Analysing qualitative data

1. Analysingqualitativedata

2. Whatistheinput? - non-numericdata - notquantified - canbe a productof all researchstrategies -> ProceduresforanalysiscanbeBOTHdeductive and inductive - computeraidedqualitativedataanalysissoftware (CAQDAS)

3. Computeraidedqualitativedataanalysissoftware (CAQDAS) …usedinpsychology, marketing research, ethnographyetc + - efficientmeanstomanage and organizedata - rigorousdataanalysis - no manual and clericaltasks - savestime - manageshugeamountsofqualitativedata - increasesflexibility - improvesvalidity and auditabilityofqualitativeresearch - - increasinglydeterministic/ rigidprocesses - privilegingofcoding - reificationofdata - increasedpressuretofocus on volume/breadthratherthanon depth/meaning - time/ energyspentlearningtousecomputerpackages - increasedcommercialism - distractionfromthe real workofanalysis

4. Differencesbetweenqualitative and quantitativedata • Quantitativedata: - Basedon meaningsderivedfromnumbers - Collectionresultinnumerical and standardiseddata - Analysisconductedthroughtheuseofdiagrams and statistics • Qualitativedata: - Basedon meaningsexpressedthroughwords - Collectionresultsinnon-standardiseddatarequiringclassificationintocategories -Analysisconductedthroughtheuseofconceptualisation

5. Preparingyourdataforanalysis: transcribingqualitativedata • non-verbalinformationmayberelevant (pauses, laugh, sighs, coughs, thetoneofthevoice, thespeedoftalk) – notonly, whattheysaybuthowtheysayit • awfullytime-consuming – 6-10 h totranscribeeveryhourofaudio-recording • Accuratenessoftranscription – datacleaning • Saveeachinterviewasseparatefile; usefilenamethatmaintainsconfidentiality/ anonymity; helpsrecognizethetheperson • Distinguishbetweeninterviewer and participant(s) visually; useotheridentifiers – questionsinitalics, topicheadingsinboldetc; beconsistentacross all transcriptions • Havingthefullquestionintranscriptmaybeofimportanceifyouwanttounderstandlaterwhatthey are talkingabout :P • Planinadvance, howtheanalysiswillfollow – e.g., ifyouusesomeCAQDAS, remember, thattheymayrequiresometimes .txtfile so all yourhighlights, capitals and italicswillbegone :P

6. Aga nüüd vaatasime seda lõiku ja sa nägid seda enne ka ja sa ütlesid, et ta võttis selle telefoni ära. Mis sa arvad, miks ta selle ära võttis? • V:Ta tahtis endale saada. • K:Vist küll. A siin oli üks teine tegelane veel. See mees. Kas tema ka midagi valesti tegi sinu arvates? • V:Jah, et ta ei hoiatand teda. • K:Aga mis sa arvad, miks ta ei hoiatanud? • V:Ei tea • K:Mis siin valesti tehti? • V:[ei saa aru] et siin nad võtsid selle koti ära ja viskasid ära, et ta ei saaks seda kätte. Too teine, kes seda pealt nägi, nemad ei hoiatand seda poissi. • K:Täpselt. Mis sa arvad, miks need kaks poissi seda väiksemat siis niimoodi kiusasid? • V:Et neile vist meeldis. • K:Aga miks see tädi, kes seal juures oli, miks ta appi ei läinud? Mis sa arvad? • V:Ta tegeles parajasti millegi muuga ja tal polnd tahtmist appi minna. • K:Jah, ma arvan, et sul on õigus. • Mis siin siis valesti tehti? • V:Et [ei sa aru] aga ta tegelt oskas seda ise ka teha. • K:A sa arvad, et oskas ise ka. A mis sa arvad, miks see tädi ei aidanud? • V: Ta ei tahtnud vist. • K:Vist jah. • Nonii, mis siin valesti tehti? • V:Et nagu üks nagu midagi ütles, mingi suvaline inimene, lihtsalt, mis kell on. Et ta küsis lihtsalt, mis kell on. Et nagu vabandada, seda ta ei ütlendki • K:Ahah. Mis sa arvad, miks ta ei tahtnud öelda? • V: Sellepärast et ta seal mõtles, et mingi suvaline inimene ja pole pole üldse lahke, et ta ei tahtnud talle ütelda.

7. Anoverviewofqualitativeanaysis: fourmaincategoriesofstrategies • Understandingthecharacteristicsoflanguage • Discoveringregulatities • Comprehendingthemeaningoftextoraction • Reflection Dimensionstodifferentiatetheapproachestoqualitativeanalysis: Lessstructured-------Morestructured Interpretivist------Procedural Inductive-------Deductive

8. Basic procedurescommontodifferentapproachesofqualitativedataanalysis: 1) categorisation Helpsyou: - Comprehendand manage yourdata; - Integraterelateddatadrawnfromdifferenttranscripts and notes; - Identifykeythemesorpatternsfromdataforfurtherexploration; - Developand/or test theoriesbased on theseapparentpatterns and relatioships; - Drawand verifyconclusions

9. Categories: • maybederivedfromthesedataorfromyourtheoreticalframework • Haveto „fit“ withwhatyouhaverevealed – withdata • Codes/ labels, giving a structureforthedata • Identificationofthecategories -> purposeofyourresearch • itispossibletointerpretethesamequalitativedataverydifferently • Internalaspectofcategory– meaningfulinrelationtothedata • Externalaspectofcategory– meaningfulinrelationtoothercategories

10. 2) „Unitising“ data - unit - chunkorbitoftextualdatathatfitsthecategory and carriesdiscretemeaning 3) Recognisingrelationships and developingcategories • searchforkeythemes/patterns /relationships • reviseyourcategories • keep anup-to-datedefinitionof all thecategories

11. 4) Developing and testinghypothesesorpropositions • testingrelationshipsbetweenvariables • seekingalternativeexplanations/ negativeexamples • consideringpossibleinterveningvariables

12. Analyticalaids: a recordofadditionalcontextualinformation • Summaries – aftereverydatacollectionset-> a summaryofthekeypointsthathavearised; thinkon alternativeideastoexploreyourquestion; identifyapparentrelationshipsbetweenthemes -> checktheirvalidity; contextualnotes – setting, changes, personsetc. • Self-memos– torecordideasaboutanyaspectofyourresearch. Omittingtorecordanidea -> itwillbelost – itisproved! • Researcher’sdiary- recordingideas -> youcanlaterfollowthedevelopmentofthembecauseofthechoronogicalform

13. Approachestoqualitativeanalysis • Deductive– using a theoreticalordescriptiveframework - useofexistingtheorytoformulateresearchquestion-> theoreticalpropositionsmaydevise a frameworktoorganise/ directthedataanalysis. • Advantage– link yourresearchintotheexistingbodyofknowledgeinyoursubjectarea • Inductive – exploringwithout a predeterminedtheoreticalordescriptiveframework -to start collectingdata/ exploringthem-> findingthemestoconcentrate on. • Analysethedataduringcollectingit, developinga conceptualframeworktoguidethesubsequentwork Inpractice-> combiningtheelementsfrombothapproachesas at certainpointsyoumay need todevelopsometheoreticalpositionto test itsapplicability; and at some moment younoticethatthetheoreticalframeworkyouchooseddoesnotyielda goodanswertoyourresearchquestion

14. Deductively-basedanalyticalprocedures • Patternmatching - predictinga patternofoutcomesbased on theoreticalpropositionstoexplainwhatyouexpecttofind. Twovariations • Explanationbuilding – anattempttobuildanexplanationwhilecollectingdata and analysingthem. processofexplanationbuilding- iterative

15. Inductively-basedanalyticalprocedures Reasonsforadoptinganinductiveapproachfortheanalysisofdata: • need foranexploratoryprojectseekingtogenerate a directionforfurtherwork • thescopeofyourresearch-> constrainedbytheoreticalpropositionsnotreflectingparticipant’sviews/ experience. Theuseofinductiveapproachshouldallow a good „fit“ betweenthetheoryyoudevelop and thesocialrealityoftheparticipants • thetheorymaybeusedtosuggestsubsequentactiontobetakenbecauseitisspecificallyderivedfromtheevents and circumstancesofthesettinginwhichtheresearchwasconducted YoushouldNOTUSEinductiveapproachtoavoid a properlevelofpreparation!

16. Datadisplay and analysis • summariseand simplifythedata; selectivelyfocus on some parts ofit; theaim istotransform and condensethedata • organiseand assembleyourreduced and selecteddataintosomediagrammaticorvisualdisplays (matrixornetwork);recognizingtherelationships and patterns/ drawingconclusions and verifyingtheseiseasierbytheuseofdatadisplays

17. Templateanalysis • list of thecodesorcategoriesthatrepresentthemesrevealedfromthedata. • Thecodeswillbepredetermined and thenamended/ addedifthedatarequiresit • Dataare coded and analysedtoidentify and explorethemes, patterns and relationships. • codesand categoriescanbeshownhierarchically • Thecodes at differentlevelofanalysismaychangetheirpositionduringtheprocess • What’sthepointof all this? • …analyticaltechniquethroughwhichtodeviseaninitialconceptualframeworkthatwillrepresent and explorekeythemes and relationshipsinthedata; helpyoutoidentifynew, emergentissuesthatarisethroughtheprocessofdatacollectionand analysis

18. Analyticinduction …inductiveversionoftheexplanation-buildingprocedure – „intensiveexaminationof a strategicallyselected number ofcases so astoempiricallyestablishthecausesof a specificphenomenon“.

19. Groundedtheory …tobuildanexplanationortogenerate a theoryaroundthecentralthemethatemergesfromyourdata. Theprocessmaybemoreorlessstructured and systematic. There are differentstagesofgroundedtheoryprocedures: • Opencoding– thedatawillbedisaggregatedintoconceptualunits and providedwith a label. Inthatwayyoumayfind a multitudeoflabels, thatyou need toplaceintobroader, relatedgroupingsorcategories. Thiswillproduce a moremanageable and focuseddataset • Axialcoding– processof looking forrelationshipsbetweenthecategoriesofdatathathaveemergedfromopencoding. Asrelationshipsbetweencategories are recognised, they are re-arrangedinto a hierarchicalform, withtheemergenceofsubcategories. The aim istoexplore and explainthephenomenonbyidentifyingwhatishappening and why; tofindoutwhatenvironmentalfactorsaffectthis; howitisbeingmanagedwithinthecontextbeingexamined, and whattheoutcomes are oftheactionthathasbeentaken. • Selectivecoding– duringdatacollection, itislikelythatyouwillfindtheprincipalcategoriesand relatedsubcategories – corecategorieswillbebaseofyourgroundedtheory

20. Quantifyingqualitativedata • tocountthefrequencyofcertainevents, particularreasonsthathavebeengiven, orinrelationtospecificreferencesto a phenomenon • frequenciescanbedisplayedas a tableordiagram • canbeproducedusingCAQDASprograms; exportedtostatisticalanalysissoftware • consideredasmethodoflimitedvalue -> donotdemonstratethenature and valueofyourqualitativedata, being a simplifiedformofit