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POL 1000 – Lecture 3: Methodology Sean Clark Lecturer, Memorial University Doctoral Fellow, CFPS Fall Session, 2011 Sean Clark Lecturer, Memorial University.

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Presentation on theme: "POL 1000 – Lecture 3: Methodology Sean Clark Lecturer, Memorial University Doctoral Fellow, CFPS Fall Session, 2011 Sean Clark Lecturer, Memorial University."— Presentation transcript:

1 POL 1000 – Lecture 3: Methodology Sean Clark Lecturer, Memorial University Doctoral Fellow, CFPS Fall Session, 2011 Sean Clark Lecturer, Memorial University Doctoral Fellow, CFPS Fall Session, 2011

2 Lecture Arc  1. What is methodology?  2. Theory.  3. Research Techniques.  1. What is methodology?  2. Theory.  3. Research Techniques.

3 Methodology  ‘Methods’: how best to conduct political research?  What questions to ask? What kinds of data to collect? How best to demonstrate causal relationships?  Positivists’ (‘soc sci IS possible’) ambition:  Describe (what is it?)  Believe can independently observe & measure phenomena (‘empirical evidence’).  Explain (why the phenomenon? what drives it?).  Outline causal relationship: ie x + y = z.  Validate thru comparison of theory with empirical evidence.  Predict (what will happen--ie policy?).  If we have x and have y, we’ll get z.  Post-positivists: ‘science’ is impossible.  “All knowledge of cultural reality is always knowledge from particular points of view.” How research is conducted will be “determined by the evaluative ideas that dominate the investigator and his age.” (Weber)  Kuhn: scientific explanations are based on the fashion of the day (i.e. Ptolemic to Copernican universes, Newtonian physics to Theory of Relativity.  Argmt: value free, objective research is impossible. Better to uncover history of meaning (thus offer few prescriptions).  ‘Methods’: how best to conduct political research?  What questions to ask? What kinds of data to collect? How best to demonstrate causal relationships?  Positivists’ (‘soc sci IS possible’) ambition:  Describe (what is it?)  Believe can independently observe & measure phenomena (‘empirical evidence’).  Explain (why the phenomenon? what drives it?).  Outline causal relationship: ie x + y = z.  Validate thru comparison of theory with empirical evidence.  Predict (what will happen--ie policy?).  If we have x and have y, we’ll get z.  Post-positivists: ‘science’ is impossible.  “All knowledge of cultural reality is always knowledge from particular points of view.” How research is conducted will be “determined by the evaluative ideas that dominate the investigator and his age.” (Weber)  Kuhn: scientific explanations are based on the fashion of the day (i.e. Ptolemic to Copernican universes, Newtonian physics to Theory of Relativity.  Argmt: value free, objective research is impossible. Better to uncover history of meaning (thus offer few prescriptions).

4 The Value of Methods  Yet positivist methods dominate modern scholarship.  Even if wary of the generalizability of its claims, positivist methods provide useful starting point.  Good place to help improve accuracy of your measures (gets you thinking about useful questions).  Even so, are heated battles w/in positivism  Ie Traditionalists vs. Behaviouralists (qualitatives vs quantitatives).  How useful are numbers? Guetzkow: very. Bull: not at all.  Yet positivist methods dominate modern scholarship.  Even if wary of the generalizability of its claims, positivist methods provide useful starting point.  Good place to help improve accuracy of your measures (gets you thinking about useful questions).  Even so, are heated battles w/in positivism  Ie Traditionalists vs. Behaviouralists (qualitatives vs quantitatives).  How useful are numbers? Guetzkow: very. Bull: not at all.

5 Theory  Theory: make a mass of details comprehensible.  Used to solve puzzles.  Are simplified models of how world actually is.  Is not personalized. Use particular cases to explain general phenomena.  Again, desire to: Describe --> Explain --> Predict.  Process:  1. “If A, then B” (hypothesis).  Use existing literature to make an informed guess.  2. Subject statement to an empirical test.  (hypothesis contrasted vs real world or factual data—ie statistics, or qualitative case studies).  Apply stimulus when can (i.e. ‘if increase X, what happens to Y?’).  However, can rarely run experiments (b/c ethics & practicality), so generally is about effort to uncover, observe, & measure trends between variables.  If hypothesis fails test, then theory is falsified.  Now either have to modify theory or start all over (but still, publish your findings).  3. Use to predict outcomes (= policy relevance).  ‘If we see A somewhere, we’ll see B too. And now we can prepare.’  Problem: world politics is complicated, thus only manage probabilities or central tendencies.  Theory: make a mass of details comprehensible.  Used to solve puzzles.  Are simplified models of how world actually is.  Is not personalized. Use particular cases to explain general phenomena.  Again, desire to: Describe --> Explain --> Predict.  Process:  1. “If A, then B” (hypothesis).  Use existing literature to make an informed guess.  2. Subject statement to an empirical test.  (hypothesis contrasted vs real world or factual data—ie statistics, or qualitative case studies).  Apply stimulus when can (i.e. ‘if increase X, what happens to Y?’).  However, can rarely run experiments (b/c ethics & practicality), so generally is about effort to uncover, observe, & measure trends between variables.  If hypothesis fails test, then theory is falsified.  Now either have to modify theory or start all over (but still, publish your findings).  3. Use to predict outcomes (= policy relevance).  ‘If we see A somewhere, we’ll see B too. And now we can prepare.’  Problem: world politics is complicated, thus only manage probabilities or central tendencies.

6 Theory Details (I)  Theories strive towards:  1. Accumulation.  "If I have seen further, it is by standing on the shoulders of giants.” (Newton)  2. Parsimony.  Simplification of a great deal of behaviour thru use of a relatively few concepts.  Theories either:  General (broad, complete accounts).  Partial or mid-range (specific instance).  Ie Waltz’s Structural Realism vs Democratic Peace Theory (explain all wars vs just those involving democracies).  Theories strive towards:  1. Accumulation.  "If I have seen further, it is by standing on the shoulders of giants.” (Newton)  2. Parsimony.  Simplification of a great deal of behaviour thru use of a relatively few concepts.  Theories either:  General (broad, complete accounts).  Partial or mid-range (specific instance).  Ie Waltz’s Structural Realism vs Democratic Peace Theory (explain all wars vs just those involving democracies).

7 Thinking Theoretically  Theses are (general, non-personal) causal statements.  Dependent & indp vars should be linked by some causal relationship or pathway.  If, then. Cause, effect. Indp var, Dep var.  Stimulation of controlled variable (indp) should lead to some sort of change in the outcome variable.  I.e.:  X = Y  X =  Y  X =  Y  A + B = C.  Through either direct experiments or indirect observation, are searching to uncover relationships btn variables.  Theses are (general, non-personal) causal statements.  Dependent & indp vars should be linked by some causal relationship or pathway.  If, then. Cause, effect. Indp var, Dep var.  Stimulation of controlled variable (indp) should lead to some sort of change in the outcome variable.  I.e.:  X = Y  X =  Y  X =  Y  A + B = C.  Through either direct experiments or indirect observation, are searching to uncover relationships btn variables.

8 Causal Diagrams  Outline relxnship btn variables involved in a theory.  Which indp (or ‘explanatory’) variables affect a phenomenon (the dep variable) & which do not?  Outside ForcesPhenomenon Independent variable A  Dependent variable Z Independent variable B  Independent variable C   Which outside force has a relxnship w the phenomenon? Is about causes & effects.  (Keep in mind, there may be intervening variables to be explained).  Remember: indp var (cause) must be completely distinct from dep var (effect).  If not, is a tautology, & devoid of explanatory power. The two must not be easily interchangeable.  From Greek ‘tautologos’ (‘repeating what has been said’).  I.e. ‘Armed conflict leads to civil war’ tell us nothing (is basically war = war).  Outline relxnship btn variables involved in a theory.  Which indp (or ‘explanatory’) variables affect a phenomenon (the dep variable) & which do not?  Outside ForcesPhenomenon Independent variable A  Dependent variable Z Independent variable B  Independent variable C   Which outside force has a relxnship w the phenomenon? Is about causes & effects.  (Keep in mind, there may be intervening variables to be explained).  Remember: indp var (cause) must be completely distinct from dep var (effect).  If not, is a tautology, & devoid of explanatory power. The two must not be easily interchangeable.  From Greek ‘tautologos’ (‘repeating what has been said’).  I.e. ‘Armed conflict leads to civil war’ tell us nothing (is basically war = war).

9 Diagram Example  Phenomenon: econ growth.  Independent variablesDependent variable Capital  Economic Growth Education  Democracy   Use empirical evidence to determine which relationship is the strongest.  Look at $, schools, elections --> which affects growth the most?  Phenomenon: econ growth.  Independent variablesDependent variable Capital  Economic Growth Education  Democracy   Use empirical evidence to determine which relationship is the strongest.  Look at $, schools, elections --> which affects growth the most?

10 Methodological Considerations  Methods consider how best to prove your causal claim.  How define your variables? (what is X? What is Y?).  Be as precise & reasoned as possible. Make sure X and Y are truly distinct (again, avoid tautology problem).  How operationalize (measure, track) your variables?  How trace over time X? How trace Y?  How know your measures are reliable & valid?  How are you sure the cases/evidence you’ve selected is an accurate reflection of the larger population from whence it’s drawn?  Reliability: accuracy of your instruments/measures.  Validity: certainty that your measures are actually capturing the phenomenon you are trying to track.  Are you measuring the right thing? Does your proxy actually reflect the phenomenon you care about?  I.e. rely on a few specific crime types to describe quality of a country’s legal system.  Or how perceptions measures tend to underweight those areas who feel prob is ‘normal’ vs places accustomed to good = quicker to grumble.  Methods consider how best to prove your causal claim.  How define your variables? (what is X? What is Y?).  Be as precise & reasoned as possible. Make sure X and Y are truly distinct (again, avoid tautology problem).  How operationalize (measure, track) your variables?  How trace over time X? How trace Y?  How know your measures are reliable & valid?  How are you sure the cases/evidence you’ve selected is an accurate reflection of the larger population from whence it’s drawn?  Reliability: accuracy of your instruments/measures.  Validity: certainty that your measures are actually capturing the phenomenon you are trying to track.  Are you measuring the right thing? Does your proxy actually reflect the phenomenon you care about?  I.e. rely on a few specific crime types to describe quality of a country’s legal system.  Or how perceptions measures tend to underweight those areas who feel prob is ‘normal’ vs places accustomed to good = quicker to grumble.

11 Research Techniques  1. Normative theory.  Theory need not be about the reality of today.  Normative theory: values & preferences.  Not what is (empirical theory), but what ought to be.  Distinguished pedigree w political philosophy, ethics of war, human rights, & global poverty.  Not descriptions of present, but guideposts to future.  2. Empirical theory.  Why do things happen? For what testable & verifiable reasons do they occur?  Not concern re right or wrong, but re what is.  1. Normative theory.  Theory need not be about the reality of today.  Normative theory: values & preferences.  Not what is (empirical theory), but what ought to be.  Distinguished pedigree w political philosophy, ethics of war, human rights, & global poverty.  Not descriptions of present, but guideposts to future.  2. Empirical theory.  Why do things happen? For what testable & verifiable reasons do they occur?  Not concern re right or wrong, but re what is.

12 Empirical Techniques  1. Case studies (‘single-n’).  Look at in-depth at specific country or instxn. What lessons can be learned? Offer great deal of knowledge about a single case.  Prob: specificity = low generalizability.  2. Statistical method (‘large-n’).  Evaluate the validity of rival hypotheses in light of quantitative evidence.  Are the variables mathematically correlated w each other, or is association no stronger than random chance?  Use laws of probability to see if variables are correlated.  Still need theory to show causal relevance (nonspuriousness).  How valid is the data? Do numbers capture the political & cultural nuance?  Lots of numbers can contain lots of errors. Plus, maybe are irrelevant.  3. Comparative method (‘small-n’).  Take causal equation (x + y = z) & compare to raw material of politics (systematic comparison of cases).  Real-world data is only way to test our theories.  Peters: “comparison…[is] the fundamental laboratory for political science.”  Most-similar method: given so similar, what unique factors make them different (i.e. colonization, re Saudi & Kuwait)?  Narrows the potential causes.  Most-different: what is common amongst disparate cases?  I.e. capitalism & US, Japan, India.  Prob: same as single-n: how can you be sure your claims are generalizable.  1. Case studies (‘single-n’).  Look at in-depth at specific country or instxn. What lessons can be learned? Offer great deal of knowledge about a single case.  Prob: specificity = low generalizability.  2. Statistical method (‘large-n’).  Evaluate the validity of rival hypotheses in light of quantitative evidence.  Are the variables mathematically correlated w each other, or is association no stronger than random chance?  Use laws of probability to see if variables are correlated.  Still need theory to show causal relevance (nonspuriousness).  How valid is the data? Do numbers capture the political & cultural nuance?  Lots of numbers can contain lots of errors. Plus, maybe are irrelevant.  3. Comparative method (‘small-n’).  Take causal equation (x + y = z) & compare to raw material of politics (systematic comparison of cases).  Real-world data is only way to test our theories.  Peters: “comparison…[is] the fundamental laboratory for political science.”  Most-similar method: given so similar, what unique factors make them different (i.e. colonization, re Saudi & Kuwait)?  Narrows the potential causes.  Most-different: what is common amongst disparate cases?  I.e. capitalism & US, Japan, India.  Prob: same as single-n: how can you be sure your claims are generalizable.

13 Empirical Techniques, II  4. Experiments.  Manipulate variables in controlled setting.  Chemists can show cause & effect clearly: if I add enough heat, then my water will boil.  One group receives stimulus (i.e. instructions, $). One does not (is ‘held constant’).  Different results = causal force at play.  I.e. Milgram obedience, behaviour economics, etc.  Prob: is incredibly difficult to run & control human activities.  Plus, what of ethics? Cannot fight another World War for a scientific experiment.  Stress of Miligram & brutality of Stanford Prison Experiment = not want to repeat.  Choice of technique you use will depend on the type of question you ask.  Your own research strengths matter, too.  4. Experiments.  Manipulate variables in controlled setting.  Chemists can show cause & effect clearly: if I add enough heat, then my water will boil.  One group receives stimulus (i.e. instructions, $). One does not (is ‘held constant’).  Different results = causal force at play.  I.e. Milgram obedience, behaviour economics, etc.  Prob: is incredibly difficult to run & control human activities.  Plus, what of ethics? Cannot fight another World War for a scientific experiment.  Stress of Miligram & brutality of Stanford Prison Experiment = not want to repeat.  Choice of technique you use will depend on the type of question you ask.  Your own research strengths matter, too.

14 A Difficult Enterprise  Correlation (apparent association) vs. causation (true cause-&-effect).  Sometimes two variables rise & fall in the same direction (‘correlation’) bc of some 3 rd, unseen force.  Useful knowledge requires nonspuriousness. Be sure that it is really X that is causing Y.  Not just that #s have to fit your story, but LOGIC as well.  Storks in N Eur rise w population. But this is not causally relevant.  ’10-’11: Blackberry use plummets in N Amer, but still tops in India. RIM: inference is we’re doing great!. Yet only bc wireless networks too poor to take advantage of Android & Apple phones.  Yet causation is difficult to establish.  Control of variables is nearly impossible.  Cases are unique (unlike chemistry ingredients) & difficult to isolate (humans are complicated & respond when watched—e.g. Hawthorne experiment).  Cases are ltd in #.  Barriers to information abound.  Cost, language, culture, chaos can all get in the way.  ==> Humility is therefore essential.  (though field still holds great potential).  Correlation (apparent association) vs. causation (true cause-&-effect).  Sometimes two variables rise & fall in the same direction (‘correlation’) bc of some 3 rd, unseen force.  Useful knowledge requires nonspuriousness. Be sure that it is really X that is causing Y.  Not just that #s have to fit your story, but LOGIC as well.  Storks in N Eur rise w population. But this is not causally relevant.  ’10-’11: Blackberry use plummets in N Amer, but still tops in India. RIM: inference is we’re doing great!. Yet only bc wireless networks too poor to take advantage of Android & Apple phones.  Yet causation is difficult to establish.  Control of variables is nearly impossible.  Cases are unique (unlike chemistry ingredients) & difficult to isolate (humans are complicated & respond when watched—e.g. Hawthorne experiment).  Cases are ltd in #.  Barriers to information abound.  Cost, language, culture, chaos can all get in the way.  ==> Humility is therefore essential.  (though field still holds great potential).

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