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Charlotte Z. Mann; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2025
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk
Myoung-jae Lee; Goeun Lee; Jin-young Choi – Sociological Methods & Research, 2025
A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…
Descriptors: Probability, Least Squares Statistics, Regression (Statistics), Causal Models
Clarissa Victoria Velez; Mileini Campez-Pardo; Jennifer Mariam Canovas; Paloma Maria Pedronzo; Yeojin Amy Ahn; Chelsea Faye Dale; Sannisha K. Dale; Lisa Gwynn; Amanda Jensen-Doss; Elizabeth R. Pulgaron; Sara Mijares St. George; Jill Ehrenreich-May – Grantee Submission, 2025
Background: Despite many adolescents experiencing mental health concerns, a substantial portion lack access to evidence-based treatments (EBTs) for psychopathology; this issue is magnified for adolescents belonging to communities considered marginalized. One way to ameliorate this is by adapting existent EBTs--typically delivered in research…
Descriptors: Prevention, High School Students, Evidence Based Practice, Therapy
Tipton, Elizabeth – American Journal of Evaluation, 2022
Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to…
Descriptors: Sampling, Sample Size, Selection, Randomized Controlled Trials
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
Yuan Tian; Xi Yang; Suhail A. Doi; Luis Furuya-Kanamori; Lifeng Lin; Joey S. W. Kwong; Chang Xu – Research Synthesis Methods, 2024
RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two…
Descriptors: Risk, Randomized Controlled Trials, Classification, Robotics
Miriam Hattle; Joie Ensor; Katie Scandrett; Marienke van Middelkoop; Danielle A. van der Windt; Melanie A. Holden; Richard D. Riley – Research Synthesis Methods, 2024
Individual participant data (IPD) meta-analysis projects obtain, harmonise, and synthesise original data from multiple studies. Many IPD meta-analyses of randomised trials are initiated to identify treatment effect modifiers at the individual level, thus requiring statistical modelling of interactions between treatment effect and participant-level…
Descriptors: Meta Analysis, Randomized Controlled Trials, Outcomes of Treatment, Evaluation Methods
Sarah E. Robertson; Jon A. Steingrimsson; Issa J. Dahabreh – Evaluation Review, 2024
When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…
Descriptors: Randomized Controlled Trials, Generalization, Inferences, Hierarchical Linear Modeling
Konstantina Chalkou; Tasnim Hamza; Pascal Benkert; Jens Kuhle; Chiara Zecca; Gabrielle Simoneau; Fabio Pellegrini; Andrea Manca; Matthias Egger; Georgia Salanti – Research Synthesis Methods, 2024
Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different…
Descriptors: Medical Research, Outcomes of Treatment, Risk, Randomized Controlled Trials
Peter Schochet – Society for Research on Educational Effectiveness, 2024
Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
Brenda Jones Harden; Tiffany L. Martoccio; Lisa J. Berlin – Prevention Science, 2025
Although there is robust evidence of the benefits of attachment-based parenting interventions, limited research has examined their impact on dyadic mutuality and toddler behavior problems. Given the central question in prevention research of what works for whom, and the documented relation of maternal psychological risk to parenting and…
Descriptors: Mothers, Psychological Patterns, Risk, Attachment Behavior
Que Zheng; Kathy Kar-man Shum – Journal of Autism and Developmental Disorders, 2025
Purpose: This study aimed to investigate the effects of a self-paced digital working memory (WM) intervention on preschoolers with ADHD symptoms and explore the relation between WM and time perception (TP) through a randomized controlled trial. Method: Fifty preschoolers between four-to-six years of age (M = 4.93 years) were randomly assigned to…
Descriptors: Randomized Controlled Trials, Short Term Memory, Intervention, Preschool Children
Evrenoglou, Theodoros; Boutron, Isabelle; Seitidis, Georgios; Ghosn, Lina; Chaimani, Anna – Research Synthesis Methods, 2023
Outputs from living evidence syntheses projects have been used widely during the pandemic by guideline developers to form evidence-based recommendations. However, the needs of different stakeholders cannot be accommodated by solely providing pre-defined non amendable numerical summaries. Stakeholders also need to understand the data and perform…
Descriptors: COVID-19, Pandemics, Meta Analysis, Computer Oriented Programs
McGuire, Stacy N.; Meadan, Hedda; Xia, Yan – Behavioral Disorders, 2023
Students who engage in challenging behavior should receive preventive and intervening supports and services in general education settings based on their individual needs. These supports are necessary for students to be successful in school, yet preservice teachers receive limited education and training in both classroom and behavior management. As…
Descriptors: Behavior Modification, Training, Beginning Teachers, Randomized Controlled Trials
Adam Sales; Sooyong Lee; Tiffany Whittaker; Hyeon-Ah Kang – Society for Research on Educational Effectiveness, 2023
Background: The data revolution in education has led to more data collection, more randomized controlled trials (RCTs), and more data collection within RCTs. Often following IES recommendations, researchers studying program effectiveness gather data on how the intervention was implemented. Educational implementation data can be complex, including…
Descriptors: Program Implementation, Data Collection, Randomized Controlled Trials, Program Effectiveness

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