Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 6 |
| Since 2017 (last 10 years) | 22 |
Descriptor
Source
Author
| Chan, Wendy | 2 |
| Higgins, Steve | 2 |
| Kasim, Adetayo | 2 |
| Singh, Akansha | 2 |
| Uwimpuhwe, Germaine | 2 |
| A. Krishnamachari | 1 |
| Adam Sales | 1 |
| Ben B. Hansen | 1 |
| Bierman, Karen L. | 1 |
| Chung, Huy Q. | 1 |
| Cody, Scott | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 19 |
| Journal Articles | 16 |
| Dissertations/Theses -… | 1 |
| Numerical/Quantitative Data | 1 |
| Reference Materials -… | 1 |
| Reports - Descriptive | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
| Indiana | 2 |
| United Kingdom (England) | 2 |
| Belgium | 1 |
| California | 1 |
| Finland | 1 |
| Florida | 1 |
| Massachusetts (Boston) | 1 |
| Norway | 1 |
| Texas | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Florida Comprehensive… | 1 |
What Works Clearinghouse Rating
| Meets WWC Standards with or without Reservations | 1 |
| Does not meet standards | 1 |
Peer reviewed
Direct linkXinhe Wang; Ben B. Hansen – Society for Research on Educational Effectiveness, 2024
Background: Clustered randomized controlled trials are commonly used to evaluate the effectiveness of treatments. Frequently, stratified or paired designs are adopted in practice. Fogarty (2018) studied variance estimators for stratified and not clustered experiments and Schochet et. al. (2022) studied that for stratified, clustered RCTs with…
Descriptors: Causal Models, Randomized Controlled Trials, Computation, Probability
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
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
Musci, Rashelle J.; Kush, Joseph M.; Masyn, Katherine E.; Esmaeili, Masoumeh Amin; Susukida, Ryoko; Goulter, Natalie; McMahon, Robert; Eddy, J. Mark; Ialongo, Nicholas S.; Tolan, Patrick; Godwin, Jennifer; Bierman, Karen L.; Bierman, Karen L.; Coie, John D.; Crowley, D. Max; Dodge, Kenneth A.; Greenberg, Mark T.; Lochman, John E.; McMahon, Robert J.; Pinderhughes, Ellen E.; Wilcox, Holly C. – Prevention Science, 2023
Psychotic-like experiences (PLEs) are common throughout childhood, and the presence of these experiences is a significant risk factor for poor mental health later in development. Given the association of PLEs with a broad number of mental health diagnoses, these experiences serve as an important malleable target for early preventive interventions.…
Descriptors: Psychosis, Symptoms (Individual Disorders), Children, Adolescents
Adam Sales; Ethan Prhiar; Thanaporn March Patikorn – Society for Research on Educational Effectiveness, 2021
In a randomized controlled trial (RCT), some subjects assigned to the treatment condition may not fully comply. Often there is interest in the effect of the treatment within the "principal stratum" of subjects who would comply if assigned to treatment. However, it is unknown which control subjects would have complied if treated and which…
Descriptors: Randomized Controlled Trials, Scores, Probability, Statistical Analysis
Uwimpuhwe, Germaine; Singh, Akansha; Higgins, Steve; Kasim, Adetayo – International Journal of Research & Method in Education, 2021
Educational researchers advocate the use of an effect size and its confidence interval to assess the effectiveness of interventions instead of relying on a p-value, which has been blamed for lack of reproducibility of research findings and the misuse of statistics. The aim of this study is to provide a framework, which can provide direct evidence…
Descriptors: Educational Research, Randomized Controlled Trials, Bayesian Statistics, Effect Size
Held, Leonhard; Matthews, Robert; Ott, Manuela; Pawel, Samuel – Research Synthesis Methods, 2022
It is now widely accepted that the standard inferential toolkit used by the scientific research community--null-hypothesis significance testing (NHST)--is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects…
Descriptors: Bayesian Statistics, Statistical Inference, Hypothesis Testing, Credibility
Download full textK. L. Anglin; A. Krishnamachari; V. Wong – Grantee Submission, 2020
This article reviews important statistical methods for estimating the impact of interventions on outcomes in education settings, particularly programs that are implemented in field, rather than laboratory, settings. We begin by describing the causal inference challenge for evaluating program effects. Then four research designs are discussed that…
Descriptors: Causal Models, Statistical Inference, Intervention, Program Evaluation
Meyer, Joerg M. – Teaching Statistics: An International Journal for Teachers, 2018
The contrary of stochastic independence splits up into two cases: pairs of events being favourable or being unfavourable. Examples show that both notions have quite unexpected properties, some of them being opposite to intuition. For example, transitivity does not hold. Stochastic dependence is also useful to explain cases of Simpson's paradox.
Descriptors: Intuition, Probability, Randomized Controlled Trials, Statistical Analysis
Finucane, Mariel McKenzie; Martinez, Ignacio; Cody, Scott – American Journal of Evaluation, 2018
In the coming years, public programs will capture even more and richer data than they do now, including data from web-based tools used by participants in employment services, from tablet-based educational curricula, and from electronic health records for Medicaid beneficiaries. Program evaluators seeking to take full advantage of these data…
Descriptors: Bayesian Statistics, Data Analysis, Program Evaluation, Randomized Controlled Trials
Uwimpuhwe, Germaine; Singh, Akansha; Higgins, Steve; Coux, Mickael; Xiao, ZhiMin; Shkedy, Ziv; Kasim, Adetayo – Journal of Experimental Education, 2022
Educational stakeholders are keen to know the magnitude and importance of different interventions. However, the way evidence is communicated to support understanding of the effectiveness of an intervention is controversial. Typically studies in education have used the standardised mean difference as a measure of the impact of interventions. This…
Descriptors: Program Effectiveness, Intervention, Multivariate Analysis, Bayesian Statistics
Qin, Xu; Hong, Guanglei – Journal of Educational and Behavioral Statistics, 2017
When a multisite randomized trial reveals between-site variation in program impact, methods are needed for further investigating heterogeneous mediation mechanisms across the sites. We conceptualize and identify a joint distribution of site-specific direct and indirect effects under the potential outcomes framework. A method-of-moments procedure…
Descriptors: Randomized Controlled Trials, Hierarchical Linear Modeling, Statistical Analysis, Probability
Steiner, Peter M.; Kim, Yongnam; Hall, Courtney E.; Su, Dan – Sociological Methods & Research, 2017
Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand…
Descriptors: Graphs, Causal Models, Quasiexperimental Design, Randomized Controlled Trials
Chan, Wendy – AERA Online Paper Repository, 2017
Policymakers are increasingly interested in the extent to which experimental results generalize from a sample to a population of inference. When the sample is not randomly selected, propensity score methods are used to reweight the sample. Subclassification by propensity score is commonly used in which the population is partitioned into strata…
Descriptors: Generalization, Classification, Randomized Controlled Trials, Inferences
Hall, Mark Monroe – ProQuest LLC, 2017
The purpose of this study was to examine the effects of proactive student-success coaching, informed by predictive analytics, on student academic performance and persistence. Specifically, semester GPA and semester-to-semester student persistence were the investigated outcomes. Uniquely, the community college focused the intervention on only…
Descriptors: Academic Achievement, Community Colleges, Two Year College Students, Coaching (Performance)
Previous Page | Next Page »
Pages: 1 | 2
