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  1. Control Tables March 7, 2011

  2. Objectives By the end of this meeting, you should be able to: • Explain the concept of statistical control. • Differentiate intervening (mediator) and interacting (moderator) variables. • Appropriately choose control variables in data analysis. • Compute a control table.

  3. Think About • When analyzing the relationship between variables, what does it mean to “control” for a variable? • We cannot always use experimental data in political science.

  4. Control Variables • Frequently when we observe a relationship between two variables, causally there may be a third variable acting as well. • Therefore we must statistically ‘control’ for that variable to see if a relationship continues between our initial two variables. • Imagine that we believe that ideology leads to vote choice. Does that finding still hold if partisanship is controlled?

  5. Intervening and Interacting Variables • An intervening variable is one where the third variable occurs between the independent and the dependent variable. • An interacting variable is one that moderates, in a casual sense, the effect of the independent variable on the dependent variable. The effects of interacting variables can be very different. • Sometimes an effect may be negative for one value but positive for another • Sometimes an effect may be stronger (but in the same direction) for one value of the variable than another.

  6. Intervening and Interacting Variables • In the previous example with ideology and vote choice, what type of variable is partisanship? • There are two general ways to answer that question. • Theoretically • Empirically

  7. Antecedent Variables • It is also important to identify if the control variable is antecedent to the independent variable, i. e. does it occur before. • If the control variable is antecedent and including it in the model eliminates the effect of the independent variable then the initial relationship must be considered spurious.

  8. Spuriousness • While control variables may help us eliminate relationships that are spurious, it is important to look for relationships that are specified by the control variable. • In these cases, there is a relationship between the independent and dependent variable but it only occurs at one level of the control variable. • For instance, you might discover that the relationship between race and turnout is spurious when education is considered except for Native Americans.

  9. How Many Control Variables to Use? • Parsimony vs. accuracy • Achen and the rule of three • The medical field: rule of thirty • Make sure to use only those that have a relationship with both the independent and dependent variable. • If the variable does not have a relationship with both the independent and dependent variable, then it is a poor control.

  10. How Many Control Variables to Use? • When testing for spuriousness only look at those variables that precede both the independent and dependent variable • Remember if a control occurs between the independent and dependent variable (i.e. is intervening) then it cannot render the initial relationship spurious. • When in doubt follow the trend of the literature.

  11. For Next Time • Read WKB chapter 14, pp. 298-313 • Answer questions 1, 2, & 3 on page 324.