Scale Validation Conducting Confirmatory Factor Analysis: A Monte Carlo Simulation Study With LISREL

Frontiers in Psychology

Daniel Ondé¹ 

关键词: Monte Carlo simulation study;    confirmatory factor analysis;    maximum likelihood;    unweighted least squares;    goodness-of-fit indices;    LISREL;   

DOI  :  10.3389/fpsyg.2018.00751

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

When psychologists are going to test their theoretical models (at the time of planning the research study), several questions may arise regarding the quality and potential accuracy of the estimation of Confirmatory Factor Analysis (CFA) models under certain applied conditions. For example, one question is the minimum sample size (N) and/or the number of indicators per factor (p/k) that is needed to estimate the CFA models properly. Many of these questions can be answered through simulation studies, because the magnitudes of the population factor loadings (λik) are known in advance. Monte Carlo simulation uses random sampling and statistical modeling to estimate mathematical functions, and is a key tool for studying analytically intractable problems (Harrison, 2010). It is quite frequent to find in the literature simulation studies that use CFA to fit measurement models. However, there is a lack of technical information in the published research to replicate this type of studies, probably due to length limitations. Furthermore, researchers and scholars must often go to numerous and technically complex sources of information to understand the laborious simulation and estimation process.

【 授权许可】

CC BY