Re: Again the requrest: SIMULATION TO INTERPRET AND PRESENT LOGIT RESULTS without formatting - hopefully looks better

["Alexander M. Jais" <jais@win.rwth-aachen.de>]

Hi Everyone,

Has perhaps anyone an idea how I can solve the problem explained below?

Would be really highly appreciated!!

All the best

Alex


--------------------------------------

Dear Statalisters,

For some reason I’ve a really hard time to figure out how I can graph my logistic regression model in Stata. I am aware of the explanation of Zellner (2009; Strategic Management Journal) and Tomz M, Wittenberg J,
King G. 2001. CLARIFY: Software for interpreting and presenting statistical results, 2.0 ed. Harvard University: Cambridge, MA).


I ve the following issue:


- My logistic regression has the following expression simplified: logit EquityMode Psychic Distance Means-Driven PsychDxMeansD


- Dependent Variable_Equity Mode: Code 0 = for Non-Equity and Code 1 = Equity


- Interaction Variable_Psychic Distance: Construct based on 6 items with 7 point likert scale


- Independent Variable_Means-Driven: : Construct based on 6 items with 7 point likert scale


- Moderation_ PsychDxMeansD: Psychic Distance times Means-Driven



Question:



* In general I want to graph figures 2 and 3 in Zellner 2009 (pls see link for source:http://faculty.fuqua.duke.edu/~charlesw/s591/Methods/c09_Bennet/SMJ_note_final.pdf).


* How do I code the Interaction Variable and Independent Variable, which are NOT binary – the explanation in Zellner shows only the binary interaction term (see lines 3, 10, … in Do file below; based
on Zellner 2009, also attached, see pages 21-23) ?

* Why is in line 8 all independent variables set to zero?

My current Do-Files looks like this:

1. set seed 9999

2. noisily estsimp logit EquityMode PsychicDistance Means-Driven
PsychDxMeansD, nolog

3. foreach var in X Y0 Y1 Y0lb Y1lb Y0ub Y1ub dY dYlb dYub {

4.gen `var' = .

5. }

6. forvalues obs = 1(1)18 {

7. replace X = .01*(`obs'+1) in `obs'

8. setx 0

9. setx Means-Driven .01*(`obs'+1)

10. foreach as_lev in 0 1 {

11.setx PsychicDistance `as_lev' PsychDxMeansD `as_lev'*.01*(`obs'+1)

12.simqi, genpr(Y`as_lev'_tmp) prval(1)

13.sum Y`as_lev'_tmp, meanonly

14.replace Y`as_lev' = r(mean) in `obs'

15._pctile Y`as_lev'_tmp, p(2.5, 97.5)

16.replace Y`as_lev'lb = r(r1) in `obs'

17.replace Y`as_lev'ub = r(r2) in `obs'

18.}

19.gen dY_tmp = Y1_tmp - Y0_tmp

20.sum dY_tmp, meanonly

21.replace dY = r(mean) in `obs'

22._pctile dY_tmp, p(2.5,97.5)

23.replace dYlb = r(r1) in `obs'

24.replace dYub = r(r2) in `obs'

25.drop *_tmp

26. }

27. twoway rbar Y0ub Y1lb X, mw msize(1) lcolor(gs0) fcolor(gs16)|| line
Y0 X, color(gs0) || rspike Y1ub Y1lb X, color(gs0) lp(dot) || line Y1 X,
color(gs0) || , yscale (r(0 1)) ylabel(0(.2)1) legend(off) xtitle("Means
Driven") ytitle("Equity Mode") graphregion(fcolor(gs16))



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