Publication Date
| In 2025 | 2 |
Descriptor
| Comparative Analysis | 2 |
| Statistical Analysis | 2 |
| Accuracy | 1 |
| Administration | 1 |
| Algorithms | 1 |
| Causal Models | 1 |
| Confidentiality | 1 |
| Disclosure | 1 |
| Evaluation Methods | 1 |
| Factor Analysis | 1 |
| Factor Structure | 1 |
| More ▼ | |
Source
| Grantee Submission | 2 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkLingbo Tong; Wen Qu; Zhiyong Zhang – Grantee Submission, 2025
Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…
Descriptors: Factor Analysis, Monte Carlo Methods, Statistical Analysis, Sample Size
Charlotte Z. Mann; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2025
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk
