Comparing nested GLMs via chi-squared and loglikelihood I want to compare two ​GLMs with binomial dependent variables. The results are: 
 m1 <- glm(symptoms ~ 1,         data=data2)
 m2 <- glm(symptoms ~ phq_index, data=data2)

The model test gives the following results: 
​ anova(m1, m2)​
         no AIC    logLik   LR.stat df  Pr(>Chisq)   
 m1      1  4473.9 -2236.0                        
 m2      9  4187.3 -2084.7  302.62  8   < 2.2e-16 ***

​I am used to comparing these kinds of models using chi-squared values, a chi-squared difference, and a chi-squared difference test. Since all other models in the paper are compared this way, and since I'd like to report them in a table together: why exactly is this model test different from my other model tests in which I get chi-squared values and difference tests? Can I obtain chi-squared values from this test? 
Results from other model comparisons (e.g., GLMER), look like this: 
    #Df AIC     BIC     logLik  Chisq   Chi     DF diff Pr(>Chisq)
m3  13  11288   11393   -5630.9 392.16          
m4  21  11212   11382   -5584.9 92.02   300.14  8       0.001

 A: The "chi-square value" you're looking for is the deviance (-2*(log likelihood), at least up to an additive constant that doesn't matter for the purposes of inference. R gives you the log-likelihood above (logLik) and the likelihood ratio statistic (LR.stat): the LR stat is twice the difference in the log-likelihoods (2*(2236.0-2084.7)).
I'm a little puzzled by your anova results, since they don't seem to match the format that I get when I run anova() on two glm() fits: in particular, stats:::anova.glm (the anova method for glm objects) doesn't print the AIC ... are m1 and m2 really glm objects?  (e.g. try class("m1"))
Can you give us a reproducible example? Here's what I get for a simple example, modified from ?glm:
## Dobson (1990) Page 93: Randomized Controlled Trial :
d.AD <- data.frame(counts=c(18,17,15,20,10,20,25,13,12),
                   outcome=gl(3,1,9),
                   treatment=gl(3,3))
glm1 <- glm(counts ~ outcome + treatment, family = poisson, data=d.AD)
glm0 <- update(glm1, . ~ 1)

Model comparison gives the residual deviances (your "chi-squared value") and the differences between them ...
anova(glm0,glm1,test="Chisq")
## Analysis of Deviance Table
## 
## Model 1: counts ~ 1
## Model 2: counts ~ outcome + treatment
##   Resid. Df Resid. Dev Df    Deviance Pr(>Chi)
## 1         8    10.5814                     
## 2         4     5.1291  4      5.4523    0.244

(if I left out the test="Chisq", I would get all of the above but without the p-value)
I see from your cross-posting at http://article.gmane.org/gmane.comp.lang.r.general/299377 that you're actually using ordinal::clm, in which case we do get output that looks like what you have above (it's important to be precise) ... the results are not identical because the model is slightly different, and you are given the log-likelihoods (=-deviance/2) rather than the deviances, but the difference between the deviances ("LR.stat") is similar.
library(ordinal)
clm1 <- clm(ordered(counts) ~
   outcome + treatment, family = poisson, data=d.AD)
clm0 <- update(clm1, . ~ 1)
anova(clm0,clm1)
##       no.par      AIC  logLik LR.stat df Pr(>Chisq)
## clm0      7    50.777 -18.389                      
## clm1     11    52.839 -15.419  5.9389  4     0.2038

