Introducing additional variables POL 242 Renan Levine

Bivariate Relationship  Y depends on X.  Explain variation in Y using different values of X.  All your analyses so far have been of this variety.  Reality: Is anything bivariate? XY

We live in a multidimensional world!  More than one “thing” affects another “thing.” True of opinions. True of polity features.  Most countries differ by many features.  Even similar countries – say, US & Canada – have different demographic and institutional features.  Time often accentuates differences. True of policy analysis  If there is a problem – caused by one factor – you might expect that policy-makers could figure out how to fix that one factor.

Conceptualize Multivariate Relationships  How does more than one independent variable influence the dependent variable? Additive Temporal  Antecedent, Intervening  SECOND HALF Specification & Control Spurious Experiments

Multivariate Relationship – I (Additive) X Y X 1 2 “Additive” Two variables affect Y, X1 and X2. May be many more X’s.

Multivariate Relationship – I (Example) Religion (Catholic) Support for Stem Cell Research Perhaps age, moral traditionalism, gender, partisanship and affection for Michael J. Fox also matters? Note: Untested relationship – may be false. Religiosity

Multivariate Relationship – I (Ex. II) Race Vote Democrat Income Is this true? Stay tuned… Any examples from your qualitative research?

Antecedent Relationship  Antecedent –adjective 1.preceding; prior: an antecedent event. –noun 2. a preceding circumstance, event, object, style, phenomenon, etc.  If there is an antecedent relationship, one variables comes before another variable in time or causality.  Limits to how well statistics can determine antecedence.

First comes love…  “First comes love, then comes marriage, then comes the baby carriage.”  Love is antecedent! DV= Baby carriage IV = Marriage (and/or some other stuff)

Intervening  The variable in between the antecedent variable and the dependent variable is the INTERVENING variable.  Has some effect on the dependent variable (if not, it is a simple X->Y relationship)

Antecedent Example  “Michigan Voter” Model (U.S.)  Party Identification -> Feelings towards Pres. Candidate -> Vote for President DV= Vote for President  Party identification precedes feelings towards candidate. Put another way: Feelings towards Presidential candidate depends on party identification.

Even more antecedent  Later: Parents’ party identification -> Party identification -> Feelings towards candidate -> Vote for President  Any examples from your research?

Qualitative Research  Great for examining what variables should be included in a multivariate relationship.  Useful for determining whether any variables are antecedent.  Remember: just because relationship doesn’t hold for one person does not mean it doesn’t hold for most people.