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Data analysis of career guidance interviews in EU countries involved in the GUIDE project using webQDA software

As part of the task for Identification of the Career Decision-Making Styles, the countries participating in the EU project GUIDE, namely, Cyprus, Denmark, Germany, Greece, Netherlands, Portugal, Slovenia, and Spain, conducted several interviews with career guidance specialists who were actively involved in career counseling activities and decisions with young professionals, for exploring their experience in observing and dealing with gender stereotypes in career choices. One of the main goals was to explore persistent common stereotypes in decision-making in career choices. The interview was composed of several parts such as general, influence, and stereotyping along with questions for collecting some personal information about the interviewees (e.g. age, work experience in Career Guidance/Counselling, etc.).

The webQDA software was used to elicit information from the data obtained through the interviews. WebQDA is a content analysis tool that allows data to be coded, enabling more agile and effective management through the classification of information and interpretation of results. Accordingly, the procedure of analyzing the data was carried out, referring to the texts of the interviews that had been coded and were subject to qualitative analysis, with the aim of identifying the arguments and the connections between them in order to establish a code for each recognized excerpt (Santos da Cruz et al., 2022).

The word cloud for the top 50 terms mentioned by interviewees is displayed in Figure 1.

Figure 1. Word cloud for the 50 most used words by the respondents.

Source: (Amorim et al., 2023)

 

The Figure reveals that the word “young” was the most frequently used, appearing 227 times. “Career” was mentioned 224 times, placing it in the second position. The other frequently mentioned words were “professions” (141 times), “parents” (108 times), “influence” (97 times), “students” (91 times), “choose” (90 times), and “gender” (83 times). This analysis helps to identify the most prevalent words in text data, revealing themes and patterns.

 

 

References
Amorim, M., Bastos, L., Saghezchi, F., Rodrigues, M., Dias, M. F., Silva, C., & Madureira, R. C. (2023). UNDERSTANDING GENDER BIAS IN CAREER DEVELOPMENT: A CASE STUDY IN EUROPEAN COUNTRIES. In ICERI2023 Proceedings (pp. 8734-8741). IATED.
Santos da CRUZ, D., Moreira da ROCHA-VEIGA, A., & Dias CAETANO, L. M. (2022). INTERLOCUTIONS BETWEEN QUALITATIVE CONTENT ANALYSIS AND WEBQDA DATA SOFTWARE IN EDUCATIONAL RESEARCH. Revista Ibero-Americana de Estudos em Educação, 17(4).