Capital Punishment Deters Su James Plank Discuss

Respond by Day 5 to two colleagues in the following way:

• In what ways do you agree or disagree that the validity issues
  in the scenarios of causal claim selected by your colleagues are the
  most important? Provide a rationale, including examples or references.

$5 Post

1. Field Experiment: Minimum wage legislation will lead to higher unemployment
2. Cross Sectional Study: Capital punishment deters crime

Internal validity is critical to the defensibility of impact
evaluations as it is defined as the accuracy of the causal claim
(Langbein, 2012). The most important validity issue within a causal
claim is controlling for other systematic variables, represented as Z_k.
Knowing the value of this variable is imperative for the evaluation to
have validity. For example, “minimum wage legislation will lead to
higher unemployment” is a causal claim that has other systematic
variables such as region, current economic conditions, or whether a
country is currently at war or in peace times. Another causal claim
example is “capital punishment deters crime” where the systematic
variables could be urban/rural populations, current unemployment rates,
or other economic conditions. In other words, it is important to know
and measure other systematic variables (Z_k) in an evaluation that could have an impact on the outcome (Y).

I believe that controlling for other systematic variables within an
evaluation is the most important issue as it significantly impacts
internal validity. If one were to conduct an evaluation regarding
legislation that increased the minimum wage to determine if it had
increased unemployment, one must look at the region as a variable, among
other variables. The impact of the legislation will have different
effects across regions, making it important to highlight where the
results were taken. This variable, represented as Z_k, will ensure that the variable maintains its internal validity when held up to scrutiny.

References

Langbein, L. (2012). Public program evaluation: A statistical guide (2nd ed.). Armonk, NY: ME Sharpe.