Tracking Opinion over Time: A method for Reducing Sampling Error
Public Opinion Quarterly
Across a wide range of applications, the Kalman filtering and smoothing algorithm provides survey researchers with a single, systematic technique by which to generate four kinds of useful information. First, it enables survey analysts to differentiate between random sampling error and true opinion change. Second, Kalman smoothing provides a means by which to accumulate information across surveys, greatly increasing the precision with which public opinion is gauged at any given point in time.
... rd, this technique provides a rigorous means by which to interpolate missing observations and calculate the uncertainty associated with these interpolations. Finally, the Kalman algorithm improves the accuracy with which public opinion may be forecasted. Our empirical examples, which focus on party identification, show that the Kalman algorithm can dramatically reduce sampling error in survey data. Since software implementing this technique is readily available, survey analysts are encouraged to use it to make more efficient use of the data at their disposal. Public opinion analysts frequently encounter problems when they attempt to track the opinions of subpopulations over time. A random national sample of 1,000 adults will contain approximately 121 African-Americans, 126 Californians, and 127 respondents over 65 years of age (U.S. Census Bureau 1995). Charting trends in opinion among such small subgroups immediately raises the question of whether movements from poll to poll are due to real opinion change or merely random sampling variability.