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Keywords Research, Management development, Model, Tests, Multiple regression analysis
Abstract Aims to commend SEM (structural equation modeling) that excels beyond multiple regression, which is a popular statistical technique to test the relationships of independent and dependent variables, in expanding the explanatory ability and statistical efficiency for parsimonious model testing with a single comprehensive method. SEM is employed to find the real "best fitting" model This article also presents an incremental approach to SEM, which is a procedural design and sounds workable for testing simple models and presents an example to test a parsimonious model of MBA knowledge and skills transfer using SFM and multiple regression. The results indicate that only one significant relationship can be justified by multiple regression. SEM on the other hand, has helped to develop new relationships based on the modification indexes, which are also theoretically accepted Finally, three relationships are shown to be significant and the "best fitting" structural model has been established
Introduction
SEM (structural equation modeling) has become one of the popular statistical tools to test the relationships proposed in a parsimonious model. It has been adopted across different disciplines for empirical studies that require quantitative analysis. Its functions have been found to be better than other multivariate techniques including multiple regression, path analysis, and factor analysis, although the latter group of analyses has unique functions and their supporters are still fond of adopting them. Although other multivariate techniques are known to be powerful in testing single relationships between the dependent and independent variables, human and behavioral issues in management are more complicated so that one dependent variable may be an independent variable in other dependence relationships. In other words, these techniques could not take into account the interaction effects among the posited variables (both dependent and independent). Therefore, a method that can examine a series of dependence relationships simultaneously helps to address complicated managerial and behavioral issues. SEM has then been advocated because it can expand the explanatory ability and statistical efficiency for model testing with a single comprehensive method (Hair et al., 1998). SEM attracts new researchers due to its powerful utility to establish measurement models and structural models. Some user-friendly commercial statistical software packages (e.g. LISREL) further extend the popularity of the use of SEM.