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Characterizing Heterogeneous Treatment Effects of Couple Relationship Education: A Machine Learning Approach
Characterizing Heterogeneous Treatment Effects of Couple Relationship Education: A Machine Learning Approach
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260311091528.5
- ISBN
- 9798270231248
- DDC
- 306.736
- 저자명
- Chen, Po-Heng
- 서명/저자
- Characterizing Heterogeneous Treatment Effects of Couple Relationship Education: A Machine Learning Approach / Po-Heng Chen
- 발행사항
- [Sl] : The University of Texas at Austin, 2025
- 형태사항
- 1 electronic resource (90 pages)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: A.
- 주기사항
- Advisors: Williamson, Hannah C. Committee members: Neff, Lisa; Timmons, Adela C.; Gleason, Marci E.J.
- 학위논문주기
- - Ph.D. : The University of Texas at Austin, 2025.
- 초록/해제
- 요약Intimate relationships form the cornerstone of personal well-being and societal stability, yet maintaining a stable and satisfying relationship is challenging for many couples. To enhance relationship functioning as well as prevent deterioration of relationship quality over time, Couple Relationship Education (CRE) was developed as a type of preventive intervention that provides intimate partners with knowledge and communication skills (Halford et al., 2001). Although CRE has been shown to be effective in some circumstances, a large body of research indicates that treatment effects of CRE exhibit a great deal of variability depending on the pretreatment conditions and the characteristics of the program attendees (Wadsworth & Markman, 2012). Given that significant federal expenditures have been invested to disseminate CRE to diverse populations of couples (via the Healthy Marriage and Relationship Education initiative), a better understanding of the heterogeneity of CRE treatment effects is needed to ensure that couples receive an intervention that is effective for them. Unfortunately, the existing literature has failed to account for the complex and intertwined nature of pretreatment risk factors, leading to inconsistent and inconclusive results. The current study addresses the complex statistical challenges by using machine learning techniques, providing a granular analysis of how each risk factor contributes to heterogeneous treatment effects of CRE. Study 1 employed causal forests (Athey et al., 2019) to investigate the extent to which pretreatment risk factors contribute to heterogeneity in treatment outcomes using data from a large-scale randomized controlled trial (RCT) of couple relationship education (N = 6,298 couples). Findings reveal heterogeneous treatment effects on relationship happiness and negative emotions and behaviors at 12-month follow-up. Participants with higher psychological distress and lower baseline relationship happiness experienced greater improvements in relationship happiness, while those with higher psychological distress and perceived stress showed more significant reductions in negative emotions and behaviors. Study 2 cross-validated these findings by applying the trained machine learning model from Study 1 to another large-scale RCT of CRE (N = 1,595 couples). Results confirmed the accuracy of the estimations and revealed similar heterogeneity in treatment outcomes, underscoring the generalizability and robustness of the Study 1 findings.
- 언어주기
- English
- 일반주제명
- Experimental psychology
- 일반주제명
- Education
- 일반주제명
- Clinical psychology
- 키워드
- Intervention
- 키워드
- Causal forests
- 기타저자
- The University of Texas at Austin Human Development and Family Sciences
- 기본자료저록
- Dissertations Abstracts International. 87-06A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
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■020 ▼a9798270231248
■040 ▼aMiAaPQD▼beng▼cMiAaPQD▼erda
■082 ▼a306.736
■1001 ▼aChen, Po-Heng▼eauthor.
■24510▼aCharacterizing Heterogeneous Treatment Effects of Couple Relationship Education: A Machine Learning Approach ▼cPo-Heng Chen
■260 ▼a[Sl]▼bThe University of Texas at Austin▼c2025
■264 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a1 electronic resource (90 pages)
■336 ▼atext▼btxt▼2rdacontent
■337 ▼acomputer▼bc▼2rdamedia
■338 ▼aonline resource▼bcr▼2rdacarrier
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: A.
■500 ▼aAdvisors: Williamson, Hannah C. Committee members: Neff, Lisa; Timmons, Adela C.; Gleason, Marci E.J.
■5021 ▼bPh.D.▼cThe University of Texas at Austin▼d2025.
■520 ▼aIntimate relationships form the cornerstone of personal well-being and societal stability, yet maintaining a stable and satisfying relationship is challenging for many couples. To enhance relationship functioning as well as prevent deterioration of relationship quality over time, Couple Relationship Education (CRE) was developed as a type of preventive intervention that provides intimate partners with knowledge and communication skills (Halford et al., 2001). Although CRE has been shown to be effective in some circumstances, a large body of research indicates that treatment effects of CRE exhibit a great deal of variability depending on the pretreatment conditions and the characteristics of the program attendees (Wadsworth & Markman, 2012). Given that significant federal expenditures have been invested to disseminate CRE to diverse populations of couples (via the Healthy Marriage and Relationship Education initiative), a better understanding of the heterogeneity of CRE treatment effects is needed to ensure that couples receive an intervention that is effective for them. Unfortunately, the existing literature has failed to account for the complex and intertwined nature of pretreatment risk factors, leading to inconsistent and inconclusive results. The current study addresses the complex statistical challenges by using machine learning techniques, providing a granular analysis of how each risk factor contributes to heterogeneous treatment effects of CRE. Study 1 employed causal forests (Athey et al., 2019) to investigate the extent to which pretreatment risk factors contribute to heterogeneity in treatment outcomes using data from a large-scale randomized controlled trial (RCT) of couple relationship education (N = 6,298 couples). Findings reveal heterogeneous treatment effects on relationship happiness and negative emotions and behaviors at 12-month follow-up. Participants with higher psychological distress and lower baseline relationship happiness experienced greater improvements in relationship happiness, while those with higher psychological distress and perceived stress showed more significant reductions in negative emotions and behaviors. Study 2 cross-validated these findings by applying the trained machine learning model from Study 1 to another large-scale RCT of CRE (N = 1,595 couples). Results confirmed the accuracy of the estimations and revealed similar heterogeneity in treatment outcomes, underscoring the generalizability and robustness of the Study 1 findings.
■546 ▼aEnglish
■590 ▼aSchool code: 0227
■650 4▼aExperimental psychology
■650 4▼aIndividual & family studies
■650 4▼aEducation
■650 4▼aClinical psychology
■653 ▼aClose relationships
■653 ▼aIntervention
■653 ▼aCausal forests
■653 ▼aSupporting healthy marriages
■653 ▼aCouple Relationship Education
■7102 ▼aThe University of Texas at Austin▼bHuman Development and Family Sciences.▼edegree granting institution.
■7201 ▼aWilliamson, Hannah C.▼edegree supervisor.
■7730 ▼tDissertations Abstracts International▼g87-06A.
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361178▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
