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Sarah Narvaiz; Qinyun Lin; Joshua M. Rosenberg; Kenneth A. Frank; Spiro J. Maroulis; Wei Wang; Ran Xu – Grantee Submission, 2024
Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). A…
Descriptors: Statistical Analysis, Computer Software, Robustness (Statistics), Statistical Inference
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David Bruns-Smith; Oliver Dukes; Avi Feller; Elizabeth L. Ogburn – Grantee Submission, 2024
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular "doubly robust" or "de-biased machine learning estimators" combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and…
Descriptors: Regression (Statistics), Weighted Scores, Data Analysis, Robustness (Statistics)
Kenneth A. Frank; Qinyun Lin; Spiro Maroulis – Grantee Submission, 2023
Beginning with debates about the effects of smoking on lung cancer, sensitivity analyses characterizing the hypothetical unobserved conditions that can alter statistical inferences have had profound impacts on public policy. One of the most ascendant techniques for sensitivity analysis is Oster's (2019) coefficient of proportionality, which…
Descriptors: Computation, Statistical Analysis, Statistical Inference, Correlation
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Ethan R. Van Norman; David A. Klingbeil; Adelle K. Sturgell – Grantee Submission, 2024
Single-case experimental designs (SCEDs) have been used with increasing frequency to identify evidence-based interventions in education. The purpose of this study was to explore how several procedural characteristics, including within-phase variability (i.e., measurement error), number of baseline observations, and number of intervention…
Descriptors: Research Design, Case Studies, Effect Size, Error of Measurement
Yuqi Gu; Elena A. Erosheva; Gongjun Xu; David B. Dunson – Grantee Submission, 2023
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…
Descriptors: Multivariate Analysis, Item Response Theory, Bayesian Statistics, Models
Haiyan Liu; Wen Qu; Zhiyong Zhang; Hao Wu – Grantee Submission, 2022
Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested…
Descriptors: Bayesian Statistics, Structural Equation Models, Statistical Inference, Statistical Distributions
Vehtari, Aki; Gelman, Andrew; Sivula, Tuomas; Jylänki, Pasi; Tran, Dustin; Sahai, Swupnil; Blomstedt, Paul; Cunningham, John P.; Schiminovich, David; Robert, Christian P. – Grantee Submission, 2020
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for…
Descriptors: Bayesian Statistics, Algorithms, Computation, Generalization
Adam C. Sales; Ethan Prihar; Johann Gagnon-Bartsch; Ashish Gurung; Neil T. Heffernan – Grantee Submission, 2022
Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…
Descriptors: Research Methodology, Educational Experiments, Causal Models, Computation
Tianci Liu; Chun Wang; Gongjun Xu – Grantee Submission, 2022
Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for the multidimensional three-parameter and…
Descriptors: Item Response Theory, Computation, Monte Carlo Methods, Algorithms
Vincent Dorie; George Perrett; Jennifer L. Hill; Benjamin Goodrich – Grantee Submission, 2022
A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…
Descriptors: Statistical Inference, Causal Models, Artificial Intelligence, Data Analysis
Yao, Yuling; Vehtari, Aki; Gelman, Andrew – Grantee Submission, 2022
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…
Descriptors: Bayesian Statistics, Computation, Markov Processes, Monte Carlo Methods
Eli Ben-Michael; Avi Feller; Erin Hartman – Grantee Submission, 2023
In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the…
Descriptors: Public Opinion, National Surveys, Elections, Political Campaigns
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Xu Qin – Grantee Submission, 2023
When designing a study for causal mediation analysis, it is crucial to conduct a power analysis to determine the sample size required to detect the causal mediation effects with sufficient power. However, the development of power analysis methods for causal mediation analysis has lagged far behind. To fill the knowledge gap, I proposed a…
Descriptors: Sample Size, Statistical Analysis, Causal Models, Mediation Theory
Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse – Grantee Submission, 2021
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes and other covariates as closely as possible. A critical feature of the original…
Descriptors: Evaluation Methods, Comparative Analysis, Regression (Statistics), Computation
Anqi Zhao; Peng Ding; Tirthankar Dasgupta – Grantee Submission, 2018
Given two 2-level factors of interest, a 2[superscript 2] split-plot design (a) takes each of the 2 [superscript 2] = 4 possible factorial combinations as a treatment, (b) identifies one factor as `whole-plot,' (c) divides the experimental units into blocks, and (d) assigns the treatments in such away that all units within the same block receive…
Descriptors: Statistical Inference, Computation, Statistical Analysis, Sampling
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