Abstract
Structural equation modeling (SEM) is a multivariate method that incorporates regression, path-analysis and factor analysis. Classical SEM requires the assumption of multivariate normality to be met and large sample size, also choice is made either to ignore uncertainties or treat the latent variables as observed. National culture Data gathered in a study or survey may be inform of ordered categories and may not follow the assumptions of multivariate normality. This restricts the use of frequentist method of estimation. A Bayesian approach to SEM allows inclusion of this uncertainty and directly models the uncertainties in predictive models. In addition Bayesian SEM does not require constant variance normal disturbances and the sample size can be a small number. The development and application of Bayesian SEM has been relatively slow but it has been made possible by Gibbs sampler. The main purpose of the study was model National Culture in Kenya based on Hofstede model and business performance. Maximum likelihood Estimation was used to estimate the parameters in Classical SEM. Gibbs sampler algorithm was employed in Bayesian approach to SEM. This study used non-informative priors. The convergence of parameter was evaluated using proportional scale reduction procedure and trace and density plots. Data was gathered from employees in Nairobi through structured questionnaires. Bayesian SEM with non-informative prior gave the best estimates indicating that personal distance, individualism and long term orientation were significantly related to business performance. However, Uncertainty Avoidance had no significant relationship with business performance.
Highlights
Most of the models in statistics can be fit in Structural equation modelling (SEM) framework Skrondal and Rabe-Hesketh (2004)
The data in this study consisted of 35 indicator variables with four exogenous latent variables and one endogenous latent variable namely one outcome latent variable for Business performance (BP) which had 6 indicator variables, and four explanatory latent variables for National Business culture namely Power Distance (PD) which had 6 indicator variables and measured the layers of management between an individual employee and the highest level of management
This study found out that though model for classical SEM fit the data reasonably well as indicated by model fits, all culture dimensions (Power Distance (PD), Uncertainty Avoidance (UA), Individualism (IND) and Long-Term Orientation (LTO)) did not significantly predict business performance (BP) at 5% level of significance since the p-values were greater than 0.05
Summary
Concepts in social and other disciplines may be multidimensional, univariate analysis which is the main form of analysis may result in estimates that are biased. Structural equation modelling (SEM) being a multivariate modelling technique can be used to examine hypothesized relationships between independent latent variables and dependent latent variables. Latent variables are quantities that are not measured directly or unobserved. Most of the models in statistics can be fit in SEM framework Skrondal and Rabe-Hesketh (2004). Majority of the literature in SEM uses frequentist estimation method where estimation is based on covariance matrix of all the outcomes and exposures that are observed Bollen (1989)
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More From: Science Journal of Applied Mathematics and Statistics
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