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The research software listed below is used / taught in different programmes at FASoS. Please check the relevant methods sections in this wiki to find out which software is recommended for different approaches to data / sources.
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The research software listed below is used / taught in different programmes at FASoS. Please check the relevant methods sections in this wiki to find out which software is recommended for different approaches to data / sources.
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<details>
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<details>
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<summary>
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<summary>
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### Atlas.ti (qualitative research)
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### Atlas.ti (qualitative research)
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</summary>
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</summary>
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[Atlas.ti](https://atlasti.com/) is a tool for qualitative research, e.g., coding interview data, for which Maastricht University provides a license key to students and staff. The software is available through the university's software store, where students can select a specialised student version. This version of _Atlas.ti_ features a dashboard that facilitates the easy creation of various projects. The top bar offers functions for adding new documents and accommodates multimedia files with a wide range of file extensions. _Atlas.ti_ allows the interactive categorisation and tagging of your data and includes visualisation options (e.g network diagrams and word clouds). However, you should only use the software if you are thoroughly familiar with its functionality and can interpret visualisations correctly. More traditional and equally effective ways of coding your data are pen and paper or spreadsheets (such as EXCEL).
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[Atlas.ti](https://atlasti.com/) is a tool for qualitative research, e.g., coding interview data, for which Maastricht University provides a license key to students and staff. The software is available through the university's software store, where students can select a specialised student version. This version of _Atlas.ti_ features a dashboard that facilitates the easy creation of various projects. The top bar offers functions for adding new documents and accommodates multimedia files with a wide range of file extensions. _Atlas.ti_ allows the interactive categorisation and tagging of your data and includes visualisation options (e.g network diagrams and word clouds). However, you should only use the software if you are thoroughly familiar with its functionality and can interpret visualisations correctly. More traditional and equally effective ways of coding your data are pen and paper or spreadsheets (such as EXCEL).
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In the BA DS programme, _Atlas.ti_ was demonstrated in the _Qualitative Research Methods_ course. In the lecture by Thom Frissen, the sample dataset consisted of nine cooking recipes, which were used to explain the process of [closed coding](https://uh-dcm.github.io/qualitative-research-and-computers/closed-coding/). Closed coding, in qualitative research, means assigning predefined codes to the research material. The term "coding" in this context refers to a deliberate process of data reduction based on a specific theoretical framework. Codes are developed within the _Atlas.ti_ "[code manager](https://doc.atlasti.com/ManualWin.v9/Managers/ManagerForCodes.html)" and then incorporated into a codebook. All codes are presented in a dropdown menu, organised alphabetically by default. Within the _Atlas.ti_ software, users can open a document and manually highlight individual lines of text or even specific words to apply their codes. The software also offers automated "[search and code](https://doc.atlasti.com/ManualWin.v9/SearchAndCode/SearchAndCode.html)" functionalities. It should be noted, however, that cooking recipes, which are heavy in nouns and short, repetitive instructions, are not necessarily representative of the texts that humanities and social sciences scholars normally use for research. Therefore, the analytical process applied to this material needs to be modified when working with interviews or social media data. For instance, _Atlas.ti_ permits the grouping of interview transcripts with field observation notes.
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In the BA DS programme, _Atlas.ti_ was demonstrated in the _Qualitative Research Methods_ course. In the lecture by Thom Frissen, the sample dataset consisted of nine cooking recipes, which were used to explain the process of [closed coding](https://uh-dcm.github.io/qualitative-research-and-computers/closed-coding/). Closed coding, in qualitative research, means assigning predefined codes to the research material. The term "coding" in this context refers to a deliberate process of data reduction based on a specific theoretical framework. Codes are developed within the _Atlas.ti_ "[code manager](https://doc.atlasti.com/ManualWin.v9/Managers/ManagerForCodes.html)" and then incorporated into a codebook. All codes are presented in a dropdown menu, organised alphabetically by default. Within the _Atlas.ti_ software, users can open a document and manually highlight individual lines of text or even specific words to apply their codes. The software also offers automated "[search and code](https://doc.atlasti.com/ManualWin.v9/SearchAndCode/SearchAndCode.html)" functionalities. It should be noted, however, that cooking recipes, which are heavy in nouns and short, repetitive instructions, are not necessarily representative of the texts that humanities and social sciences scholars normally use for research. Therefore, the analytical process applied to this material needs to be modified when working with interviews or social media data. For instance, _Atlas.ti_ permits the grouping of interview transcripts with field observation notes.
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When it comes to saving research results, _Atlas.ti_ provides various export options, including PDFs with annotations or spreadsheet versions of the codebook. Users can include visualisations, but it is imperative to always retain the coded documents, a list of all codes, and the associated quotations for documentation.
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When it comes to saving research results, _Atlas.ti_ provides various export options, including PDFs with annotations or spreadsheet versions of the codebook. Users can include visualisations, but it is imperative to always retain the coded documents, a list of all codes, and the associated quotations for documentation.
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Atlas.ti maintains [an official YouTube channel](https://www.youtube.com/channel/UCYR-VG5Ar7-Idr0W1WWy6Yw) with user tutorials. We highly recommend watching these videos before working with the software.
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Atlas.ti maintains [an official YouTube channel](https://www.youtube.com/channel/UCYR-VG5Ar7-Idr0W1WWy6Yw) with user tutorials. We highly recommend watching these videos before working with the software.
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\[_Summary by Monika Barget_\]
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\[_Summary by Monika Barget_\]
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</details>
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</details>
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<details>
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<summary>
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<details>
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<summary>
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### Qualtrics (surveys)
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### DSRI (Data Science Research Infrastructure)
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</summary>
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</summary>
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[Qualtrics](https://library.maastrichtuniversity.nl/apps-tools/qualtrics/) is a digital survey tool that students and staff can use for free at Maastricht University. Please contact your faculty's ICT service and briefly mention your research purpose to receive access. _Qualtrics_ permits you to create surveys from templates and to disseminate them online. The question types you may select range from free text responses to dropdown menus and scales. When it comes to analysing the participants' responses, you can either export the data and explore them with Python, R or other ready-made software, or you can use the many data filtering and data visualisation options within _Qualtrics_:
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The Data Science Research Infrastructure (DSRI) is available for thesis students at Maastricht University to collect, process, and analyse data. Many students use it to scrape web data (e.g., YouTube comments) for qualitative and computational research projects. If you are using a Git repository or Docker image that includes a course name (e.g. "Machines of Knowledge") although you have never taken the course yourself, this is not an issue. It only indicates the material was originally developed for that course. However, you always have to use the most recent repository version. For example, if you are working in 2024, use the repository labeled with "2024" because earlier versions (e.g., "2023") may lack key updates. If you encounter technical issues, contact the DSRI Support Team directly. They have backend access and can troubleshoot issues much faster than your supervisor or course instructor:
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1) _Qualtrics_ permits you to [filter results](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/filtering-results/) based on your respondents' age, nationality, education or whatever personal attributes you have asked them to provide. You can also filter data by time if your survey covered a longer period.
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2) By default, _Qualtrics_ shows you the results of your survey in table format and as [bar charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/bar-chart/). However, bar charts are not always the most useful representation of data. The visualisation of results can, therefore, be changed. You can, for example, also create [pie charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/pie-chart/), [line charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/line-chart/), and [gauge charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/gauge-chart/), or change the colours and labels.
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📧 DSRI-SUPPORT-L@maastrichtuniversity.nl
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3) All charts can be exported individually, but you can also export complete reports. The "[default report](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/reports-overview/)" contains all the responses. In addition, you can also create selected reports based on your filters.
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\[_Summary by Monika Barget_\]
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The following **video** explains the process step-by-step:
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</details>
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[Qualtrics Edit Reports with Breakouts and Filters](https://www.youtube.com/watch?v=Igq2y0pVHCc)
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That said, it is best to avoid copying and pasting tables / graphs from Qualtrics into your research paper or thesis without explaining and contextualising what we see in your own words. All charts included in academic papers also need proper captions. It is vital to use tables and graphs in line with the APA7 formatting conventions. R and R-Studio, to which BA DS students have been introduced in the _Qualitative Data Analysis_ course, can also help you analyse and visualise data collected with _Qualtrics_, especially when the options provided by _Qualtrics_ are too limited.
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<details>
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<summary>
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\[_Summary by Monika Barget_\]
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### Qualtrics (surveys)
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</details>
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<details>
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</summary>
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<summary>
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[Qualtrics](https://library.maastrichtuniversity.nl/apps-tools/qualtrics/) is a digital survey tool that students and staff can use for free at Maastricht University. Please contact your faculty's ICT service and briefly mention your research purpose to receive access. _Qualtrics_ permits you to create surveys from templates and to disseminate them online. The question types you may select range from free text responses to dropdown menus and scales. When it comes to analysing the participants' responses, you can either export the data and explore them with Python, R or other ready-made software, or you can use the many data filtering and data visualisation options within _Qualtrics_:
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### Voyant Tools (distant reading)
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1) _Qualtrics_ permits you to [filter results](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/filtering-results/) based on your respondents' age, nationality, education or whatever personal attributes you have asked them to provide. You can also filter data by time if your survey covered a longer period.
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</summary>
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2) By default, _Qualtrics_ shows you the results of your survey in table format and as [bar charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/bar-chart/). However, bar charts are not always the most useful representation of data. The visualisation of results can, therefore, be changed. You can, for example, also create [pie charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/pie-chart/), [line charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/line-chart/), and [gauge charts](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/visualizations/charts/gauge-chart/), or change the colours and labels.
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3) All charts can be exported individually, but you can also export complete reports. The "[default report](https://www.qualtrics.com/support/survey-platform/reports-module/results-section/reports-overview/)" contains all the responses. In addition, you can also create selected reports based on your filters.
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[Voyant Tools](https://voyant-tools.org/) is a collection of analytical tools for computational text analysis (also known as _distant reading_). Distant reading helps you get high-level insights into large text corpora but can also encourage you to understand a small collection of texts in a new way. The focus is on finding patterns: Which words are most frequent? Which words co-occur throughout the text? How does the vocabulary used change in the course of a narrative?
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The following **video** explains the process step-by-step:
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Monika Barget created the following slides for teaching essential text analysis / distant reading with Voyant Tools to MA students in the humanities and social sciences:
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[Qualtrics Edit Reports with Breakouts and Filters](https://www.youtube.com/watch?v=Igq2y0pVHCc)
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[Introduction to text analysis with Voyant Tools](https://zenodo.org/record/7348456#.Y4X0SX3MJPZ)
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That said, it is best to avoid copying and pasting tables / graphs from Qualtrics into your research paper or thesis without explaining and contextualising what we see in your own words. All charts included in academic papers also need proper captions. It is vital to use tables and graphs in line with the APA7 formatting conventions. R and R-Studio, to which BA DS students have been introduced in the _Qualitative Data Analysis_ course, can also help you analyse and visualise data collected with _Qualtrics_, especially when the options provided by _Qualtrics_ are too limited.
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The focus is on analysing social media data (e.g. YouTube comments and tweets) and the students' ability to interpret frequently used Natural Language Processing (NLP) visualisations.
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\[_Summary by Monika Barget_\]
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\[_Summary by Monika Barget_\]
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</details>
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</details>
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<details>
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<details>
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<summary>
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<summary>
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### Voyant Tools (distant reading)
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### R and Rstudio (Programming)
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</summary>
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</summary>
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[Voyant Tools](https://voyant-tools.org/) is a collection of analytical tools for computational text analysis (also known as _distant reading_). Distant reading helps you get high-level insights into large text corpora but can also encourage you to understand a small collection of texts in a new way. The focus is on finding patterns: Which words are most frequent? Which words co-occur throughout the text? How does the vocabulary used change in the course of a narrative?
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The programming language [R](https://www.r-project.org/) and its integrated development environment (IDE) [RStudio](https://posit.co/products/open-source/rstudio/) are used for quantitative data analysis, automated text analysis, data visualisations, data science pipelining, machine learning, web development (RShiny) and writing reports (RMarkdown). R is a lightweight, open-source and very powerful programming language.
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Monika Barget created the following slides for teaching essential text analysis / distant reading with Voyant Tools to MA students in the humanities and social sciences:
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\[_Summary by Thomas Frissen_\]
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[Introduction to text analysis with Voyant Tools](https://zenodo.org/record/7348456#.Y4X0SX3MJPZ)
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</details> |
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The focus is on analysing social media data (e.g. YouTube comments and tweets) and the students' ability to interpret frequently used Natural Language Processing (NLP) visualisations.
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\[_Summary by Monika Barget_\]
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</details>
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<details>
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<summary>
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### R and Rstudio (Programming)
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</summary>
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The programming language [R](https://www.r-project.org/) and its integrated development environment (IDE) [RStudio](https://posit.co/products/open-source/rstudio/) are used for quantitative data analysis, automated text analysis, data visualisations, data science pipelining, machine learning, web development (RShiny) and writing reports (RMarkdown). R is a lightweight, open-source and very powerful programming language.
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\[_Summary by Thomas Frissen_\]
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</details> |
