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The use of structural equation modelling (SEM) has steadily increased in behavioural science where

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The use of structural equation modelling (SEM) has steadily increased in behavioural science where two submodels are identified including a measurement model and a structural model. In this study the research paradigm indicates and concurrently strives to combine measurement and structural model for complete parameter tests. SEM is a quantitative data analytical technique which specifies, estimates and tests theoretical relationships between observed endogenous variables and latent, unobserved exogenous variables. (Byrne, 2001) The SEM is a statistical methodology that takes a confirmatory that is, hypothesis testing approach to the analysis of a structural theory. This theory represents causal processes that generate observations on …show more content…

Taking the sample sensitivity and model specification into account root mean square error of approximation (RMSEA), incremental fit index (IFI), Tucker Lewis index or Non-normed fit index (TLI or NNFI) and comparative fit index (CFI) are considered in this study for evaluating fit indices. The grounds for reporting these indices as fit measures are discussed in the following paragraph.

The RMSEA first developed by Steiger and Lind (1980) tells us how well the model with unknown but optimally chosen parameter estimates would fit the populations’ covariation matrix. In recent years it has been regarded as ‘the most informative fit indices’ (Diamantopoulos & Siguaw, 2000) due to its sensitivity to the number of estimated parameters in the model. As it is a parsimony adjusted index it takes into account the error of approximation which is not affected by sample size and reduces the stringent requirement on 2 that the model holds exactly in the population. MacCallum, Browne and Sugawara (1996) have used RMSEA values of 0.01, 0.05 and 0.08 to indicate excellent, good and mediocre fit, respectively and 0.10 as the cut-off for poor fitting model. Hulland, Chow and Lam (1996) proposed that RMSEA values between 0.05 and 0.10 are sometimes considered adequate fit. The current study uses the RMSEA value below and equal to 0.05 to indicate good fit and a value from

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