Research has shown that small signal stability detection in power systems could become more critical as large renewable resources come on-line and replace more traditional generation facilities. Simultaneously, the proliferation of long term recording equipment to detect small signal stability problems have allowed engineers a greater view into the small signal phenomenon. However, collected field data can be subject to noise contamination as well as other effects making stability assessment a challenge. In this paper a recently proposed small signal estimator is compared to other prediction algorithms under various test conditions. The purpose of this comparative assessment is to judge the relative performance amongst these estimators in predicting small signal stability with and without noise present within artificial measurements. Results show the new estimator can outperform the other algorithms under simulated conditions for stationary signals but its limited for non-stationary signals.