Ideal Point or Dominance Process? Unfolding Tree Approaches to Likert Scale Data with Multi-Process Models

Datos Bibliográficos

ID19290374
AutoresBiao Zeng (0000-0003-1974-5806, Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University), Hongbo Wen (0000-0001-8620-9734, Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, autor de correspondencia), Moongee Jeon (0000-0002-5880-4146, Department of Education, University of California, autor de correspondencia)
Año2025
Volumen60
Número5
Páginas898-929
Fecha de publicación2025-09-03
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaMultivariate Behavioral Research (JOURNAL)
Identificadores de la revistaISSN: 0027-3171 • E-ISSN: 1532-7906
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2025.2496505
PMID40420796
OpenAlexW4410788313
IdiomaEN
Referencias citadas55

This study introduces a new multi-process analytical framework based on the ideal point assumption for analyzing Likert scale data with three newly developed Unfolding Tree (UTree) models. Through simulations, we tested the performance of proposed models and existing Item Response Tree (IRTree) models across various conditions. Subsequently, empirical data were utilized to analyze and compare the UTree models relative to IRTree models, exploring respondents' decision-making processes and underlying latent traits. Simulation results showed that fit indices could effectively discern the correct model underlying the data. When the correct model was employed, both IRTree and UTree accurately retrieved item and individual parameters, with the recovery precision improving as the number of items and sample size increased. Conversely, when an incorrect model was utilized, the mis-specified model consistently returned biased results in estimating individual parameters, which was pronounced when the respondents followed an ideal point response process. Empirical findings highlight that respondents' decisions align with the ideal point process rather than the dominance process. The respondents' choices of extreme response options are more driven by target traits than by extreme response style. Furthermore, evidence indicates the presence of two distinct but moderately correlated target traits throughout the different decision stages

Data mining · Dominance (genetics) · Econometrics · Epistemology · Ideal (ethics) · Ideal point · Likert scale · Process (computing) · Scale (ratio) · Statistics · Advanced Causal Inference Techniques · Chemistry · Computer Science · Mathematics · Mental Health Research Topics · Psychology · Psychometric Methodologies and Testing