This page explores the process of qualitative data analysis. Before doing any analysis it is important to be clear about the purpose of the analysis: what are you trying to do, to answer? Note that qualitative research is generally exploratory. It could, but rarely is, explanatory. In general explanatory research tends to be quantitative as you need large representative samples to be able to generalise any findings. Qualitative research could also be confirmatory, that is testing predetermined ideas. It is unlikely to be comparative research, as comparative research involves comparing, for example two groups of participants. Comparative research requires absolute consistency and tends to be quantitative.
- Another Look At Qualitative Data Analysis For Machine Learning
- Another Look At Qualitative Data Analysis For Mac Download
Computer Assisted Qualitative Data AnalysiS: An Alternative Tool for Less. And the integration of quantitative data with qualitative data to generate interactive visualizations can really help the researcher see their data from a multitude of angles. Which means Mac and Windows people can work together (QSR will be releasing a Mac.
Analysing data involves:. Taking apart and reassemble for a purpose, according to some consistent criteria.
Sorting, organising, reducing, describing. Making sense, drawing conclusions, explaining How this is done depends on the research question – what are you trying to find out, understand – but also on. Your research paradigm. Research paradigms and data analysis influences our belief on how we make sense as it indicates what can be made sense of. E.g. Are we looking at establishing cause and effect relations between variables that can be isolated from their background? Or trying to untangle how meanings have been constructed culturally or socially? For more read section. The chosen research design(s) E.g.
Narrative research is looking for narratives (on events, on experience), phenomenology for lived experience. See section on for more information on designs.
Another useful online resource: Concluding: making meaning Good analysis goes beyond what people say. Ricoeur (1976) refers to this last stage as the appropriation stage – where new understandings are developed from the data. Codes, as categories of meanings, and concepts within them are compared and contrasted and, where appropriate, linked and examined taking into account the context in which the experiences described in the text took place. You re-organise all the different codes data to find some new order, logic.you look at all your codes, examine relationships, see what is connected with what, how. Context is important at this stage as it supports your interpretation. Note that you are inferring through induction (inferring to observation) or what is referred to as abduction or inference to best explanation of observation.
Generally you are not deducing which refers to inferring that something is true if the premises from which it is inferred are true. You may need to rearrange your coded data various times before coming up with the new text, the explanatory text.
You are expecting to transform the data and generate a new text resulting from. Re-conceptualisation. Theorisation (insights, concepts, propositions, models). Relations established with existing research The aim of this is to develop a new understanding, a new meaning which is no longer the private meaning, as experienced by participants, but a public meaning as revealed by the text (Ricoeur, 1976).
Whether you go back to participants or not at this stage is up to you, or to your research approach. Any claims you make have to be the result of a clearly outlined process and have to be based on the data you used. Potential issues To ensure solid and good research.
Be aware of the influence of the researcher on the meaning making at all times. Reflexivity is crucial. Read Le Gallais (2008). Make explicit the relationship between researcher, participant and research data. Account for any discrepancies. Have a consistent approach to meaning and coding. Do not treat codes as fixed and mechanical categories, they have to come from the data.
Make sure context is taken into account when and as required. Take into account that elicited and naturally occurring data are not exactly the same Reporting qualitative data Requires a consistent account of the research, the research approach, methods and research purpose/questions. References Charmaz, K.
Good Days, Bad Days: The Self in Chronic Illness and time, Rutgers University Press, New Brunswick. Chenail, R.J. Presenting qualitative data. The Qualitative Report, 2/3, 1-9. Retrieved from. Chidlow A., Plakoyiannaki E.
‘Translation in cross-language international business research: Beyond equivalence’. Journal of International Business Studies 45, 562–582. (1993), Qualitative Data Analysis, Routledge & Kegan, London. & Luff, D.(2009)., Resource Pack produced by The NIHR RDS for the East Midlands / The NIHR RDS for Yorkshire and the Humber Lapadat, Judith C. ‘Problematizing transcription: purpose, paradigm and quality’. International Journal of Social Research Methodology 3/3, 203-219. Le Gallais, Tricia (2008).
‘Wherever I go there I am: reflections on reflexivity and the research stance’, Reflective Practice: International and Multidisciplinary Perspectives, 9/2, 145-155. Minichiello, V. In-depth interviewing: Principles, techniques, analysis (2nd ed). Longman, Melbourne. (2009)., Academy of Management Journal 52/5, 8. Qualitative data analysis.
Qualitative research methods: A health focus. Oxford University Press. Interpretation Theory: Discourse And The Surplus Of Meaning. The Texas Christian University Press: Forth Worth.
‘Not everything can be reduced to numbers’, pp. 149-173 in Health research.
New York: Oxford University Press. Silverman, D. Interpreting Qualitative Data: Methods for Analysing Talk, Text and Interaction (3rd ed).
Sage, London. & Corbin, J.(1990). Basics of Qualitative Research, Sage, Newbury Park. Wong, J.P.H., & Poon, M.K.L. ‘Bringing translation out of the shadows: Translation as an issue of methodological significance in cross-cultural qualitative research’. Journal of Transcultural Nursing, 21/2, 151-158.
Frankly speaking, there isn’t a long list of free qualitative data analysis software for MAC. Still, there is a choice of tools to help you sort, organize and analyze a large amount of text or other types of data. Nowadays, computer-assisted qualitative data analysis software (CAQDAS) is a must for researchers and data professionals. You can use it to analyze a focus group transcripts, journal articles, legal documents, books, images, paintings, and many other types of information. Of course the free options have a lot of limitations but still, you can use them to get done a lot of work. Here is a list of the best free qualitative data analysis tools for MAC, you can consider if you lack the budget.
RDQA RDQA is a R package for Qualitative Data Analysis, and it is a free qualitative analysis software application ( with BSD license). It works on Linux/FreeBSD, Mac OSX and Windows. You can use it to enter data into a database and codify the data by 4 levels: cases, codes, code categories, and annotations. RDQA is used mainly by small and midsize organizations but it also has large enterprise users. RDQA is easy to use tool by many statisticians and data experts when performing analysis of textual data. However, for now, it only supports just plain text formatted data. Kye features and benefits. Import documents from plain text or on-the-fly.
Import PDF highlights. Support non-English documents.
File Editing after coding. Advanced Graphic user interface.
Memos of documents, coding, project, codes, files and more. Single-file (.rqda) format, which is basically a SQLite database. Organize codes into code categories, which is key to theory building. Organize files into file categories. Apply attributes to file, which is useful for content analysis.
Rename files, codes, code categories, and cases. Calculate the relation between two codings, given the coding indexes. Give a summary of coding and inter-code relationships And more.
Another Look At Qualitative Data Analysis For Machine Learning
Website: 2. TAMS Analyzer When it comes to free qualitative data analysis software for MAC, this is one of the most popular choices. TAMS (stands for Text Analysis Markup System) is an open source tool that allows you to quickly code sections of text. The software runs on Macintosh and Linux platforms. Despite TAMS is generally employed to encode discourse and ethnographic documents (such as interviews), you can also use it to analyze almost any plain text. You can import documents, fields notes or interview transcript and seamlessly code them by clicking on code from your list.
The Macintosh version of the program also includes full support for transcription (back space, insert time code, jump to time code, etc.) when working with data on sound files. Key features and benefits. PDF coding and analysis support. Image (jpg, etc.) coding. Layout for video coding. Multi-user support using MySQL as a server.
Multimedia support. XML file formats. Hot code sets. Ability to set comments for both ranges of text and individual tags Many others. Website: 3. HyperRESEARCH HyperRESEARCH is a flexible and powerful tool that assists you in data analysis and theory building. From coding and retrieval to analysis and reporting, the tool got you covered.
HyperRESEARCH is an intuitive software solution that requires near-zero learning curve to help you get started. The program is for MAC and Windows and allows you to move your work seamlessly between Mac and Windows. They offer premium and free option. You can download the Free Limited Edition. The size and complexity of your study is limited, but otherwise, you can access all the features. You can use the Free Limited Edition as long as you like.
Key features and benefits. Very easy to use – you can start coding right away with simple, intuitive steps. Any piece of data in a source for coding—text, image, audio, video, or PDF (coming soon). Go beyond coding and categorizing—use the Theory Builder to frame and test a hypothesis. See your codes in the margin of your data for easy scanning. Protect privacy by automatically masking sensitive information.
Many others. Website: 4. QDA Miner Lite QDA Miner Lite is also one of the top free qualitative data analysis software for MAC.
Another Look At Qualitative Data Analysis For Mac Download
You can use the tool for the analysis of textual data (such as interview and news transcripts), open-ended answers from a, as well as for the analysis of still images. Generally, QDA Miner Lite is a Windows application. However, there are ways to run it on a Mac OS computer. Key CAQDAS features and benefits. Importation of documents from plain text, RTF, HTML, PDF and also data from Excel, MS Access, CSV, and other text files. Importation from transcription tools and from other qualitative coding software such as Altas.ti, HyperResearch, and many others. Intuitive coding using codes organized in a tree structure.
Code frequency analysis with charts and tag clouds. Export tables to XLS, Tab Delimited, CSV formats, and Word format. Export graphs to BMP, PNG, JPEG, WMF formats. Interface and help file in English, French, and Spanish. Website: 5. KH Coder KH Coder is a free software for qualitative data analysis, quantitative content analysis, and text mining for MAC and Windows. The tool provides a variety of statistical analysis functions by using MySQL, ChaSen, and R as back-end tools.
You can analyze Japanese, English, French, German, Italian, Portuguese, Spanish, Chinese (simplified), Korean and Russian language data and text with KH Coder. KH Coder provides an opportunity for a variety of and functions using tools such as Stanford POS Tagger, Snowball stemmer, MySQL and R. Key features and benefits.