. While the first is a paid program, the second is based on R and is available free of charge. The IRAMUTEQ program (Ratinaud, 2008-2025) falls within this framework of using software to facilitate data analysis and interpretation, which has already been studied and used by other authors (Silva-Alonso et al. 2024; Sousa, 2021; Souza et al., 2018). In addition to its exclusive use for qualitative methodology, the IRAMUTEQ program is particularly relevant for analyzing results in MMR, which has come to be known as the third paradigm. MMR emerged at the end of the 20th century as a pragmatic approach that integrates qualitative and quantitative methodologies. This program is very accessible and useful, but some practical issues need to be solved. On the one hand, there are some language issues to be improved. The program was developed by Ratinaud originally in the French language, and the software has other several language dictionaries available that can be updated (Camargo & Justo, 2013). So that many studies have already been conducted with IRAMUTEQ in Brazil (Portuguese language), but other languages still need to be treated. One of these is the Spanish language, which is included in the software’s dictionaries, but need to be improved by integrating explicit vocabulary of different domains. Moreover, there are other dictionaries that haven’t been included in the software, such as the one of the Basque language (Abasolo and Eguskiza, 2024). To make available qualitative and MMR in other languages, many strategies must be considered. On the other hand, in some cases databases cause some trouble related to how these should be created and treated to facilitate the corpus reading process to the software. Different practical strategies may help create the right text database for the software and for the purposes of the studies. Finally, the sample size is another aspect that can generate inconveniences when using the IRAMUTEQ program. Even when the sample size can be much smaller than in quantitative research, a minimum is always required. But sometimes small and significant samples can offer rich qualitative data. So, practical strategies to solve sample related problems would be important in the qualitative and MMR fields. Accordingly, this panel aims to discuss the use of the CAQDAS in qualitative data analysis and mixed studies, and to continue with the discussion initiated in the Panel Discussion v2-jul2024 by integrating the knowledge about IRAMUTEQ and its applicability in qualitative and mixed studies that participants have developed in the last year. Thus, the objective of this panel discussion is to facilitate practical applications about how to optimize the use of IRAMUTEQ for quantitative and qualitative use, such as when using MMR, from a general perspective. The procedure to reach the objective mentioned will be based on an introductory presentation of qualitative and mixed methods and the IRAMUTEQ software, a broader presentation of practical aspects of using IRAMUTEQ and the practical use of mixed methodology using IRAMUTEQ and its optimization, and a presentation of practical examples. Moreover, a discussion will be conducted throughout the theoretical presentation by leading an interactive session, where the exchange of opinions and experiences will be fostered.