Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 2 |
Descriptor
| Bayesian Statistics | 2 |
| Computation | 2 |
| Monte Carlo Methods | 2 |
| Randomized Controlled Trials | 2 |
| Educational Research | 1 |
| Elementary School Students | 1 |
| Grade 1 | 1 |
| Hierarchical Linear Modeling | 1 |
| Intervention | 1 |
| Maximum Likelihood Statistics | 1 |
| Meta Analysis | 1 |
| More ▼ | |
Publication Type
| Information Analyses | 1 |
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Early Childhood Education | 1 |
| Elementary Education | 1 |
| Grade 1 | 1 |
| Primary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkQi, Hongchao; Rizopoulos, Dimitris; Rosmalen, Joost – Research Synthesis Methods, 2023
The meta-analytic-predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the new trial when historical data is available, and the MAP approach is used in the analysis. In…
Descriptors: Sample Size, Computation, Meta Analysis, Bayesian Statistics
Download full textMay, Henry – Society for Research on Educational Effectiveness, 2014
Interest in variation in program impacts--How big is it? What might explain it?--has inspired recent work on the analysis of data from multi-site experiments. One critical aspect of this problem involves the use of random or fixed effect estimates to visualize the distribution of impact estimates across a sample of sites. Unfortunately, unless the…
Descriptors: Educational Research, Program Effectiveness, Research Problems, Computation
