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Using Text Analysis in Mediation Analysis
Using Text Analysis in Mediation Analysis
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153101
- ISBN
- 9798384087632
- DDC
- 310
- 서명/저자
- Using Text Analysis in Mediation Analysis
- 발행사항
- [Sl] : The Ohio State University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 114 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Allenby, Greg M.;Li, H. Alice.
- 학위논문주기
- Thesis (Ph.D.)--The Ohio State University, 2024.
- 초록/해제
- 요약Text data is widely used in marketing research. This dissertation proposes two new methods that utilize text data in parallel and serial mediation analyses. A parallel mediation model that uses text data to identify multiple mediators in parallel mediation analysis is proposed. The model is based on the Latent Dirichlet Allocation (LDA) model that incorporates treatment and outcome variables. Treatment variables can affect topic composition in the text data, with topic probabilities used to predict outcomes via a logistic regression model. Lexical priors are introduced to seed topics that researchers consider relevant to an analysis, while non-seeded topics allow researchers to find other potential mediation paths. The resulting analysis of mediation replaces the use of rating scales with text that more flexibly reflects the reasons for respondent choices. The assessment of stimuli's effect on topic probabilities provides information on which aspects of stimuli contribute to the change in respondents' choices of words and their latent meanings behind these words. Consumers often engage in complex reasoning when exposed to new information contained in advertisements and websites. In this dissertation, a serial mediation method is proposed to understand consumers? thoughts about new information in a serial mediation framework using textual and fixed-point rating data. Treatment variables are assumed to affect the topic composition of the text data, which is then related to the rating data and an outcome variable. The proposed model flexibly identifies mediators and relationships in situations where scales are not well developed. Apart from the additional insights revealed from the textual data, the proposed model predictively outperforms existing models of mediation.
- 일반주제명
- Statistics
- 키워드
- Topic modeling
- 키워드
- Lexical priors
- 키워드
- Machine learning
- 기타저자
- The Ohio State University Business Administration
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aZhang, Judy Zijing.
■24510▼aUsing Text Analysis in Mediation Analysis
■260 ▼a[Sl]▼bThe Ohio State University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a114 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Allenby, Greg M.;Li, H. Alice.
■5021 ▼aThesis (Ph.D.)--The Ohio State University, 2024.
■520 ▼aText data is widely used in marketing research. This dissertation proposes two new methods that utilize text data in parallel and serial mediation analyses. A parallel mediation model that uses text data to identify multiple mediators in parallel mediation analysis is proposed. The model is based on the Latent Dirichlet Allocation (LDA) model that incorporates treatment and outcome variables. Treatment variables can affect topic composition in the text data, with topic probabilities used to predict outcomes via a logistic regression model. Lexical priors are introduced to seed topics that researchers consider relevant to an analysis, while non-seeded topics allow researchers to find other potential mediation paths. The resulting analysis of mediation replaces the use of rating scales with text that more flexibly reflects the reasons for respondent choices. The assessment of stimuli's effect on topic probabilities provides information on which aspects of stimuli contribute to the change in respondents' choices of words and their latent meanings behind these words. Consumers often engage in complex reasoning when exposed to new information contained in advertisements and websites. In this dissertation, a serial mediation method is proposed to understand consumers? thoughts about new information in a serial mediation framework using textual and fixed-point rating data. Treatment variables are assumed to affect the topic composition of the text data, which is then related to the rating data and an outcome variable. The proposed model flexibly identifies mediators and relationships in situations where scales are not well developed. Apart from the additional insights revealed from the textual data, the proposed model predictively outperforms existing models of mediation.
■590 ▼aSchool code: 0168.
■650 4▼aStatistics
■653 ▼aTopic modeling
■653 ▼aLexical priors
■653 ▼aLatent Dirichlet Allocation
■653 ▼aGrade of membership
■653 ▼aMachine learning
■653 ▼aHeterogeneous effects
■690 ▼a0310
■690 ▼a0463
■690 ▼a0338
■71020▼aThe Ohio State University▼bBusiness Administration.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0168
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164908▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
