# 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.

Read the rest of this entry »

# Overview of Meta-Analysis, Part 4 (of 7): Data Exploration

**Posted:**March 28, 2012 |

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

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

**Tags:**coding, data management, effect size, meta-analysis, missing data, moderator, outlier, reporting guidance, sample size, significance testing | 1 Comment

This seven-part overview’s first three parts focused on collecting data used in meta-analyses: estimates of effect size (ES), sample sizes or conditional variances (CVs) to quantify ES sampling error or (im)precision, and ES features. The overview’s subsequent four parts address analyzing these data and presenting results. In this fourth part I begin by describing **preliminary analyses** that can help identify errors and issues to attend to in primary analyses. (Part 1 of this overview lists the topics for all seven parts.)

Read the rest of this entry »