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AARHUS UNIVERSITY DEPARTMENT OF POLITICAL SCIENCE Process Tracing methods – an introduction Ph.D. workshop University of Konstanz, Germany March 16, 2012 Derek Beach, PhD Associate Professor Department of Political Science University of Aarhus, Denmark Email: [email protected]
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Page 1: Process Tracing methods – an introduction

AARHUS UNIVERSITY

DEPARTMENT OF POLITICAL SCIENCE

Process Tracing methods – an introduction Ph.D. workshop

University of Konstanz, Germany

March 16, 2012

Derek Beach, PhD Associate Professor Department of Political Science University of Aarhus, Denmark

Email: [email protected]

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AARHUS UNIVERSITY

DEPARTMENT OF POLITICAL SCIENCE

Outline

1.  WhatisProcessTracing?

2.  Whatarecausalmechanisms?

3.  ThreevariantsofPT

4.  CausalinferenceinPT

5.  Studyingcausalmechanisms?

6.  WhencanPTbeused,andnotused?

2

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1.WhatisProcesstracing?

Single case researchmethod that canbeused tomakewithin‐case inferencesabout

presence/absenceofcausalmechanisms

3

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DEPARTMENT OF POLITICAL SCIENCE

1.WhatisProcesstracing?

‘thecause‐effect linkthatconnects independentvariableandoutcomeisunwrapped

anddividedintosmallersteps;thentheinvestigatorlooksforobservableevidence

ofeachstep.’(VanEvera1997:64).

‐focusisonstudyingcausalmechanismsusingin­depthsinglecasestudy

4

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DEPARTMENT OF POLITICAL SCIENCE

1.WhatisProcesstracing?

KKV,Gerring–casestudymethodsmoreanalogoustomedicalexperiment

‐inperfectworldmeasureeffectoftandconsameunit(UtandUc)

‐analyzemeancausaleffects

PT–closertocriminaltrial

‐ evidence assessed for each part of explanation (mechanism) to detect whether it can be concluded

beyondreasonabledoubtthatmechanismexisted

6

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DEPARTMENT OF POLITICAL SCIENCE

2.Whatarecausalmechanisms?

:atheoryofasystemofinterlockingpartsthattransmitscausalforcesfromXtoY

(Glennan,1996,2002;Bunge,1997,2004;Bhaskar,1979).

7

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DEPARTMENT OF POLITICAL SCIENCE 8

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DEPARTMENT OF POLITICAL SCIENCE 9

USgovernmentworksforensuringan‘OpenDoor’,de^inedasaninternationalpoliticalsystem

conducivetotradeand

investment,inWesternEuropeUSstrivesfor

singlemarket

USdecisionmakersbelievethatprosperityisthekeytoUSsecurity

USdecisionmakersbelievethatUSprosperitydependson

foreignmarkets,inparticularontheeconomicrevivalof

WesternEuropeafterWWII

(withclosure,thefearisthatUSwouldneedaregimented,state‐plannedeconomy)

USgovernmentusestoolsavailabletopressureWesternEuropetoadopt

economicopenness(e.g.usingtheMarshall

Plan)

USgrandstrategy=

extraregionalhegemony‐USactsasregional

stabilizerinWesternEurope‐USensuresthatcountriesaregovernedby‘rightkind’ofgovernment

RelativepowerofUSvis­a­visothergreatpowers

X Causalmechanism(OpenDoor) outcome

Layne’s case-specific Open Door mechanism

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DEPARTMENT OF POLITICAL SCIENCE

Regularityunderstandingofcausality

‘…thedifferencebetweenthesystematiccomponentofobservationsmadewhenthe

explanatoryvariabletakesonevalueandthesystematiccomponentofcomparable

observationswhentheexplanatoryvariablestakesonanothervalue.’

(King,KeohaneandVerba,1994:81‐82,italicsadded).

10

2.Whatarecausalmechanisms?

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DEPARTMENT OF POLITICAL SCIENCE

Mechanismicunderstandingofcausality

‐ Openup‘blackbox’betweenXandY

‐the dynamic, interactive in^luence of causes upon outcomes, and in particular how

causal forces are transmitted through a series of interlocking parts of a causal

mechanismtocontributetoproduceanoutcome.

11

2.Whatarecausalmechanisms?

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DEPARTMENT OF POLITICAL SCIENCE

2.Whatarecausalmechanisms?

‘…Amechanismisasetof interactingparts–anassemblyofelementsproducingan

effectnotinherentinanyoneofthem.Amechanismisnotsomuchabout‘nutsand

bolts’ asabout ‘cogsandwheels’ – thewheelworkoragencybywhichaneffect is

produced.’(Hernes,1998:78,italicsadded)

12

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AARHUS UNIVERSITY

DEPARTMENT OF POLITICAL SCIENCE

2.Whatarecausalmechanisms?

Parts = factors that are individually necessary parts of mechanism, composed of

entitiesthatengageinactivities(notinterveningvariables!)

Entities=objectengaginginactivities(noun)

Activities=producersofchangeorwhattransmitscausalforcesthroughCM(verbs)

13

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AARHUS UNIVERSITY

DEPARTMENT OF POLITICAL SCIENCE

2.Whatarecausalmechanisms?

14

X Y

Scope conditions

*

causal mechanism

activities

entities

part 1 part 2

noun

verb

noun

verb

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AARHUS UNIVERSITY

DEPARTMENT OF POLITICAL SCIENCE

2.Whatarecausalmechanisms?

‐  MechanismsareNOTaseriesofinterveningvariables

‐  (examplefromRosato,2003:585)

15

Democracy accountability groupconstraint Peace

Independentvariable

Dependentvariable

Causalmechanism

Interveningvariable1

Interveningvariable2

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2.Whatarecausalmechanisms?

16

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Discussion

1.  Developaplausiblecausalmechanismthatcanexplainwhyeconomic

development(X)contributestoproducedemocratization(Y)throughthecreation

ofaneducatedmiddleclass.

17

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DEPARTMENT OF POLITICAL SCIENCE

3.ThreevariantsofProcessTracing

1.  Theory‐testing

2.  Theory‐building

3.  Explainingoutcome

18

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DEPARTMENT OF POLITICAL SCIENCE

3.ThreevariantsofProcessTracing

19

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DEPARTMENT OF POLITICAL SCIENCE 20

Theory‐testing

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DEPARTMENT OF POLITICAL SCIENCE 21

Theory‐building

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DEPARTMENT OF POLITICAL SCIENCE 22

Explainingoutcome

Empirical,case­speci.ic

level

’Facts’ofthecase(egasempiricalnarrative)

Inductivepath

Suf^icientexplanationofoutcome?

Deductivepath

either

Continueuntil

suf^icientexplanation

Theoreticallevel

Causalmechanisms=>systematicCM,case‐speci^ic(non‐systematic)CM,case‐speci^iccombinationofsystematicCM(eclectictheorization)

1

3

1

2

3

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DEPARTMENT OF POLITICAL SCIENCE

4.CausalinferenceinPT

‐  KKV,Gerringsuggestthatthereisonelogicofinferenceinallpoliticalscience

‘thedifferencesbetweenthequantitativeandqualitativetraditionsareonlystylisticand

are methodologically and substantively unimportant. All good research can be

understood–indeed,isbestunderstood–toderivefromthesameunderlyinglogicof

inference.’(King,KeohaneandVerba,1994:4).

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X n1 n2 n3 Y

observable implications of

each part

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DEPARTMENT OF POLITICAL SCIENCE

4.CausalinferenceinPT

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DEPARTMENT OF POLITICAL SCIENCE

4.CausalinferenceinPT

‐  Bayesianlogicofinference=analystgivesgreaterweighttoevidencethatisexpected

aprioritobelessprobablebaseduponourpreviousknowledgeofphenomenon.

‐  ‘What is important isnotthenumberofpiecesofevidencewithinacasethat ^itone

explanationoranother,butthelikelihoodof^indingcertainevidenceifatheoryistrue

versus the likelihood of ^inding this evidence if the alternative explanation is

true.’(Bennett2006:341).

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4.CausalinferenceinPT

Bayes’formula

posteriorprobability=priorprobabilityxlikelihoodratio

27

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DEPARTMENT OF POLITICAL SCIENCE

4.CausalinferenceinPT

posteriorprobability=theposteriorprobabilityofthedegreeofcon^idencewehave

inthevalidityofahypothesis(h)abouttheexistenceofapartofacausalmechanism

aftercollectingevidence(e).

p(h│e)

28

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4.CausalinferenceinPT

Prior=degreeofcon^idencethattheresearcherhasinthevalidityofahypothesis

priortogatheringevidence,baseduponexistingtheorization,empiricalstudiesand

otherformsofexpertknowledge.

p(h)

29

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DEPARTMENT OF POLITICAL SCIENCE

4.CausalinferenceinPT

Likelihood ratio = expected probability of ^inding evidence supporting a hypothesis

basedupontheresearcher’sinterpretationoftheprobabilityof^indingitinrelation

to the hypothesis and background knowledge informed by previous studies

(p(e│h), compared with the expected probability of ^inding the evidence if the

hypothesisisnottrue(p(e│~h).

p(e│~h)/p(e│h)

30

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4.CausalinferenceinPT

Bayes’formula

31

p(h|e)= p(h)

p(h)+p(e|~h)*p(~h)p(e|h)

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4.CausalinferenceinPT

SilverBlazeexample–testingpartofmechanism(whetherhorseabductedbyinsider)

‐Prior=low(whywouldinsiderkidnapownhorse!)=20%(p(~h)=80%)

‐Likelihoodoftest=p(e|h)=90%,p(e|~h)=10%

32

0.692= 0.2

0.2+(0.1/0.9)*0.8

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4.CausalinferenceinPT

Whatif50‐50test?

‐Prior=low=20%(p(~h)=80%)

‐  Likelihoodoftest=p(e|h)=50%,p(e|~h)=50%

33

p(h|e)= p(h)

p(h)+p(e|~h)*p(~h)p(e|h)

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4.CausalinferenceinPT

Whatifhighcon^idenceinprior?

‐Prior=low=70%(p(~h)=30%)

‐  Likelihoodoftest=p(e|h)=80%,p(e|~h)=20%

34

p(h|e)= p(h)

p(h)+p(e|~h)*p(~h)p(e|h)

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5.Studyingcausalmechanisms

‐ developstrongempiricaltestsforwhetherallpartsofcausalmechanismarepresent

ornot

‐ logicofempiricaltestinginprocesstracing=>ifweexpectedXtocauseY,eachpart

of the mechanism between X and Y should leave the predicted empirical

manifestationswhichcanbeobservedintheempiricalmaterial.

35

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5.Studyingcausalmechanisms

‐Detectingthesemanifestations=>developmentofcarefullyformulatedpredictions

ofwhatevidenceweshouldexpecttoseeifthehypothesizedpartofthemechanism

exists

‐Predictionstranslatetheoreticalconceptsofthecausalmechanismintocase­speciFic

observablemanifestations(expectedevidence).

36

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5.Studyingcausalmechanisms

Empiricalpredictions‐4differenttypesofevidence

1.  Patternevidence=statisticalpatternsintheevidence.

2.  Sequenceevidence=temporalandspatialchronologyofevents

3.  Traceevidence=mereexistenceprovidesproof

4.  Accountevidence=contentofempiricalmaterial

37

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5.Studyingcausalmechanisms

‐  uniquepredictions=>empiricalpredictionsthatdonotoverlapwiththoseofother

theories=>con^irmatorypowerifefound

‐  Uniqueness corresponds to the likelihood ratio, where predictions are developed

thatmaximizethevalueofp(e|h)inrelationtop(e|~h).

38

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5.Studyingcausalmechanisms

‐  certain prediction => prediction is unequivocal and the prediction (e) must be

observedorelsethetheoryfailstheempiricaltest=>discon^irmatorypowerifenot

found

39

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Certainty(ifenotfound~discon>irmatorypower)

Uniqueness(ifefound–con>irmatory

power)

High

High

Low

Low

‘Hoop’tests‘Doubly‐decisive’

tests

‘Smoking‐gun’tests‘Straw‐in‐the‐wind’

tests

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DEPARTMENT OF POLITICAL SCIENCE

5.Studyingcausalmechanisms

straw­in­the­windtest=empiricalpredictionsthathavealowlevelofuniquenessand

alowlevelofcertainty(lowcon^irmatoryanddiscon^irmatorypower)

‐dolittletoupdateourcon^idenceinahypothesisirrespectiveofwhetherwe^indeor

~e,asbothpassedandfailedtestsareoflittleifanyinferentialrelevanceforus.

41

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DEPARTMENT OF POLITICAL SCIENCE

5.Studyingcausalmechanisms

Hoop tests=predictions thatarecertainbutnotunique (lowcon^irmatoryandhigh

discon^irmatorypower)

‐  failure of test (^inding~e) reduces our con^idence in thehypothesis but ^indinge

doesnotenableupdating.

42

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DEPARTMENT OF POLITICAL SCIENCE

5.Studyingcausalmechanisms

Smokinggun tests =highlyuniquebuthave loworno certainty in theirpredictions

(highcon^irmatoryandlowdiscon^irmatorypower)

‐ Likelihood ratio is small (^inding e given h highly probable whereas ~h is highly

improbable),therebygreatlyincreasingourcon^idenceinthevalidityofhifwe^ind

e.Ifnot^inde=>noupdating.

43

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5.Studyingcausalmechanisms

Doubly decisive tests => both certainty and unique (high con^irmatory and

discon^irmatorypower)

‐  evidence has to be found or our con^idence in the validity of the hypothesis is

reduced(updatingwhen~e)

‐  at the same time the test is able to discriminate strongly between evidence that

supportsthehypothesisandalternatives(smalllikelihoodratio),enablingupdating

whenwe^inde.44

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5.Studyingcausalmechanisms

45

Supranationalactorsenjoy

privilegedaccesstoorabilityto

processinformationand

ideas

Nationalgovernmentsunableorunwillingtoaccessand

processcriticalinformationandideas

Informationalasymmetries

inducebottlenecksinperformingthreekeytasks:policy

initiation,mediationand

socialmobilization

Supranationalactorscanmosteffectivelyinitiate,

mediateandmobilize

In^luenceofsupranationalactorsoninterstatebargaining

outcomesinEUnegotiations

Activitiesof

supranationalactors

X Causalmechanism(supranationalentrepreneurship) Y

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DEPARTMENT OF POLITICAL SCIENCE

5.Studyingcausalmechanisms

Moravcsikexample:testof‘theCommissionhasprivilegedaccesstoinformation’.

‐  strawinthewind=‘expecttoseethattheCommissionhasmanycivilservants’

‐  strongertest=‘expecttoseethattheCommissioninthemostsensitiveareasof

negotiationswasmuchbetterinformedaboutthecontentandstate‐of‐playofthe

negotiationsthangovernments,possessingmoredetailedsubstantiveissuebriefsand

moreaccurateandupdatedinformationonthestateofplay’46

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Discussion

1.  Operationalize an empirical test drawn from your own research,

describingtheuniquenessandcertainty.

47

Case study methodology – small-n research designs

Derek Beach, PhD

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DEPARTMENT OF POLITICAL SCIENCE

6.TheusesofPT

48

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6.TheusesofPT–nesting?

•  Systematicfactorsonlyincross‐case(Rohl^ing)•  Deterministictheory

•  LNAwhentraditionalstatisticalanalysis=probabilistic(meancausaleffectsacrosspopulation)•  SNA(PT)=deterministicontology

•  DivorcingXfromX+CM•  canXbemeaningfullydivorcedfromCMifwePTstudiesaretocommunicatewithothermethods?•  Arewestudyingtwodifferentthings:LNA=X:Y/PT=X+CM=>Y•  Onesolution=usecon^igurationaltheories

•  FxX=liberalideasorX1(liberalideas)+X2(liberalgroups)+X3(responsivegov)

49

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6.TheusesofPT–nesting?

•  ExplainingoutcomePTcannotbenestedforseveralreasons:

1. Useofnon‐systematicfactorsinaccountingforY(minimalsuf^iciency)2. Eclectic,non‐systematic(case‐speci^iic)combinationoftheories,withtheoriesused

inpragmaticfashionasheuristictoolstoaccountforoutcome(moreidiographic

focus)

**deeplyinterestedinthecase

**howeverEOPTcanhavesomeexportable^indings–‘lessons’

50

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6.TheusesofPT–nesting?

•  Theory‐testingstudiescanbenestedintwosituations1)havestrongX:YcorrelationfromLNAresearch

•  DoesXcauseYinmannerpredictedbytheory?(Owen)

•  Isthereacausalrelationship,orisitspurious?

2)well‐developedtheorybutisthereempiricalsupport(whensmallscopeofN)

**problemwithprobabilistic/deterministictheorization+whatwearestudying…

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6.TheusesofPT–nesting?

•  Theory‐buildingstudiescanbenestedintwosituations1)havestrongX:YcorrelationfrompriorresearchbutnoideahowXcausedY

2)KnowYbutunclearaboutwhatcausedit(whatisX?)

**challengeofidentifyingnon‐systematicfactorsinsinglecasestudy

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Whatcasesarerelevantfor:

‐  Theory‐testingofeconomicdevelopment‐>democracy

‐  Theory‐buildingexplainingwhylowincomecountriescanbecomedemocratic

Case study methodology – small-n research designs

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Case study methodology – small-n research designs

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6.TheusesofPT

‐  Strongwithin­caseinferencescanbemadeusingin‐depthsinglecasestudy

‐  Nocross­caseinferencescanbemadewithPT

‐  WhetherPTcanbeusedinconjunctionwithothermethodsdependsuponthe

variantofPT(yesfortheory‐testingandbuilding,noforexplainingoutcome)

57


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