Charles Laurin
About
Former IES employee at NCER
Associated IES Content
Statistical and Research Methodology in Education
The goal of the Statistical and Research Methodology in Education (Stats/Methods) grant program (84.305D) is to provide a wide range of methodological and statistical tools that will better enable applied education scientists to conduct rigorous education research.
Grant
Consequences of Selective Reporting Bias in Education Research
The most popular meta-analytic methods have serious limitations in diagnosing and adjusting for selective reporting, especially when there are dependencies among multiple effects from primary studies, a widespread occurrence. For meta-analysis of independent effects, selection models have shown promise in flexibly capturing complex reporting patterns while providing adjusted meta-analytic estimates, but no existing model also simultaneously addresses effect size dependencies. The purpose of ...
Federal funding program:
Award number:
R305D220026
Grant
Integrated Replication Designs for Identifying Generalizability Boundaries of Causal Effects
The purpose of this grant is to develop an approach for identifying generalizability boundaries, which describe conditions under which effects are expected to replicate across variations in units, treatments, outcomes, settings, and times. The researchers will use principles of fractional and confounded factorial designs to plan integrated fractional replication designs and use subject-matter theory to specify causal estimands of interest, along with hypothesized moderators. The research wil...
Federal funding program:
Award number:
R305D220034
Grant
Design Comparable Effect Sizes for Single Case Designs
An important function of effect size measures is to represent the results of studies in a manner that is as comparable as possible. Single-case designs are more difficult to compare across studies, given the typically low number of participants involved in such studies. The purpose of this project is to develop design comparable effect size measures for alternating treatments designs and for changing criterion designs.
Federal funding program:
Award number:
R305D220052
Grant
Quantifying the Robustness of Causal Inferences: Extensions and Application to Existing Databases
The purpose of this project is to advance, extend, and apply existing sensitivity analysis techniques to make them most useful for education research and practice. Specifically, the researchers will expand the Impact Threshold for a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR) approaches to a variety of designs and design features, such as differential attrition, moderation analyses, and regression discontinuity designs. In doing so, the researchers hope t...
Federal funding program:
Award number:
R305D220022
Grant
Investigating the Potential of Machine Learning Methods for Identifying Impact Variation in Randomized Control Trials
In research on the effects of education interventions, there is interest not only in the overall average treatment effect but also in effects for subgroups of sample members and whether those effects differ. The purpose of this grant is to investigate the value and limitations of using machine learning to detect the presence of heterogeneous subgroup impacts in randomized control trials (RCTs) in education and other policy domains. The machine learning approach does not require researchers t...
Federal funding program:
Award number:
R305D220028
Grant
Identifying Best Practices for Estimating Average Treatment Effects in Cluster Randomized Trials: Estimands, Estimators, and Estimates
The purpose of this grant is to provide guidance to applied researchers on the best estimands and estimators of those estimands for analyses of cluster randomized control trials (CRTs).
Federal funding program:
Award number:
R305D220046
Grant
Generalized, Multilevel, and Longitudinal Psychometric Models for Evaluating Educational Interventions
Diagnostic classification models (DCMs) can provide useful information about students' strengths and weaknesses in an academic domain beyond the norm-referenced or percentile results typically achieved through item response theory. DCMs, however, need to be able to accommodate the multilevel framework in which most education takes place, or they run the risk of providing substantially inaccurate results. The purpose of this project is to develop and make accessible to applied researchers a m...
Federal funding program:
Award number:
R305D220020
Grant
Extending Dynamic Fit Index Cutoffs for Latent Variable Models
The goal of this proposed project is to provide empirical researchers with more generalizable, accessible, and accurate guidelines for assessing model fit in latent variable models. Traditional cutoffs have multiple known shortcomings, including the tendency to favor models with low construct reliability over those with high construct reliability. To address some of the drawbacks of traditional fit indices, the research team will expand the scope, testing, and software availability of a rela...
Federal funding program:
Award number:
R305D220003
Grant
Multilevel Regression Discontinuity Design With Latent Variables
A regression discontinuity (RD) design is often employed to provide causal evidence when a randomized control trial is practically infeasible or unethical. Conventional RD models assume that all running variables, covariates, and outcomes are observed variables. The purpose of this grant is to extend the modeling framework to augment the structural RD model with multilevel latent variable measurement models for any or all of the variables.
Federal funding program:
Award number:
R305D220030
Grant
Bayesian Probabilistic Forecasting with International Large-Scale Assessments
The purpose of this project is to develop models and methods to demonstrate that international large-scale assessments, in particular PISA, can be used to forecast international trends in literacy and numeracy outcomes.
Federal funding program:
Award number:
R305D220012
Grant
Fair Prediction of College-Student Success Using Multivariate Adaptive Regression Splines
The purpose of this project is to develop a new fair Multivariate Adaptive Regression Splines (MARS) statistical model to predict college-student success. MARS is a parsimonious non-parametric regression model that can identify useful input variables through a built-in feature-selection step when many potential variables are considered. MARS also renders an easily interpretable model, making it more helpful for use in higher education settings.
Federal funding program:
Award number:
R305D220055
Grant
Methods and Software for Handling Network Data and Text Data in Structural Equation Modeling
The purpose of this grant is to combine structural equation modeling (SEM) techniques and data science methods to model network and text data. Network and text data are increasingly collected in many fields of research, business, and government. For example, to study student behaviors, it is important to understand the context of behaviors because students are not independent entities but are typically connected with one another, which naturally leads to the collection and analysis of networ...
Federal funding program:
Award number:
R305D210023
Grant
Statistical Innovations for Clustered Observational Studies
In many observational research settings in education, the treatment is allocated to entire clusters of students, instead of to individual students. These clustered observational studies (COSs) arise when the treatment is applied at the group level (e.g., teachers, classes, or schools) with outcomes of interest measured at the student level. The intervention is not randomized, but it is clustered such that all students within a given cluster are treatment or control students. There is very li...
Federal funding program:
Award number:
R305D210014
Grant
Direct Adjustment in Combination With Robust or Nonlinear Regression: Software and Methods for RDDs, RCTs and Matched Observational Studies
The purpose of this grant is to develop open-source software that will enable researchers to separate the two functions of classical analysis of covariance - covariance adjustment and treatment effect estimation - into distinct modules for the purpose of optimally estimating standard errors. Covariates play an essential role in education evaluations. In observational studies, regression discontinuity studies, and randomized experiments with attrition, covariates can be used to enhance interp...
Federal funding program:
Award number:
R305D210029
Grant
Moderation and Non-compliance in Multi-Site Trials with Measurement Error and Missing Data
The purpose of this grant is to produce a general statistical framework and an associated set of statistical guidelines and tools for studying such impact variability. The project will address three major design issues: 1) A treatment effect may be moderated by covariates that are measured with error; 2) The covariates and outcome, whether continuous or discrete, may be only partially observed; and 3) Compliance to treatment assignment may be imperfect. The project will also extend these iss...
Federal funding program:
Award number:
R305D210022
Grant
Innovative Statistical Learning Methods and Software for Large-Scale Assessment
When the constructs that educational assessments try to measure are multifaceted, whether by design or not, multidimensional item response theory (MIRT), also known as item factor analysis, provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. The advancement of computational and statistical techniques has allowed for increased usage of MIRT models, but even with state-of-the-art algorithms, the computation can still be time-consuming, espe...
Federal funding program:
Award number:
R305D200015
Grant
Developing Computational Tools for Model-Based Oral Reading Fluency Assessments
The purpose of this study is to expand the existing estimation model available in a computer-based oral reading fluency (ORF) assessment which was developed as part of a previous IES grant (R305A140203). The extensions of the previous model will include the development of a sentence-level model that takes into account between-sentence dependency and incomplete reading.
Federal funding program:
Award number:
R305D200038
Grant
Improving Methods for Policy Impact Evaluation with Group Panel Data in Education Research
When a randomized control trial is infeasible in an education setting, researchers can use quasi-experimental research designs. A commonly used approach is to use repeated observations of aggregate data, known as group panel data, before and after a new policy or intervention is put in place. To estimate the effects, researchers typically rely on either a comparative interrupted time series (CITS) or, increasingly, the synthetic control method (SCM), but there is not a clear set of best prac...
Federal funding program:
Award number:
R305D200010
Grant
thinkCausal: Practical Tools for Understanding and Implementing Causal Inference Methods
The purpose of this grant is to develop a highly scaffolded multi-purpose causal inference software package, thinkCausal, with the Bayesian Additive Regression Trees (BART) predictive algorithm as a foundation. This will allow education researchers from varied backgrounds to access and better understand these versatile estimation tools.
Federal funding program:
Award number:
R305D200019
Grant
Deriving and Developing Tools to Estimate Optimal Data Points for Quasi-Experimental Designs
Education policies and interventions are often implemented in ways that do not render it feasible to use a randomized control trial to test their effects. The purposes of this study are to derive theory for computing exact statistical power for three common quasi-experimental designs (QEDs) - nonequivalent control group designs, difference-in-differences, and interrupted time series - and to develop software for computing statistical power using the derived approaches for those QEDs.
Federal funding program:
Award number:
R305D200045
Grant
Empirical Benchmarks for Interpreting Effect Size and Design Parameters for Planning Multilevel Randomized Trials on Social and Behavioral Outcomes
Benchmarks for multilevel trials
Federal funding program:
Award number:
R305D190013
Grant
Bayesian Analysis of Academic Outcomes from Single-Case Experimental Designs
Single-Case Experiment Designs (SCEDs) are a flexible methodology in which applied education researchers and practitioners can evaluate the effectiveness of academic interventions with students that have severe learning needs. Replication of functional relations within and across participants, and across studies is necessary to establish evidence-based practices using SCEDs. However, research regarding how outcomes from SCEDs should be summarized within and across studies to identify evidenc...
Federal funding program:
Award number:
R305D190023
Grant
Examining the Changes in Methodology that Occur Between the Design and Implementation of Field Trials in Education
The purpose of this project was to gather evidence about the changes in methodology that occur between the design and implementation of cluster randomized trials (when groups are randomly assigned to treatment or control conditions) used in education research. Specifically, the researchers examined the changes from what is planned to what is implemented in: (1) the research design; (2) sample sizes; (3) outcome measures; (4) the intervention itself; and (5) expected rates of attrition. The r...
Federal funding program:
Award number:
R305D090013
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