[R-sig-ME] Random effect variance = zero

[Aurore Paligot]

Hello Everybody, I am new at using mixed models, and I would like some advice about some results that I obtained and that seem counter-intuitive to me.  As an output of a test, I obtainded a variance of zero for a random factor. 

Data
I am looking at the distance between the hands in symmetrical signs of a sign language. This is my dependent  variable. I have four signers (speakers), recorded in four different contexts. I have 320 observations in total : 20 for each signer in each context. 
Research question
I want to see whether there is a relationship between the distance between the hands and the context of use (more or less formal). Context is defined here as a fixed factor with four levels : C1, C2, C3, C4. 
Formula
Context.model = lmer (Distance ~ Context + (1|Signer), data=context) 
Results
For the random factor "Signer", the variance and standard deviation are both equal to zero: 
Linear mixed model fit by REML ['lmerMod']Formula: Ecart ~ Contexte + (1 | Locuteur)   Data: context
REML criterion at convergence: 2986.4
Scaled residuals:     Min      1Q  Median      3Q     Max -1.6085 -0.5709 -0.1486  0.2404  7.0084 
Random effects: Groups   Name        Variance Std.Dev. Locuteur (Intercept)   0.0     0.00    Residual                       725.7    26.94   Number of obs: 319, groups:  Locuteur, 4
Fixed effects:            Estimate Std. Error t value(Intercept)  4.10312    3.01187   1.362ContexteC2  14.60662    4.27288   3.418ContexteC3  -0.09983    4.27288  -0.023ContexteC4  23.22922    4.24626   5.471
Correlation of Fixed Effects:           (Intr) CntxC2 CntxC3ContexteC2 -0.705              ContexteC3 -0.705  0.497       ContexteC4 -0.709  0.500  0.500
Questions 
How is it possible?  Can it be considered as a reasonable output? 
I found this information about the variance estimates of zero. Could this explanation apply to my study? 

"It is possible to end up with a school variance estimate of zero. This fact often puzzles the researcher since each school will most certainly not have the same mean test result. An estimated among-school variance being zero, however, does not mean that each school has the same mean, but rather that the clustering of the students within schools does not help explain any of the overall variability present in test results. In this case, test results of students can be considered as all independent of each other regardless if they are from the same school or not. "( http://www.cscu.cornell.edu/news/statnews/stnews69.pdf )
If not, where could the problem come from? Is the formula that I used correct? Is a mixed-model appropriate for this type of question? 
I would really appreciate some clarification if someone already faced this type of problem ! 

Best regards, 
Aurore 		 	   		  
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