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Psychometric and Machine Learning Approaches to Diagnostic Assessment
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Psychometric and Machine Learning Approaches to Diagnostic Assessment. |
| 개인저자 | Gonzalez, Oscar. |
| 단체저자명 | Arizona State University. Psychology. |
| 발행사항 | [S.l.]: Arizona State University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 306 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438196704 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: David P. MacKinnon |
| 요약 | The goal of diagnostic assessment is to discriminate between groups. In many cases, a binary decision is made conditional on a cut score from a continuous scale. Psychometric methods can improve assessment by modeling a latent variable using ite |
| 일반주제명 | Quantitative psychology. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
