3.3.1 Regression
To further consider the validity of the STP approach, linear regression analyses were completed to determine the extent to which the component measurements obtained from this approach accounted for the between-participant variance of components obtained the from averaged ERPs. A separate regression analysis was conducted for each component (i.e., P1, N1, P2, N2 and P3) using the same general model of four input variables entered in three steps. Step 1 included the participants’ mean amplitude of the STP (baseline-to-peak) component; in Step 2, participants’ the standard deviations of both the latency and amplitude of the STP component were added; in Step 3, participants’ mean noise value was added. For all components, the regression models showed a significant amount of variance accounted for in each successive step. Therefore, R 2, R 2Change, and the standardized coefficients only after the third step are reported in Table 10.
In the models of each component, the results after the third step indicate that of the four input variables, the mean amplitude variable from the STP approach had the highest beta weight and accounted for a significant amount of variability in its corresponding component measurement from the averaged ERP. Noise accounted for a significant amount of variability in the early components (P1 and N1); however, the beta weight values decreased suggesting it accounted for a smaller portion of the variability in the later components. Although the beta weights of the standard deviation of latencies were significant indicating that the latencies obtained in the STP approach accounted for some variability, this was much less so than the mean amplitude or noise. Across all components, the beta weights of the standard deviation of the amplitude were not significant. Interestingly, the results of the second step, the step prior to the addition of the noise factor, the beta weights of the standard deviation of the amplitude were significant in each of the regression analyses. The fact the amplitude variability is no longer a significant predictor of the averaged ERP component when noise is introduced to the prediction model suggest that these two variables are colinear, i.e., they share a common portion of the variance. Taken together, these results of these regression analyses indicate that the single trial peak measures are strong predictors of the stimulus-locked averaged ERPs, and thus, may be considered valid measures of brain activity.