Structural Equation Modeling Of Bilateral Latent Structure Of Satisfaction With Life And Quality Of Life

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Structural Equation Modeling (SEM) is a method which uses various types of models to depict relationships among observed variables, with the same basic goal of providing a quantitative test of a theoretical model hypothesized by the researcher. Several instruments including World Health Organization (WHOQoL-BREF) for non HIV patients, WHOQoL-HIV (BREF) for HIV patients and Satisfaction with Life Scale (SWLS) have been provided for assessing quality of life. These instruments may be non-country or regional specific. The aim of the study therefore, was to investigate the bilateral structure between the instruments as well as to reduce the number of the variables in the instruments. The specific objectives are to (i) assess the impact of the variables of the instruments on their various latent constructs through their bilateral structure; (ii) formulate the SEMs for non-HIV and HIV patients; (iii) compare Maximum Likelihood (ML) and Generalized Least Squares (GLS) estimates of the equations in (ii); (iv) regroup variables in WHOQoL-HIV (BREF) and formulate their SEMs and (v) obtain a reduced instrument from the combined WHO QoL instruments and assess the effectiveness.rnThe two estimators used on the instruments for parameter estimation in the SEMs were ML and GLS.The binary logistic regression model was also used on the combined instruments. Life data were collected from over 300 patients from each of the two randomly selected states out of the six states in the Southern Western Nigeria. AMOS version 21 Statistical software was used to analyze the data. rnThe following were the major findings of the study:rni. Most of the variables in the instrument had significant impact on their latent constructs (domains). Also there were variations in the path diagrams for the WHO QoL instruments.rnii. The SEMs were formulated and it was noted that for non-infected patients, psychology domain had the most substantial causal effect on their quality of life, while, physical domain had the most substantial causal effect on the quality of life of HIV patients.rniii. Using some goodness of fit criteria, GLS estimation method was more appropriate in analysing ordinal categorical data, as in this study than ML method.rniv. Reclassification of the domains in the WHOQoL-HIV (BREF) showed that some variables like pain and discomfort; dependence on medication were miss grouped.rnv. The study established that not all the models formulated using WHOQoL instruments were admissible in the proposed regrouped variables.rnvi. The positive predictive value and the negative predictive value of the merged reduced instrument was as good as the standard WHO quality of life instruments.rnIn conclusion, the instrument with the merged reduced variables can be used to obtain data for both groups of patients together as well as for each group separately. Moreover, the GLS estimation method should be used when analyzing data from these instruments. It is therefore recommended that the merged reduced instrument should be used for both patients and the GLS used for analysis.

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Structural Equation Modeling Of Bilateral Latent Structure Of Satisfaction With Life And Quality Of Life

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