Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 3 |
Descriptor
Probability | 3 |
Statistical Inference | 3 |
Accuracy | 2 |
Computation | 2 |
Longitudinal Studies | 2 |
Sampling | 2 |
Bayesian Statistics | 1 |
Comparative Analysis | 1 |
Data Collection | 1 |
Educational Attainment | 1 |
Educational Research | 1 |
More ▼ |
Source
Journal of Experimental… | 3 |
Publication Type
Journal Articles | 3 |
Reports - Research | 2 |
Numerical/Quantitative Data | 1 |
Reports - Descriptive | 1 |
Education Level
Early Childhood Education | 1 |
Elementary Education | 1 |
Grade 10 | 1 |
High Schools | 1 |
Kindergarten | 1 |
Primary Education | 1 |
Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Longitudinal… | 1 |
What Works Clearinghouse Rating
Shieh, Gwowen – Journal of Experimental Education, 2019
The analysis of covariance (ANCOVA) is a useful statistical procedure that incorporates covariate features into the adjustment of treatment effects. The consequences of omitted prognostic covariates on the statistical inferences of ANCOVA are well documented in the literature. However, the corresponding influence on sample-size calculations for…
Descriptors: Sample Size, Statistical Analysis, Computation, Accuracy
Shen, Ting; Konstantopoulos, Spyros – Journal of Experimental Education, 2022
Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…
Descriptors: Data Collection, Educational Research, Hierarchical Linear Modeling, Bayesian Statistics
Bai, Haiyan – Journal of Experimental Education, 2013
Propensity score estimation plays a fundamental role in propensity score matching for reducing group selection bias in observational data. To increase the accuracy of propensity score estimation, the author developed a bootstrap propensity score. The commonly used propensity score matching methods: nearest neighbor matching, caliper matching, and…
Descriptors: Statistical Inference, Sampling, Probability, Computation