TITLE:
Validation of general linear modeling for identifying factors associated with Quality of Life: A comparison with structural equation modeling
AUTHORS:
Naoko Kumagai, Motonori Hatta, Yashiyasu Okuhara, Hideki Origasa
KEYWORDS:
General Liner Modeling; Latent Variable; Standardized Path Coefficient; Standard Partial Regression Coefficient; Structural Equation Modeling
JOURNAL NAME:
Health,
Vol.5 No.11,
November
27,
2013
ABSTRACT:
Purpose: General linear modeling (GLM) is usually applied to investigate factors associated with the domains of Quality of Life (QOL). A summation score in a specific sub-domain is regressed by a statistical model including factors that are associated with the sub-domain. However, using the summation score ignores the influence of individual questions. Structural equation modeling (SEM) can account for the influence of each question’s score by compositing a latent variable from each question of a sub-domain. The objective of this study is to determine whether a conventional approach such as GLM, with its use of the summation score, is valid from the standpoint of the SEM approach. Method: We used the Japanese version of the Maugeri Foundation Respiratory Failure Questionnaire, a QOL measure, on 94 patients with heart failure. The daily activity sub-domain of the questionnaire was selected together with its four accompanying factors, namely, living together, occupation, gender, and the New York Heart Association’s cardiac function scale (NYHA). The association level between individual factors and the daily activity sub-domain was estimated using SEMand GLM, respectively. The standard partial regression coefficients of GLM and standardized path coefficients of SEM were compared. Ifthese coefficients were similar (absolute value of the difference －0.06 and －0.07 for the GLM and SEM. Likewise, the estimates of occupation, gender, and NYHA were －0.18 and －0.20, －0.08 and －0.08, 0.51 and 0.54, respectively. The absolute values of the difference for each factor were 0.01, 0.02, 0.00, and 0.03, respectively. All differences were less than 0.05. This means that these two approaches lead to similar conclusions. Conclusion: GLM is a valid method for exploring association factors with a domain in QOL.