# Posts

### Distribution of the number of significant effect sizes

A while back, I posted the outline of a problem about the number of significant effect size estimates in a study that reports multiple outcomes. This problem interests me because it connects to the issue of selective reporting of study results, which creates problems for meta-analysis.

### Approximating the distribution of cluster-robust Wald statistics

$\def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\cor{{\text{cor}}} \def\bm{\mathbf} \def\bs{\boldsymbol}$ In Tipton and Pustejovsky (2015), we examined several different small-sample approximations for cluster-robust Wald test statistics, which are like $$F$$ statistics but based on cluster-robust variance estimators.

### Implementing Consul's generalized Poisson distribution in Stan

$\def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol}$ For a project I am working on, we are using Stan to fit generalized random effects location-scale models to a bunch of count data.

### Implementing Efron's double Poisson distribution in Stan

$\def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol}$ For a project I am working on, we are using Stan to fit generalized random effects location-scale models to a bunch of count data.

### Cluster-Bootstrapping a meta-analytic selection model

In this post, we will sketch out what we think is a promising and pragmatic method for examining selective reporting while also accounting for effect size dependency. The method is to use a cluster-level bootstrap, which involves re-sampling clusters of observations to approximate the sampling distribution of an estimator. To illustrate this technique, we will demonstrate how to bootstrap a Vevea-Hedges selection model.

### Cohen's $d_z$ makes me dizzy when considering measurement error

Meta-analyses in education, psychology, and related fields rely heavily of Cohen’s $d$, or the standardized mean difference effect size, for quantitatively describing the magnitude and direction of intervention effects. In these fields, Cohen’s $d$ is so pervasive that its use is nearly automatic, and analysts rarely question its utility or consider alternatives (response ratios, anyone? POMP?). Despite this state of affairs, working with Cohen’s $d$ is theoretically challenging because the standardized mean difference metric does not have a singular definition. Rather, its definition depends on the choice of the standardizing variance used in the denominator.

### Corrigendum to Pustejovsky and Tipton (2018), redux

In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares. We were recently alerted that Theorem 2 in the paper is incorrect as stated. It turns out, the conditions in the original version of the theorem are too general. A more limited version of the Theorem does actually hold, but only for models estimated using ordinary (unweighted) least squares, under a working model that assumes independent, homoskedastic errors. In this post, I’ll give the revised theorem, following the notation and setup of the previous post (so better read that first, or what follows won’t make much sense!).

### Corrigendum to Pustejovsky and Tipton (2018)

In my 2018 paper with Beth Tipton, published in the Journal of Business and Economic Statistics, we considered how to do cluster-robust variance estimation in fixed effects models estimated by weighted (or unweighted) least squares. A careful reader recently alerted us to a problem with Theorem 2 in the paper, which concerns a computational short cut for a certain cluster-robust variance estimator in models with cluster-specific fixed effects. The theorem is incorrect as stated, and we are currently working on issuing a correction for the published version of the paper. In the interim, this post details the problem with Theorem 2. I’ll first review the CR2 variance estimator, then describe the assertion of the theorem, and then provide a numerical counter-example demonstrating that the assertion is not correct as stated.

### Variance component estimates in meta-analysis with mis-specified sampling correlation

$\def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}}$ In a recent paper with Beth Tipton, we proposed new working models for meta-analyses involving dependent effect sizes. The central idea of our approach is to use a working model that captures the main features of the effect size data, such as by allowing for both between- and within-study heterogeneity in the true effect sizes (rather than only between-study heterogeneity).

### Implications of mean-variance relationships for standardized mean differences

A question came up on the R-SIG-meta-analysis listserv about whether it was reasonable to use the standardized mean difference metric for synthesizing studies where the outcomes are measured as proportions. I think this is an interesting question because, while the SMD could work perfectly fine as an effect size metric for proportions, there are also other alternatives that could be considered, such as odds ratios or response ratios or raw differences in proportions. Further, there are some situations where the SMD has disadvantages for synthesizing contrasts between proportions. Thus, it’s a situation where one has to make a choice about the effect size metric, and where the most common metric (the SMD) might not be the right answer. In this post, I want to provide a bit more detail regarding why I think mean-variance relationships in raw data can signal that the standardized mean differences might be less useful as an effect size metric compared to alternatives.