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Structural Equation Model to Predict Subjective Quality of Life: A Comparison of Scales with Different Numerical Anchoring

OBJECTIVE: The main aim of the current survey was to evaluate a hypothesized model on subjective quality of life (SQOL), and to survey the role of scale anchoring on satisfaction and dissatisfaction ratings. METHOD: The sample consisted of 456 volunteering students who were randomly assigned in to t...

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Detalles Bibliográficos
Autor principal: Mazaheri, Mehrdad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Tehran University of Medical Sciences 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3395924/
https://www.ncbi.nlm.nih.gov/pubmed/22952507
Descripción
Sumario:OBJECTIVE: The main aim of the current survey was to evaluate a hypothesized model on subjective quality of life (SQOL), and to survey the role of scale anchoring on satisfaction and dissatisfaction ratings. METHOD: The sample consisted of 456 volunteering students who were randomly assigned in to two different conditions, and rated their current overall life (dis)satisfaction and their (dis)satisfaction on six different domains of life. Each condition used one of the two rating scale formats; the formats differed in anchoring (−5 to +5 and 0 to 10). In order to find how the six different domains of life combine to produce an overall measure of subjective quality of life, a SQOL model was designed; and the strength of this hypothesized model of SQOL was examined using structural equation modeling. RESULTS: The results of testing for multiple group invariance of the hypothesized model indicated a cross-validity for the studied model for measuring SQOL. Our results also indicated that comparing the two different response formats, only for scores derived from Horizontal (0 to 10) response format, all the paths in the model were found to be significant. CONCLUSION: The results of the confirmatory factor analysis (CFA) support the conclusion that the proposed model of SQOL fit the data well, and is able to predict SQOL.