Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 2 |
Descriptor
| Construct Validity | 2 |
| Factor Analysis | 2 |
| Monte Carlo Methods | 2 |
| Sample Size | 2 |
| Athletic Coaches | 1 |
| Criteria | 1 |
| Data | 1 |
| Decision Making | 1 |
| Exercise | 1 |
| High Schools | 1 |
| Measures (Individuals) | 1 |
| More ▼ | |
Publication Type
| Dissertations/Theses -… | 1 |
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| High Schools | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Direct linkPornchanok Ruengvirayudh – ProQuest LLC, 2018
Determining the number of dimensions underlying many variables in the data or many items in the test is a crucial process prior to performing exploratory factor analysis. Failure to do so leads to serious consequences concerning construct validity. Parallel analysis (PA) has been found to be useful to determine the number of dimensions (i.e.,…
Descriptors: Monte Carlo Methods, Tests, Data, Sample Size
Peer reviewedMyers, Nicholas D.; Ahn, Soyeon; Jin, Ying – Research Quarterly for Exercise and Sport, 2011
Monte Carlo methods can be used in data analytic situations (e.g., validity studies) to make decisions about sample size and to estimate power. The purpose of using Monte Carlo methods in a validity study is to improve the methodological approach within a study where the primary focus is on construct validity issues and not on advancing…
Descriptors: Sample Size, Monte Carlo Methods, Construct Validity, Validity
