SA108 Introduction to NVivo for Qualitative Data AnalysisMarie-Hélène ParéMarie-Hélène Paré is eLearning consultant and lectures program evaluation in the Master in HealthSocial Work at the Open University of Catalonia and a freelance methodologist in qualitative dataanalysis. She was educated in Quebec, Beirut and Oxford. A clinician by training, she worked forseveral years in psychosocial care with survivors of war rape and war trauma in humanitarianemergencies for MSF, MDM and UNRWA in war-torn countries. She moved to academia to researchcommunity participation in MHPSS which she researches using mixed methods. Marie-Hélène haslectured qualitative data analysis in more than forty universities and research centres worldwide. Sheteaches qualitative data analysis at the ECPR Method School since 2009 and also teaches at the IPSANUS Summer School at the National University of Singapore.Prerequisite knowledgeNo prerequisite knowledge of NVivo required. Knowledge of qualitative research necessary.This course uses NVivo 12 Pro for WindowsThis is a bring-your-laptop course for NVivo 12 Pro for Windows. You can download the 14-day freetrial here. This course is unsuitable for NVivo for Mac as this version is incomplete compared toWindows. You can run NVivo 12 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, andonly, your Mac meets the system requirements here. You must ensure that NVivo works well on yourmachine regardless of the OS used as no technical assistance will be provided at the Summer Schoolby the instructor, teaching assistants, ECPR staff, or CEU IT services. Please see the softwareinstallation instructions in the section Software and hardware requirements below.Short course outlineThis course is designed for participants who plan to use NVivo for the management, coding, analysisand reporting of qualitative data. The course content is spread over four modules and includessetting up a project; organising and classifying data; managing a literature review; coding andanalysing textual, multimedia and internet data; and reporting qualitative findings usingvisualisations. The course is entirely hands-on and uses sample data to learn NVivo’s basic andadvanced functionalities. The course does not cover how to analyse qualitative data using specificanalytic methods such as thematic analysis, grounded theory, or content analysis. If you are lookingfor such course, see the course Advanced Qualitative Data Analysis at the ECPR Winter School inBamberg.Long course outlineNVivo is software programme for qualitative data analysis. It is a powerful platform that supportstext, multimedia, pictures, and PDFs; open-ended surveys from Excel and Survey Monkey;bibliographic meta-data from reference manager software; social media data from Facebook,Twitter, LinkedIn, YouTube as well as webpages; notes taken with Evernote and OneNote; and emailsof Outlook. NVivo supports a range of inductive and deductive methods to qualitative analysis suchas thematic and content analysis, within and cross-case analysis, discourse, conversational andnarrative analysis, grounded theory, analytical induction, and qualitative evidence synthesis. Theobjective of this course is to provide participants with knowledge and skills to use the basic andadvanced features of NVivo in their own research. The course content is spread over four modulesand includes setting up a project; organising and classifying data; managing a literature review;coding and analysing text, multimedia and internet data; seeking patterns and discovering1

relationships; and reporting qualitative findings using visualisations. Details of the four modules arepresented below.Module 1 Data ManagementThe course opens with an introduction ofthe NVivo interface, its structure andunderlying logic. We create an NVivoproject, import, organise, and classify data.We learn to manage a literature reviewusing annotations, cross-references, andmemo links for easy access and retrieval.Our attention then turns to handlingmultimedia data starting with thegeneration of verbatim transcripts, videosummaries and picture logs. We then use externals to link an NVivo project to outside information,as well as memos where the analytic process is recorded. Module 1 concludes with lexical queries,which search for frequency and context of keywords in textual data. We analyse the outputs usingword clouds, dendograms, and word trees.Module 2: Data CodingModule 2 introduces the different techniques to automatically andmanually code qualitative data in NVivo. We start by autocodingdata from structured interviews, which allows sorting large sectionsof data in thematic sections. Such data sorting - known as ‘broadbrush’ coding - is very useful when one wants to examineeverything that was said about a specific question or theme across adataset, without having to open every single piece of interviewwhere the question was asked. Formatting tips in Word accompanythis topic.We move on to manual coding and learn the different techniques tocode data inductively; that is, using a bottom-up approach. Keynotions underlying the coding process such as coding unit, semanticexclusiveness, and semantic exhaustiveness, are exemplified withthe material at hand. The use of relationship nodes is tried outwhen one wants to formalise associations between the codes for hypothesis generation and/orfalsification. Module 2 concludes with visualisations that map the coding process and comparecoding across sources and cases.Module 3: Data AnalysisModule 3 covers the range of functionalitiesto prepare and conduct qualitative analysis.Since social research frequently gatherqualitative data as well variable data, socomparisons can be made across cases,settings and contexts, we look at theprocedures to create case classifications andassign variables to cases. We then turn to theSearch Folder to efficiently retrieves casesthat match a specific sociodemographicprofile. This allows us to create sets to beused for the cross-case comparison.2

We then move on to coding-based queries which retrieve data based on boolean operators thatsearch for data overlap, inclusion, proximity, or exclusion. We start with coding queries that searchfor data coded at some nodes but only when mentioned by cases that match specific attributes. Forcross-case analysis, we run a matrix coding queries which cross-tabulate cases with codes, and weinterpret the results using different outputs: coding density, case number, and relative percentage.Our interpretation is recorded in memos and is linked back to theory. Module 3 concludes withrunning group query to explore association between coded items across a dataset.Module 4: Data VisualisationModule 4 proposes different graphic displays toeffectively communicate one’s research findings. Wefirst discuss the rationale for choosing certain displaysagainst others. We learn to generate maps, charts,diagrams, and dendograms. Moving on to building asolid audit trail to back up results and substantiate one’sclaims, we learn how to export qualitative findings outof NVivo. The usefulness of generating node summaryreports, which provide a detailed synthesis of the scopeof a node in a project, is also covered. When workingwith colleagues who don’t use NVivo, the possibility toexport project data in mini websites using HTML files ispresented.Module 4 concludes with the ABC of coordinating teamwork with an emphasis on the golden rules for successful data management, splitting and mergingproject files in a master project, and the measurement of intercoder reliability between coders.Day-to-day scheduleThursdayTopic(s)Data organisation andexplorationFridayData coding and comparisonDetails1. Import, organise and classify data2. Manage a literature review3. Work with internet and multimedia data4. Run textual queries5. Link your project to external information6. Record the research process in memos1. Apply your research design in NVivo2. Autocode structured data3. Code data inductively4. Manage a coding scheme3

SaturdayData analysis and visualisation5. hypothesesMap the coding processSearch, locate, and group itemsRun coding and matrix queriesPresent findings with visualisationsGenerate summary reportsExport content out of NVivoCoordinate teamworkDay-to-day reading listThe NVivo Pro Started Guide (see here for download) is the main text of the course. Those who wishto deepen understanding of using NVivo in qualitative research can do the optional readings ofBazeley & Jackson (2013) Qualitative Data Analysis with NVivo (2nd ed.). Please note that this bookwas written for NVivo 10 and the interface and some functionalities are now outdated with version12.ThursdayFridaySaturdayReadingsData organisation and explorationCompulsory text NVivo 11 Pro Started Guide: pp.5-7; 10-14; 17-23; 37-38Optional text Bazeley & Jackson: format data: 59-61; download data with NCapture: 173-177;import data: (internals) 24-34; 45-46; 61-66; (open-ended surveys) 199-203;(social media) 171-176; 209-211; (multimedia) 154-167; transcription: 167-169;externals: 62-63; literature review: 178-194; links and memos: 34-45; text-basedqueries: 110-117; 249-250Data coding and comparisonCompulsory text NVivo 11 Pro Started Guide: pp.24-36Optional text Bazeley & Jackson: autocoding: 108-110; (datasets) 207-208; codes and coding:68-94; coding scheme: 95-106; 117-119; relationship nodes: 230-234; cases andvariables: 50-56; (from surveys) 122-139; 205-207Data analysis and visualisationCompulsory text NVivo 11 Pro Started Guide: pp.40-42; 43-48Optional text Bazeley & Jackson: sets: 106-107; 146-153; coding-based queries: 141-146; 242248; 250-257; cross-case analysis and theory-building: 257- 265; visualisations:(model) 28-30; 217-230; 234-241; reports: 265-269; export content out of NVivo:119-121; 139-140; team work: 270-296Software and hardware requirementsThis course requires that you run NVivo 12 Pro for Windows on your laptop. You can download the14-day free trial here. DO NOT COME TO THE COURSE WITH NVIVO FOR MAC as this version isincomplete compared to NVivo 12 Pro for Windows. Mac users should consult the compatibilityoptions and system requirements to run NVivo 12 Pro for Windows using Boot camp or Parallels ontheir Mac. You must ensure that NVivo works well on your machine regardless of the OS used as notechnical assistance will be provided at the Summer School by the instructor, teaching assistants,4

ECPR staff, or CEU IT services. Once NVivo is installed on your laptop, verify that it works properly.Follow the instructions below. NVivo.In the Start screen, in the New section, click Sample Project.NVivo opens a copy of the sample project which is stored in your default project location.If you can’t open the Sample project, contact QSR international by submitting a supportrequest form online (see section Contact Us Online at the bottom).NVivo system requirementsProcessorMemoryDisplayOperating systemHard diskMinimum1.2 GHz single-core processor (32bit) 1.4 GHz single-core processor(64-bit)2 GB RAM or more1024 x 768 screen resolutionRecommended2.0 GHz dual-core processor or faster4 GB RAM or more1680 x 1050 screen resolution orhigherMicrosoft Windows 7Microsoft Windows 7 or laterApproximately 5 GB of available Approximately 8 GB of availablehard-disk spacehard-disk spaceLiterature and studies that used NVivoAuld, G. W., Diker, A., Bock, M. A., Boushey, C., J, Bruhn, C. M., Cluskey, M., . . . Zaghloul, S. (2007).Development of a Decision Tree to Determine Appropriateness of NVivo in Analyzing QualitativeData Sets. Journal of Nutrition Education and Behavior, 39(1), 37-47.Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2004). Maximising transparency in a doctoralthesis: the complexity of writing about the use of QSR* NVIVO within grounded theory study.Qualitative Research, 4(2), 247-265.Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2006). Using computer-assisted qualitative dataanalysis software to develop a grounded theory project. Field Methods, 18(3), 245-266.Davidson, J. (2012). The Journal Project: Qualitative Computing and the Technology/Aesthetics Dividein Qualitative Research. Forum Qualitative Sozialforschung / Forum: Qualitative Social 8/3376.Fàbregues, S., Paré, M.-H., & Meneses, J. (2018). Operationalizing and Conceptualizing Quality inMixed Methods Research: A Multiple Case Study of the Disciplines of Education, Nursing,Psychology, and Sociology. Journal of Mixed Methods Research.Hays, R., & Daker-White, G. (2015). The consensus? A qualitative analysis of opinionsexpressed on Twitter. BMC Public Health, 15, 838. doi: 10.1186/s12889-015-2180-9.Hutchison, A. J., Johnston, L. H., & Breckon, J. D. (2010). Using QSR-NVivo to facilitate thedevelopment of a grounded theory project: an account of a worked example. InternationalJournal of Social Research Methodology, 13(4), 283-202.Johnston, L. H. (2006). Software and method: Reflections on teaching and using QSR NVivo indoctoral research. International Journal of Social Research Methodology, 9(5), 379-391.Leech, N. L., & Onwuegbuzie, A. J. (2011). Beyond Constant Comparison Qualitative Data Analysis:Using NVivo. School Psychology Quarterly, 26(1), 70-84.Rich, M., & Patashnick, J. (2011). Narrative research with audiovisual data: VideoIntervention/Prevention Assessment (VIA) and NVivo. International Journal of Social ResearchMethodology, 5(3), 245-261.5

Siccama, C., & Penna, S. (2008). Enhancing Validity of a Qualitative Dissertation Research Study byUsing NVIVO. Qualitative Research Journal, 8(2), 91-103.Wainwright, M., & Russell, A. (2010). Using NVivo Audio-Coding: Practical, Sensorial andEpistemological Considerations. Social Research Update, 60(1), 1-4.Welsh, E. (2002). Dealing with Data: Using NVivo in the Qualitative Data Analysis Process. ForumQualitative Sozialforschung / Forum: Qualitative Social Research, 3(2), Art. 26. Retrieved , P., López-Sánchez, F., & Sánchez-Gómez, M. C. (2012). Content analysis researchmethod with Nvivo-6 software in a PhD thesis: an approach to the long-term psychologicaleffects on Chilean ex-prisoners survivors of experiences of torture and imprisonment. Quality &Quantity, 46(1), 379–3906

word clouds, dendograms, and word trees. Module 2: Data Coding Module 2 introduces the different techniques to automatically and manually code qualitative data in NVivo. We start by autocoding data from structured interviews, which allows sorting large sections of data in thematic sections. Such data sorting - known as 'broad-