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Dear all,I am working on a manuscript of my meta-analysis on effectiveness of a program. As indicators of heterogeneity in the effect size estimates, I have obtained the Q statistic and the I^2 statistic from the program Comprehensive Meta-Analysis (CMA) 3.0. Now I am using the formula on page 123 of Borenstein, Hedges, Higgins, & Rothstein (2009) to compute the 95% confidence intervals of the I^2 statistics. But some problems occur.1) When there is only 1 study (k=1), should we (or can we) still compute the Q and I^2 statistic? Because I still obtained 0.00 for both Q and I^2 in CMA. However when I go ahead to compute the 95%CI of I^2, it cannot be computed. I suppose heterogeneity issue would not be irrelevant when k=1?2) Is it possible and reasonable for the lower limit of the 95% confidence interval of I^2 be negative values? The range apparently seems too large...? What does a negative value of I^2 imply?The 3 problematic cases for this question:Q=5.708, df=4, I^2=29.92 [-81.34, 72.92]Q=3.527, df=1, I^2=71.65 [-26.04, 93.62]Q=1.17, df=4, I^2=0.00 [-1646.34, 33.07]2) Another problematic case is that, the computed lower limit of I^2 is larger than the point estimate of I^2 statistic. Does this imply my computation error or other possible issues?Q=117.220, df=12, I^2=71.65 [84.34, 93.31]the reference is: Borenstein, Hedges, Higgins, & Rothstein, 2009http://onlinelibrary.wiley.com/book/10.1002/97804707433863) Regarding the article below, may I know if anyone could share with me, in simpler ways, how to conceptualize, conduct, and interpret the testing of potential publication bias alongside moderators such as PET-PEESE in this article? I am required to conduct this test in my meta-analysis but I don't quite understand the simulation analyses in this paper.Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. Research Synthesis Methods, 5(1), 60-78.Any help would be greatly appreciated. Much thanks for your help in advance! Thanks!!Regards,KevriaSee More

Tips, techniques, and procedures to evaluate the extent to which effect size magnitude varies across a series of studies.

Dear all,I am conducting a meta-analysis on the effectiveness of an intervention on two outcomes (attitudinal and behavioral). The effect size measure is Cohen's d. After computing the Cohen's d, I want to proceed to subgroup analysis and meta-regression. I am looking for resources or answers to the 2 questions I came across below.For instance, one moderator is "interactive approach VS didactic approach", and another one is gender (male VS female). 1) Referring to the Cochrane's Handbook (http://handbook.cochrane.org/chapter_9/9_6_5_1_ensure_that_there_ar... (http://handbook.cochrane.org/chapter_9/9_6_5_1_ensure_that_there_are_adequate_studies_to_justify.htm)), it stated that at least ten studies in a meta-analysis are required. Does this "10 studies" mean that there should be at least 10 data EACH for interactive approach and didactic approach respectively? Or just 10 studies included in the meta-analysis as a whole?3) Some reviewers said individual-level moderators (e.g. percentage of male, mean age) cannot not be used to conduct subgroup analysis; only the study-level moderators (e.g. publication year, location of study) can be used. Is this always true? What is the rationale?Thank you all for the patience and help in advance!Regards,GloriaSee More

Tips and techniques for the analysis of meta-analytic databases and for interpretations of such analyses.

A technique to synthesizing correlation/covariance matrices for the purpose of fitting structural equation models.

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