Interpretation of strange posterior standard deviations

[Kyle Manning]

Hello, 

I've been modeling a variable with a lognormal distribution by using

family="lognormal". 

When I do this, I end up with a model where the posterior standard deviations for each data point, a.k.a.

model$summary.fitted.values$sd

Are all incredibly small, for example

1. 0.000421568997572139
2. 0.000421568997572139
3. 0.000746665312948463
4. 0.000746665312948463

I thought doing exp(model$summary.fitted.values$sd) would give the correct values, but then the values are all just above 1, such as 

1. 1.00042165787027
2. 1.00042165787027
3. 1.00074694413689
4. 1.00074694413689

I've tried many different priors but I always see the above effect with standard deviations.

Am I doing something incorrect? I would expect the standard deviations for my data to be much bigger, and certainly not all similar. If it matters, I am using a model with 2 fixed effects and ~8 random effects.

Thanks,

Kyle