To calculate Q-squared (Q²) values for sensitivity analysis in your study using AMOS and the CB-SEM (Covariance-Based Structural Equation Modeling) approach, you can follow these steps: First, ensure that your model is properly specified and validated using the standard fit indices (e.g., CFI, RMSEA). Next, you'll need to perform a bootstrapping procedure, which is often recommended to assess the predictive validity of your model. In AMOS, go to the "Analysis Properties," enable bootstrapping, and select the number of samples (usually 1000 or more). Once the bootstrapping is complete, you can extract the Q² values, which measure the model’s predictive relevance for dependent constructs by comparing the predicted values to the observed values in your dataset. Specifically, Q² is calculated as Q2=1−SSresSStotalQ² = 1 - \frac{SS_{res}}{SS_{total}}Q2=1−SStotal​SSres​​, where SSresSS_{res}SSres​ is the sum of squared residuals and SStotalSS_{total}SStotal​ is the total sum of squares. A Q² value greater than zero indicates that the model has predictive relevance. You should also discuss sensitivity analysis by examining how changes in key parameters or constructs impact the Q² values, which provides insights into the robustness of your model. Additionally, consider conducting a sensitivity analysis by systematically varying model parameters or inputs and observing how these changes affect the Q² results, ensuring that you address the evaluator's concerns thoroughly in your thesis.

Sharif Ahamed Thank You for your valuable input. Ma'am, Kindly enlighten how to extract Q square values post the bootstrapping procedure from the AMOS output.

Is this the calculation we must Amos does? Q² = 1 - \frac{SS_{res}}{SS_{total}}Q2=1−SStotal​SSres​​