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What Is QDA?

Qualitative Data Analysis (QDA) helps you synthesize and structure information (like a scientific theory) from unstructured data such as interviews (interview analysis). Through qualitative coding (or labeling) of data, theoretical constructs are represented as codes in a code sytem which is hierarchically structured. Not only interviews can be coded though. You can also use QDA for your literature analysis or systematic literature review (SLR), where you upload the papers you want to include in your review to QDAcity and code them. All data that you can put into text form or a PDF can be coded in QDAcity. Rigorous application of appropriate QDA methods and practices are integral for valid and reliable results of your study.

Methods and Processes

There are many methodological frameworks which you can use and, if you comply with them cite in your analysis (paper, report, thesis). They vary significantly in complexity and frequently govern not only the analysis but also sampling and data gathering. Chose one (light-weight or opinionated) that fits your purpose/research question. Some common examples are:
Corbin and Strauss (2014)
Constructivist epistemological perspective:Charmaz (2014)

Best Practices

When conducting qualitative research which involves the qualitative Coding of data using qualitative data analysis (QDA), following tried and true best practices is important to ensure validity of your findings. It is also important to use the right terminology when communicating your findings. We recommend going through the following list of methods and practices, being aware of them, and when writing about your findings explicitly mention which ones you followed. In a survey we did with reviewers of top-tier research journals we found that the expectations of reviewers to follow these practices were often significantly higher than was reported on in published literature.
Member checks require the researcher to mirror the research results to participants of the study to ensure that the results reflect the understanding and experience of the participants.
Using peer debriefing researchers share their findings, interpretations, and experiences with a group of colleagues or experts in order to gain critical feedback and enhance the rigor and Credibility of their work. It involves open discussions, reflection, and constructive criticism aimed at refining the research process and ensuring the validity and reliability of the study's findings. (Spall, 1998).
Attention to negative cases establishes structural corroboration or coherence during the analysis by systematically disconfirming evidence. Researchers actively search for contradicting patterns and explanations. Deviant case analysis and polar sampling support aspects of this practice.
Several investigators involve in the analysis process and it is expected that they arrive at the same conclusion Guion et al. (2011)
Guion et al. (2011) defines data triangulation as: "using different sources of information in order to increase the validity of a study".
Guion et al. (2011) define the theory triangulation as: "the use of multiple perspectives to interpret a single set of data."
Guion et al. (2011) define the environmental triangulation as: "the use of different locations, settings, and other key factors related to the environment in which the study took place, such as the time, day, or season."
Guion et al. (2011) define methodological triangulation as: "the use of multiple qualitative and/or quantitative methods to study the program."
Establishing referential adequacy requires the gathering of additional data after fieldwork has been concluded in order to compare it with study results Guba and Lincoln (1985).
Thick description in qualitative research refers to a detailed and contextually rich account of a social or cultural phenomenon, capturing not only the surface-level actions and behaviors but also the underlying meanings, symbols, and social dynamics that shape them. It emphasizes the researcher's immersion in the research setting, enabling a deeper understanding of the complexities and nuances inherent in the subject of study.
Ruby (1980) defines that reflexity requires the researcher to "systematically and rigorously reveal their methodology and themselves as the instrument of data generation"
An audit trail is material generated during the research process (Rodgers and Cowles, 1993).
Two or more researchers Coding the same data. Discrepancies are discussed and resolved to arrive at a shared interpretation of all codes.
The state at which the further gathering of data no longer yields novel insight on the phenomenon
A document of codes with definitions of concepts and categories, as well as instructions on when to employ a particular code, and when not to. (MacQueen et al., 1998)
Prolonged engagement means spending enough time with the phenomenon being studied to reflect on potential biases and to allow participants to adjust to the situation being studied (Creswell and Poth, 2012)


We provide step-by-step guides on different stages of the research and qualitative coding process and how to implement some of the practices mentioned above.
Look through the overview of How-Tos here and click the button below a description to read the full guide.

This section will be extended with additional content soon.

Transcription of an interview

After recording an interview you will want to transcribe it in order to code the data with QDAcity. We help you with an automated transcription. However you will have to check and correct it where necessary. QDAcity can help you with that, too. This guide shows you how.
β†’Interview Transcription HowTo

Teaching using QDAcity

QDAcity supports teaching of methods competencies for qualitative coding and interview analysis to a large number of students. In this guide we show how to set up a course and which types of exercises for qualitative data analysis (QDA) are currently supported.
β†’Teaching HowTo

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