# 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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# Overview of Meta-Analysis, Part 2 (of 7): Sampling Error

**Posted:**March 11, 2012 |

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

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

**Tags:**binary outcome, conditional variance, correlation, dependence, effect size, heterogeneity, meta-analysis, missing data, multivariate effect size, primary-study design, sample size, standardized mean difference | Leave a comment

In Part 1 of this seven-part overview of meta-analysis, I introduced Conn, Hafdahl, Cooper, Brown, and Lusk’s (2009) quantitative review of workplace exercise interventions and discussed extracting effect-size (ES) estimates. Building on that material, in this second part I’ll address **obtaining info about an ES’s sampling error**, which plays a critical role in most modern meta-analytic methods. (Part 1 of this overview lists topics in the subsequent five posts.)

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# Overview of Meta-Analysis, Part 1 (of 7): Effect Sizes

**Posted:**February 27, 2012 |

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

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

**Tags:**binary outcome, correlation, dependence, effect size, meta-analysis, missing data, multivariate effect size, standardized mean difference, substantive application | Leave a comment

This post is the first in a seven-part overview of common meta-analytic tasks. In this first part I’ll introduce a real-world **substantive application of meta-analysis** and address **estimating effect sizes** (ESs). Subsequent parts will focus on the following topics:

- Part 2: obtaining information about ES sampling error
- Part 3: collecting features of ESs
- Part 4: exploring data
- Part 5: fitting meta-analytic models to ESs (subparts 5a, 5b, and 5c)
- Part 6: checking for potential problems
- Part 7: expressing results informatively