Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 6 |
Descriptor
Source
Journal of Educational and… | 9 |
Author
Rubin, Donald B. | 2 |
Draper, David | 1 |
Frank, Kenneth A. | 1 |
Jo, Booil | 1 |
Kim, Yongnam | 1 |
Miyazaki, Yasuo | 1 |
Raudenbush, Stephen W. | 1 |
Sang-June Park | 1 |
Shin, Yongyun | 1 |
Sobel, Michael E. | 1 |
Youjae Yi | 1 |
More ▼ |
Publication Type
Journal Articles | 9 |
Reports - Evaluative | 9 |
Education Level
Elementary Education | 2 |
Grade 1 | 2 |
Grade 2 | 1 |
Grade 3 | 1 |
Kindergarten | 1 |
Audience
Teachers | 1 |
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Sang-June Park; Youjae Yi – Journal of Educational and Behavioral Statistics, 2024
Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…
Descriptors: Interaction, Predictor Variables, Causal Models, Mathematical Models
Kim, Yongnam – Journal of Educational and Behavioral Statistics, 2019
Suppression effects in multiple linear regression are one of the most elusive phenomena in the educational and psychological measurement literature. The question is, How can including a variable, which is completely unrelated to the criterion variable, in regression models significantly increase the predictive power of the regression models? In…
Descriptors: Multiple Regression Analysis, Causal Models, Predictor Variables
Sobel, Michael E. – Journal of Educational and Behavioral Statistics, 2008
Treatments in randomized studies are often targeted to key mediating variables. Researchers want to know if the treatment is effective and how the mediators affect the outcome. The data are often analyzed using structural equation models (SEMs), and model coefficients are interpreted as effects. However, only assignment to treatment groups is…
Descriptors: Structural Equation Models, Causal Models, Identification
Shin, Yongyun; Raudenbush, Stephen W. – Journal of Educational and Behavioral Statistics, 2011
This article addresses three questions: Does reduced class size cause higher academic achievement in reading, mathematics, listening, and word recognition skills? If it does, how large are these effects? Does the magnitude of such effects vary significantly across schools? The authors analyze data from Tennessee's Student/Teacher Achievement Ratio…
Descriptors: Small Classes, Correlation, Reading Achievement, Mathematics Achievement
Zhang, Junni L.; Rubin, Donald B. – Journal of Educational and Behavioral Statistics, 2003
The topic of "truncation by death" in randomized experiments arises in many fields, such as medicine, economics and education. Traditional approaches addressing this issue ignore the fact that the outcome after the truncation is neither "censored" nor "missing," but should be treated as being defined on an extended sample space. Using an…
Descriptors: Experiments, Predictor Variables, Bayesian Statistics, Death
Jo, Booil – Journal of Educational and Behavioral Statistics, 2008
An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors. The core of this approach is to fully clarify bias mechanisms of considered models and to connect these models based on common parameters. Focusing on intention-to-treat analysis,…
Descriptors: Evaluation Methods, Intention, Research Methodology, Causal Models
Miyazaki, Yasuo; Frank, Kenneth A. – Journal of Educational and Behavioral Statistics, 2006
In this article the authors develop a model that employs a factor analysis structure at Level 2 of a two-level hierarchical linear model (HLM). The model (HLM2F) imposes a structure on a deficient rank Level 2 covariance matrix [tau], and facilitates estimation of a relatively large [tau] matrix. Maximum likelihood estimators are derived via the…
Descriptors: Methods, Factor Analysis, Computation, Causal Models
Rubin, Donald B. – Journal of Educational and Behavioral Statistics, 2004
Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both…
Descriptors: Undergraduate Students, Public Policy, Statistical Inference, Graduate Students

Draper, David – Journal of Educational and Behavioral Statistics, 1995
The use of hierarchical models in social science research is discussed, with emphasis on causal inference and consideration of the limitations of hierarchical models. The increased use of Gibbs sampling and other Markov-chain Monte Carlo methods in the application of hierarchical models is recommended. (SLD)
Descriptors: Causal Models, Comparative Analysis, Markov Processes, Maximum Likelihood Statistics