Among the squared and interaction terms, pH × pH is statistically important based on the p values.
Statistical modelling was accomplished by response surface fitting regression analysis with a polynomial to check the linear, squared and interaction effects of the yarn and fabric input parameters together on thermo-physiological properties of plated fabrics.
The model including SF-36 dimensions, squared and interaction terms estimated using random effects GLS has the most accurate predictions of all models estimated here and existing mapping functions as indicated by MAE (0.127) and MSE (0.030).
Chlorotic area (mm) 4.31 *** 1.08 *** 4.31 *** *,**,***denote significance at P < 0.05, P < 0.01 and P < 0.001 respectively, N.S. = non-significant; MS D. stramonium = Mean square D. stramonium, MSConc = Mean square alkaloid concentration, MSInter = Mean square interaction.
A summary chi-square interaction statistic should be non-significant, showing no deviation from model expectation.
Thus a summary chi-square interaction fit statistic should be non significant, as should the individual item chi-square statistics after Bonferroni adjustment to the alpha level.
The overall fit to the model was assessed using the item-trait chi-square interaction statistic, with a Bonferroni adjustment to the probability value.
Again, given limitations of sample size, the scale showed good fit to Rasch model expectations (Chi-Square Interaction p = 0.21) and satisfied the test for unidimensionality (independent t-test 10.8% 95% CI 5 17%).
The results confirmed the robustness of the scale, all individual items were shown to fit model expectations (Table 1), with overall fit (Chi-Square Interaction) showing a non-significant deviation from model expectation (p = 0.23) and strict unidimensionality (significant independent t-tests = 7.2% (CI: 3 11%).
The final individual item Fit Residual mean was -0.794 (SD = 1.335); the person Fit Residual mean was -0.432 (SD = 1.150) and the total chi-square interaction value was 97.364 (df = 78, p = 0.068), all of which indicated fit to the Rasch model.
