The focus of the present paper in on structural equation models and the latent variable models that are included in SEM.Īlthough SEM was developed in the early 1900s as a result of Spearman's (1904) development of factor analysis and Wright's (1918, 1921) invention of path analysis, the first basic introductory textbook on SEM was not published until 1984. A broad categorization of statistical methods is termed 'latent variable models', which include factor analysis, item response theory, latent class models, and structural equation models. First a description of SEM is provided, followed by applications to research. The purpose of the present paper is to consider the potential advances that SEM can make in medical and health sciences research and provide a five step approach to implementing SEM research in epidemiology and medical research. With its strength as a statistical tool to analyze complex relationships among variables, and even posit and test causal relationships with non-experimental data, it allows researchers to explain the development of phenomena such as disease and health behaviors. In a recent (2009) commentary in the International Journal of Epidemiology, Tu expressed concern about the scarcity of SEM models in epidemiological research and urged epidemiologists to use SEM models more frequently. An increase in use of sophisticated tools of analysis reflects the increase in complexity of empirical models and theoretical developments seen in the published research over the years. This suggests that researchers recognize its application to a variety of research questions, types of data, and methods of study. In psychology, for example, the citation frequency of SEM has steadily increased from 164 in 1994 to 343 in 2000 and then to 742 in the last year (based on the citation frequency of SEM and M of PsychINFO database 1970-2010). The use of SEM has now become widespread across research domains. The purpose of the present paper, however, is to introduce structural equation modeling through explanation and demonstration of its methods in an attempt to disseminate it more widely in medical and health sciences research. Specific principles and examples for the field of medical education were utilized. In a recent paper, we provided a "how to" for medical education researchers. Although its application has been seen in many disciplines, it has yet to be extensively used in medical research and epidemiology. Perhaps its greatest advantage is the ability to manage measurement error, which is one of the greatest limitations of most studies. It provides a flexible framework for developing and analyzing complex relationships among multiple variables that allow researchers to test the validity of theory using empirical models. ![]() ![]() Its applications range from analysis of simple relationships between variables to complex analyses of measurement equivalence for first and higher-order constructs. Structural equation modeling (SEM) is a powerful multivariate analysis technique that is widely used in the social sciences.
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