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Introducing additional variables

This piece delves into the intricacies of multivariate relationships, emphasizing that while traditional analyses often focus on bivariate relationships between one independent variable (X) and a dependent variable (Y), real-world scenarios involve multiple factors. We examine how various independent variables, such as age, gender, and societal influences, can interact and affect outcomes like voting behavior or support for policies. The article introduces concepts of additive, antecedent, and intervening variables, highlighting the importance of qualitative research in understanding these dynamics.

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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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