Hi all,
I am working on my thesis (part of the analysis is to find whether having a Chief Innovation Officer (CINO) affects Sales growth performance, especially under certain contingencies). When I run a simple model, without adding interaction between CINO and contingencies, everything works fine:
However, when I try to add an interaction between CINO and a contingency variable (which in this case is the proportion of years in which the firm had an outsider CEO), things go haywire:
As you can see, the F value and Prov>F is missing. I tried mean centering poc0, but that produced similar results. Can someone please help me out with this? Thank you!
Best,
Mohsin
I am working on my thesis (part of the analysis is to find whether having a Chief Innovation Officer (CINO) affects Sales growth performance, especially under certain contingencies). When I run a simple model, without adding interaction between CINO and contingencies, everything works fine:
:
regress avsg cino avten pcoo avtmt pdc avri avhhi poc0 avlemp avtd, vce(robust)
Linear regression Number of obs = 94
F( 10, 83) = 3.71
Prob > F = 0.0004
R-squared = 0.2258
Root MSE = .11445
------------------------------------------------------------------------------
| Robust
avsg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cino | .0521228 .0295207 1.77 0.081 -.0065927 .1108383
avten | .0038515 .0026968 1.43 0.157 -.0015123 .0092154
pcoo | -.0199879 .0409653 -0.49 0.627 -.1014662 .0614904
avtmt | -.0021819 .003144 -0.69 0.490 -.0084352 .0040713
pdc | -.0176846 .0287924 -0.61 0.541 -.0749514 .0395823
avri | .0392595 .08647 0.45 0.651 -.1327258 .2112448
avhhi | .0000196 .0000236 0.83 0.409 -.0000273 .0000665
poc0 | -.0182638 .0313006 -0.58 0.561 -.0805194 .0439918
avlemp | -.0257163 .0112342 -2.29 0.025 -.0480607 -.0033718
avtd | -.028726 .0265449 -1.08 0.282 -.0815228 .0240708
_cons | .0944653 .0427932 2.21 0.030 .0093514 .1795793
------------------------------------------------------------------------------
:
regress avsg i.cino##c.poc0 avten pcoo avtmt pdc avri avhhi avlemp avtd, vce(robust)
Linear regression Number of obs = 94
F( 10, 82) = .
Prob > F = .
R-squared = 0.2264
Root MSE = .1151
------------------------------------------------------------------------------
| Robust
avsg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.cino | .0462768 .0342008 1.35 0.180 -.0217593 .114313
poc0 | -.0182985 .0314439 -0.58 0.562 -.0808503 .0442534
|
cino#c.poc0 |
1 | .1641756 .2237474 0.73 0.465 -.2809293 .6092805
|
avten | .0039453 .002788 1.42 0.161 -.0016009 .0094916
pcoo | -.020044 .0413273 -0.49 0.629 -.1022572 .0621691
avtmt | -.0020835 .0032113 -0.65 0.518 -.0084718 .0043049
pdc | -.0178229 .0290359 -0.61 0.541 -.0755844 .0399387
avri | .0341651 .090117 0.38 0.706 -.1451063 .2134365
avhhi | .00002 .0000237 0.84 0.402 -.0000272 .0000672
avlemp | -.0256099 .0113393 -2.26 0.027 -.0481673 -.0030525
avtd | -.0292053 .0268835 -1.09 0.281 -.0826851 .0242746
_cons | .0925489 .0446197 2.07 0.041 .0037862 .1813116
------------------------------------------------------------------------------
Best,
Mohsin