Parameter Estimation of Structural Equation Modeling Using Bayesian Approach

Abstract

Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.

Identifying Significant Cost-Influencing Factors for Sustainable Development in Construction Industry Using Structural Equation Modelling

“…Bayesian SEM tends to allow for model development to be performed even if some essential assumptions are not fulfilled. Bayesian SEM depends on the number of observations [77]. In covariance SEM, the estimated parameter is not considered as a random variable, while in Bayesian SEM it is considered to be a random variable that has a distribution termed prior distribution.…”

“…Plausible reasoning attempts to validate or invalidate hypotheses using uncertain information and can be used to reason about the truth of single hypothesis (H0 or H1). In this study, hypotheses are used in the Bayesian SEM method: H0 : the exogenous variable has no significant effect on the endogenous variable H1 : the exogenous variable has a significant effect on the endogenous variable e acceptance or rejection of based on the presence or absence of zero value in a credible interval on each parameter [77,84]. Parameter is said to be significant (reject H0)) if credible interval does not contain the zero value and means that the exogenous variable has a significant effect on the endogenous variable.…”

Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost

“…Bayesian SEM tends to allow that the model development can be performed even if some essential assumptions are not fulfilled. Unlike the SEM method relying on variance covariance matrix, Bayesian SEM depends on the number of observations [67]. In SEM, the estimated parameter is not considered as a random variable, while in Bayesian SEM it is considered as a random variable that has a distribution referred to as prior distribution.…”

“…Parameters significance testing can be conducted by using 95 percent confidence interval which is the lower limit percentiles of 2.5 percent and the upper limit percentiles of 97.5 percent of the posterior distribution [70]. The significance of a parameter depends on whether or not a zero value lies in a confidence interval [67]. If a confidence interval does not contain a zero value, the parameter is significant.…”