Can data analysis using software provide rigor?
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Can data analysis using software provide rigor?

Figure 2 – Coding involving multiple researchers in webQDA

In this example, we designate as internal validation the process involving more than one element. If it is an individual project, the user may resort to external validation, using, for example, one of the validation techniques mentioned above.

Any user can enter comments (figure 3). This feature allows the project manager or the team to receive expert feedback on the data available in the Source System. Comments can be listed by source or by user.

Figure 3 – Inserting comments – webQDA

The logbook (figure 4) presents the entries of the different users in chronological order. Entries can also be viewed by date range or by keyword. For example, one may only want to access entries that talk about encoding.

Figure 4 – Logbook – webQDA

Considering the advances in research and technology, through the discussion of current challenges and needs for the promotion of data analysis in qualitative research in a broader and more flexible way, the use of software when done rigorously and systematically, enhances the deepening analysis of the data as well as the quality of the results obtained. The proper use of a CAQDAS, through its different functionalities, allows giving rigor to the analysis of qualitative data. However, this capability does not replace the responsibilities of the researcher/user.


Bardin, L. (2014) Análise de Conteúdo. 3a. Edições 70.

Baugh, J., Hallcom, A. S. and Harris, M. E. (2010) ‘Computer Assisted Qualitative Data Analysis Software: A Practical Perspective for Applied Research’, Revista Del Instituto Internacional de Costos, (enero/juni(6)).

Booth, A., Hannes, K., Harden, A., Noyes, J., Harris, J., & Tong, A. (2014). COREQ (Consolidated Criteria for Reporting Qualitative Studies). Guidelines for Reporting Health Research: A User’s Manual, 214–226.

Costa, A. P. (2020). Trabalho Colaborativo: codificação em tempo real. Blog webQDA.

Costa, A. P., Moreira, A., & Souza, F. N. de. (2019). webQDA – Qualitative Data Analysis (3.1). Aveiro University and MicroIO.

Costa, A. P., & Minayo, M. C. de S. (2019). Building criteria to evaluate qualitative research papers: a tool for peer reviewers. Revista Da Escola de Enfermagem Da USP, 53, 1–7.×2018041403448

Costa, E. P. da, & Costa, A. P. (2017). O trabalho colaborativo apoiado pelas tecnologias: o exemplo da investigação qualitativa. Educação a Distância e Práticas Educativas Comunicacionais e Interculturais, 17(2), 61–69.

Costa, A. P. (2016). Cloud Computing em Investigação Qualitativa: Investigação Colaborativa através do software webQDA. Fronteiras: Journal of Social, Technological and Environmental Science, 5(2), 153–161.

Costa, A. P., Souza, F. N. de, Reis, L. P., & Freitas, F. M. (2016). Funcionalidades para a Promoção do Trabalho Colaborativo em Investigação Qualitativa: O caso software webQDA. In Á. Rocha, L. P. Reis, M. P. Cota, O. S. Suárez, & R. Gonçalves (Eds.), Actas de la 11 a Conferencia Ibérica de Sistemas y Tecnologías de Información (pp. 935–940). AISTI – Associação Ibérica de Sistemas e Tecnologias de Informação.

Creswell, J. W., & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry. Theory Into Practice, 39(3).

Gibbs, G. R., Friese, S., & Mangabeira, W. C. (2002). The Use of New Technology in Qualitative Research. Introduction to Issue 3(2) of FQS. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Vol 3, No 2 (2002): Using Technology in the Qualitative Research Process, 3(2).

Lessard-Hébert, M., Boutin, G., & Goyette, G. (1990). Investigação Qualitativa Fundamentos e práticas (I. Piaget (ed.)).

Tong, A., Flemming, K., McInnes, E., Oliver, S., & Craig, J. (2012). Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Medical Research Methodology, 12(1), 181.

Vala, J. (1989). Identités sociales et représentations du pouvoir. Revue Internationale de Psychologie Sociale, 3, 451–470.

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General tips for encoding Qualitative Data

One of the main errors verified in research is the lack of planning of adequate methods for data analysis. For example, to develop a data collection instrument, it is necessary to pay attention to the tools used to obtain results (analysis). Analysing qualitative data is not a task without difficulties, as the non-numeric and unstructured data corpus is generally diffuse and complex. There are no clear and widely accepted rules on how to analyse non-numeric and unstructured data.
The seven essential steps or subtasks that we will describe below are transversal or generic to qualitative data analysis techniques. The technique’s focus on the analysis rests on specific choices according to each objective and research questions.
For very practical purposes, it can be said that qualitative research of scientific nature has three stages: (1) an exploratory phase; (2) fieldwork; (3) analysis and treatment of the empirical and documentary material. The exploratory phase consists of the production of the research project and all the procedures necessary for preparation to enter the field. The fieldwork phase constitutes the primordial moment for understanding, in intersubjectivity, the empirical reality under study.