The phrase "fit for evaluating" is correct and usable in written English.
You can use this phrase when expressing that something is suitable for the purpose of evaluating or judging something. For example: "This data is fit for evaluating the effectiveness of our marketing strategies."
In addition, Yusof et al. [ 38] proposed a human, organization, and technology-fit (HOT-fit) framework for evaluating the success of health information systems by emphasizing a good fit among human, organizational, and technical elements of the system.
Five fit indices have been developed for evaluating how the model fits the data, namely the Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI), and RMSEA (Root Mean Square Error of Approximation).
NFI (>0.90 acceptable fit), CFI (>0.90 acceptable fit), Root Mean Square Residual (<0.08 acceptable fit) were calculated and chi-squared statistics were used for evaluating model fit [ 24].
Normed fit index (NFI), comparative fit index (CFI) and chi-squared statistics were used for evaluating model fit.
Common guidelines for evaluating fit indices are CFI and TLI values greater than.90 and.95 for acceptable and excellent data fits and RMSEA values less than.05 and.08 for close and reasonable fits to the data (Marsh, Hau, & Wen, 2004).
As well, our proposed criteria are consistent with variables identified by the SAMHSA's National Registry of Effective and Promising Practices (NREPP) [ 41], as important for evaluating fit and those identified as important in understanding the acceptance and use of new practices by policy makers and service providers [ 42].
The test for evaluating the fit of the model with conditional independence (goodness-of-fit test) proved to be adjusted (P value = 0.938 for model A and 0.836 for model B).
Criteria for evaluating the fit of models are proposed and approaches to assessing the utility of results suggested.
Usual tools for evaluating SEM fit such as the analysis of residuals are available not only for input covariance matrix but also for individual data points.
IRT was mostly used for evaluating model fit, unidimensionality and differential item functioning, the distribution of items and persons along the underlying scale, and reliability.
