# 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 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.)

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# Overview of Meta-Analysis, Part 3 (of 7): Effect-Size Features

**Posted:**March 19, 2012 |

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

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

**Tags:**coding, effect size, inclusion/exclusion, individual participant data, meta-analysis, missing data, moderator, primary-study quality | Leave a comment

In Part 2 of this seven-part overview, I described obtaining the sample size(s) or conditional variance (CV) associated with an effect-size (ES) estimate to quantify this estimate’s sampling error or (im)precision. Here in Part 3 I’ll address **coding features linked to ESs**. Whereas this overview’s first three parts focus on collecting data used in a research synthesis, its subsequent four parts will address meta-analyzing these data and presenting results. (Part 1 lists the topics for all seven parts.)

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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