# 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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# Sneak Preview: Degraded Effect Sizes and Tricky Conditional Variances

**Posted:**April 2, 2012 |

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

**Filed under:**Sneak Preview |

**Tags:**Bayesian analysis, conditional variance, correlation, effect size, meta-analysis, missing data, resampling, significance testing, standardized mean difference, vote counting | Leave a comment

My post on data exploration more than half completed my seven-part overview of meta-analysis. As a diversion while I write Part 5, let’s consider two of several methodological issues I plan to discuss in this blog: degraded effect sizes (ESs) and tricky conditional variances (CVs). My main aim here is to pique your interest in future posts by offering a glimpse at ways to manage selected challenges that routine meta-analytic techniques don’t address. These “teaser” descriptions will be quite superficial. I plan to elaborate on each of these challenges—as well as many others—after laying a foundation in my seven-part overview.

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