In Case of Doubt See the Manual: A Comparative Analysis of (Self)Learning Packages Qualitative Research Software

Freitas, Fábio; Ribeiro, Jaime; Brandão, Catarina; Souza, Francislê Neri de; Costa, António Pedro; Reis, Luís Paulo

doi:10.1007/978-3-319-61121-1_16

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…It was based on the QDAS tools’ respective online user manuals. These are the main communication method with end-users (Freitas et al, 2018) and the functions the manuals described provided the initial source for comparison. Since this checklist confirmed marked contrasts, the researchers tested organisation of Twitter data via each QDAS.…”

Section: Methodsmentioning

confidence: 99%

Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising Live Twitter Data

Noakes,

Harpur,

Uys

2023

Social Science Computer Review

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Qualitative data analysis software (QDAS) packages that support live data extraction are a relatively recent innovation. Little has been written concerning the research implications of differences in such QDAS packages’ functionalities, and how such disparities might contribute to contrasting analytical opportunities. Consequently, early-stage researchers may experience difficulties in choosing an apt QDAS for Twitter analysis. In response to both methodological gaps, this paper presents a software comparison across the four QDAS tools that support live Twitter data imports, namely, ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™. The authors’ QDAS features checklist for these tools spotlights many differences in their functionalities. These disparities were tested through data imports and thematic coding that was derived from the same queries and codebook. The authors’ resultant QDAS experiences were compared during the first activity of a broad qualitative analysis process, ‘organising data’. Notwithstanding large difference in QDAS pricing, it was surprising how much the tools varied for aspects of qualitative research organisation. Notably, the quantum of data extracted for the same query differed, largely due to contrasts in the types and amount of data that the four QDAS could extract. Variations in how each supported visual organisation also shaped researchers’ opportunities for becoming familiar with Twitter users and their tweet content. Such disparities suggest that choosing a suitable QDAS for organising live Twitter data must dovetail with a researcher’s focus: ATLAS.ti accommodates scholars focused on wrangling unstructured data for personal meaning-making, while MAXQDA suits the mixed-methods researcher. QDA Miner’s easy-to-learn user interface suits a highly efficient implementation of methods, whilst NVivo supports relatively rapid analysis of tweet content. Such findings may help guide Twitter social science researchers and others in QDAS tool selection. Future research can explore disparities in other qualitative research phases, or contrast data extraction routes for a variety of microblogging services.

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Section: Methodsmentioning

confidence: 99%

Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising Live Twitter Data

Noakes,

Harpur,

Uys

2023

Social Science Computer Review

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“…Researchers commonly resort to computer-assisted QDA software (CAQDAS) to streamline the process of organizing, coding, and analyzing data, making it more efficient and accessible. MAXQDA, 1 Atlas.ti, 2 and NVivo 3 are popular examples among social scientists (Freitas et al, 2017). These tools help researchers manage large volumes of unstructured data, assign codes, and identify patterns and themes, thus improving the rigor and consistency of qualitative research.…”

Section: Background and Related Workmentioning

confidence: 99%

Bridging Qualitative Data Silos: The Potential of Reusing Codings Through Machine Learning Based Cross-Study Code Linking

Wildemann,

Niederée,

Elejalde

2023

Social Science Computer Review

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For qualitative data analysis (QDA), researchers assign codes to text segments to arrange the information into topics or concepts. These annotations facilitate information retrieval and the identification of emerging patterns in unstructured data. However, this metadata is typically not published or reused after the research. Subsequent studies with similar research questions require a new definition of codes and do not benefit from other analysts’ experience. Machine learning (ML) based classification seeded with such data remains a challenging task due to the ambiguity of code definitions and the inherent subjectivity of the exercise. Previous attempts to support QDA using ML rely on linear models and only examined individual datasets that were either smaller or coded specifically for this purpose. However, we show that modern approaches effectively capture at least part of the codes’ semantics and may generalize to multiple studies. We analyze the performance of multiple classifiers across three large real-world datasets. Furthermore, we propose an ML-based approach to identify semantic relations of codes in different studies to show thematic faceting, enhance retrieval of related content, or bootstrap the coding process. These are encouraging results that suggest how analysts might benefit from prior interpretation efforts, potentially yielding new insights into qualitative data.

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“…Code development and application takes hours of concentrated work, which is hard to perform reliably at scale [10], even for moderately sized datasets. With access to larger datasets and advances in computer-supported analysis, the adoption of qualitative data analysis systems (QDAS) has increased substantially [14,16].…”

Section: Related Work 21 Coding In Qualitative Data Analysismentioning

confidence: 99%

“…A summative evaluation of an intervention or artifact is concerned with its impact on the efectiveness and resulting outcomes [38]. As such, we evaluated Cody's efectiveness compared to MAXQDA, one of the most well-known QDAS [16]. For the summative evaluation, we used the second version of Cody (see Figures 2 and 5a).…”

Section: Summativementioning

confidence: 99%

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Cody: An AI-Based System to Semi-Automate Coding for Qualitative Research

Rietz

Maedche

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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In Case of Doubt See the Manual: A Comparative Analysis of (Self)Learning Packages Qualitative Research Software

Cited by 10 publications

References 13 publications

Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising Live Twitter Data

Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising Live Twitter Data

Bridging Qualitative Data Silos: The Potential of Reusing Codings Through Machine Learning Based Cross-Study Code Linking

Cody: An AI-Based System to Semi-Automate Coding for Qualitative Research

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