GFI and AGFI coefficients of higher than 0.85 indicate good fit (Cole,
1987). RMSEA values of lower than 0.10 and χ2/df values in the range of
2-5 also show good fit (Jöreskog & Sörbom, 2001). The path diagram of
the scale that had these values is shown in Figure 1.
The measurement model is the measurement component of a SEM. An
essential purpose of the measurement model is to describe how well the
observed indicator variables serve as a measurement instrument for the
underlying latent variables or factors. Measurement models are usually
carried out and evaluated by CFA. A measurement model or CFA proposes
links or relations between the observed indicator variables and the
underlying latent variables/factors that they are designed to measure,
and then tests them against the data to “confirm” the proposed
factorial structure (Jichuan; and Xiaoqian, 2020)
Model fit Indexes values are X2/sd, CFI , GFI, RMSEA.
Comparative fit index , (CFI) CFI is one of several
indices available to assess model fit. A value between 0.90 and 0.95
indicates acceptable fit, and above 0.95 indicates good fit (Bowen &
Guo, 2020). The comparative fit index (CFI) analyzes the model fit by
examining the discrepancy between the data and the hypothesized model,
while adjusting for the issues of sample size inherent in the
chi-squared test of model fit (Gatignon, 2010).
Chi-square (χ2), The most basic and common fit statistic used to
evaluatestructural equation models; chi-square should always be provided
in reports on SEM analyses. Chi-square values resulting in a
nonsignificant p-value (i.e., p # 0.05) indicate good model fit. The
chi-square statistic is directly affected by the size of the sample
being used to test the model (Bowen & Guo, 2020).
Root mean square error of approximation (RMSEA) This is the most
popular measure of model fit for SEM. The root mean square error of
approximation (RMSEA) is a standardized measure of error of
approximation. The error of approximation means the lack of fit of the
specified model to the population. (Jichuan; and Xiaoqian, 2020)
Goodness of fit index and adjusted goodness of fit index
The goodness of fit index (GFI) is a measure of fit between the
hypothesized model and the observed covariance matrix. The adjusted
goodness of fit index (AGFI) corrects the GFI, which is affected by the
number of indicators of each latent variable. The GFI and AGFI range
between 0 and 1, with a value of over .9 generally indicating acceptable
model fit (Baumgartner & Hombur, 1996).