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Transcription as Friend and Foe
01: Exactly. And, as far as doing it, the other, I think the biggest
obstacle, is training. Is getting=
G Is opportunity.
01 It is an opportunity. But . . .
Group ((chuckles))
01 . . . it is an obstacle as far as the [district is concerned.]=
G =[It is hard to not say it.]=
01 Because they will not give that time to really teach and train.
Even, you know, I'm gonna walk in as the . . . the real Luddite.
And be able to walk out and feel like I can go out and use the
equipment. Not just say it.
11 Yeah.](https://image.slidesharecdn.com/qualitativemessymarch2013-130327002548-phpapp01/85/Messy-Research-How-to-Make-Qualitative-Data-Quantifiable-and-Make-Messy-Data-Understandable-23-320.jpg)
Uploaded byGigi Johnson
Messy Research: How to Make Qualitative Data Quantifiable and Make Messy Data Understandable
This document discusses qualitative research methods for business. It addresses challenges in making qualitative data understandable for real decisions. It discusses why businesses conduct research, how to determine what data and analysis is needed, and issues with determining "truth" in business contexts. Finally, it discusses four types of qualitative data and focuses on making qualitative data more quantitative by addressing issues like validity, sample sizes, and coding consistency.
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Messy Research: How to Make Qualitative Data Quantifiable and Make Messy Data Understandable
- 1. 1 MESSY RESEARCH How toMake Qualitative Data Quantifiable and Make Messy Data Understandable Dr. Gigi Johnson
- 2. Core ⢠When to chose it Qualitative ⢠Major challenges in design Issues and analysis ⢠How to tell messy stories for real decisions and action by business
- 3. 3 Why ⢠Make a decision Business ⢠Deploy resources as an Research organization ? ⢠Convince others in organization ⢠Rule out alternatives ⢠Influence certain people in the company ⢠Understand change ahead of company
- 4. 4 How Much DoWe Need? ⢠How much data and what data? ⢠From where? ⢠What analysis do we need to do? ⢠What narrative/presentation is enough? Often, in business research, we tend to focus on volumes . . . . . . missing focus on analysis and presentation for decision-making
- 5. 5 What is Truthin Business? ⢠What is enough information to make a decisionâor real âTruthâ? ⢠How much information of what type is âenoughâ? ⢠How messy will this be and still be ânormalâ? ⢠Who can best find the data? ⢠Which people know what part of the answers we need? ⢠Is the core data we need reachable? ⢠What is our role as researcher with perspective?
- 6. 6 4 Types ofQualitative Data Leech, N. L., & Onwuegbuzie, A. J. , 2005
- 7. 7 Focus on Quantitative Issues Design characteristic ⢠Validity ⢠Sample sizes ⢠External Validity: ⢠Statistical significance generalize findings across ⢠Sampling bias populations, tasks, and ⢠Coding consistency environments (Campbell ⢠Control groups & Stanley, 1966) ⢠Pre- and post-testing ⢠Internal Validity: Design rule out other factors ⢠Instrument design (tested other than the validity) Independent Variable
- 8. 8 Related Issues withQualitative Issues Design characteristic ⢠Trustworthiness ⢠Triangulation ⢠Code/Recoding ⢠Truth Value/Credibility ⢠Technique ⢠Applicability/Fitness/ ⢠Member Check (show analysis Transferability to participants) (Janesick, ⢠Consistency/ 2000; Merriam, 1998) Dependability ⢠Interview corroboration ⢠Neutrality/ ⢠Peer debriefing Confirmability ⢠Auditability ⢠Bracketing ⢠Balance ⢠(Guba 1981, Schmid, 1981) ⢠(Lincoln & Guba, 1985)
- 9. 9 WHAT IS QUALITATIVE? Whenshould we use it?
- 10. 10 Qualitative vs. Quantitative Quantitative Qualitative â˘Helpful when âanswering â˘Looking at single case or questions of who, where, small number of cases how many, how much, and â˘Looking at in-context what is the relationship situation, framed by words between specific variablesâ and narratives (Adler, 1996, p. 5) â˘Looking for in-context â˘Striving for causation or for relationships and generalizing to larger connections populations â˘Creating hypotheses or instruments for quantitative
- 11. 11 Qualitative Can EnrichQuantitative Examples: ďâPrebriefingâ (Collins et al., 2006), checking potential quantitative survey participants for willingness and suitability ďPilot study to assess the appropriateness of an instrument like a questionnaire or survey ďRuling out hypotheses
- 12. 12 Challenge of Qualitative ⢠Difficulty in capturing lived experiences via text ⢠Creating a âbricolageâ â an assemblage of representations that fit a complex situation (Denzin & Lincoln, 2005) Use of Qualitative Analytical tools helps connect this complex in-context environment into a way that others can understand.
- 13. 13 DESIGNING THE RESEARCH Goals Narratives Populations& Samples Data Instruments
- 14.
- 15. 15 Populations and âLevelâ â˘Populations: Total target group ⢠AMR: Group could be a regional or business population, or could be members at a level in the organization ⢠Sample: Group in study
- 16. 16 âWhoâ has theData? ⢠Thinking in terms of Five Forces ⢠Vendors, Customers, Competitors ⢠Reasons to share ⢠âKnowingâ ⢠Belief, research, or connections ⢠Expert does not mean âknowsâ real information ⢠Similar question: Secondary Research and connecting Primary to it
- 17. 17 Research Methods ⢠DocumentAnalysis ⢠Focus Groups ⢠Observations ⢠Interviews ⢠Shadowing ⢠Participant Observation ⢠Literature Review ⢠Oral History/Ethnography ⢠Social Network Analysis (SNA) ⢠Quantifying/mapping context
- 18. 18 Literature Review ⢠Checkout what research has been done on the research methods that you are considering, e.g., focus groups, narrative research, document analysis ⢠Google Scholar: Good launching pad
- 19. 19 Sampling Methods andSize ⢠Quantitative: Concern with probabilities and similarities to overall population ⢠Qualitative? ⢠Snowball sampling: uses social networks and connections to identify unknown populations ⢠Convenience sampling ⢠Judgment Sample: based on framework of variables from researchers ⢠Maximum Sampling, Extreme ⢠How much is enough? ⢠Saturation (repeated patterns) (Rubin & Rubin, 1995).
- 20. 20 Instruments ⢠Creating aQuestionnaire ⢠Focus Group â Outline, Objectives ⢠Surveys â may be instruments already tested for validity ⢠Interviews ⢠Open Ended ⢠Semi-Structured ⢠Test and plan coding methods upfront; what will you input the answers into? ⢠Grounded Theory: Grand Tour Question(s)
- 21. 21 DOING THE DARNED RESEARCH Recordingand measurement Transcription Field Notes
- 22. 22 Recording as Strategy Methods ďVideo ďAudio (including cell phone) Issues ďAffect on Outcomes: Performance ďSecurity/Storage ďPermission ďTranscription ďGlitches/Errors: Multiple devices
- 23. 23 Transcription as Friendand Foe 01: Exactly. And, as far as doing it, the other, I think the biggest obstacle, is training. Is getting= G Is opportunity. 01 It is an opportunity. But . . . Group ((chuckles)) 01 . . . it is an obstacle as far as the [district is concerned.]= G =[It is hard to not say it.]= 01 Because they will not give that time to really teach and train. Even, you know, I'm gonna walk in as the . . . the real Luddite. And be able to walk out and feel like I can go out and use the equipment. Not just say it. 11 Yeah.
- 24. 24 Undercurrents from FieldNotes ď Individual impressions ď Notes before and after sessions ď Bring your own biases, context, and observations to the table
- 25. 25 ANALYZING THE DARNED RESEARCH UsuallyNOT in qualitative business research plans
- 26. 26 Designing the Analysis â˘Not just casually connecting ⢠Causality vs. Correlation ⢠Two analysis directions ⢠Old-fashioned and robust ⢠Excel worksheets or written on documents ⢠Hand coding and counting ⢠Alternatives ⢠Computer-assisted data qualitative data analysis software (CAQDAS)
- 27. 27 Recursive Abstraction ⢠Fancy phrase for summarizing, then summarizing the summaries ⢠Usual accidental business research method ⢠Helps to have consistent methods for summarizing between coders/team members, or a coding worksheet
- 28. 28 Coding ⢠Chunking textdata, then adding a code ⢠You can code and iteratively recode/emergent (Tesch, 1990). ⢠Method: aimed to continue to narratively code while bridging to new ideas and surfacing new categories until you began to find pattern codes and themes (Miles & Huberman, 1994).
- 29. 29 Key Phrase Frequency â˘Word counts are based on the belief that all people have distinctive vocabulary and word usage patterns. ⢠âLinguistic fingerprintsâ (Pennebaker, Mehl, & Niederhoffer, 2003, p. 568). ⢠Gives context to words like âmany,â âfrequently,â etc. terms are fundamentally quantitative.
- 30. 30 KWIC Keywords-in-context (KWIC; Fielding& Lee, 1998) â˘Data analysis method that reveals how respondents use words in context â˘Compares words that appear before and after âkey wordsâ
- 31. 31 Narrative Analysis (NA) â˘(Nearly) all qualitative research is filtered by contexts, beliefs, and methods of communication ⢠NA evaluates patterns, threads, tensions, and themes within the transcripts and field notes (Clandinin & Connelly, 1994, 2000; Ryan & Bernard, 2000). ⢠Can pull out portions of text where themes are mentioned (Ryan & Bernard, 2000)
- 32. 32 Triangulation ⢠Assesses theintegrity of the inferences that one draws from more than one vantage point (Lincoln & Guba, 1985) ⢠Use of multiple data sources, multiple researchers, perspectives, tools, and/or methods (Denzin, 1989; Schwandt, 2001) ⢠Adds confirmability, dependability, and credibility to data collection
- 33. 33 USING ANALYSIS TOOLS Examples QuantitativeAnalysis (CAQDAS) Transcription Data Visualization
- 34. 34 2 Reasons forTools ⢠Help the team gather, sort, visualize, and engage messy and abundant qualitative data ⢠Explain and convince client of validity of research done ⢠Ability to walk through analytical process and explain the patterns in the data
- 35. 35 Example: Express Scribe http://www.nch.com.au/scribe/index.html
- 36.
- 37. 37 CAQDAS â˘Tools for recording, storing, indexing, content searching, mapping/networking, and sorting data (Lewins & Silver, 2005; Morse & Richards, 2002) The University of Surreyâs CAQDAS Networking Project Reviews: http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing/
- 38.
- 39. 39 DATA VISUALIZATION AND INFOGRAPHICS Findingthe Story Telling the Story Persuading Change with the Story
- 40. 40 Using Data toTell and Be the Story ⢠Abundant Data (âBig Dataâ) from in-context data collection in our connected world ⢠Social Network Analysis (SNA) â how we are all connected ⢠Big company problem â large volumes of data to digest and act upon ⢠âInvestigating relationshipsâ â not just for presentation, but for research teams to visualize emerging patterns
- 41. 41 Concept Mapping: One Methodof Data Visualization
- 42. 42 ⢠Great list: Data http://dailytekk.com/2012/02/27/over-100-incredible-infographic-tools-a Visualizatio ⢠Piktochart â Transforms your information into memorable presentations. ⢠Infogr.am - Create interactive charts and infographics. n Infographics for ⢠Gephi â Like Photoshop for data. Graph visualization and manipulation software. Decisions ⢠Tableau Public - Free data visualization software. ⢠Free Vector Infographic Kit â Vector infographic elements from (others in MediaLoot. Appendix slides) ⢠Weave â Web-based analysis and visualization environment. ⢠iCharts â Charts made easy. ⢠ChartsBin â A web-based data visualization tool. ⢠GeoCommons â See your data on a map. ⢠VIDI â A suite of powerful Drupal visualization modules. ⢠Prefuse â Information visualization software. ⢠StatSilk â Desktop and online software for mapping and visualization. ⢠Gliffy â Online diagram and flowchart software. ⢠Hohli â Online charts builder. ⢠Many Eyes â Lets you upload data and create visualizations. ⢠Google Chart Tools â Display live data on your site.
- 43. 43 Questions ? Dr. Gigi Johnson @maremel Maremel Institute
- 44. 44 Playing ⢠Wordle â http://www.wordle.com with â fun tool to turn words from Words documents into word maps ⢠Tagxedo -- http://www.tagxedo.com â similar to Wordle, Tagxedo lets you create word clouds and sculptures from URLs, Tweets, and other social media documents, as well as export them into a variety of formats.
- 45. 45 We can tinker with maps, both as pre- Playing made images as well as data-driven with Maps tools. â˘Web Resources Depot -- http://www.webresourcesdepot.com/free-vector-w -- shares a variety of world map images for use â˘Free PSD Files -- http://freepsdfiles.net/3d-renders/3-isolated-3d-us -- This site has easy images for further editing for presentations â˘GunnMap -- http://www.gunn.co.nz/map -- creates world maps with your data â˘StatPlanet Map Maker -- http://www.statsilk.com/software/statplanet -- also creates interactive maps
- 46. 46 ⢠Several tools let you expand how you lay out Playing concept maps and linked ideas: ⢠FreeMind -- http://freemind.sourceforge.net â I enjoy with this free tool. Graphically simple, it lets you play with a free tool for mind mapping that can be adapted Concept into all sorts of other applications. ⢠Webspiration â http://www.webspirationpro.com â I Maps miss its freemium mode; it now has a trial period and then costs $6/month. I found Inspiration and Webspiration wonderful for group presentations and immediate work. ⢠MindManager -- http://www.mindjet.com â this concept management tool starts at $20/month for one and discounts for group collaboration. ⢠MindNode -- http://www.mindnode.com â This tools for Mac computers comes at a moderate price -- $20 for the mac and $10 as an iOS Apps. ⢠VUE by Tufts -- http://vue.tufts.edu -- I really enjoy this âVisual Understanding Environmentâ tool, which combines concept maps with search and graphics. â˘
- 47. 47 ⢠Prezi -- http://www.prezi.com -- My recent Presentations undergraduate class spent half of their projects in Prezi, which has a zooming camera metaphor across a vast digital white board. They enjoyed putting in music, video, and other embedded content. I got a bit dizzy, but enjoyed the creativity. ⢠Sliderocket -- http://www.sliderocket.com -- Several of my students enjoyed using Sliderocket for class presentations. It gave them a robust and elegant toolset to work with. ⢠Brainshark -- http://www.brainshark.com -- Friends who are professional business development executives heartily recommend Brainshark as a way to pre- package and present content at a distance.
- 48. 48 ⢠Google Charts API - http://code.google.com/apis/chart/ -- you can use Graphs Google Charts to create animations in charts, dashboards, and lots of other goodies ⢠Gliffy -- http://www.gliffy.com/ -- I just found Gliffy, a great diverse creator and of charts and graphs. Different versions of it work with different social workspace/sharing software: ⢠Hohli -- http://charts.hohli.com â free online chart builder Charts ⢠Creately -- http://creately.com -- (paid but cheap at $5/month/person) is a online tool to build charts, and collaborate around them ⢠Many Eyes -- http://www-958.ibm.com/software/data/cognos/manyeyes/ -- an experiment by IBM Research and the IBM Cognos software group let users create and evaluate data visualizations. ⢠GGobi -- http://www.ggobi.org/ -- free data visualization tool for your datasets ⢠Mondrian -- http://www.rosuda.org/Mondrian/ -- open source toolset for charting and graphing data plots and more complex graphs and data- driven visuals ⢠OpenDX -- http://www.opendx.org -- Older open source software, based on IBMâs visualization data explorer. ⢠Spotfire -- https://silverspotfire.tibco.com â a whole visualization suite, free for individuals for the first year, then $99/year thereafter. ⢠Visualizefree -- http://www.visualizefree.com/ -- Sampler of more complex system; shows real-time images from the FAA of flights as a sample ⢠Mycrocosm -- http://mycro.media.mit.edu/ -- quirky tool to create displays of your own personal data that you can input by cell or email and track
- 49. 49 ⢠Hans Roslingâs Gapminder Playing Foundation worked with Trendalyzer, which then was sold to Google in 2007, then with folded away when Google Labs. Motion ⢠VIDI -- http://www.dataviz.org/ -- VIDI Data, run by the Jefferson Institute, provides a Charts visualization module for Drupal CMS to show motion charts, timelines, geodata, and comparative data. ⢠TrendCompass -- http://epicsyst.com/trendcompass -- lets you add your own data to their data visualization tool if you register ⢠Eurostat Explorer -- http://www.ncomva.se/flash/explorer/eur o/ -- sample with EU data that can be played with using a motion graphic.
- 50. 50 Playing ⢠Tweakersoftâs Vector Designer -- http://www.tweakersoft.com/vect with ordesigner.html -- This $20 Mac Images App helps users create simple vector designs. ⢠GIMP -- http://www.gimp.org -- For those who would want to tinker with Photoshop, but wince at the pricetag, GIMP (âGNU Image Manipulation Programâ) is an open source alternative. ⢠Inkscape -- http://inkscape.org/ -- open source vector graphics â˘
- 51. 51 Playing There are lots of extensive tools to work with large public databases. with Data â˘Google Public data Resources -- http://www.google.com/publicdata -- From the creators of abundant and specialized search comes search just for public data sources â˘Visualizing.org -- http://www.visualizing.org/data/br owse and http://www.visualizing.org/ data/channels -- Visualizing provides links to all sorts of sample and interesting data sets
- 52. 52 ⢠KDnuggets News newsletter on Data Mining Additiona and Knowledge Discovery l Tools -- http://www.kdnuggets.com/software/visualiza tion.html -- longer list of free and paid data visualization tools
- 53. 53 Select References ⢠Leech,N. L., & Onwuegbuzie, A. J. (2007). An array of qualitative data analysis tools: A call for qualitative data analysis triangulation. School Psychology Quarterly, 22, 557-584. ⢠Lewins, A., & Silver, C. (2007). Using Software in Qualitative Research: A Step-by-Step Guide, Sage. ⢠Lewins, A. (2008). CAQDAS: Computer Assisted Qualitative Data Analysis' in (ed) N. Gilbert, Researching Social Life (ed.)(3rd ed). London: Sage. ⢠Lewis, R.B., & Maas, S.M. (2007). QDA Miner 2.0: Mixed-Model Qualitative Data Analysis Software, Field Methods 19: 87-108 ⢠Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage. ⢠Silver, C., & Fielding, N. (2008). Using computer packages in qualitative research. In C. Willig & W. Stainton-Rogers (eds.), The Sage Handbook of Qualitative Research in Psychology. London: Sage.
Editor's Notes
- #3Â When should we use qualitative research versus other methods? What are the major challenges in design and analysis ? What should we consider for an AMR Project with qualitative research versus what we might do for a published research project or for our own professional work?