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From |
John Antonakis <John.Antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Clarification: Instrumental variable design: no control group |

Date |
Sun, 28 Sep 2008 22:55:26 +0200 |

Hi again:

Sorry, what I said below about the raters is unclear, as was my explanation on the design. I would just like to clarify two things. Raters were in groups, and the raters rated 4 participants (hence the rater fixed effects). The y variable is actually a speech; Time 1 and Time 2 speeches were randomly assigned to the groups of raters (but groups never saw the same participant twice). The DV was measured two different ways: an absolute measure (how good the participant was at giving the speech) and a relative measure (the raters rank ordered the 4 speeches from best to worst).

Thanks,

John.

Here goes again.................

I have estimated the following model in STATA:

(where participant i is measured on two occassions)

xi: xtivreg y (mv = prepost) controls i.rater i.participant, re robust

Where:

prepost = a dummy variable (0=pretreatement, 1=posttreatment)

mv = the manipulated check (i.e., a manipulation check to see whether

the participants actually responded to the treatment)

controls = refers to participant individual difference controls (e.g.,

personality variables, age, set)

i.rater = rater fixed effects (given that groups of raters rated the

participant on the DV)

i.participant = participant fixed effects (given that participants were

rated twice)

Now this design does not have a control group. It would have been

stronger if it did, but I could not randomly assign individuals to a

treatment and control. Everyone had to receive the treatment. The way to

mitigate this limitation was to measure the manipulation and then

regress the DV on the manipulation (instrumented by the prepost dummy).

My thinking is that the manipulation check serves as the independent

endogenous variable and the intervention dummy variable as the exogenous

treatment. If the change in y is predicted by the manipulation check,

which in turn is predicted by the prepost treatment, then I can be more

certain about our causal deductions as compared a simple repeated

measures design in which the manipulation check does not serve as the

endogenous independent variable. Because the manipulation check is

modelled as a predictor, other putative causes can, for most intents and

purposes, be ruled out.

Has anyone seen a published example of such as design that they can

point me to? If so, is there a particular name given to this design or

is it simply a IV design? Also, any suggestions on how to improve causal

deduction?

Thanks,

J.

--

____________________________________________________

Prof. John Antonakis

Associate Dean

Faculty of Business and Economics

University of Lausanne

Internef #618

CH-1015 Lausanne-Dorigny

Switzerland

Tel ++41 (0)21 692-3438

Fax ++41 (0)21 692-3305

http://www.hec.unil.ch/people/jantonakis&cl=en

____________________________________________________

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