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Factored Regression Specification for Composites With Categorical Item-Level Missing Data
Factored Regression Specification for Composites With Categorical Item-Level Missing Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152017
- ISBN
- 9798382838557
- DDC
- 151
- 서명/저자
- Factored Regression Specification for Composites With Categorical Item-Level Missing Data
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 107 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Du, Han.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Composites are widely used in the behavioral and social sciences where multiple items measure a construct of interest. Researchers often use composites to measure abstract concepts such as depression and anxiety. However, missing items are prevalent in the field, either due to participants skipping items or a planned missingness design. A researcher can choose either an item-level missing data treatment or a scale-level missing data treatment, but studies have demonstrated that item-level missing data treatment is superior because it maximizes power and precision. Item-level missing data handling though, can be challenging because missing data models can become very complex especially when there are many total items and small sample sizes. Recently in the literature, there have been many studies focused on advancing factored regression specifications and a recently published paper applied this to composite scores. The method was very favorable compared to other gold standard methods, but simulation studies had limited scope on categorical items. This dissertation extends the factored regression specification to examine how it performs under various scenarios categorical item distribution types and response format options. Overall, the simulation results suggest that the proposed method can be very effective compared to the gold standard methods under most conditions, especially when the number of items is very large, and the sample size is relatively small. A real data analysis illustrates the application of the proposed methods and the other gold standard methods.
- 일반주제명
- Quantitative psychology
- 일반주제명
- Statistics
- 일반주제명
- Psychology
- 일반주제명
- Epidemiology
- 키워드
- Composites
- 키워드
- Missing data
- 키워드
- Depression
- 기타저자
- University of California, Los Angeles Psychology 0780
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382838557
■035 ▼a(MiAaPQ)AAI31331845
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a151
■1001 ▼aAlacam, Egamaria.
■24510▼aFactored Regression Specification for Composites With Categorical Item-Level Missing Data
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a107 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Du, Han.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aComposites are widely used in the behavioral and social sciences where multiple items measure a construct of interest. Researchers often use composites to measure abstract concepts such as depression and anxiety. However, missing items are prevalent in the field, either due to participants skipping items or a planned missingness design. A researcher can choose either an item-level missing data treatment or a scale-level missing data treatment, but studies have demonstrated that item-level missing data treatment is superior because it maximizes power and precision. Item-level missing data handling though, can be challenging because missing data models can become very complex especially when there are many total items and small sample sizes. Recently in the literature, there have been many studies focused on advancing factored regression specifications and a recently published paper applied this to composite scores. The method was very favorable compared to other gold standard methods, but simulation studies had limited scope on categorical items. This dissertation extends the factored regression specification to examine how it performs under various scenarios categorical item distribution types and response format options. Overall, the simulation results suggest that the proposed method can be very effective compared to the gold standard methods under most conditions, especially when the number of items is very large, and the sample size is relatively small. A real data analysis illustrates the application of the proposed methods and the other gold standard methods.
■590 ▼aSchool code: 0031.
■650 4▼aQuantitative psychology
■650 4▼aStatistics
■650 4▼aPsychology
■650 4▼aEpidemiology
■653 ▼aComposites
■653 ▼aFactored regression specification
■653 ▼aMissing data
■653 ▼aGold standard methods
■653 ▼aDepression
■690 ▼a0632
■690 ▼a0621
■690 ▼a0766
■690 ▼a0463
■71020▼aUniversity of California, Los Angeles▼bPsychology 0780.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162477▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
