Interchangeability between factor analysis, logistic IRT, and normal ogive IRT

Frontiers in Psychology

Psychology

Eunseong Cho¹ 

[1] null;

关键词: item response theory;    logistic model;    normal ogive model;    Monte Carlo simulation;    normal distribution;    logistic distribution;    factor analysis;   

DOI  :  10.3389/fpsyg.2023.1267219

 received in 2023-07-26, accepted in 2023-09-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

In existing studies, it has been argued that factor analysis (FA) is equivalent to item response theory (IRT) and that IRT models that use different functions (i.e., logistic and normal ogive) are also interchangeable. However, these arguments have weak links. The proof of equivalence between FA and normal ogive IRT assumes a normal distribution. The interchangeability between the logistic and normal ogive IRT models depends on a scaling constant, but few scholars have examined whether the usual values of 1.7 or 1.702 maximize interchangeability. This study addresses these issues through Monte Carlo simulations. First, the FA model produces almost identical results to those of the normal ogive model even under severe nonnormality. Second, no single scaling constant maximizes the interchangeability between logistic and normal ogive models. Instead, users should choose different scaling constants depending on their purpose in using a model and the number of response categories (i.e., dichotomous or polytomous). Third, the interchangeability between logistic and normal ogive models is determined by several conditions. The interchangeability is high if the data are dichotomous or if the latent variables follow a symmetric distribution, and vice versa. In summary, the interchangeability between FA and normal ogive models is greater than expected, but that between logistic and normal ogive models is not.

【 授权许可】

Unknown   
Copyright © 2023 Cho.