[R-sig-ME] is a mixed effect model appropiate?

Tamara R iss3ngard at gmail.com
Wed Aug 9 21:26:23 CEST 2017

Hi, i'm working with survey data regarding leptospirosis knowledge,
attitudes and practices on residents from three slum settlements and i'm
using socio-demographic indicators, knowledge score and attitude score as
predictors of preventive practices score.
I started analyzing my data as a linear model with both categorical and
continuous predictors:

glm(practices~site + sex + education + occupation + knowledge score +
attitude score

But discussing the results with my phD advisor she suggested me to put site
as a random effect in a linear mixed model because of lack of independence
between observations from the same site:

lmer(practices~sex + education + occupation + knowledge score + attitude
score + (1|site))

Thing is that i have less than 100 observations and the variance of random
effects equals to 0. I read in a previous post on this group that it
indicates that the model could be simplified by removing the random effect
but i wish to know if simplifying my model (going back to the original
regression model) will be appropiate to model the lack of independence of
the data or should i also include random slopes for knowledge and attitude
scores into the model? Thanks in advance

Tamara Ricardo
Lic. en Biodiversidad - Becaria CONICET
FHUC - Universidad Nacional del Litoral
Ciudad Universitaria - Pje. el Pozo
Santa Fe (3000) - Argentina

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list