# 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 5b (of 7): Primary Meta-Analyses (cont.)

**Posted:**April 30, 2012 |

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

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

**Tags:**between-studies variance component, conditional variance, fixed effect, heterogeneity, interval estimation, math notation, meta-analysis, meta-regression, random effect, significance testing, standardized mean difference | 2 Comments

This is the second of three posts in Part 5 of my overview of meta-analysis. In Part 5a I described six conventional models for meta-analysis, each of which combines within-study and between-studies models. In this second post I first comment on **nested models** then describe **estimation and inference for two models without covariates**—procedures for fitting these models to effect-size (ES) estimates and quantifying uncertainty about their focal (hyper)parameters. In the third post, Part 5c, I’ll do the same for two models with covariates and also comment on extensions and variants of these models and procedures.

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# Overview of Meta-Analysis, Part 5a (of 7): Primary Meta-Analyses

**Posted:**April 12, 2012 |

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

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

**Tags:**between-studies variance component, categorical data, conditional variance, effect size, fixed effect, heterogeneity, meta-analysis, meta-regression, moderator, multilevel model, random effect | 2 Comments

The previous four parts of this seven-part overview of meta-analysis focused on obtaining data and preparing them for the central task addressed in this fifth part: meta-analyzing effect-size (ES) estimates, which I’ll cover in three subparts focused on **meta-analytic models** (Part 5a) and **procedures for fitting them to ESs** (Parts 5b and 5c). In the last two parts (6 and 7) I’ll address follow-up techniques to assess potential problems with these primary analyses, as well as useful ways to report these analyses’ results. (Topics for all seven parts of this overview are listed in Part 1.)

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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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