James E. Pustejovsky

I am a statistician and associate professor in the School of Education at the University of Wisconsin-Madison, where I teach in the Educational Psychology Department and the graduate program in Quantitative Methods. My research involves developing statistical methods for problems in education, psychology, and other areas of social science research, with a focus on methods related to research synthesis and meta-analysis.

Interests

  • Meta-analysis
  • Causal inference
  • Robust statistical methods
  • Education statistics
  • Single case experimental designs

Education

  • PhD in Statistics, 2013

    Northwestern University

  • BA in Economics, 2003

    Boston College

Recent Posts

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

I spend more time than I probably should discussing meta-analysis problems on the R-SIG-meta-analysis listserv. The questions that folks pose there are often quite interesting—especially when they’re motivated by issues that they’re wrestling with while trying to complete meta-analysis projects in their diverse fields.

Inverting partitioned matrices

There’s lots of linear algebra out there that’s quite useful for statistics, but that I never learned in school or never had cause to study in depth. In the same spirit as my previous post on the Woodbury identity, I thought I would share my notes on another helpful bit of math about matrices.

Standardized mean differences in single-group, repeated measures designs

I received a question from a colleague about computing variances and covariances for standardized mean difference effect sizes from a design involving a single group, measured repeatedly over time.

Finding the distribution of significant effect sizes

In basic meta-analysis, where each study contributes just a single effect size estimate, there has been a lot of work devoted to developing models for selective reporting. Most of these models formulate the selection process as a function of the statistical significance of the effect size estimate; some also allow for the possibility that the precision of the study’s effect influences the probability of selection (i.

Working papers

Between-case standardized mean differences: Flexible methods for single-case designs

Single-case designs (SCDs) are a class of research methods for evaluating the effects of academic and behavioral interventions in educational and clinical settings. Although visual analysis is …

Single case design research in Special Education: Next generation standards and considerations

Single case design has a long history of use for assessing intervention effectiveness for children with disabilities. Although these designs have been widely employed for more than 50 years, recent …

Investigating narrative performance in children with developmental language disorder: A systematic review and meta-analysis

Purpose: Speech-language pathologists (SLPs) typically examine narrative performance when completing a comprehensive language assessment. However, there is significant variability in the methodologies …

Power approximations for overall average effects in meta-analysis of dependent effect sizes

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power …

Comparison of competing approaches to analyzing cross-classified data: Random effects models, ordinary least squares, or fixed effects with cluster robust standard errors

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in education. However, when the focus of a study is on the regression coefficients at level …

Recent Publications

Multi-level meta-analysis of single-case experimental designs using robust variance estimation

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under …

Meta-Analysis with robust variance estimation: Expanding the range of working models

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to …

Recent Presentations

The state of single case synthesis: Premises, tools, and possibilities

Selective reporting in meta-analysis of dependent effect size estimates

Publication bias and other forms of selective outcome reporting are important threats to the validity of findings from research syntheses—even undermining their special status for informing evidence-based practice and policy guidance.

Easy, cluster-robust standard errors with the clubSandwich package

Cluster-robust variance estimation methods (also known as sandwich estimators, linearization estimators, or simply “clustered” standard errors) are a standard inferential tool in many …

Four things every quantitative social scientist should know about meta-analysis

Meta-analysis is a set of statistical tools for synthesizing results across multiple sources of evidence. Meta-analyses of intervention research are often taken as a gold standard for informing …

Synthesis of dependent effect sizes: Robust variance estimation with clubSandwich

Large meta-analyses often involve dependent effect sizes, but where the exact form of the dependence is unknown. Meta-analysis with robust variance estimation handles this problem through …

Software

lmeInfo

Information Matrices for ‘lmeStruct’ and ‘glsStruct’ Objects

simhelpers

Helper package to assist in running simulation studies

ARPobservation

Simulate systematic direct observation data

clubSandwich

Cluster-robust variance estimation

scdhlm

Between-case SMD for single-case designs

SingleCaseES

Single-case design effect size calculator

wildmeta

Cluster-wild bootstrap for meta-regression

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Young Ri Lee

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Megha Joshi

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Christopher Runyon

Measurement Scientist

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