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Structural equation and log-linear modeling: a comparison of methods in the analysis of a study on caregivers' health
Bin Zhu1, Stephen D Walter, Peter L Rosenbaum
1Centre for Clinical Epidemiology and Community Studies, Sir Mortimer B, Davis-Jewish General Hospital, Montreal, Quebec, Canada. bzhu@epid.jgh.mcgill.ca
Background:
In this paper we compare the results in an analysis of determinants of caregivers' health derived from two approaches, a structural equation model and a log-linear model, using the same data set.
Methods:
The data were collected from a cross-sectional population-based sample of 468 families in Ontario, Canada who had a child with cerebral palsy (CP). The self-completed questionnaires and the home-based interviews used in this study included scales reflecting socio-economic status, child and caregiver characteristics, and the physical and psychological well-being of the caregivers. Both analytic models were used to evaluate the relationships between child behaviour, caregiving demands, coping factors, and the well-being of primary caregivers of children with CP.
Results:
The results were compared, together with an assessment of the positive and negative aspects of each approach, including their practical and conceptual implications.
Conclusion:
No important differences were found in the substantive conclusions of the two analyses. The broad confirmation of the Structural Equation Modeling (SEM) results by the Log-linear Modeling (LLM) provided some reassurance that the SEM had been adequately specified, and that it broadly fitted the data.
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Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as: