MEANING OF PCLOSE INDEX IN AMOS
In AMOS software, the “PClose” index (Probability Close to Zero) is an index that measures the suitability of the model in predicting relationships between variables. PClose is used to evaluate the statistical properties of the model.
The PClose value ranges from 0 to 1, and the closer the value is to 1, the better the model fits the data. More precisely, PClose evaluates whether the estimated values of the parameters in the model are significantly different from zero.
If the PClose value is high (close to 1), it means that the parameter estimates in the model are not significantly different from zero, and the model can be considered a good fit to the data. If the PClose value is low (near 0), it means there is a significant difference between the parameter estimate and the null value, and the model may not fit the data.
PClose is an important index in evaluating the statistical properties of the model, but is not enough to comprehensively evaluate the effectiveness and quality of the model. It is also necessary to consider other indices such as RMSEA, CFI, TLI, and chi-square to get an overview of the model.
Evaluating the suitability of the research model, in addition to the PClose index, also includes RMSEA, CFI, GFI, IFI, TLI, P value, Chi-square, etc. is very important. We can see that in many studies, authors rarely mention the PClose index, but instead refer to indexes such as RMSEA, CFI, GFI, etc. is because of their feasibility and ease of understanding. However, the choice of specific model evaluation index depends on the research objectives, context and specific data analysis.
Note, PClose and RMSEA do not have a direct relationship. The RMSEA index measures the model’s fit to the data, assessing how well the model fits the actual data. The lower the RMSEA value, the better the data fit and the model is satisfactory in describing the data. Typically, an RMSEA value below 0.05 indicates a good model fit, a value between 0.05 and 0.08 indicates an average model fit, and a value above 0.10 indicates a poor model fit.
