**Evaluating a Control Variable
**By Daniel Palazzolo, Ph.D.

We need to go through
a few steps to assess the effects of a control variable.

**
1. Test a bivariate relationship**—referred to as the “original
relationship.” It must be statistically significant and at least
reasonably strong to begin with. Record all the relevant information associated
with a bivariate analysis. Also, check the case processing box to be sure
there are sufficient cases. If there are fewer than 1,000 cases, it will
be very difficult to test a control variable.

2. Consider a control variable

3. Assess the impact of the control variabl

4. Write the results in clear, commonsense terms

**Scenario 1: Spurious.** The control variable renders the
original relationship insignificant.

Column %: Column percentages grow closer together for all values of the
control variable, to the point that there no longer appears to be a difference
between the values of the independent variable in the original relationship.

__Strength Measure__: Measures are much weaker, approximating zero
for all values of the control variable.

__
Significance__: The relationships for all values of the control variable
are no longer significant.

**Scenario 2: No change or Enhanced.** We see no major changes
in the original relationship when we inspect the values of the control
variable.

__Column %__: In this case the column percentages for each value of
the control variable remain roughly the same, and differences in the column
percentages remain roughly the same.

__
Strength Measure__: Remains roughly the same; a small change may occur,
but nothing substantial.

In the case of an “enhanced” result, while the control variable does not alter the original relationship, it may be directly related to the dependent variable. In this case, we will see a difference in the row % totals between the original and the control variables. The way to affirm this is to run a separate crosstab between the control and the dependent variable.

**Scenario
3: Specified (or Conditional).** The control variable has differential
effects on the original relationship, i.e. the original remains the same
for at least one value of the control variable, but changes for at least
one value of the control variable.

__Column %__: The column percentages remain the same for some of the
values of the control variable, but they either grow closer together or
further apart for one or more values of the control variable.

__Strength Measure__: Roughly the same for at least one value of the
control variable, but lower or higher for at least one value of the control
variable.

__
Significance__: Significant for at least one value of the control variable,
but not significant for at least one value of the control variable.

**Scenario 4: Exaggerated/Suppressed.** In some instances,
the original relationship changes for all values of the control variable;
showing either a weakening of the original relationship, in which case
we say it was exaggerated, or a strengthening of the original relationship,
in which we say it was suppressed. This is different from a spurious relationship
(in which the control variable renders the original relationship insignificant),
or an enhanced relationship (where the original relationship stays in
tact and the control has an independent effect on the dependent variable),
or a specified relationship (where the results of the control differ with
different values of the control variable).

__
Column %__: In this case the column percentages for each value of the
control variable are closer together (exaggerated) or further apart (suppressed).

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