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This article offers a step-by-step description of how qualitative data analysis software can be used for a qualitative content analysis of newspaper articles. Using NVivo as an example, it illustrates how software tools can facilitate analytical flexibility and how they can enhance transparency and trustworthiness of the qualitative research process. Following a brief discussion of the key characteristics, advantages and limitations of qualitative data analysis software, the article describes a qualitative content analysis of 230 newspaper articles, conducted to determine international media perceptions of New Zealand's environmental performance in connection with climate change and carbon emissions. The article proposes a multi-level coding approach during the analysis of news texts that combines quantitative and qualitative elements, allowing the researcher to move back and forth in coding and between analytical levels. The article concludes that while qualitative data analysis software, such as NVivo, will not do the analysis for the researcher, it can make the analytical process more flexible, transparent and ultimately more trustworthy.URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs150283

The analysis of data represents the most important and difficult step in the qualitative research process. Thus, recently, a few authors have written methodological works that contain discussion of an array of qualitative data analysis approaches. Yet, despite the call of Leech and Onwuegbuzie (2007) a decade ago for qualitative researchers to analyze a given set of qualitative data in multiple ways, this practice has been largely ignored. Thus, in this article, we bolster the argument for conducting multiple data analyses. In particular, we use data stemming from an interview to demonstrate how using five qualitative data analysis approaches (e.g., constant comparison analysis, discourse analysis) helped to enhance what we refer to as analysis saturation, thereby increasing verstehen (i.e., understanding).

This paper describes recent issues and developments in Qualitative Data Analysis Software (QDAS) as presented in the opening plenary at the KWALON 2016 conference. From a user perspective, it reflects current features and functionality, including the use of artificial intelligence and machine learning; implications of the cloud; user friendliness; the role of digital archives; and the development of a common exchange format. This user perspective is complemented with the views of software developers who took part in the “Rotterdam Exchange Format Initiative,” an outcome of the conference.

The potential use of artificial intelligence programs such as a ChatGPT to analyze qualitative data raises any number of questions, most notably whether it is possible to produce similar results without the demanding process of manual coding. In addition, there are questions about both the simplicity of using ChatGPT for qualitative data analysis and the potential time savings that it might provide This article addresses these questions by using ChatGPT to reinvestigate two qualitative datasets that were previously analyzed by more traditional methods. In particular, it examines the extent to which the responses from ChatGPT can recreate the themes that were originally chosen to summarize the two previous analyses. The results show that ChatGPT performed reasonably well, but in both cases it was less successful at locating subtle, interpretive themes, and more successful at reproducing concrete, descriptive themes. In doing so, the program was quite easy to use and required very little effort in comparison to approaches that rely on manual coding. It is important to recognize, however, that both coding and approaches based on artificial intelligence are simply tools that must be applied within a larger analytic process. Overall, this exploration suggests that artificial intelligence may well have the power to disrupt the coding of data segments as a dominant paradigm for qualitative data analysis.

Purpose Qualitative research has gained in importance in the social sciences. General knowledge about qualitative data analysis, how to code qualitative data and decisions concerning related research design in the analytical process are all important for novice researchers. The purpose of this paper is to offer researchers who are new to qualitative research a thorough yet practical introduction to the vocabulary and craft of coding. Design/methodology/approach Having pooled, their experience in coding qualitative material and teaching students how to code, in this paper, the authors synthesize the extensive literature on coding in the form of a hands-on review. Findings The aim of this paper is to provide a thorough yet practical presentation of the vocabulary and craft of coding. The authors, thus, discuss the central choices that have to be made before, during and after coding, providing support for novices in practicing careful and enlightening coding work, and joining in the debate on practices and quality in qualitative research. Originality/value While much material on coding exists, it tends to be either too comprehensive or too superficial to be practically useful for the novice researcher. This paper, thus, focusses on the central decisions that need to be made when engaging in qualitative data coding in order to help researchers new to qualitative research engage in thorough coding in order to enhance the quality of their analyses and findings, as well as improve quantitative researchers’ understanding of qualitative coding.

As qualitative research becomes increasingly recognized and valued, it is imperative that it is conducted in a rigorous and methodical manner to yield meaningful and useful results. To be accepted as trustworthy, qualitative researchers must demonstrate that data analysis has been conducted in a precise, consistent, and exhaustive manner through recording, systematizing, and disclosing the methods of analysis with enough detail to enable the reader to determine whether the process is credible. Although there are numerous examples of how to conduct qualitative research, few sophisticated tools are available to researchers for conducting a rigorous and relevant thematic analysis. The purpose of this article is to guide researchers using thematic analysis as a research method. We offer personal insights and practical examples, while exploring issues of rigor and trustworthiness. The process of conducting a thematic analysis is illustrated through the presentation of an auditable decision trail, guiding interpreting and representing textual data. We detail our step-by-step approach to exploring the effectiveness of strategic clinical networks in Alberta, Canada, in our mixed methods case study. This article contributes a purposeful approach to thematic analysis in order to systematize and increase the traceability and verification of the analysis.

Objective. In the course of daily teaching responsibilities, pharmacy educators collect rich data that can provide valuable insight into student learning. This article describes the qualitative data analysis method of content analysis, which can be useful to pharmacy educators because of its application in the investigation of a wide variety of data sources, including textual, visual, and audio files. Findings. Both manifest and latent content analysis approaches are described, with several examples used to illustrate the processes. This article also offers insights into the variety of relevant terms and visualizations found in the content analysis literature. Finally, common threats to the reliability and validity of content analysis are discussed, along with suitable strategies to mitigate these risks during analysis. Summary. This review of content analysis as a qualitative data analysis method will provide clarity and actionable instruction for both novice and experienced pharmacy education researchers.

In this essay, we discuss how tables can be used to ensure—and reassure about—trustworthiness in qualitative research. We posit that in qualitative research, tables help not only increase transparency about data collection, analysis, and findings, but also—and no less importantly—organize and analyze data effectively. We present some of the tables most frequently used by qualitative researchers, explain their uses, discuss how they enhance trustworthiness, and provide illustrative examples to inspire readers in their use of tables in their own research.

This article is part of a series on African primary care research and gives practical guidance on qualitative data analysis and the presentation of qualitative findings. After an overview of qualitative methods and analytical approaches, the article focuses particularly on content analysis, using the framework method as an example. The steps of familiarisation, creating a thematic index, indexing, charting, interpretation and confirmation are described. Key concepts with regard to establishing the quality and trustworthiness of data analysis are described. Finally, an approach to the presentation of qualitative findings is given.

In der ersten Märzwoche 2016 trafen sich ca. 120 Forschende aus 12 Ländern (u.a. Japan, Syrien, den USA und der Türkei) in Berlin, um sich über Nutzungsmöglichkeiten qualitativer Software zu informieren. Die 18. Conference on Computer-Assisted Qualitative Data Analysis (CAQD) erlaubte im Rahmen verschiedener Workshops sowie mittels einer Poster-Session und von Nutzer/innenforen, Best-Practice-Beispiele (auch informell) zu diskutieren. Hinzu kamen Keynotes von John CRESWELL bzw. Udo KUCKARTZ und Stefan RÄDIKER, die über Potenziale des Softwareeinsatzes im Themenfeld Mixed Methods informierten.URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs170146