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Socio-demographic Characteristics of the Total Sample and Regional Sub-samples Structural Equation Model (SEM) with AMOS software to analyze the data. In the analysis of Structural Equation Model variables can be distinguished into a latent variable (exogenous variables and endogenous variables) and observed variable. Time budget pressure, task complexity, and information technology are the exogenous latent variables, for the endogenous latent variables there is a dysfunctional auditor behavior, and fraud detection. Table 1

Table 1 Socio-demographic Characteristics of the Total Sample and Regional Sub-samples Structural Equation Model (SEM) with AMOS software to analyze the data. In the analysis of Structural Equation Model variables can be distinguished into a latent variable (exogenous variables and endogenous variables) and observed variable. Time budget pressure, task complexity, and information technology are the exogenous latent variables, for the endogenous latent variables there is a dysfunctional auditor behavior, and fraud detection. Table 1

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Table 3 - Loading Factor Score
Table 3 - Loading Factor Score
Table 4 - Model Fit Table 4 explains the result of the model. The model was not fit in the first test, it modified the nodel with seeing modification indices. Covariance line in error of the observed variable was given is based on the suggestion from modification indices. Subsequently, a value which meets the ‘equirement of fit model was received. After modification, the value of chi-square is few, and the srobability is more than significant level (0.05). RMSEA value is fewer than 0.08, and CFI value is nore than 0.90. It means that our model is fit and it can be used to predict the effect of exogenous variable on endogenous variables. X’: Chi-square is to test whether the population variance that is estimated equal to the sample covariance. (The value must be a few); Df: degree of freedom; P: Probability is test of significance to the difference of data covariance matrix with estimated covariance matrix (Value > 0.05); X’/df: Chi-square divided degree of freedom; RMSEA: The Root Mean Square Error of Approximation (0.08 > Value); CFI: Comparative Fit Index is test of model feasibility (Value > 0.94).
Table 4 - Model Fit Table 4 explains the result of the model. The model was not fit in the first test, it modified the nodel with seeing modification indices. Covariance line in error of the observed variable was given is based on the suggestion from modification indices. Subsequently, a value which meets the ‘equirement of fit model was received. After modification, the value of chi-square is few, and the srobability is more than significant level (0.05). RMSEA value is fewer than 0.08, and CFI value is nore than 0.90. It means that our model is fit and it can be used to predict the effect of exogenous variable on endogenous variables. X’: Chi-square is to test whether the population variance that is estimated equal to the sample covariance. (The value must be a few); Df: degree of freedom; P: Probability is test of significance to the difference of data covariance matrix with estimated covariance matrix (Value > 0.05); X’/df: Chi-square divided degree of freedom; RMSEA: The Root Mean Square Error of Approximation (0.08 > Value); CFI: Comparative Fit Index is test of model feasibility (Value > 0.94).
4.2 Estimation Parameter value
4.2 Estimation Parameter value
Related topics:
BusinessPsychologyInformation TechnologyFraud DetectionDysfunctional FamilyDysfunctional Audit BehaviorFinance and Investment Banking
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