arXiv subjects

Elizabeth Tipton

Publications and source records attributed to Elizabeth Tipton.

5 recordsLinked to original sources

arXiv2026

Meta-Analysis Without Normality: Estimating the True Effect Distribution with Penalized Gaussian Mixtures

Standard random-effects meta-analysis relies heavily on the assumption that the underlying true effects are normally distributed. In the social sciences, where evidence synthesis increasingly involves large, highly heterogeneous datasets, this assumption is often restrictive and unjustified. Misspecification of the random-effects distribution prevents the detection of asymmetry or multimodality, potentially leading to erroneous conclusions regarding the prevalence of adverse effects or the existence of specific subgroups. This paper introduces a Penalized Gaussian Mixture (PGM) framework designed to recover the entire probability density function of true effects without enforcing a rigid parametric shape. The method adapts to different non-normal scenarios, including skewed and multimodal distributions, while reducing to the normal case when supported by the data. A simulation study demonstrates that in large, highly heterogeneous meta-analyses, PGM yields substantially more accurate estimates of tail probabilities and the density function than standard methods when normality is violated, without substantially compromising efficiency under normality. An empirical application to environmental education data illustrates the practical utility of the method. The proposed framework provides researchers with a robust tool to move beyond simple summary statistics and characterize the complex nature of the true effect distribution in the real world.

stat.ME↗

arXiv2023

Designing Randomized Experiments to Predict Unit-Specific Treatment Effects

Typically, a randomized experiment is designed to test a hypothesis about the average treatment effect and sometimes hypotheses about treatment effect variation. The results of such a study may then be used to inform policy and practice for units not in the study. In this paper, we argue that given this use, randomized experiments should instead be designed to predict unit-specific treatment effects in a well-defined population. We then consider how different sampling processes and models affect the bias, variance, and mean squared prediction error of these predictions. The results indicate, for example, that problems of generalizability (differences between samples and populations) can greatly affect bias both in predictive models and in measures of error in these models. We also examine when the average treatment effect estimate outperforms unit-specific treatment effect predictive models and implications of this for planning studies.

stat.ME↗

arXiv2023

MetaExplorer: Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

Scientists often use meta-analysis to characterize the impact of an intervention on some outcome of interest across a body of literature. However, threats to the utility and validity of meta-analytic estimates arise when scientists average over potentially important variations in context like different research designs. Uncertainty about quality and commensurability of evidence casts doubt on results from meta-analysis, yet existing software tools for meta-analysis do not necessarily emphasize addressing these concerns in their workflows. We present MetaExplorer, a prototype system for meta-analysis that we developed using iterative design with meta-analysis experts to provide a guided process for eliciting assessments of uncertainty and reasoning about how to incorporate them during statistical inference. Our qualitative evaluation of MetaExplorer with experienced meta-analysts shows that imposing a structured workflow both elevates the perceived importance of epistemic concerns and presents opportunities for tools to engage users in dialogue around goals and standards for evidence aggregation.

cs.HC↗

arXiv2016

Small sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models

In longitudinal panels and other regression models with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) in order to account for heteroskedasticity and un-modeled dependence among the errors. CRVE is asymptotically consistent as the number of independent clusters increases, but can be biased downward for sample sizes often found in applied work, leading to hypothesis tests with overly liberal rejection rates. One solution is to use bias-reduced linearization (BRL), which corrects the CRVE so that it is unbiased under a working model, and t-tests with Satterthwaite degrees of freedom. We propose a generalization of BRL that can be applied in models with arbitrary sets of fixed effects, where the original BRL method is undefined, and describe how to apply the method when the regression is estimated after absorbing the fixed effects. We also propose a small-sample test for multiple-parameter hypotheses, which generalizes the Satterthwaite approximation for t-tests. In simulations covering a variety of study designs, we find that conventional cluster-robust Wald tests can severely under-reject while the proposed small-sample test maintains Type I error very close to nominal levels.

stat.ME↗

arXiv2015

robumeta: An R-package for robust variance estimation in meta-analysis

Meta-regression models are commonly used to synthesize and compare effect sizes. Unfortunately, traditional meta-regression methods are ill-equipped to handle the complex and often unknown correlations among non-independent effect sizes. Robust variance estimation (RVE) is a recently proposed meta-analytic method for dealing with dependent effect sizes. The robumeta package provides functions for performing robust variance meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown.

stat.ME↗