Study&Guide&Unit&1&Epistemology-What&do&we&know&and&how&do&we&know&it?&• Traditionally&we&know&through&recognized&authorities,&precedent,&intuition,&common&sense,&and&observation.&&Inference1Inference&requires&quantitative&and&qualitative&judgments&How$we$make$inferences$• We&cannot&observe&causation&• We&don’t&see&x&and&y&happen&• We&observe&correlation&• We&see&x&and&y&move&together&• We&come&up&with&a&causal&story&to&explain&why&the&correlation&takes&place&&Deterministic3Relationships-&Cause(X)&always&leads&to&effect(Y)&• When&X&occurs,&Y&will&also&occur&with&CERTAINTY&&&Probabilistic3Relationship-&Cause(X)&usually&leads&to&effect(Y)&• When&X&occurs,&Y&will&tend&to&occur,&but&NOT&with&certainty&&Previous3Research3and3theories-When&examining&research&that&has&already&been&done&keep&these&questions&in&mind:&• What&(if&any)&other&causes&of&the&dependent&variable&did&the&previous&researchers&miss?&• Can&their&theory&be&applied&elsewhere?&• If&we&believe&their&findings,&are&there&further&implications?&• How&might&this&theory&work&at&different&levels&of&aggregation&(micro-macro)&&Induction3(EmpiricsTheory)&• Observe&something&and&come&up&with&a&theory&• Example$390%$of$humans$are$right$handed,$Joe$is$human.$Therefore$the$probability$the$Joe$is$right$handed$is$90%$$Deduction&(TheoryEmpirics)&• Build&a&theory&on&assumptions,&then&observe&• Example3$All$men$are$mortal,$Socrates$is$a$man.$Therefore$Socrates$is$mortal.$$Independent3Variable-&the&suspected&cause&&Dependent3Variable-the&something&because&the&effect&of&the&outcome&&***Independent(X)3causes3dependent(Y)***33Conceptual3Definitions-the&meaning&we&assign&to&terms,&concepts&area&a&share&understanding&• Concepts&are&our&perception&of&“things”&with&common&characteristics&&• Simple,&concepts&are&tools&used&to&describe&the&world&3Correlation-&two&variables&seems&to&“move&together”&3Causation-changes&in&one&variable&leads&the&changes&in&another&variable&***Correlation3does3not3cause3causation***33The3Fallacy3of3affirming3the3consequent-&We&have&evidence&and&reason&in&our&faovr,&therefore,&we&have&provided&our&theory.&All&we&can&say&is&we&have&yet&to&falsify&our&theory&3Time1Series-&in&which&the&spatial&dimension&is&the&same&for&all&cases&and&the&dependent&variable&is&measured&at&multiple&points&in&time&3Cross3sectional-&in&which&the&time&dimension&is&the&same&for&all&cases&and&the&dependent&variable&is&measured&for&multiple&spatial&units&Starting3with3a3puzzle-&what$is$the$general$phenomenon$you$are$seeking$to$explain?&• The&puzzle&is&the&two&cases&to&have&similar&outcomes,&but&they&have&different&outcomes&• We&begin&with&a&puzzle&instead&of&a&question&because&a&puzzle&requires&some&knowledge&of&events&and&puzzles&make&us&look&for&non&obvious&causes&3Means3of3evaluating3theories3Generalization&• Make&your&nouns&and&verbs&more&general&(President&Obama&wants&to&be&reelectedPresidents&want&to&be&reelectedPoliticians&want&to&be&reelected)&Falsifiable&• Ability&of&hypothesis&to&fail&to&be&falsified.&Helps&to&discern&the&worthiness&of&a&theory&&• Falsify&the&null&hypothesis?&Your&theory&is&sa fe& for&now&Parsimony&(Simple,$stupid)&• To&make&a&theory&more&general,&we&often&have&to&give&up&parsimony,&and&to&make&a&theory&more&parsimonious,&we&often&have&to&give&up&generality.&If&we&can&make&our&theory&more&general&or&more&parsimonious&and&without&sacrifice,&we&should&do&so.&3Formal3theory-&Begin&with&assumptions&and&use&math&and&game&theory&to&develop&theoretical&models&3Rational3Choice3Theory-&you&know&what&you&prefer&and&your&preferences&are&consistent&• If&I&prefer&A&over&B&and&I&prefer&B&over&C,&then&I&prefer&A&over&C&3Reliability-&how&consistent&will&the&process&by&which&we&measure&these&concepts&be?&(is$it$consistent?)$3Validity3–how&well&does&the&operational&definition&match&the&theoretical&definition?&(are$we$measuring$what$we$want$to$measure?)&Hypotheses3The$hypothesis$is$a$testable$statement,$derived$from$theory$that$indicates$a$cause$and$effect$between$two$concepts$Requirements&for&hypothesis&• Must&offer&a&cause(X)&and&effect(Y)&• Must&specify&the&direction&of&that&relationship&(positive&or&negative)&• Must&specify&the&type&of&relationship&(linear&or&curvilinear)&• Must&specify&units&of&variation&of&interest&&&Units-individuals&or&aggregate&Variation-overtime?&Between&units?&• Must&be&falsifiable&(were¬&wrong&yet)&&&&&&&&&&&&&&Linear$graph$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$Curvilinear$graph$&Testing&hypotheses&• We&must&operationalize&our&concepts&• First&we&need&solid&theoretical&definition&• Operational&definition&explains&what&the&concept&will&look&like&and&how&it&is&to&be&tapped&or&measured&in&the&empirical&world&• Null&hypothesis-the&expected&relationship&if&our&theory&is&wrong,&implicit&in&all&hypothesis&(When&we&test&our&hypothesis,&what&we&are&doing&is&attempting&to&reject&the&null&hypotheses)&3Bivariate3Relationships133• ONE&independent&variable&causes&ONE&dependent&variable&&3Endogenous3Relationships3• X&causes&Y&but&Y&can&cause&X&too&•
View Full Document
Unlocking...