Book Review: Hunter,J. E.,& Schmidt,F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Thousand Oaks,CA: Sage

Cortina, J.M. , & Nouri, H. (2000). Effect sizes for ANOVA designs. Thousand Oaks, CA: Sage.

Gleser, L.J. , & Olkin, I. (1994). Stochastically dependent effect sizes. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 339-355). New York: Russell Sage.

Hunter, J.E. , & Schmidt, F.L. (1990). Methods of meta-analysis: Correcting error and bias in research findings. Newbury Park, CA: Sage.

Kalaian, H.A. , & Raudenbush, S.W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1, 227-235.

Lipsey, M.W. , & Wilson, D.B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.

Murphy, K.R. (2003). Validity generalization: A critical review. Mahwah, NJ: Lawrence Erlbaum.

Oswald, F.L. , & Johnson, J.W. (1998). On the robustness, bias and stability of statistics from meta-analysis of correlation coefficients: Some initial Monte-Carlo findings . Journal of Applied Psychology, 83, 164-178.

Raudenbush, S.W. (1994). Random effects models. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 301-321). New York: Russell Sage.

Schulze, R. (2004). Meta-analysis: A comparison of approaches. Cambridge, MA: Hogrefe & Huber.

Viechtbauer, W. (2005). Bias and efficiency of meta-analytic variance estimators in the random-effects model. Journal of Educational and Behavioral Statistics, 30, 261-293.