Time Series Analyses on Opinion Survey Data: The Method and Evaluations of Samplemiser
When researchers analyze time series survey data, it is of importance to distinguish random sampling error from volatility of time varying. This study aims at introducing a statistical method of Samplemiser developed by U.S. politics scientists for distinguishing genuine movements in public opinion from random movements produced by sampling error. In this work, we first explain the applicability and importance of Samplemiser by a preliminary example. We then present the methodology of Samplemiser, the Kalman filtering and smoothing algorithm, and empirically apply this approach to the TVBS time series opinion survey data of Chen Shui Bian and Lien Chan in the 2004 Taiwan presidential election. The findings indicate that the smoothing algorithm reduces random sampling error in survey data, which implies that the data throughout the smoothing algorithm accurately gauge public opinion trends. The results also show that the estimates for autoregressive parameter (by which last period’s opinion affecting current opinion) influence the accuracy with which public opinion may be forecasted. We conclude that Samplemiser with the wed-based statistical software is an available and beneficial approach although it still has some limitations.