# Sneak Preview 2: Outliers, Metric Transformation, and ES Distribution

**Posted:**May 31, 2012 |

**Author:**A. R. Hafdahl |

**Filed under:**Sneak Preview |

**Tags:**assumption violation, between-studies variance component, conditional variance, correlation, effect size, fixed effect, heterogeneity, interval estimation, meta-analysis, outlier, random effect, substantive application | Leave a comment

My previous three posts on fitting models to effect sizes (ESs)—Parts 5a, 5b, and 5c—were the core of my seven-part overview of meta-analysis. With only two posts remaining in the overview, I’ll pause again to describe three more methodological issues I plan to discuss: **potential outliers**, **transforming ES metrics**, and the **distribution of ES parameters**. As in my first sneak preview—about degraded ESs and tricky conditional variances (CVs)—I’ll keep these “teaser” descriptions fairly short, mainly to pique your interest; each issue deserves at least one dedicated post with more detail.

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# Overview of Meta-Analysis, Part 5c (of 7): Primary Meta-Analyses (cont.)

**Posted:**May 13, 2012 |

**Author:**A. R. Hafdahl |

**Filed under:**Overview of Meta-Analysis |

**Tags:**Bayesian analysis, between-studies variance component, dependence, fixed effect, heterogeneity, interval estimation, meta-analysis, meta-regression, model comparison, moderator, multivariate effect size, random effect, significance testing, standardized mean difference | 1 Comment

This is the last of three posts in Part 5 of my overview of meta-analysis. In Part 5a I described six conventional meta-analytic models for effect-size (ES) estimates, and in Part 5b I described estimation and inference for two of those models without covariates. In this post I’ll extend the methods of Part 5b to two **models with covariates** and comment on **extensions and other variants** of these models and procedures, to hint at the wide variety of situations that arise in meta-analysis. In Parts 6 and 7 of the overview, I’ll address follow-up procedures and ways to report results, respectively.

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