概化理论
潜变量
维数(图论)
人格
心理学
比例(比率)
潜变量模型
变量(数学)
计量经济学
结构方程建模
构造(python库)
抽象
因子分析
社会心理学
统计
数学
计算机科学
发展心理学
组合数学
物理
数学分析
量子力学
认识论
程序设计语言
哲学
作者
Richard P. Bagozzi,Todd F. Heatherton
标识
DOI:10.1080/10705519409539961
摘要
This article proposes a framework for representing personality constructs at four levels of abstraction. The total aggregation model is the composite formed by the sum of scores on all items in a scale. The partial aggregation model treats separate dimensions of a personality construct as indicators of a single latent variable, with each dimension being an aggregation of items. The partial disaggregation model represents each dimension as a separate latent variable, either freely correlated with the other dimensions or loading on one or more than one higher order factor; the measures of the dimensions are multiple indicators formed as aggregates of subsets of items. The total disaggregation model also represents each dimension as a separate latent variable but, unlike the partial disaggregation model, uses each item in the scale as an indicator of its respective factor. Illustrations of the models are provided on the State Self‐Esteem Scale—including tests of psychometric properties, invariance, and generalizability.
科研通智能强力驱动
Strongly Powered by AbleSci AI