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MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Qualitative Research II:
The Analysis of Qualitativ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Lecture Aim and Objectives
• Aim
To introduce stude...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
PhD research far-west Nepal
Masculinity, Modernity ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
PhD – Ethnographic Methods/ Analysis
• Household su...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Post-doc - prison masculinities in
Scotland
Data co...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Non-numerical – converse of quantitative data
• T...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Examples of Qualitative Data Sources
• Interviews
•...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Identify
similarities
Extract themes
Identify
relat...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
To draw conclusions
To develop theories
To test hyp...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Principles of Analysing Qualitative Data
1 Proceed ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Qualitative Research:
Common Features of Analytic M...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
The Analysis Continuum
Raw Data
Descriptive
Stateme...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
The Credibility of Qualitative Analysis
The credibi...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Data Analysis During Collection
• Analysis often no...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Data Analysis After Collection
• One way is to foll...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Data Analysis Strategies
• Identifying themes
• Beg...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
A common approach for analyzing qualitative data
is...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
What is the Process of Data Analysis?
Codes the Tex...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Step 1. Get to know your data. Good qualitative dat...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Step 2. Focus the analysis
• Review the purpose of ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Step 3. Categorize information
Some people call thi...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
How do You Prepare and Organize the Data?
• Develop...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
A Visual Model of the Coding Process in
Qualitative...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Approaches to coding
• A priori codes are developed...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
In vivo codes
• Treat data as answers to open-ended...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
A priori codes
• Categories are created ahead of ti...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Step 4. Identify patterns within and between
catego...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Working with others (key stakeholders, other progra...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Step 5. Interpretation
• Stand back and think about...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
How do You Validate the Accuracy of Your
Findings?
...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Qualitative analysis is not as easy as it
looks…
• ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Common failings part one…
(Robson, 1995: 374-5)
• d...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Common failings part two…
• Listing all narrative c...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Practical exercise
Read the text (which is an extra...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
1. The walk from the prison entrance to the prison ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
1. The walk from the prison entrance to the prison ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
1. The walk from the prison entrance to the prison ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Qualitative Data Management Tools
• Enables efficie...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Divide data into meaningful units
• Use words/phr...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Types of Code
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Types of Code
• This took place at Head
Office
• Th...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Interpersonal
relationships
Family
Parent-child
Spo...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Create database for storing different
sources (te...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Qualitative Analysis Using Nvivo
Import Code
Query ...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Source – your data
• Documents, audio, video, ima...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Descriptive code
• Classification/attributeWhat i...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Descriptive coding
•Research design, project outlin...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Install NCapture for Internet Explorer/Chrome
• C...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Code data at multiple nodes
• Use descriptive, th...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Complex package that can take time to
learn
• Can...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• NVivo Toolkit:
http://explore.qsrinternational.co...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Qualitative Data Analysis with Nvivo
• Bazeley, P...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Conclusions
• Admit limitations in data collection
...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
• Bazeley, P .Jackson, K (2014) Qualitative data an...
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
matthew.maycock@glasgow.ac.uk
www.matthewmaycock.co...
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The analysis of qualitative data 22nd Oct 2015

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The analysis of qualitative data 22nd Oct 2015

  1. 1. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Qualitative Research II: The Analysis of Qualitative Data Dr Matt Maycock MRC/CSO Social and Public Health Sciences Unit 23rd October 2015
  2. 2. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Lecture Aim and Objectives • Aim To introduce students to the analysis of qualitative data • Objectives By the end you will have an appreciation of: 1 The principles of analysing qualitative data 2 The Qualitative Analytical Process 3 Qualitative Data Management Tools (Nvivo)
  3. 3. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. PhD research far-west Nepal Masculinity, Modernity and Bonded Labour: Continuity and Change amongst the Kamaiya of Kailali District, far-west Nepal (School of International Development, UEA, Norwich) Background to my research… Yearlong fieldwork in Nepal: Three month language training and key informant interviews Nine months in two fieldsites
  4. 4. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. PhD – Ethnographic Methods/ Analysis • Household survey • Life History interviews • Participant observation • Photographs • Participatory Development Tools (such as resource mapping) • Data analysis took as long as data collection • I used Nvivo extensively to support and shape my data analysis
  5. 5. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Post-doc - prison masculinities in Scotland Data collection • Observation of sessions • Interviews with participants who completed programme • Interviews with participants who did not complete • Focus group with prison staff Ongoing Data Analysis Grounded theory I have used Nvivo extensively Coding framework developed collaboratively
  6. 6. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Non-numerical – converse of quantitative data • Typically word based – but may include imagery, video, etc. • Can record attitudes, behaviours, experiences, motivations, etc. • Descriptive – describing events/opinions etc. • Explanatory – explaining events/opinions etc. What is Qualitative Data?
  7. 7. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Examples of Qualitative Data Sources • Interviews • Focus groups • Speeches • Questionnaires • Journals/diaries • Documents • Observation • Audio/visual materials • Websites • Social media
  8. 8. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Identify similarities Extract themes Identify relationships Highlight differences Create generalisations (?) Analysing Qualitative Data
  9. 9. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. To draw conclusions To develop theories To test hypotheses Objectives of Qualitative Analysis
  10. 10. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Principles of Analysing Qualitative Data 1 Proceed systematically and rigorously (minimise human error) 2 Record process, memos, journals, etc. 3 Focus on responding to research questions 4 Appropriate level of interpretation appropriate for situation 5 Time (process of inquiry and analysis are often simultaneous) 6 Seek to explain or enlighten 7 Evolutionary/emerging
  11. 11. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Qualitative Research: Common Features of Analytic Methods (Miles and Huberman, 1994) 1 Affixing codes to a set of field notes drawn from data collection 2 Noting reflections or other remarks in margin 3 Sorting or shifting through the materials to identify similar phrases, relationships between themes, distinct differences between subgroups and common sequences 4 Isolating patterns and processes, commonalties and differences, and taking them out to the filed in the next wave of data collection 5 Gradually elaborating a small set of generalisations that cover the consistencies discerned in the data base 6 Confronting those generalisations with a formalised body of knowledge in the from of constructs or theories
  12. 12. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. The Analysis Continuum Raw Data Descriptive Statements Interpretation
  13. 13. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. The Credibility of Qualitative Analysis The credibility for qualitative inquiry depends on three distinct but related inquiry elements: 1 Rigorous techniques and methods for gathering high- quality data that is carefully analysed, with attention to issues of validity, reliability, and triangulation 2 The credibility of the researcher, which is dependent on training, experience, track record, status, and presentation of self 3 Philosophical belief in the phenomenological paradigm, that is, a fundamental appreciation of naturalistic inquiry, qualitative methods, inductive analysis and holistic thinking
  14. 14. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Data Analysis During Collection • Analysis often not left until the end • To avoid collecting data that are not important the researcher must ask: • How am I going to make sense of this data? • As they collect data the researcher must ask • Why do the participants act as they do? • What does this focus mean? • What else do I want to know? • What new ideas have emerged? • Is this new information?
  15. 15. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Data Analysis After Collection • One way is to follow three iterative steps 1. Become familiar with the data through 1. Reading 2. Memoing 2. Exam the data in depth to provide detailed descriptions of the setting, participants, and activities. 3. Categorizing and coding pieces of data and grouping them into themes.
  16. 16. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Data Analysis Strategies • Identifying themes • Begin with big picture and list “themes” that emerge. • Events that keep repeating themselves • Coding qualitative data • Reduce data to a manageable form • Often done by writing notes on note cards or in coding software (such as Nvivo) and sorting into themes. • Predetermined categories (A priori) vs. emerging categories (In vivo)
  17. 17. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. A common approach for analyzing qualitative data is called content analysis. It involves 5 steps: 1. Get to know your data 2. Focus the analysis on your research questions 3. Categorize the information • Identify themes or pattern • Organize them into coherent categories 4. Identify patterns and connections within and between categories 5. Interpretation – bring it all together
  18. 18. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. What is the Process of Data Analysis? Codes the Text for Description to be Used in the Research Report/ thesis Codes the Text for Themes to be Used in the Research Report / thesis The Researcher Codes the Data (i.e., locates text segments and assigns a code to label them) The Researcher Prepares Data for analysis ( e.g., transcribes fieldnotes) The Researcher Collects Data (i.e., a text file, such as fieldnotes, transcriptions, optically scanned material) The Researcher Reads Through Data ( i.e., obtains general sense of material) SimultaneousInteractive
  19. 19. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Step 1. Get to know your data. Good qualitative data analysis depends upon understanding your data. Spend time getting to “know” your data. • Read and re-read the text • Listen to tape recordings if you have them; transcribe data • Check the quality of the data. Is it complete and understandable. It it likely to add meaning and value? Was it collected in an unbiased way?
  20. 20. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Step 2. Focus the analysis • Review the purpose of the evaluation and what you wanted to find out (keep referring to your research questions). • Based on your ‘getting to know your data’, think about a few questions that you want your analysis to answer and write them down. • You might focus your analysis by question, topic, time period, event, individual or group.
  21. 21. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Step 3. Categorize information Some people call this process ‘coding’ the data. It involves reading the data and giving labels or codes to the themes and ideas that you find. You may have themes or ideas you search for (pre-set categories) and/or create categories (emergent categories) as you work with the data. I will discuss using Nvivo to do this in more detail shortly
  22. 22. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. How do You Prepare and Organize the Data? • Develop a matrix or table of sources that can be used to organize the material • Organize material by type • Keep duplicate copies of materials • Transcribe data • Prepare data for hand (if you don’t like computers) or computer analysis (and select computer program)
  23. 23. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. A Visual Model of the Coding Process in Qualitative Research Reduce Codes to 5-7 Themes Initially read through data Divide text into segments of information Label segments of information with codes Reduce Overlap and redundancy of codes Collapse codes into themes Many Pages of Text Many Segments of Text 30-40 codes Codes reduced to 20
  24. 24. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Approaches to coding • A priori codes are developed before examining the data • In vivo codes are derived from the data • Co-occurring codes partially or completely overlap
  25. 25. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. In vivo codes • Treat data as answers to open-ended questions • ask data specific questions • assign codes for answers • record theoretical notes Strauss and Corbin, 1998, Ron Wardell, EVDS 617 course notes
  26. 26. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. A priori codes • Categories are created ahead of time • from existing literature • from previous open coding • Code the data just like open coding Ron Wardell, EVDS 617 course notes
  27. 27. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Step 4. Identify patterns within and between categories • Once you have identified the categories, you might: • Sort and assemble all data by theme • Sort and assemble data into larger categories • Count the number of times certain themes arise to show relative importance (not suitable for statistical analysis) • Show relationships among categories
  28. 28. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Working with others (key stakeholders, other program staff, participants) in the coding and interpretation process is helpful. For example, several people might review the data independently to identify categories. Then, you can compare categories and resolve any discrepancies. How else might you involve others in your qualitative data analysis? Collaboration with coding
  29. 29. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Step 5. Interpretation • Stand back and think about what you’ve learned. What do these categories and patterns mean? What is really important • What did you learn? • Interpretation is not neutral • Reflect about the personal meaning of the data • Compare and contrast personal viewpoints with the literature • Address limitations of the study • Make suggestions for future research
  30. 30. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. How do You Validate the Accuracy of Your Findings? • Member/participant checking: Members/participants check the accuracy of the account • Triangulation: Using corroborating evidence • Triangulation involves gathering data on the same theme from a variety of sources. Mixed methods approaches are useful here. Triangulation can be useful in data analysis whether or not there are correspondences or discrepancies. • External: Hiring the services of an individual outside the study to review the study. Or you can ask a friend…
  31. 31. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Qualitative analysis is not as easy as it looks… • Qualitative analysis is ‘as much a test of the enquirer as it is a test of the data: • first and foremost, analysis is a test of the...ability to think - to process information in a meaningful and useful manner...qualitative analysis remains much closer to codified common-sense than the complexities of statistical analysis of quantitative data’ (Robson 1995: 374). • As a result it is important to recognise some common failings...
  32. 32. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Common failings part one… (Robson, 1995: 374-5) • data overload - too much to process and remember • information availability - information which is difficult to get hold of (wrongly) gets less attention • positive instances - a tendency to ignore evidence which conflicts with hypotheses • uneven reliability - that some sources are more reliable than others sometimes gets ignored • fictional base - tendency to compare with a base or average when no base data is available • confidence in judgement - excessive confidence once a judgement is made • co-occurrence - interpreted as strong evidence of correlation • inconsistency - repeated evaluations of the same data which differ
  33. 33. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Common failings part two… • Listing all narrative comments without doing any analysis • Including information that makes it possible to identify the respondent. • Generalizing from comments to the whole group. Qualitative information seeks to provide unique insights, understanding and explanation – it is not to be generalized. • Using quotes to provide a positive spin. Consider your purpose for including quotes. • If you have a large amount of data analysis is challenging • No standard processes for coding or extracting themes • Time constraints
  34. 34. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Practical exercise Read the text (which is an extract from my fieldnotes from my post-doc research) on the following page and assign each line of the text to one of the codes below: • CODE A - The prison context • CODE B - Researcher reflections • CODE C – Health in prison
  35. 35. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. 1. The walk from the prison entrance to the prison gym passes through 2. old and new parts of the prison, fenced outside spaces, gardens and 3. various noticeably non-descript rooms, but does not include direct 4. passage through any of the prison accommodation areas (halls). The 5. sound of rattling keys is the soundtrack to this walk. There are a 6. various motivational messages located on the walk up the two flights 7. of stairs to get to the prison gym, including: “we live longer healthier 8. lives”. Going through the nine barriers and gates to get to the gym 9. becomes a kind of routine through which one becomes enveloped 10. within the prison, it encloses around you. “Entering the gym” (Field note extract, December 2014, Session one, Prison B) CODE A - The prison context CODE B - Researcher reflections CODE C – Health in prison
  36. 36. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. 1. The walk from the prison entrance to the prison gym passes through 2. old and new parts of the prison, fenced outside spaces, gardens and 3. various noticeably non-descript rooms, but does not include direct 4. passage through any of the prison accommodation areas (halls). The 5. sound of rattling keys is the soundtrack to this walk. There are a 6. various motivational messages located on the walk up the two flights 7. of stairs to get to the prison gym, including: “we live longer healthier 8. lives”. Going through the nine barriers and gates to get to the gym 9. becomes a kind of routine through which one becomes enveloped 10. within the prison, it encloses around you. “Entering the gym” (Field note extract, December 2014, Session one, Prison B) CODE A - The prison context CODE B - Researcher reflections CODE C – Health in prison
  37. 37. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. 1. The walk from the prison entrance to the prison gym passes through 2. old and new parts of the prison, fenced outside spaces, gardens and 3. various noticeably non-descript rooms, but does not include direct 4. passage through any of the prison accommodation areas (halls). The 5. sound of rattling keys is the soundtrack to this walk. There are a 6. various motivational messages located on the walk up the two flights 7. of stairs to get to the prison gym, including: “we live longer healthier 8. lives”. Going through the nine barriers and gates to get to the gym 9. becomes a kind of routine through which one becomes enveloped 10. within the prison, it encloses around you. “Entering the gym” (Field note extract, December 2014, Session one, Prison B) CODE A - The prison context CODE B - Researcher reflections CODE C – Health in prison
  38. 38. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Qualitative Data Management Tools • Enables efficient data management by supporting the processes of indexing, searching and hence data theorising • Creates an environment to store and explore data and ideas, it does not determine the research approach. • The major advantage of the package is that it enables an efficient and flexible approach to rigorously and systematically analysing qualitative data. • Such tools include, Nvivo, ATLAS.ti, MAXQDA • You can buy Nvivo for £30 from IT, or it is installed on some computers around the campus
  39. 39. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
  40. 40. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Divide data into meaningful units • Use words/phrases e.g. ‘physical environment’, ‘interpersonal relationships’ • Codes can be ‘data-driven’ or ‘theory- driven’ • A priori codes are developed before examining the data • In vivo codes are derived from the data • Co-occurring codes partially or completely overlap • In NVivo, codes are stored within Nodes • Keep a master list of codes used Coding Data
  41. 41. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Types of Code
  42. 42. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Types of Code • This took place at Head Office • This is about discrimination against women • This is a reflection on misogyny in the workplace Analytic Descriptive Thematic
  43. 43. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Interpersonal relationships Family Parent-child Spouse/ partner Sibling Non-family Friends Work colleagues Tree (Hierarchical) Coding
  44. 44. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Create database for storing different sources (text, audio, video, web resources, etc.) Manage data • Annotate data • Attach memos to filesManage ideas • Identify commonly occurring words • Collate data relating to a theme or concept Query data • Illustrate relationships using models • Report knowledge developed from data Model & report How can NVivo Help?
  45. 45. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Qualitative Analysis Using Nvivo Import Code Query & Visualise AnnotateSummarise
  46. 46. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
  47. 47. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Source – your data • Documents, audio, video, images, etc. • Memo – item in a project linked to a document or node • Node – a code or concept (theme node) or a component of your project e.g. participant or location (case node) • Can be free or tree • Classification – applied to a case or participant e.g. person, organisation, etc. • Attributes – data (demographics) known about a case (participant) recorded separately from the case Terminology
  48. 48. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Descriptive code • Classification/attributeWhat is this? • Thematic code • Annotation/memo Why is this interesting? • Analytic code • Memo Why is this relevant to my research question? Coding in NVivo
  49. 49. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Descriptive coding •Research design, project outline •Folders, templates, case nodes Thematic coding •Finding obvious themes, autocoding •In Vivo coding Analytic coding •Creating node hierarchies •Using queries, matrices Developing an NVivo Project Structuringphase Creative/analyticphase Optionalanalyticiterations Source: Edhlund, B & Mcdougall, A (2013), NVivo 10 Essentials, p. 14
  50. 50. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Install NCapture for Internet Explorer/Chrome • Capture content from: • Web pages • Online PDFs • Facebook • Twitter • LinkedIn • YouTube • http://help- ncapture.qsrinternational.com/desktop/welcome/welcome.htm • http://nsmnss.blogspot.co.uk/2014/08/7-ways-nvivo-helps- researchers-handle.html Web Pages and Social Media Data
  51. 51. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Code data at multiple nodes • Use descriptive, thematic and analytic codes • Keep a record of your codes and the themes that evolve • Use a Word Frequency query to help you identify key phrases • Use Text Search queries to help you explore themes • Take time to reflect on what you have found and record ideas using memos • Keep a journal of your analysis process Tips for using Nvivo
  52. 52. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Complex package that can take time to learn • Can distance researcher from their data • Researcher can get caught in ‘coding trap’ • Can identify references to phrases but cannot discern different contexts • Will not compensate for poor data or weak interpretive skills! Nvivo - Limitations
  53. 53. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • NVivo Toolkit: http://explore.qsrinternational.com/nvivo-toolkit • Getting Started Guide: http://download.qsrinternational.com/Document/NVivo1 0/NVivo10-Getting-Started-Guide.pdf • QSR website: http://www.qsrinternational.com/support.aspx • QSR Support - @QSRSup - on Twitter: https://twitter.com/QSRSup • QSR on Facebook: http://www.facebook.com/qsrinternational • QSR on YouTube: https://www.youtube.com/user/QSRInternational Online Resources
  54. 54. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Qualitative Data Analysis with Nvivo • Bazeley, P & Jackson, K (2013) • NVivo 10 Essentials • Edhlund, B & Mcdougall, A (2013) • Using QSR‐NVivo to facilitate the development of a grounded theory project: an account of a worked example • Andrew John Hutchison, Lynne Halley Johnston, Jeff David Breckon • International Journal of Social Research Methodology • Vol. 13, Iss. 4, 2010 • Using NVivo to Answer the Challenges of Qualitative Research in Professional Communication: Benefits and Best Practices Tutorial • Hoover, R.S.; Koerber, A.L., • Professional Communication, IEEE Transactions on • Vol.54, no.1, pp.68,82, March 2011 doi: 10.1109/TPC.2009.2036896 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=533 7919&isnumber=5718246 Literature
  55. 55. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Conclusions • Admit limitations in data collection • strength, not weakness • contributes to the research validity • demonstrates your reflexivity as researcher • Use qualitative data analysis software (such as Nvivo) as a tool to support your analysis. Such software is only as good as your analytical approach. • Finally ...Good qualitative research depends upon good analysis not just description. • Your analysis will only be as good as your data
  56. 56. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. • Bazeley, P .Jackson, K (2014) Qualitative data analysis with Nvivo. London: SAGE • Bazeley, P (2013) Qualitative Data Analysis: Practical Strategies. London: SAGE • Bryman, A. (2012). Social research methods (2nd ed.). Oxford: Oxford University Press. Chapter 24 • Fairclough, N. (2003). Analysing discourse. Textual analysis for social research. London: Routledge. • Grbich, C. (2012). Qualitative data analysis: An introduction. London: Sage. • Madison, D S (2011) Critical Ethnography: Method, Ethics, and Performance. London: SAGE Chapters 8 & 9 • Miles, M (2013) Qualitative Data Analysis: A Methods Sourcebook. London: SAGE • Ritchie, J. Lewis, J (2013) Qualitative Research Practice: A Guide for Social Science Students and Researchers. London: Sage. Chapters 10 & 11 • Robson, C (2011) Real World Research (third edition). London: John Wiley & Sons. Part V • Saldana, J (2012) The Coding Manual for Qualitative Researchers. London: SAGE • Silverman, D. (2015). Interpreting qualitative data: Methods for analysing talk, text and interaction. London: Sage. Further reading…
  57. 57. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. matthew.maycock@glasgow.ac.uk www.matthewmaycock.com Contact details…

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