The analysis of qualitative data 22nd Oct 2015

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Qualitative Research II: The Analysis of Qualitative Data

Dr Matt MaycockMRC/CSO Social and Public Health Sciences Unit23rd October 2015

MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.

Lecture Aim and ObjectivesAimTo introduce students to the analysis of qualitative data

ObjectivesBy the end you will have an appreciation of:

The principles of analysing qualitative dataThe Qualitative Analytical ProcessQualitative Data Management Tools (Nvivo)


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PhD research far-west NepalMasculinity, 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 researchYearlong fieldwork in Nepal:Three month language training and key informant interviewsNine months in two fieldsites


PhD Ethnographic Methods/ AnalysisHousehold 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


Post-doc - prison masculinities in ScotlandData collection Observation of sessions

Interviews with participants who completed programme

Interviews with participants who did not complete

Focus group with prison staffOngoing Data Analysis

Grounded theory

I have used Nvivo extensively Coding framework developed collaboratively


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Non-numerical converse of quantitative dataTypically 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?


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Examples of Qualitative Data SourcesInterviewsFocus groupsSpeechesQuestionnairesJournals/diariesDocumentsObservationAudio/visual materialsWebsitesSocial media


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Analysing Qualitative Data


Sort through data to identify similar phrasesExtract themes based on phrasesIdentify relationships between themesHighlight differences between subgroupsIdentify patterns and processesBuild on consistencies to create generalisationsIterative process need to review initial findings to inform further analysis

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Objectives of Qualitative Analysis


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Principles of Analysing Qualitative DataProceed systematically and rigorously (minimise human error)Record process, memos, journals, etc.Focus on responding to research questionsAppropriate level of interpretation appropriate for situationTime (process of inquiry and analysis are often simultaneous)Seek to explain or enlightenEvolutionary/emerging


Qualitative Research: Common Features of Analytic Methods(Miles and Huberman, 1994)Affixing codes to a set of field notes drawn from data collectionNoting reflections or other remarks in marginSorting or shifting through the materials to identify similar phrases, relationships between themes, distinct differences between subgroups and common sequencesIsolating patterns and processes, commonalties and differences, and taking them out to the filed in the next wave of data collectionGradually elaborating a small set of generalisations that cover the consistencies discerned in the data baseConfronting those generalisations with a formalised body of knowledge in the from of constructs or theories


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The Analysis ContinuumRaw DataDescriptiveStatementsInterpretation


The Credibility of Qualitative AnalysisThe credibility for qualitative inquiry depends on three distinct but related inquiry elements:Rigorous techniques and methods for gathering high-quality data that is carefully analysed, with attention to issues of validity, reliability, and triangulationThe credibility of the researcher, which is dependent on training, experience, track record, status, and presentation of selfPhilosophical belief in the phenomenological paradigm, that is, a fundamental appreciation of naturalistic inquiry, qualitative methods, inductive analysis and holistic thinking


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Data Analysis During CollectionAnalysis often not left until the endTo 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 askWhy 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?


Data Analysis After CollectionOne way is to follow three iterative stepsBecome familiar with the data throughReadingMemoingExam the data in depth to provide detailed descriptions of the setting, participants, and activities.Categorizing and coding pieces of data and grouping them into themes.


Data Analysis StrategiesIdentifying themesBegin with big picture and list themes that emerge.Events that keep repeating themselvesCoding qualitative dataReduce data to a manageable formOften 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)


A common approach for analyzing qualitative data is called content analysis. It involves 5 steps:

Get to know your data Focus the analysis on your research questionsCategorize the information Identify themes or patternOrganize them into coherent categoriesIdentify patterns and connections within and between categories Interpretation bring it all together


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What is the Process of Data Analysis?Codes the Text forDescription to be Usedin the Research Report/ thesisCodes the Text forThemes to be Usedin the Research Report / thesisThe 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)

Simultaneous

Interactive


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?


Step 2. Focus the analysisReview 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.


Step 3. Categorize informationSome 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


How do You Prepare and Organize the Data?Develop a matrix or table of sources that can be used to organize the materialOrganize material by typeKeep duplicate copies of materialsTranscribe dataPrepare data for hand (if you dont like computers) or computer analysis (and select computer program)


A Visual Model of the Coding Process in Qualitative Research

Reduce Codes to5-7 ThemesInitially readthrough dataDivide text into segmentsof informationLabel segments of informationwith codesReduce Overlap andredundancyof codesCollapsecodes intothemes

ManyPages of TextManySegments of Text30-40codesCodesreducedto 20


Approaches to codingA priori codes are developed before examining the data

In vivo codes are derived from the data

Co-occurring codes partially or completely overlap


In vivo codesTreat data as answers to open-ended questions

ask data specific questionsassign codes for answersrecord theoretical notesStrauss and Corbin, 1998, Ron Wardell, EVDS 617 course notes


A priori codesCategories are created ahead of timefrom existing literaturefrom previous open coding

Code the data just like open coding

Ron Wardell, EVDS 617 course notes


Step 4. Identify patterns within and between categoriesOnce you have identified the categories, you might:Sort and assemble all data by themeSort and assemble data into larger categoriesCount the number of times certain themes arise to show relative importance (not suitable for statistical analysis)Show relationships among categories


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


Step 5. InterpretationStand back and think about what youve learned. What do these categories and patterns mean? What is really importantWhat did you learn?

Interpretation is not neutralReflect about the personal meaning of the dataCompare and contrast personal viewpoints with the literatureAddress limitations of the studyMake suggestions for future research


How do You Validate the Accuracy of Your Findings?Member/participant checking: Members/participants check the accuracy of the account

Triangulation: Using corroborating evidenceTriangulation 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


Qualitative analysis is not as easy as it looksQualitative 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...


Common failings part one (Robson, 1995: 374-5)data overload - too much to process and rememberinformation availability - information which is difficult to get hold of (wrongly) gets less attentionpositive instances - a tendency to ignore evidence which conflicts with hypothesesuneven reliability - that some sources are more reliable than others sometimes gets ignoredfictional base - tendency to compare with a base or average when no base data is availableconfidence in judgement - excessive confidence once a judgement is madeco-occurrence - interpreted as strong evidence of correlationinconsistency - repeated evaluations of the same data which differ


Common failings part two Listing all narrative comments without doing any analysisIncluding 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 challengingNo standard processes for coding or extracting themesTime constraints


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 contextCODE B - Researcher reflectionsCODE C Health in prison


The walk from the prison entrance to the prison gym passes through old and new parts of the prison, fenced outside spaces, gardens andvarious noticeably non-descript rooms, but does not include directpassage through any of the prison accommodation areas (halls). Thesound of rattling keys is the soundtrack to this walk. There are avarious motivational messages located on the walk up the two flightsof stairs to get to the prison gym, including: we live longer healthierlives. Going through the nine barriers and gates to get to the gymbecomes a kind of routine through which one becomes envelopedwithin the prison, it encloses around you.

Entering the gym (Field note extract, December 2014, Session one, Prison B)











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


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Divide data into meaningful unitsUse words/phrases e.g. physical environment, interpersonal relationshipsCodes can be data-driven or theory-drivenA priori codes are developed before examining the dataIn vivo codes are derived from the dataCo-occurring codes partially or completely overlapIn NVivo, codes are stored within NodesKeep a master list of codes used

Coding Data


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Types of Code


Types of CodeThis took place at Head OfficeThis is about discrimination against womenThis is a reflection on misogyny in the workplaceAnalyticDescriptiveThematic


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Tree (Hierarchical) Coding


Usually need to explain how coding was carried out and present a table of codes for thesis or journal articles43

How can NVivo Help?


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Qualitative Analysis Using NvivoImport

Code

Query & Visualise

Annotate

Summarise


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Source your dataDocuments, audio, video, images, etc.Memo item in a project linked to a document or nodeNode a code or concept (theme node) or a component of your project e.g. participant or location (case node)Can be free or treeClassification applied to a case or participant e.g. person, organisation, etc.Attributes data (demographics) known about a case (participant) recorded separately from the caseTerminology


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Coding in NVivo


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Developing an NVivo ProjectStructuring phaseCreative/analytic phaseOptional analytic iterationsSource: Edhlund, B & Mcdougall, A (2013), NVivo 10 Essentials, p. 14


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Install NCapture for Internet Explorer/ChromeCapture content from:Web pagesOnline PDFsFacebookTwitterLinkedInYouTubehttp://help-ncapture.qsrinternational.com/desktop/welcome/welcome.htmhttp://nsmnss.blogspot.co.uk/2014/08/7-ways-nvivo-helps-researchers-handle.htmlWeb Pages and Social Media Data


Code data at multiple nodesUse descriptive, thematic and analytic codesKeep a record of your codes and the themes that evolveUse a Word Frequency query to help you identify key phrasesUse Text Search queries to help you explore themesTake time to reflect on what you have found and record ideas using memosKeep a journal of your analysis process

Tips for using Nvivo


Place an underscore at the beginning of a document name e.g. _Journal so that it will appear at the top of any alphabetical list51

Complex package that can take time to learnCan distance researcher from their dataResearcher can get caught in coding trapCan identify references to phrases but cannot discern different contextsWill not compensate for poor data or weak interpretive skills!Nvivo - Limitations


Does not prescribe a method but rather supports a range of methodological approachesEmphasis on storage and retrieval rather than analysisAutomated or keyword searches are no substitute for interpretive coding52

NVivo Toolkit: http://explore.qsrinternational.com/nvivo-toolkitGetting Started Guide: http://download.qsrinternational.com/Document/NVivo10/NVivo10-Getting-Started-Guide.pdfQSR website: http://www.qsrinternational.com/support.aspxQSR Support - @QSRSup - on Twitter: https://twitter.com/QSRSupQSR on Facebook: http://www.facebook.com/qsrinternationalQSR on YouTube: https://www.youtube.com/user/QSRInternational Online Resources


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Qualitative Data Analysis with NvivoBazeley, P & Jackson, K (2013)NVivo 10 Essentials Edhlund, B & Mcdougall, A (2013)Using QSRNVivo to facilitate the development of a grounded theory project: an account of a worked exampleAndrew John Hutchison, Lynne Halley Johnston, Jeff David Breckon International Journal of Social Research Methodology Vol. 13, Iss. 4, 2010Using NVivo to Answer the Challenges of Qualitative Research in Professional Communication: Benefits and Best Practices TutorialHoover, R.S.; Koerber, A.L., Professional Communication, IEEE Transactions onVol.54, no.1, pp.68,82, March 2011doi: 10.1109/TPC.2009.2036896URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5337919&isnumber=5718246Literature


ConclusionsAdmit limitations in data collectionstrength, not weaknesscontributes to the research validitydemonstrates 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


Bazeley, P .Jackson, K (2014) Qualitative data analysis with Nvivo. London: SAGEBazeley, P (2013) Qualitative Data Analysis: Practical Strategies. London: SAGEBryman, A. (2012). Social research methods (2nd ed.). Oxford: Oxford University Press. Chapter 24Fairclough, 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 & 9Miles, M (2013)Qualitative Data Analysis: A Methods Sourcebook. London: SAGERitchie, J. Lewis, J (2013)Qualitative Research Practice: A Guide for Social Science Students and Researchers. London: Sage. Chapters 10 & 11Robson, C (2011) Real World Research (third edition). London: John Wiley & Sons. Part VSaldana, J (2012)The Coding Manual for Qualitative Researchers. London: SAGESilverman, D. (2015). Interpreting qualitative data: Methods for analysing talk, text and interaction. London: Sage.Further reading


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The analysis of qualitative data 22nd Oct 2015

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