Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Data Analysis | 2 |
| Sample Size | 2 |
| Statistical Data | 2 |
| Computation | 1 |
| Data | 1 |
| Data Collection | 1 |
| Error Correction | 1 |
| Evaluation Criteria | 1 |
| Evaluation Methods | 1 |
| Goodness of Fit | 1 |
| Growth Models | 1 |
| More ▼ | |
Author
| Ahmadi, Mohammad | 1 |
| Dileepan, Parthasarati | 1 |
| Harring, Jeffrey R. | 1 |
| McNeish, Daniel | 1 |
| Wheatley, Kathleen K. | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Evaluative | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkAhmadi, Mohammad; Dileepan, Parthasarati; Wheatley, Kathleen K. – Journal of Education for Business, 2016
This is the decade of data analytics and big data, but not everyone agrees with the definition of big data. Some researchers see it as the future of data analysis, while others consider it as hype and foresee its demise in the near future. No matter how it is defined, big data for the time being is having its glory moment. The most important…
Descriptors: Strategic Planning, Data, Data Analysis, Statistical Analysis
McNeish, Daniel; Harring, Jeffrey R. – Educational and Psychological Measurement, 2017
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
Descriptors: Growth Models, Goodness of Fit, Error Correction, Sampling
