Hello, I had a quick question. I want to assess whether a count model using data from a survey suffers from overdispersion. Can I test for overdispersion using the alpha parameter from an nbreg model? I understand why the LR test is not produced when using survey-weighted data, but can I trust the alpha parameter to properly assess overdispersion? In the case below, because the confidence interval for the alpha parameter does not contain 0, is there evidence of overdispersion?
Code:
. local contrls ///
> c.ppage ///
> i.race_alt ///
> i.income ///
> i.personal_debt ///
> i.region ///
> i.housetype_alt ///
> i.employment ///
> i.arrested ///
> i.asud ///
> i.LifetimePTSDcriteria_positive ///
> i.any_lifetime_suicide_attempt ///
>
. svy:nbreg home_loss `contrls', exposure(total_years) irr
(running nbreg on estimation sample)
Survey: Negative binomial regression
Number of strata = 1 Number of obs = 1,283
Number of PSUs = 1,283 Population size = 1,258.0535
Design df = 1,282
F(19, 1264) = 5.59
Dispersion: mean Prob > F = 0.0000
-------------------------------------------------------------------------------
| Linearized
home_loss | IRR std. err. t P>|t| [95% conf. interval]
--------------+----------------------------------------------------------------
ppage | .982977 .0126402 -1.34 0.182 .9584894 1.00809
|
race_alt |
1.Non-White | 1.858577 .5590613 2.06 0.040 1.030141 3.35324
|
income |
15,000-$~999 | 2.323929 1.060695 1.85 0.065 .9491779 5.689813
$30,000-~999 | .8925358 .4195413 -0.24 0.809 .3549242 2.24448
>= $60,0000 | .2782457 .1839597 -1.93 0.053 .0760558 1.017946
|
personal_debt |
2.less tha.. | 1.968493 .9617833 1.39 0.166 .7548373 5.133508
3.$15,00~000 | 1.336246 .7598767 0.51 0.610 .4379052 4.077487
4.$35,00~000 | 1.107194 .7143556 0.16 0.875 .3122585 3.925846
|
region |
Midwest | 2.420599 1.614974 1.33 0.185 .6538554 8.961153
South | 2.357167 1.494536 1.35 0.176 .6794933 8.177029
West | .8547957 .6390102 -0.21 0.834 .197214 3.704989
|
housetype_alt |
2.Apartmen.. | 1.482899 .5088747 1.15 0.251 .7563719 2.907285
3.Other | 1.184448 .8015107 0.25 0.803 .3140271 4.467501
|
employment |
Working pa.. | .1982927 .1220356 -2.63 0.009 .0592864 .6632205
Not working | .4308464 .184369 -1.97 0.049 .1860928 .9975055
|
arrested |
1.Arrested | 1.616884 .5399287 1.44 0.150 .8397831 3.113082
|
asud |
Yes | 1.941994 .8153613 1.58 0.114 .8521709 4.42557
|
LifetimePTS~e |
Yes | 1.944238 .8600343 1.50 0.133 .8163272 4.63057
|
any_lifeti~pt |
Yes | 4.136422 1.841605 3.19 0.001 1.72702 9.907229
_cons | .0172554 .0184194 -3.80 0.000 .0021254 .1400889
ln(total_y~s) | 1 (exposure)
--------------+----------------------------------------------------------------
/lnalpha | .8077066 .4349825 -.0456491 1.661062
--------------+----------------------------------------------------------------
alpha | 2.242759 .9755607 .9553772 5.264901
-------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation to incidence-rate
ratios.
Note: _cons estimates baseline incidence rate.
