Showing posts with label CAQDAS. Show all posts
Showing posts with label CAQDAS. Show all posts
Tuesday, 26 September 2023
Noteworthy disparities with four CAQDAS tools: explorations in organising live Twitter (now known as X) data
Written for researchers interested in extracting live X (formerly Twitter) data via Qualitative Data Analysis Software tools
QDAS tools that support live data extraction are a relatively recent innovation. At the time of our fieldwork, four prominent QDAS provided this: only ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™ had Twitter data import functionalities. Little has been written concerning the research implications of differences between their functionalities, and how such disparities might contribute to contrasting analytical opportunities. Consequently, early-stage researchers may experience difficulties in choosing an apt QDAS to extract live data for Twitter academic research.
In response to both methodological gaps, we spent almost a year working on a software comparison to address the research question (RQ) 'How do QDAS packages differ in what they offer for live Twitter data research during the organisational stage of qualitative analysis?'. Comparing their possible disparities seems worthwhile since what QDAS cannot, or poorly, support may strongly impact researchers’ microblogging data, its organisation, and scholars’ potential findings. In the preliminary phase of research, we developed a features checklist for each package, based on their online manuals, product descriptions and forum feedback related to live Twitter imports. This checklist confirmed wide-ranging disparities between QDAS, which were not unexpected since they are priced very differently- ranging from $600 for an ATLAS.ti subscription, to $3,650 for a QDAMiner (as part of the Provalis Research’s ProSuite package, which also includes WordStat 10 & Simstat).
To ensure that each week's Twitter data extractions could produce much data for potential evaluation, we focused on extracting and organising communiqués from the national electrical company, the Electricity Supply Commission (Eskom). ‘Load-shedding’ is the Pan South African Language Board’s word of the year for 2022 (PanSALB, 2022), due to it most frequent use in credible print, broadcast and online media. Invented as a euphemism by Eskom’s public-relations team, load-shedding describes electricity blackouts. Since 2007, planned rolling blackouts have been used in a rotating schedule for periods ‘where short supply threatens the integrity of the grid’ (McGregor & Nuttall, 2013). In the weeks up to, and during, the researchers’ fieldwork, Eskom, and the different stages of loadshedding strongly trended on Twitter. These tweets reflected the depth of public disapproval, discontent, anger, frustration, and general concern.
QDAS packages commonly serve as tools that researchers can use for four broad activities in the qualitative analysis process (Gilbert, Jackson, & di Gregorio, 2014). These are (a) organising- coding sets, families and hyperlinking; (b) exploring - models, maps, networks, coding and text searches; (c) reflecting - through memoing, annotating and mapping; and (d) integrating qualitative data through memoing with hyperlinks and merging projects (Davidson & di Gregorio, 2011; Di Gregorio, 2010; Lewins & Silver, 2014).
To ensure that each week's Twitter data extractions could produce much data for potential evaluation, we focused on extracting and organising communiqués from the national electrical company, the Electricity Supply Commission (Eskom). ‘Load-shedding’ is the Pan South African Language Board’s word of the year for 2022 (PanSALB, 2022), due to it most frequent use in credible print, broadcast and online media. Invented as a euphemism by Eskom’s public-relations team, load-shedding describes electricity blackouts. Since 2007, planned rolling blackouts have been used in a rotating schedule for periods ‘where short supply threatens the integrity of the grid’ (McGregor & Nuttall, 2013). In the weeks up to, and during, the researchers’ fieldwork, Eskom, and the different stages of loadshedding strongly trended on Twitter. These tweets reflected the depth of public disapproval, discontent, anger, frustration, and general concern.
QDAS packages commonly serve as tools that researchers can use for four broad activities in the qualitative analysis process (Gilbert, Jackson, & di Gregorio, 2014). These are (a) organising- coding sets, families and hyperlinking; (b) exploring - models, maps, networks, coding and text searches; (c) reflecting - through memoing, annotating and mapping; and (d) integrating qualitative data through memoing with hyperlinks and merging projects (Davidson & di Gregorio, 2011; Di Gregorio, 2010; Lewins & Silver, 2014).
Notwithstanding the contrasts in the costs for different QDAS packages, it was still surprising how much the QDAS tools varied for the first activity, (a) ‘organising data’ in our qualitative research project: 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 and thematic analysis 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.
We hope that these findings might help guide Twitter social science researchers and others in QDAS tool selection. Our research has suggested recommendations for these tools developers to follow for potentially improving the user experience for Twitter researchers. Future research might explore disparities in other qualitative research phases, or contrast data extraction routes for a variety of microblogging services. More broadly, an opportunity for a methodological contribution exists regarding research that can define a strong rationale for the software comparison method.
The authors greatly appreciate the SSRC's editor, Professor Stephen Lyon, advice on improving our final manuscript. We also thank The Noakes Foundation for its grant AFSDV02- our interdisciplinary software comparison would not have been possible without funding to cover subscriptions to the most extensive versions of MAXQDA Analytics Pro and QDA Miner. All authors are affiliated with the Cape Peninsula University of Technology (CPUT) and appreciate CPUT's provision of licensed versions of ATLAS.ti.
Please comment below if you have any questions or comments regarding our paper?
Please comment below if you have any questions or comments regarding our paper?
Labels:
academic
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analysis
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CAQDAS
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QDAS
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qualitative
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research
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social_network
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software_comparison
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Twitter
Location: Cape Town, Western Cape Province, RSA
Cape Town, South Africa
Thursday, 22 June 2023
Recommendations for QDAS developers from 'Noteworthy disparities with four CAQDAS tools- explorations in organising live Twitter data', forthcoming
Dr Corrie Uys, Dr Pat Harpur and I are working on a manuscript that explores the research implications of differences in Qualitative data analysis software (QDAS) packages’ support for live Twitter data imports. This paper's software comparison contrasts the four prominent QDAS tools that support such imports, namely ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™. We discuss key discrepancies in their use during the organisational phase of qualitative research and address related methodological issues.
Outside the paper's scope, our software comparison also uncovered several suggestions that developers of these QDAS tools might follow to improve the user experience for Twitter researchers:
Outside the paper's scope, our software comparison also uncovered several suggestions that developers of these QDAS tools might follow to improve the user experience for Twitter researchers:
1 Make tweets easier to sort & link them to their original context
QDAS typically present a myriad of isolated tweets in one spreadsheet document that seems to divorce tweets from their conversational context. Researchers would benefit from being able to order and sort tweets as data. QDAS should also provide the option to quickly link to the original tweet in Twitter. Only NVivo made it relatively efficient to see the original context of a tweet in a Twitter discussion.
2 Provide more extensive support for modes and Twitter affordances
Linking to the original context with Twitter is particularly important where audio, emoji, font, image, and video modes and Twitter affordances for hashtagging and @mentions disappear. These may not be imported into QDAS spreadsheets as QDAS tools differ widely in the data they extract for Twitter affordances and modes.
3 Support conversational analysis
Research into Twitter conversations was poorly supported by all four QDAS tools. Each presented a myriad of isolated tweets, with no way to display the original conversational thread. QDAS and Twitter should work together for providing qualitative researchers with ready access to Twitter exchanges. The added benefits of API2 functionality (such as conversation tracking) seem MIA in QDAS. Such integration would seem a useful step for promoting wider research into healthy conversations that Twitter described in 2018 as an important business priority.
4 Provide examples for live Twitter data analysis
QDAS companies that provide Twitter import functionality should provide resources that address not only how to extract data, but also examples of how their software is used in analysing microblogging data. While Twitter is actively encouraging and training academic researchers to transform raw JSON into CSV files for research purposes, QDAS companies seem to provide scant examples for live Twitter data analysis. The online resources they provide could be improved by adding examples. For example, we look forward to seeing how QDAS are used in analysing Twitter conversation threads.
5 Spotlight the black box of Twitter data organisation
QDAS developers could make the ‘black box’ of Twitter data organisation visible by showing a model of the data undergirding the tweets, and also the spreadsheet's data excludes. Researchers could benefit from such an overview for the great deal of Twitter fields that are missing.
6 Missing in extraction
Another black box concerns the process of data extraction from Twitter. While the functionality of running live imports for select criteria is efficient, more information could be shared regarding the context of the extraction. For example, what are the internal and external limits on the maximum number of tweets a QDAS can import.
Do let us know what you think of these suggestions by submitting a comment below, or contacting me.
Do let us know what you think of these suggestions by submitting a comment below, or contacting me.
Labels:
Atlas.ti
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CAQDAS
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development
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MAXQDA
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nvivo
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QDAMiner
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QDAS
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recommendations
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research
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software
Location: Cape Town, Western Cape Province, RSA
Cape Town, South Africa
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