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多维迫选问卷评分的显性特质剖析法

The Dominant Trait Profile Method of Scoring Multidimensional Forced-Choice Questionnaires.

作者信息

Dimitrov Dimiter M

机构信息

Education & Training Evaluation Commission, Riyadh, Saudi Arabia.

George Mason University, Fairfax, VA, USA.

出版信息

Educ Psychol Meas. 2025 Aug 14:00131644251360386. doi: 10.1177/00131644251360386.

DOI:10.1177/00131644251360386
PMID:40827249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12356822/
Abstract

Proposed is a new method of scoring multidimensional forced-choice (MFC) questionnaires referred to as the dominant trait profile (DTP) method. The DTP method identifies a dominant response vector (DRV) for each trait-a vector of binary scores for preferences in item pairs within MFC blocks from the perspective of a respondent for whom the trait under consideration dominates over the other traits being measured. The respondents' observed response vectors are matched to the DRV for each trait to produce (1/0) matching scores that are then analyzed via latent trait modeling, with scaling options (a) bounded D-scale (from 0 to 1), or (b) item response theory logit scale. The DTP method allows for the comparison of individuals on a trait of interest, as well as their standing in relation to a dominant trait "standard" (criterion). The study results indicate that DTP-based trait estimates are highly correlated with those produced by the popular Thurstonian item response theory model and the Zinnes and Griggs pairwise preference item response theory model, while avoiding the complexity of their designs and some computations issues.

摘要

本文提出了一种新的多维强制选择(MFC)问卷评分方法,即显性特征剖析(DTP)法。DTP法为每个特征确定一个显性反应向量(DRV)——从所考虑特征相对于其他被测量特征占主导地位的受访者角度出发,针对MFC模块中项目对的偏好形成的二元评分向量。将受访者观察到的反应向量与每个特征的DRV进行匹配,以产生(1/0)匹配分数,然后通过潜在特征建模进行分析,缩放选项有(a)有界D量表(从0到1)或(b)项目反应理论对数量表。DTP法允许对个体在感兴趣的特征上进行比较,以及他们相对于显性特征“标准”(标准)的地位。研究结果表明,基于DTP的特征估计与流行的瑟斯顿项目反应理论模型以及津尼斯和格里格斯成对偏好项目反应理论模型产生的估计高度相关,同时避免了它们设计的复杂性和一些计算问题。