Greetings.
I'm running some models under R using glmmPQL from MASS. These are
three-level models (two grouped levels and the individual level) with
dichotomous outcomes. There are several statistics of interest; for the
moment, I have two specific questions:
1.) This question refers to the following model (I present first
the call, then the output of summary():
morality.restr.1.pql<-glmmPQL(random = ~ 1 | groupid/participantid,
fixed = r.logic.morality ~ 1,
data = fgdata.df[coded.logic,],
na.action=na.omit,
niter=50,
family=binomial)
Linear mixed-effects model fit by maximum likelihood
Data: fgdata.df[coded.logic, ]
AIC BIC logLik
4427.735 4447.531 -2209.868
Random effects:
Formula: ~1 | groupid
(Intercept)
StdDev: 0.3312237
Formula: ~1 | participantid %in% groupid
(Intercept) Residual
StdDev: 0.3651775 0.9765288
Variance function:
Structure: fixed weights
Formula: ~invwt
Fixed effects: r.logic.morality ~ 1
Value Std.Error DF t-value p-value
(Intercept) -0.1699931 0.1039887 905 -1.634727 0.1025
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.2951648 -0.8865510 -0.7183326 1.0428044 1.6135857
Number of Observations: 1042
Number of Groups:
groupid participantid %in% groupid
20 137
Raudenbush & Bryk (1992; 2002) suggest that the Intraclass Correlation is
a useful statistic for a hierarchical linear model. My understanding is
that this statistic is the proportion of the model's total variance that
is "explained" by each level of the model. I have calculated this for
level 2 as 0.3312237^2 / (0.3312237^2 + 0.3651775^2 + 0.9765288^2) and for
level 3 as 0.3651775^2 / (0.3312237^2 + 0.3651775^2 +
0.9765288^2). However, Guo and Zhao imply that the total variance for a
dichotomous-outcome (logistic) model should be a constant, specifically
pi^2/3. Clearly pi^2/3 is a very different number from (0.3312237^2 +
0.3651775^2 + 0.9765288^2). Can anyone shed light on this? Does this
calculation make sense at all?
2.) There is the possibility in these models of using some
cross-classification. The lowest unit of analysis here is the
utterance: one statement made in a group discussion. Each statement is
(currently) nested within a speaker, who is in turn nested within a
group. The complication is that each statement is *also* nested within one
of four scenarios, and the scenarios are repeated across the 20
groups. Using the scenario as a fixed covariate in the model results (or
seems to) in erronenously assuming too many degrees of freedom, since
utterances are clustered within scenarios. But cross-classifying the
scenario * group into 80 clusters seems like it will seriously impede
intepretation. Any advice? Ultimately it may not be terribly important to
include the scenario as a covariate, but I would like to be able to do so
if necessary.
Thanks for any advice.
----------------------------------------------------------------------
Andrew J Perrin - http://www.unc.edu/~aperrin
Assistant Professor of Sociology, U of North Carolina, Chapel Hill
clists at perrin.socsci.unc.edu * andrew_perrin (at) unc.edu
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