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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
Kaitlyn G. Fitzgerald; Elizabeth Tipton – Grantee Submission, 2024
This article presents methods for using extant data to improve the properties of estimators of the standardized mean difference (SMD) effect size. Because samples recruited into education research studies are often more homogeneous than the populations of policy interest, the variation in educational outcomes can be smaller in these samples than…
Descriptors: Data Use, Computation, Effect Size, Meta Analysis
Peter M. Steiner; Patrick Sheehan; Vivian C. Wong – Grantee Submission, 2023
Given recent evidence challenging the replicability of results in the social and behavioral sciences, critical questions have been raised about appropriate measures for determining replication success in comparing effect estimates across studies. At issue is the fact that conclusions about replication success often depend on the measure used for…
Descriptors: Replication (Evaluation), Measurement Techniques, Statistical Analysis, Effect Size
Ziqian Xu; Fei Gao; Anqi Fa; Wen Qu; Zhiyong Zhang – Grantee Submission, 2024
Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this…
Descriptors: Statistical Analysis, Sample Size, Mediation Theory, Monte Carlo Methods
Chenchen Ma; Gongjun Xu – Grantee Submission, 2022
Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications of CDMs, certain hierarchical structures among the latent attributes are assumed by researchers to characterize their dependence structure. Specifically, a directed acyclic…
Descriptors: Vertical Organization, Models, Evaluation, Statistical Analysis
Nianbo Dong; Benjamin Kelcey; Jessaca Spybrook; Yanli Xie; Dung Pham; Peilin Qiu; Ning Sui – Grantee Submission, 2024
Multisite trials that randomize individuals (e.g., students) within sites (e.g., schools) or clusters (e.g., teachers/classrooms) within sites (e.g., schools) are commonly used for program evaluation because they provide opportunities to learn about treatment effects as well as their heterogeneity across sites and subgroups (defined by moderating…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Educational Research, Effect Size
Guanglei Hong; Fan Yang; Xu Qin – Grantee Submission, 2023
In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of post-treatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due…
Descriptors: Causal Models, Mediation Theory, Research Problems, Statistical Inference
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
Liyang Sun; Jesse M. Shapiro – Grantee Submission, 2022
Linear panel models featuring unit and time fixed effects appear in many areas of empirical economics. An active literature studies the interpretation of the ordinary least squares estimator of the model, commonly called the two-way fixed effects (TWFE) estimator, in the presence of unmodeled coefficient heterogeneity. We illustrate some…
Descriptors: Statistical Analysis, Computation, Research Design, Economics
Larry V. Hedges; William R. Shadish; Prathiba Natesan Batley – Grantee Submission, 2022
Currently the design standards for single case experimental designs (SCEDs) are based on validity considerations as prescribed by the What Works Clearinghouse. However, there is a need for design considerations such as power based on statistical analyses. We compute and derive power using computations for (AB)[superscript k] designs with multiple…
Descriptors: Statistical Analysis, Research Design, Computation, Case Studies
Regan Mozer; Luke Miratrix – Grantee Submission, 2024
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…
Descriptors: Artificial Intelligence, Coding, Efficiency, Statistical Inference
Prathiba Natesan Batley; Madhav Thamaran; Larry Vernon Hedges – Grantee Submission, 2023
Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for…
Descriptors: Calculators, Computer Oriented Programs, Computation, Research Design
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2021
Score differencing is one of six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…
Descriptors: Probability, Bayesian Statistics, Cheating, Statistical Analysis
Clintin P. Davis-Stober; Jason Dana; David Kellen; Sara D. McMullin; Wes Bonifay – Grantee Submission, 2023
Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a…
Descriptors: Research Methodology, Sample Size, Effect Size, Hypothesis Testing
Kenneth A. Frank; Qinyun Lin; Ran Xu; Spiro Maroulis; Anna Mueller – Grantee Submission, 2023
Social scientists seeking to inform policy or public action must carefully consider how to identify effects and express inferences because actions based on invalid inferences will not yield the intended results. Recognizing the complexities and uncertainties of social science, we seek to inform inevitable debates about causal inferences by…
Descriptors: Social Sciences, Research Methodology, Statistical Inference, Robustness (Statistics)