Data, methods and units of analysis

1. Data, methods and units of analysis PhD Research Design Course Tobias Hagmann, Roskilde University thagmann@ruc.dk January 30, 2018

2. Objectives of this session Think through four key questions that are vital for your doctoral project and its research design, namely: • What is my unit and level of analysis? • What’s my data like and where can I find it? • What is my thesis (really) about? • How do I do it?

3. Question 1Whatis my unit and level of analysis?

4. Unit and level of analysis -> unit of analysis and level of analysisarecrucial for a good research design 1) unit of analysis: • whatexactly do I study? • sometimes referred to as ‘variables’ (dependent/independent) • causal relations between units of analysis (ex: relation between X and Y) • don’tconfuseconcepts with units of analysis (although overlap) 2) levelof analysis: • geographical perimeter, conceptualboundary, level of aggregation • knowing the boundaries of one’s research topic (and unit of analysis), but alsoitsrelevance for the broader population of units

5. Don’tbemislead by yourdiscipline • The unit of analysis is in the eye of the beholder. (‘Le point de vue créel’objet’, Bachelard). • Academic disciplines have theirfavorite units of analysis(whichoftenbecame ‘normalized’). • Knowledge is compartementalized as differentdisciplines ‘colonize’ particular units of analysis, eventhoughtheyrefer to same or similarempiricalrealities and social phenomena. • Be conscience of howyousituateyourselfwithinyourdiscipline (and field of study).

6. Discuss with yourneighbour • What is mymain unit of analysis? • What is mymainlevel of analysis? • How is what I do different from whatothersaredoing, have done?

7. Question 2What’smy data like and wherecan I find it?

8. Empirical reality is complex • Social scienciststry to find, at times impose uniform, neatlydelineatedrationalities, logics, and causalities, but empirical reality and data areoften • ambiguous and contradictory • incomplete and messy • the result of preselection and previous definitions • difficult, at times impossible to access • Do not assumethatyourtheory is more complicatedthanyourempiricalmaterial! • The betteryourknowyour ‘empirics’, your ‘data’, the betteryourthesis. • Good PhDthesestypically provide original (primary) empiricalmaterial.

9. Locatingprimary data • the betterweknowwhereour data ‘sits’ (or ‘lives’), the higher the chance thatwecanaccess and document it • our ‘data’ exists in multiple forms: • primary versus secondary • visible and invisible • individual and collective • expert versus popular • embodied and disembodied • material and symbolic • locating data is a continuos ‘try and error’ process (need to remain open throughout research process)

10. Actors and data • social actors (= human beings) produce and store information or ‘data’ in different forms: • throughtheireverydayexperiences (practices) • through learning and exchanging with otherhumans (knowledge) • by forming moral judgementsabout the world (moral conceptions) • by codifying data themselves (databases, archives) • but alsoforget and ‘cloud’ information includingtheirownexperiences(temporality of remembered events) => all of the above is data wecanuse to explain social phenomena

11. Sites and data • data and actorsarelinked to particular sites, places and spaces, whichneed to beidentified • a research site has its • own potential and limitation for data collection(ex. marketplace vs. private home) • is differentiated in terms of access (public, semi-public, ‘specialized’, private) • has itsownspatiality and temporality • the more youbecomeassociated with a site, the better chances for data collection (but alsorisk of being ‘sucked in’)

12. Application and discussion Take some 10 minutes and produce a tableindicating • maindifferent types of data thatinformsyourthesis • actors or institutions thatpossess or producethis data • sites whereyoucangather (or have gathered) this data Try to be as specific as possible!

13. Question 3What is mythesis (really) about?

14. Thinking of yourthesis as a ‘case’ • Not case study research design, but: doctoral research object as a case thatrelates to a broader population of cases • Forces us to thinkthrough relations between: • the specific and the general • the concrete and the abstract • the unique and the generic • Identifydifferentlevels of abstractionthatarealwasys present in a (doctoral) research project

15. Key points Flyvbjerg (2006) • Misunderstanding 1: General, theoretical (context-independent) knowledge is more valuable than concrete, practical (context-dependent) knowledge. • Misunderstanding 2: One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. • Misunderstanding 3: The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are moresuitablefor hypotheses testing and theory building. • Misunderstanding 4: The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. • Misunderstanding 5: It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies.

16. Flyvbjerg 2006

17. Key points Lund (2014)

18. The ‘bible’ of case study research design • Yin, R. K. (2003). Case Study Research: Design and Methods. London and New Delhi, SAGE Publications.

19. Discussion in class • What is your case? • What does it contribute to your discipline or field? • What is particular about your case (what makes it special?)? • What are the limits of generalizability of your case? • How does your case connect to broader theories and theory development? • Who will care about your case (which stakeholders)?

20. Question 4How do I do it?

21. Methodologicalchoices • In principlechoice of methods is guided by research question and objective. • In reality it is often the result of a mixture of: • pragmatism • personalpreference • ‘howothers have done it’ • Keep in mind: methodsareoften the most ‘practical’ and ‘applied’ skillthatyouuse for yourthesis. Choosethemwisely. Theywillco-determineyour future academicprofile.

22. Data access and the researcher • Research design and field research data collectionneed to takeintoaccount the researcher’spositionality, in particulargender, age, nationality, religion, class etc. • Choose a data collectionstrategythatmaximisesyour ‘identity’ and positionality(and identifyyour limits explicitly). • Considerpersonalskills, preferences and interests in designing data collectionstrategy. • Use ‘positionality’ as a comparativeadvantage: what kind of data canyouaccess and analyzethatnobodyelsecan?

23. Differentmethods, mixingmethods • qualitative vs. quantitative research traditions (Goertz & Mahoney, 2012) • differentlogics of inference: typically large-N cross-case analysis vs. small N or in-case analysis • in reality every social phenomenoncanbebothqualified and quantified (and data can at times betransformed from one to another) • short-sighted ‘hatred’ between the two research traditions • different types of mixed methodsapproaches (seeCreswell, 2013) • ‘complementary social science’ (Blok & Pedersen, 2014)

24. Discussion in class • What is your positionality? • Which kind of data, actors and sites can you access easily – which ones require extra effort – and which ones are off limit? • How will ‘informants’ perceive you and your research topic? • How do your personal skills, preferences and interests inform your data collection strategy? • What kind of data can you produce that nobody else can? • What motivates your choice of method?