R help - Apr 2010 - contradictory output between ncv.test() and gvlma()

Can anyone tell me why the ncv.test output and the gvlma output would be
contradictory on the question of heteroscedasticity?  Below, the ncv.test
output reveals a problem with heteroscedasticity, but the gvlma output says
that the assumptions are acceptable.  How is this reconciled?
> ncv.test(defmodA)
Non-constant Variance Score Test
Variance formula: ~ fitted.values
Chisquare = 7.360374    Df = 1     p = 0.00666769
> gvlma(defmodA)
Call:
lm(formula = DefPunWmn1 ~ DefPersBenef, data = Data)

Coefficients:
 (Intercept)  DefPersBenef
      1.2579        0.1572


ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance =  0.05

Call:
 gvlma(x = defmodA)

                     Value   p-value                   Decision
Global Stat        37.3746 1.508e-07 Assumptions NOT satisfied!
Skewness           32.8916 9.744e-09 Assumptions NOT satisfied!
Kurtosis            2.6248 1.052e-01    Assumptions acceptable.
Link Function       0.3684 5.439e-01    Assumptions acceptable.
Heteroscedasticity  1.4899 2.222e-01    Assumptions acceptable.

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