MARC보기
LDR01536nmm uu200397 4500
001000000331793
00520240805165358
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438196704
035 ▼a (MiAaPQ)AAI10825882
035 ▼a (MiAaPQ)asu:18086
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 151
1001 ▼a Gonzalez, Oscar.
24510 ▼a Psychometric and Machine Learning Approaches to Diagnostic Assessment.
260 ▼a [S.l.] : ▼b Arizona State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 306 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: David P. MacKinnon
5021 ▼a Thesis (Ph.D.)--Arizona State University, 2018.
520 ▼a 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
590 ▼a School code: 0010.
650 4 ▼a Quantitative psychology.
650 4 ▼a Artificial intelligence.
690 ▼a 0632
690 ▼a 0800
71020 ▼a Arizona State University. ▼b Psychology.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0010
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998815 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자