Publications · 2022 · Journal article

Analyzing differences between parent- and self-report measures with a latent space approach

Dongyoung Go, Minjeong Jeon, Saebyul Lee, Ick Hoon Jin, Hae-Jeong Park

PLOS ONE 17(6), e0269376Corresponding author

Abstract

We explore potential cross-informant discrepancies between child- and parent-report measures with an example of the Child Behavior Checklist (CBCL) and the Youth Self Report (YSR), parent- and self-report measures on children's behavioral and emotional problems. We propose a new way of examining the parent- and child-report differences with an interaction map estimated using a Latent Space Item Response Model (LSIRM). The interaction map enables the investigation of the dependency between items, between respondents, and between items and respondents, which is not possible with the conventional approach. The LSIRM captures the differential positions of items and respondents in the latent spaces for CBCL and YSR and identifies the relationships between each respondent and item according to their dependent structures. The results suggest that the analysis of item response in the latent space using the LSIRM is beneficial in uncovering the differential structures embedded in the response data obtained from different perspectives in children and their parents. This study also argues that the differential hidden structures of children and parents' responses should be taken together to evaluate children's behavioral problems.

Korean summary

아동의 행동·정서 문제를 평가할 때 부모가 보고한 결과와 아동 스스로 보고한 결과가 왜, 어떻게 다른지를 새로운 통계 기법으로 들여다본 연구입니다. 부모용 아동행동평가척도(CBCL)와 청소년 자기보고(YSR) 자료에 잠재공간 문항반응모형(LSIRM)을 적용해, 문항과 응답자를 같은 잠재 공간에 배치한 상호작용 지도를 추정했습니다. 이 방법은 전통적 분석으로는 파악하기 어려운 문항 간, 응답자 간, 문항과 응답자 간의 의존 구조를 드러내며, 부모와 아동의 응답에서 문항과 응답자의 위치가 서로 다르게 나타남을 보여주었습니다. 두 관점의 응답에 숨어 있는 서로 다른 구조를 함께 고려해야 아동의 행동 문제를 올바르게 평가할 수 있다는 점을 시사한 연구입니다.

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