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Introducing additional variables

Introducing additional variables. POL 242 Renan Levine. X. Y. 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?. We live in a multidimensional world!.

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Introducing additional variables

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  1. Introducing additional variables POL 242 Renan Levine

  2. X Y 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?

  3. 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.

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

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

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

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

  8. 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.

  9. 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)

  10. 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)

  11. 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.

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

  13. 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.

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