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James E. Pustejovsky

Pusto’s package downloads

programming
Rstats
Back in 2019, I posted an analysis of the R packages that I have developed, looking at how frequently they have been downloaded, comparing them to other topically related…
Sep 22, 2026
James E. Pustejovsky

Meta-Analysis of Variance for Monte Carlo Simulations

distribution theory
experimental design
simulation
Monte Carlo simulation studies are the workhorse of quantitative methodology. Whether you are developing new methods, trying to understand existing ones, or figuring out how…
Jul 31, 2026
James E. Pustejovsky

Calibration models for Kullback-Leibler divergence

distribution theory
Kullback-Leibler-divergence
For a new project, I’ve been thinking about model evaluation and model selection in meta-analysis. You can’t get very far into the literature on this without running into…
Jul 12, 2026
James E. Pustejovsky

Generating selectively reported results in meta-analysis

meta-analysis
publication bias
selective reporting
For one of her dissertation studies, my advisee Paulina Grekov is looking at factors that influence the performance of publication bias adjustment methods in meta-analysis…
Apr 11, 2026
James E. Pustejovsky

p-uniform* is a three-parameter selection model

meta-analysis
selective reporting
estimation
Robbie van Aert and Marcel van Assen have a new article out on a method for estimating average effect size in meta-analyses subject to publication bias, called p-uniform*.…
Mar 8, 2026
James E. Pustejovsky

Notes on regression adjustment for selectively reported results in meta-analysis

effect size
distribution theory
selective reporting
Several of the most prominent methods for investigating selective reporting bias in meta-analyis take the form of simple linear regressions, estimated by weighted least…
Jan 17, 2026
James E. Pustejovsky

Variance stabilization of Cohen’s d

effect size
standardized mean difference
delta method
In a recent Psychological Methods paper, Haaf & Rouder (2023) suggest conducting meta-analysis of standardized mean differences after transforming them to point biserial…
Jul 23, 2025
James E. Pustejovsky

Interactive display of simulation results with Observable.js

simulation
programming
Monte Carlo simulations are an essential and ubiquitous feature of my work as a statistical methodologist. With my students and collaborators, I have conducted simulations…
Jan 19, 2025
James E. Pustejovsky

A bigger simulation of bootstrap confidence interval variations

programming
Rstats
bootstrap
In my previous post, I demonstrated some new brand-new additions to the simhelpers package: utilities for calculating bootstrap confidence intervals. That post walked…
Jan 18, 2025
James E. Pustejovsky

Bootstrap confidence interval variations

programming
Rstats
bootstrap
I recently added some new utilities for calculating bootstrap confidence intervals to the simhelpers package. The functions are designed to make it a bit more convenient to…
Jan 15, 2025
James E. Pustejovsky

A quirk of nlme::varIdent

Rstats
programming
hierarchical models
nlme

Wherein I document how to control the reference level in models that use nlme::varIdent() for specifying a variance structure.
Dec 28, 2024
James E. Pustejovsky

Beta-density selection models for meta-analysis

effect size
distribution theory
selective reporting
I still have meta-analytic selection models on the brain. As part of an IES-funded project with colleagues from the American Institutes for Research, I’ve been working on…
Nov 8, 2024
James E. Pustejovsky

Copas selection models for meta-analysis

effect size
distribution theory
selective reporting
Recently, I’ve been studying and tinkering with meta-analytic models for selective reporting of findings from primary studies. I’ve mostly focused on step-function selection…
Jul 27, 2024
James E. Pustejovsky

Step-function selection models for meta-analysis

effect size
distribution theory
selective reporting
In a recent post I looked at the distribution of statistically significant effect sizes in studies that report multiple outcomes, when those studies are subject to a certain…
Jul 9, 2024
James E. Pustejovsky

Simulating bootstrap confidence intervals

programming
Rstats
bootstrap
Bootstrapping is a brilliant statistical idea. I realize that it’s become pretty routine, these days, and maybe has lost some of the magical veneer it once had, but I will…
Jul 1, 2024
James E. Pustejovsky

Correlations between z-transformed correlation coefficients

effect size
correlation
distribution theory
meta-analysis
For a little meta-analysis project that I’m working on with Jingru, we are dealing with a database of correlation coefficients, where some of the included studies report…
Jun 6, 2024
James E. Pustejovsky

Distribution of the number of significant effect sizes

effect size
distribution theory
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…
Mar 28, 2024
James E. Pustejovsky

Approximating the distribution of cluster-robust Wald statistics

robust variance estimation
distribution theory
In Tipton and Pustejovsky (2015), we examined several different small-sample approximations for cluster-robust Wald test statistics, which are like \(F\) statistics but…
Mar 24, 2024
James E. Pustejovsky

Implementing Consul’s generalized Poisson distribution in Stan

Bayes
simulation
distribution theory
generalized linear model
programming
Rstats
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. In a previous post, I walked through our…
Dec 6, 2023
James E. Pustejovsky

Implementing Efron’s double Poisson distribution in Stan

Bayes
simulation
distribution theory
generalized linear model
programming
Rstats
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. We’re interested in using the…
Sep 15, 2023
James E. Pustejovsky

Cluster-Bootstrapping a meta-analytic selection model

bootstrap
dependent effect sizes
meta-analysis
publication bias
programming
Rstats

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.
Mar 30, 2023
James E. Pustejovsky, Megha Joshi

Cohen’s \(d_z\) makes me dizzy when considering measurement error

effect size
standardized mean difference
design-comparable SMD
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.
Feb 17, 2023
James E. Pustejovsky

Corrigendum to Pustejovsky and Tipton (2018), redux

robust variance estimation
econometrics
matrix algebra

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.
Nov 7, 2022
James E. Pustejovsky

Corrigendum to Pustejovsky and Tipton (2018)

robust variance estimation
econometrics
matrix algebra

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.
Sep 28, 2022
James E. Pustejovsky

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

meta-analysis
dependent effect sizes
distribution theory
hierarchical models
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…
Nov 28, 2021
James E. Pustejovsky

Implications of mean-variance relationships for standardized mean differences

standardized mean difference
response ratio
distribution theory
meta-analysis

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.
Nov 2, 2021
James E. Pustejovsky

Inverting partitioned matrices

matrix algebra

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. At some point in high school or college, you might have learned how to invert a small matrix by hand. It turns out that there’s a straight-forward generalization of this formula to matrices of arbitrary size, but that are partitioned into four pieces.
Oct 20, 2021
James E. Pustejovsky

Standardized mean differences in single-group, repeated measures designs

effect size
standardized mean difference
distribution theory
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…
Oct 6, 2021
James E. Pustejovsky

Finding the distribution of significant effect sizes

effect size
distribution theory
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.…
Apr 27, 2021
James E. Pustejovsky

The Woodbury identity

hierarchical models
matrix algebra
As in many parts of life, statistics is full of little bits of knowledge that are useful if you happen to know them, but which hardly anybody ever bothers to mention. You…
Dec 4, 2020
James E. Pustejovsky

An ANCOVA puzzler

meta-analysis
effect size
standardized mean difference
Doing effect size calculations for meta-analysis is a good way to lose your faith in humanity—or at least your faith in researchers’ abilities to do anything like sensible…
Nov 24, 2020
James E. Pustejovsky

From Longhorn to Badger

It’s taken me a while to finally get around to updating my website with some personal news. I’ve moved from UT Austin to the UW Madison School of Education, where I am now…
Aug 28, 2020
James E. Pustejovsky

What do meta-analysts mean by ‘multivariate’ meta-analysis?

meta-analysis
multivariate
dependent effect sizes
If you’ve ever had class with me or attended one of my presentations, you’ve probably heard me grouse about how statisticians are mostly awful about naming things.1 A lot of…
Jun 27, 2020
James E. Pustejovsky

Weighting in multivariate meta-analysis

meta-analysis
weighting
One common question about multivariate/multi-level meta-analysis is how such models assign weight to individual effect size estimates. When a version of the question came up…
Jun 9, 2020
James E. Pustejovsky

An update on code folding with blogdown + Academic theme

programming
Rstats
2024-06-08 I have ported my website to Quarto, which has native support for code folding. I’m leaving this post up despite the fact that the functionality is no longer…
May 3, 2020
James E. Pustejovsky

Simulating correlated standardized mean differences for meta-analysis

effect size
standardized mean difference
meta-analysis
simulation
programming
distribution theory
As I’ve discussed in previous posts, meta-analyses in psychology, education, and other areas often include studies that contribute multiple, statistically dependent effect…
Sep 30, 2019
James E. Pustejovsky

Sometimes, aggregating effect sizes is fine

effect size
meta-analysis
dependent effect sizes
In meta-analyses of psychology, education, and other social science research, it is very common that some of the included studies report more than one relevant effect size.…
Jul 2, 2019
James E. Pustejovsky

Code folding with blogdown + Academic theme

programming
Rstats
2024-06-08 I have ported my website to Quarto, which has native support for code folding. I’m leaving this post up despite the fact that the functionality is no longer…
Apr 14, 2019
James E. Pustejovsky

CRAN downloads of my packages

programming
Rstats
meta-analysis
At AERA this past weekend, one of the recurring themes was how software availability (and its usability and default features) influences how people conduct meta-analyses.…
Apr 9, 2019
James E. Pustejovsky

Systematic Reviews and Meta-analysis SIG at AERA 2019

meta-analysis
AERA
This year, Dr. Laura Dunne and I are serving as program co-chairs for the AERA special interest group on Systematic Reviews and Meta-Analysis, which is a great group of…
Mar 26, 2019
James E. Pustejovsky

A handmade clubSandwich for multi-site trials

sandwiches
robust variance estimation
econometrics
weighting
I’m just back from the Society for Research on Educational Effectiveness meetings, where I presented work on small-sample corrections for cluster-robust variance estimators…
Mar 9, 2019
James E. Pustejovsky

Effective sample size aggregation

econometrics
causal inference
weighting
In settings with independent observations, sample size is one way to quickly characterize the precision of an estimate. But what if your estimate is based on weighted data…
Jan 22, 2019
James E. Pustejovsky

Easily simulate thousands of single-case designs

behavioral observation
simulation
single-case design
Earlier this month, I taught at the Summer Research Training Institute on Single-Case Intervention Design and Analysis workshop, sponsored by the Institute of Education…
Jun 21, 2018
James E. Pustejovsky

New paper: A gradual effects model for single-case designs

single-case design
effect size
generalized linear model
I’m very happy to share a new paper, co-authored with my student Danny Swan, “A gradual effects model for single-case designs,” which is now available online at Multivariate…
May 14, 2018
James E. Pustejovsky

clubSandwich at the Austin R User Group Meetup

Rstats
robust variance estimation
sandwiches
Last night I attended a joint meetup between the Austin R User Group and R Ladies Austin, which was great fun. The evening featured several lightning talks on a range of…
Apr 26, 2018
James E. Pustejovsky

Sampling variance of Pearson r in a two-level design

effect size
correlation
meta-analysis
delta method
distribution theory
Consider Pearson’s correlation coefficient, \(r\), calculated from two variables \(X\) and \(Y\) with population correlation \(\rho\). If one calculates \(r\) from a simple…
Apr 19, 2018
James E. Pustejovsky

The multivariate delta method

delta method
distribution theory
The delta method is surely one of the most useful techniques in classical statistical theory. It’s perhaps a bit odd to put it this way, but I would say that the delta…
Apr 11, 2018
James E. Pustejovsky

New paper: Using response ratios for meta-analyzing SCDs with behavioral outcomes

single-case design
effect size
response ratio
meta-analysis
I’m pleased to announce that my article “Using response ratios for meta-analyzing SCDs with behavioral outcomes” has been accepted at Journal of School Psychology. There’s a…
Mar 16, 2018
James E. Pustejovsky

New paper: procedural sensitivities of effect size measures for SCDs

single-case design
effect size
response ratio
non-overlap measures
simulation
I’m very happy to share that my article “Procedural sensitivities of effect sizes for single-case designs with directly observed behavioral outcome measures” has been…
Jan 11, 2018
James E. Pustejovsky

Back from the IES PI meeting

single-case design
hypothesis testing
I’m just back from the Institute of Education Sciences’ Principle Investigators conference in Washington D.C. It was an envigorating trip for me, and not only because of the…
Jan 10, 2018
James E. Pustejovsky

2SLS standard errors and the delta-method

instrumental variables
causal inference
delta method
distribution theory
I just covered instrumental variables in my course on causal inference, and so I have two-stage least squares (2SLS) estimation on the brain. In this post I’ll share…
Oct 7, 2017
James E. Pustejovsky

Pooling clubSandwich results across multiple imputations

missing data
sandwiches
small-sample
Rstats
A colleague recently asked me about how to apply cluster-robust hypothesis tests and confidence intervals, as calculated with the clubSandwich package, when dealing with…
Sep 27, 2017
James E. Pustejovsky

Imputing covariance matrices for meta-analysis of correlated effects

meta-analysis
sandwiches
robust variance estimation
Rstats
In many systematic reviews, it is common for eligible studies to contribute effect size estimates from not just one, but multiple relevant outcome measures, for a common…
Aug 10, 2017
James E. Pustejovsky

The siren song of significance

pre-registration
hypothesis testing
How is statistical analysis like the Odyssey? Here’s an analogy that I used in my research methods course last semester to explain the purpose of study pre-registration. If…
Jun 19, 2017
James E. Pustejovsky

You wanna PEESE of d’s?

meta-analysis
publication bias
standardized mean difference
simulation
Publication bias—or more generally, outcome reporting bias or dissemination bias—is recognized as a critical threat to the validity of findings from research syntheses. In…
Apr 27, 2017
James E. Pustejovsky

New working paper: Using log response ratios for meta-analyzing SCDs with behavioral outcomes

single-case design
effect size
response ratio
meta-analysis
One of the papers that came out of my dissertation work (Pustejovsky, 2015) introduced an effect size metric called the log response ratio (or LRR) for use in meta-analysis…
Apr 26, 2017
James E. Pustejovsky

Presentation at IES 2016 PI meeting

single-case design
effect size
I am just back from the Institute of Education Sciences 2016 Principal Investigators meeting. Rob Horner had organized a session titled “Single-case methods: Current status…
Dec 19, 2016
James E. Pustejovsky

New tutorial paper on BC-SMD effect sizes

single-case design
effect size
design-comparable SMD
I’m pleased to announce that the Campbell Collaboration has just published a new discussion paper that I wrote with my colleagues Jeff Valentine and Emily Tanner-Smith about …
Dec 19, 2016
James E. Pustejovsky

Bug in nlme::lme with fixed sigma and REML estimation

Rstats
programming
hierarchical models
nlme
About one year ago, the nlme package introduced a feature that allowed the user to specify a fixed value for the residual variance in linear mixed effect models fitted with l…
Nov 7, 2016
James E. Pustejovsky

What is Tau-U?

effect size
single-case design
non-overlap measures
Parker, Vannest, Davis, and Sauber (2011) proposed the Tau-U index—actually several indices, rather—as effect size measures for single-case designs. The original paper…
Nov 3, 2016
James E. Pustejovsky

New working paper: Procedural sensitivities of SCD effect sizes

effect size
single-case design
response ratio
non-overlap measures
simulation
I’ve just posted a new version of my working paper, Procedural sensitivities of effect sizes for single-case designs with behavioral outcome measures. The abstract is below.…
Oct 17, 2016
James E. Pustejovsky

Simulation studies in R (Fall, 2016 version)

Rstats
simulation
programming
In today’s Quant Methods colloquium, I gave an introduction to the logic and purposes of Monte Carlo simulation studies, with examples written in R.
Sep 28, 2016
James E. Pustejovsky

Bug in nlme::getVarCov

Rstats
programming
hierarchical models
nlme
The bug documented below was corrected a while ago, in version 3.1-150 of the nlme package. See the bug report and the Changelog entry for 2020-09-24.
Aug 10, 2016
James E. Pustejovsky

Alternative formulas for the standardized mean difference

meta-analysis
effect size
distribution theory
standardized mean difference
The standardized mean difference (SMD) is surely one of the best known and most widely used effect size metrics used in meta-analysis. In generic terms, the SMD parameter is…
Jun 3, 2016
James E. Pustejovsky

Assigning after dplyr

Rstats
programming
Hadley Wickham’s dplyr and tidyr packages completely changed the way I do data manipulation/munging in R. These packages make it possible to write shorter, faster, more…
May 13, 2016
James E. Pustejovsky

Unlucky randomization

experimental design
A colleague asked me the other day:
May 11, 2016
James E. Pustejovsky

The sampling distribution of sample variances

distribution theory
A colleague and her students asked me the other day whether I knew of a citation that gives the covariance between the sample variances of two outcomes from a common sample.…
Apr 25, 2016
James E. Pustejovsky

Tau-U

effect size
single-case design
non-overlap measures
Parker, Vannest, Davis, and Sauber (2011) proposed Tau-U as an effect size measure for use in single-case designs that exhibit baseline trend. In their original paper, they…
Mar 23, 2016
James E. Pustejovsky

Standard errors and confidence intervals for NAP

effect size
single-case design
non-overlap measures
distribution theory
Parker and Vannest (2009) proposed non-overlap of all pairs (NAP) as an effect size index for use in single-case research. NAP is defined in terms of all pair-wise…
Feb 28, 2016
James E. Pustejovsky

Estimating average effects in regression discontinuities with covariate interactions

econometrics
Rstats
causal inference
regression discontinuity
Regression discontinuity designs (RDDs) are now a widely used tool for program evaluation in economics and many other fields. RDDs occur in situations where some…
Jan 27, 2016
James E. Pustejovsky

Regression discontinuities with covariate interactions in the rdd package

econometrics
Rstats
causal inference
regression discontinuity
NOTE (2019-09-24): This post pertains to version 0.56 of the rdd package. The problems described in this post have been corrected in version 0.57 of the package, which was…
Jan 25, 2016
James E. Pustejovsky

Clustered standard errors and hypothesis tests in fixed effects models

econometrics
fixed effects
sandwiches
Rstats
UPDATED April 09, 2024 to use current syntax for constraints argument in clubSandwich::Wald_test().
Jan 10, 2016
James E. Pustejovsky

Special Education Pro-Sem

meta-analysis
single-case design
effect size
Yesterday evening I again had the pleasure of visiting Dr. Barnes’ pro seminar for first year students in Special Education, where I shared some of my work on research…
Nov 24, 2015
James E. Pustejovsky

Correlations between standardized mean differences

meta-analysis
effect size
standardized mean difference
distribution theory
Several students and colleagues have asked me recently about an issue that comes up in multivariate meta-analysis when some of the studies include multiple treatment groups…
Sep 17, 2015
James E. Pustejovsky

Fatal crashes in Austin/Travis County

transportation
I have been hearing quite a bit lately about how there have been an unusually large number of fatal automobile crashes in Austin this year, resulting in a total of 69…
Aug 20, 2015
James E. Pustejovsky

The clubSandwich package for meta-analysis with RVE

meta-analysis
robust variance estimation
sandwiches
Rstats
UPDATED April 09, 2024 to use current syntax for constraints argument in clubSandwich::Wald_test().
Jul 10, 2015
James E. Pustejovsky

New article: Four methods for analyzing PIR data

behavioral observation
single-case design
My article with Daniel Swan, “Four methods for analyzing partial interval recording data, with application to single-case research” has been accepted for publication in…
Feb 11, 2015
James E. Pustejovsky

Getting started with scdhlm

single-case design
design-comparable SMD
Rstats
UPDATED 10/2/2016 after posting the package to CRAN
Oct 19, 2014
James E. Pustejovsky

New article: Alternating renewal process models for behavioral observation

behavioral observation
alternating renewal process
Rstats
My article with Chris Runyon, titled “Alternating renewal process models for behavioral observation: Simulation methods, software , and validity illustrations” has been…
Oct 16, 2014
James E. Pustejovsky

Wanted: PIR data

behavioral observation
Partial interval recording (PIR) is one method for recording data during systematic direct observation of a behavior. While a convenient method, PIR has the key drawback…
Sep 3, 2014
James E. Pustejovsky

New article: Design-comparable effect sizes in multiple baseline designs: A general modeling framework

single-case design
effect size
hierarchical models
design-comparable SMD
My article with Larry Hedges and Will Shadish, titled “Design-comparable effect sizes in multiple baseline designs: A general modeling framework” has been accepted at…
Jul 20, 2014
James E. Pustejovsky

ARPobservation now on CRAN

behavioral observation
alternating renewal process
Rstats
Version 1.0 of the ARPobservation package is now available on the Comprehensive R Archive Network. This makes it even easier to install. Here’s the package description:
May 31, 2014
James E. Pustejovsky

Meta-sandwich with extra mustard

meta-analysis
sandwiches
Rstats
robust variance estimation
In an earlier post about sandwich standard errors for multi-variate meta-analysis, I mentioned that Beth Tipton has recently proposed small-sample corrections for the…
Apr 26, 2014
James E. Pustejovsky

Another meta-sandwich

meta-analysis
sandwiches
Rstats
robust variance estimation
In a previous post, I provided some code to do robust variance estimation with metafor and sandwich. Here’s another example, replicating some more of the calculations from Ta…
Apr 23, 2014
James E. Pustejovsky

A meta-sandwich

meta-analysis
sandwiches
Rstats
robust variance estimation
A common problem arising in many areas of meta-analysis is how to synthesize a set of effect sizes when the set includes multiple effect size estimates from the same study.…
Apr 21, 2014
James E. Pustejovsky

Special Education Pro-Sem

meta-analysis
single-case design
effect size
Dr. Marcia Barnes from the department of Special Education invited me to visit her pro-seminar this afternoon and talk about some of my work on meta-analytic methods for…
Apr 10, 2014
James E. Pustejovsky

Update: parallel R on the TACC

Rstats
programming
simulation
TACC
I have learned from Mr. Yaakoub El Khamra that he and the good folks at TACC have made some modifications to TACC’s custom MPI implementation and R build in order to correct…
Apr 8, 2014
James E. Pustejovsky

New article: Measurement-comparable effect sizes for single-case studies of free-operant behavior

behavioral observation
single-case design
effect size
response ratio
My article “Measurement-comparable effect sizes for single-case studies of free-operant behavior” has been accepted at Psychological Methods. Postprint and supporting…
Feb 4, 2014
James E. Pustejovsky

Running R in parallel on the TACC

Rstats
programming
simulation
TACC
UPDATE (4/8/2014): I have learned from Mr. Yaakoub El Khamra that he and the good folks at TACC have made some modifications to TACC’s custom MPI implementation and R build…
Dec 20, 2013
James E. Pustejovsky

Designing simulation studies using R

Rstats
simulation
Here are the slides from my presentation at this afternoon’s Quant. Methods brown bag. I gave a very quick introduction to using R for conducting simulation studies. I hope…
Dec 6, 2013
James E. Pustejovsky

To what extent does partial interval recording over-estimate prevalence?

behavioral observation
simulation
alternating renewal process
It is well known that the partial interval recording procedure produces an over-estimate of the prevalence of a behavior. Here I will demonstrate how to use the…
Oct 26, 2013
James E. Pustejovsky

ARPobservation: Basic use

behavioral observation
alternating renewal process
Rstats
The ARPobservation package provides a set of tools for simulating data generated by different procedures for direct observation of behavior. This is accomplished in two…
Oct 25, 2013
James E. Pustejovsky

Getting started with ARPobservation

behavioral observation
simulation
Rstats
UPDATED 5/29/2014 after posting the package to CRAN
Oct 24, 2013
James E. Pustejovsky

Reliability of UnGraphed single-case data: An example using the Shogren dataset

single-case design
inter-rater reliability
In one example from my dissertation, I re-analyzed a systematic review by Shogren and colleagues, titled “The effect of choice-making as an intervention for problem…
Oct 23, 2013
James E. Pustejovsky

Another project idea: Meta-analytic methods for correlational data

meta-analysis
robust variance estimation
correlation
Several different approaches have been proposed for meta-analysis of correlation coefficients. One of the major differences between approaches is the choice of scale…
Sep 13, 2013
James E. Pustejovsky

Current projects

behavioral observation
single-case design
Interested in working with me? See below for descriptions of several potential projects. If you have interest and abilities that line up with one of these, feel free to…
Aug 20, 2013
James E. Pustejovsky
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