Underdispersion in multilevel logistic regression

Hi folks,

I'm doing some multilevel logistic models with lmer() and I noticed that the
estimated scale in my model (see code & results below) suggests the presence
of under-dispersion. Are there any guidelines on when the scale is
sufficiently far from 1 that one should conclude that underdispersion (or
overdispersion) is serious enough to warrant switching from family =
binomial(logit) to family = quasibinomial(logit)?

model.17f <- lmer(formula = EventTV ~ Period + OR1pc + OR1flyer + OR2pctv

+
+                             OR2flyertv + Spanish_Version + MUDwell + 0 +
+                             (1 | ClusterID) + (1 | SurveyID),
+                 data=RS05.Round1A, family = binomial(logit),
method="Laplace")

summary(model.17f)

Generalized linear mixed model fit using Laplace
Formula: EventTV ~ Period + OR1pc + OR1flyer + OR2pctv + OR2flyertv +
Spanish_Version + MUDwell + 0 + (1 | ClusterID) + (1 | SurveyID)
   Data: RS05.Round1A
 Family: binomial(logit link)
  AIC  BIC logLik deviance
 5324 5578  -2631     5262
Random effects:
 Groups    Name Variance   Std.Dev.
 SurveyID       1.4455e+00 1.2023e+00
 ClusterID      5.0000e-10 2.2361e-05
number of obs: 27460, groups: SurveyID, 1787; ClusterID, 52

Estimated scale (compare to  1 )  0.6785013



Steven J. Pierce, M.S.
Doctoral Student in Ecological/Community Psychology
Department of Psychology
Michigan State University
240B Psychology Building
East Lansing, MI 48824-1116

E-mail: pierces1 at msu.edu
Web: http://www.psychology.msu.edu/eco/