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) | 3 |
Descriptor
| Factor Analysis | 3 |
| Generalization | 3 |
| Monte Carlo Methods | 3 |
| Foreign Countries | 2 |
| Academic Achievement | 1 |
| Adolescents | 1 |
| Benchmarking | 1 |
| Computation | 1 |
| Cutting Scores | 1 |
| Data Analysis | 1 |
| Data Collection | 1 |
| More ▼ | |
Author
| Arslan, Gökmen | 1 |
| Daniel McNeish | 1 |
| Finch, Holmes | 1 |
| Melissa G. Wolf | 1 |
| Monahan, Patrick | 1 |
| Renshaw, Tyler L. | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
| Reports - Descriptive | 1 |
| Tests/Questionnaires | 1 |
Education Level
| High Schools | 1 |
| Secondary Education | 1 |
Audience
| Researchers | 1 |
Location
| Dominican Republic | 1 |
| Turkey | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Beck Depression Inventory | 1 |
What Works Clearinghouse Rating
Melissa G. Wolf; Daniel McNeish – Grantee Submission, 2023
To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of 0.08, 0.06, and 0.96, respectively, established by Hu and Bentler (1999). However, these indices are affected by model characteristics and their sensitivity to…
Descriptors: Programming Languages, Cutting Scores, Benchmarking, Factor Analysis
Renshaw, Tyler L.; Arslan, Gökmen – Canadian Journal of School Psychology, 2016
The present study reports on the first investigation of the generalizability of the psychometric properties of the Student Subjective Wellbeing Questionnaire (SSWQ) beyond the original development and replication studies. Previous studies tested an English version of the SSWQ with urban, mostly Black/African American, low socioeconomic status,…
Descriptors: Generalization, Psychometrics, Well Being, Questionnaires
Finch, Holmes; Monahan, Patrick – Applied Measurement in Education, 2008
This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…
Descriptors: Monte Carlo Methods, Factor Analysis, Generalization, Methods

Peer reviewed
Direct link
