Control Tables
March 7, 2011
Objectives
By the end of this meeting, you should be able
to:
a) Explain the concept of statistical control.
b) Differentiate intervening (mediator) and
interacting (moderator) variables.
c) Appropriately choose control variables in
data analysis.
d) Compute a control table.
Think About
a) When analyzing the relationship between
variables, what does it mean to “control”
for a variable?
b) We cannot always use experimental data
in political science.
Control Variables
a) Frequently when we observe a
relationship between two variables,
causally there may be a third variable
acting as well.
b) Therefore we must statistically ‘control’
for that variable to see if a relationship
continues between our initial two
variables.
c) Imagine that we believe that ideology
leads to vote choice. Does that finding still
hold if partisanship is controlled?
Intervening and Interacting
Variables
a) An intervening variable is one where the third
variable occurs between the independent and the
dependent variable.
b) 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.
Intervening and Interacting
Variables
a) In the previous example with ideology
and vote choice, what type of variable is
partisanship?
b) There are two general ways to answer that
question.
•
•
Theoretically
Empirically
Antecedent Variables
a) It is also important to identify if the
control variable is antecedent to the
independent variable, i. e. does it occur
before.
b) 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.
Spuriousness
a) 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.
b) In these cases, there is a relationship between the
independent and dependent variable but it only
occurs at one level of the control variable.
c) For instance, you might discover that the
relationship between race and turnout is
spurious when education is considered except
for Native Americans.
How Many Control Variables to
Use?
a) Parsimony vs. accuracy
•
•
Achen and the rule of three
The medical field: rule of thirty
b) 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.
How Many Control Variables to
Use?
c) 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.
d) When in doubt follow the trend of the
literature.
For Next Time
a) Read WKB chapter 14, pp. 298-313
b) Answer questions 1, 2, & 3 on page 324.