Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 2 |
Descriptor
Author
| Dong, Nianbo | 1 |
| Erez Yoeli | 1 |
| Gollob, Harry | 1 |
| Hannah Li | 1 |
| Hedges, Larry V. | 1 |
| Jonas Jonasson | 1 |
| Justin Boutilier | 1 |
| Lipsey, Mark | 1 |
| Reichardt, Charles | 1 |
| Rindskopf, David | 1 |
| Sobel, Michael E. | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 5 |
| Journal Articles | 4 |
| Opinion Papers | 3 |
| Reports - Evaluative | 2 |
| Speeches/Meeting Papers | 2 |
| Reports - Descriptive | 1 |
Education Level
| Higher Education | 1 |
Audience
| Researchers | 2 |
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Justin Boutilier; Jonas Jonasson; Hannah Li; Erez Yoeli – Society for Research on Educational Effectiveness, 2024
Background: Randomized controlled trials (RCTs), or experiments, are the gold standard for intervention evaluation. However, the main appeal of RCTs--the clean identification of causal effects--can be compromised by interference, when one subject's actions can influence another subject's behavior or outcomes. In this paper, we formalize and study…
Descriptors: Randomized Controlled Trials, Intervention, Mathematical Models, Interference (Learning)
Dong, Nianbo; Lipsey, Mark – Society for Research on Educational Effectiveness, 2010
This study uses simulation techniques to examine the statistical power of the group- randomized design and the matched-pair (MP) randomized block design under various parameter combinations. Both nearest neighbor matching and random matching are used for the MP design. The power of each design for any parameter combination was calculated from…
Descriptors: Simulation, Statistical Analysis, Cluster Grouping, Mathematical Models
Peer reviewedSobel, Michael E. – Psychometrika, 1990
Total, direct, and indirect effects in linear structural equation models are examined. Formulas currently given for direct and total effects are reported, and causation is considered. It is concluded that in many instances the effects do not support the interpretations given in the literature. (SLD)
Descriptors: Effect Size, Equations (Mathematics), Mathematical Models, Statistical Analysis
Peer reviewedRindskopf, David – New Directions for Program Evaluation, 1986
Modeling the process by which participants are selected into groups, rather than adjusting for preexisting group differences, provides the basis for several new approaches to the analysis of data from nonrandomized studies. Econometric approaches, the propensity scores approach, and the relative assignment variable approach to the modeling of…
Descriptors: Effect Size, Experimental Groups, Intelligence Quotient, Mathematical Models
Peer reviewedReichardt, Charles; Gollob, Harry – New Directions for Program Evaluation, 1986
Causal models often omit variables that should be included, use variables that are measured fallibly, and ignore time lags. Such practices can lead to severely biased estimates of effects. The discussion explains these biases and shows how to take them into account. (Author)
Descriptors: Effect Size, Error of Measurement, High Schools, Mathematical Models
Peer reviewedHedges, Larry V. – New Directions for Program Evaluation, 1984
The adequacy of traditional effect size measures for research synthesis is challenged. Analogues to analysis of variance and multiple regression analysis for effect sizes are presented. The importance of tests for the consistency of effect sizes in interpreting results, and problems in obtaining well-specified models for meta-analysis are…
Descriptors: Analysis of Variance, Effect Size, Mathematical Models, Meta Analysis
Strube, Michael J. – 1986
A general model is described which can be used to represent the four common types of meta-analysis: (1) estimation of effect size by combining study outcomes; (2) estimation of effect size by contrasting study outcomes; (3) estimation of statistical significance by combining study outcomes; and (4) estimation of statistical significance by…
Descriptors: Comparative Analysis, Effect Size, Mathematical Models, Meta Analysis
Wongbundhit, Yuwadee – 1984
Two approaches for conceptualizing and estimating a contextual model for analyzing student achievement are the separate equation approach and the single equation approach. The separate equation method determines the relationship between the individual-level independent variable and the individual-level dependent variable within each group. It then…
Descriptors: Academic Achievement, Comparative Analysis, Computer Simulation, Educational Research

Direct link
