Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea.
Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea.
PLoS One. 2022 Jun 29;17(6):e0269376. doi: 10.1371/journal.pone.0269376. eCollection 2022.
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.
我们以儿童行为检查表(CBCL)和青少年自我报告(YSR)为例,探讨了儿童和父母报告的行为和情绪问题测量指标之间可能存在的交叉报告差异。我们提出了一种新的方法,通过使用潜在空间项目反应模型(LSIRM)估计的交互图来检查父母和孩子报告之间的差异。交互图可以调查项目之间、受访者之间以及项目和受访者之间的依赖关系,而传统方法则无法做到这一点。LSIRM 捕捉了 CBCL 和 YSR 中项目和受访者在潜在空间中的差异位置,并根据其依赖结构确定每个受访者和项目之间的关系。结果表明,使用 LSIRM 在潜在空间中分析项目反应有助于揭示从儿童及其父母不同角度获得的反应数据中嵌入的差异结构。本研究还认为,应该综合考虑儿童和家长反应的差异隐藏结构,以评估儿童的行为问题。