Calculating effect sizes without SDs or sample sizes for meta-analysis I'm coding articles for a meta-analysis looking at group differences on dichotomous and continuous outcomes. Groups are not the same size. Continuous outcome ES is standardized mean difference and dichotomous outcome ES is logOR. 
I had two questions about calculating ESs.
1) Can I calculate an effect size using group means ONLY (i.e., if SDs or CIs are not provided)? 
2) I'm running into an issue where some articles present the sample sizes for both groups, later present the means/SDs for both groups, and then present an ANOVA where the df is smaller than I would expect it to be given the sample size presented. This suggests that there was missing data for that analysis, but I can't know which group it's missing from, so I don't know the group sample sizes. What are best practices for handling this situation? I don't want to bias the ESs with inflated sample sizes. Can I calculate an ES using the F value without knowing the sample size for the groups? Should I subtract subjects from both groups equally?
 A: Based on additional investigation, this is what I came up with:

1) Can I calculate an effect size using group means ONLY (i.e., if SDs or CIs are not provided)?

Nope.

2) I'm running into an issue where some articles present the sample sizes for both groups, later present the means/SDs for both groups, and then present an ANOVA where the df is smaller than I would expect it to be given the sample size presented. This suggests that there was missing data for that analysis, but I can't know which group it's missing from, so I don't know the group sample sizes. What are best practices for handling this situation? I don't want to bias the ESs with inflated sample sizes. Can I calculate an ES using the F value without knowing the sample size for the groups? Should I subtract subjects from both groups equally?

The solution I settled on was to assume that subjects were missing at random from both groups and proportionally reduce the groups to create a total N consistent with the df. For example, if the df suggested a sample size of 24, but group A was stated to be 10 people and group B was stated to be 20 people (suggesting a loss of 6 subjects), then I dropped 2 people from group A and 4 from group B.
