%0 Journal Article
%T Structural equation and log-linear modeling: a comparison of methods in the analysis of a study on caregivers' health
%A Bin Zhu
%A Stephen D Walter
%A Peter L Rosenbaum
%A Dianne J Russell
%A Parminder Raina
%J BMC Medical Research Methodology
%D 2006
%I BioMed Central
%R 10.1186/1471-2288-6-49
%X 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.The results were compared, together with an assessment of the positive and negative aspects of each approach, including their practical and conceptual implications.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.The use of SEM analysis has increased in recent years, especially in social science, education, business, medicine and biological science [1]. The capacity of SEM to distinguish between indirect and direct relationships between variables and to specify structural relations among latent variables differentiates SEM from other simpler modeling processes. Also, the flexibility of SEM allows the researcher to model data structures which violate traditional model assumptions, such as heterogeneous error variances and correlated errors. However, the application of SEM models is often complex in practice, and it requires both theory and data considerations to drive the decision-making in its development and validation. Judgement is required throughout the process, and a strong background in the content area and the causal hypothesis framework by the investigators is important. Particularly controversial areas are the testing of model fit and the iterative model r
%U http://www.biomedcentral.com/1471-2288/6/49