Pollsters and survey analysts seek to track public opinion and other sorts of social phenomena over time. The difficulty, however, is that survey results fluctuate due to an unknown combination of real opinion change and random sampling variability.
Linear filtering enables survey analysts to improve the forecasting accuracy of tracking polls. Linear smoothing enables researchers to look back over a series of polls and reassess the true state of opinion at previous time points.
This website provides an easy-to-use interface with statistical routines that perform filtering and smoothing of survey percentages. Not many statistical packages contain filtering routines as sophisticated and useful as those presented here, and none to our knowledge readily handle complications posed by (1) unequal time intervals between polls and (2) sampling error that varies from one poll to the next. It helps to have some background in statistics, but anyone willing to fiddle around a bit can get the hang of what's going on.
Users enter pertinent information about their tracking polls (e.g., how they are spaced in time, sample sizes, and results). If users input several tracking polls, they may filter these polls while at the same time estimating the (1) variability in true opinion from one period to the next and (2) estimating the autoregressive parameter that links opinion from one point in time to the next. Users with fewer just a handful of polls are advised to stipulate one or both of these parameters.
Current techniques for analyzing polls waste information. Either old polls are discarded or they are lumped together with current polls through averaging. Both approaches are wasteful and lead to needless inaccuracy. By correctly weighting current and past polling information, Samplemiser enables poll-readers to make more efficient use of tracking polls. This is where the "miser" part comes in: Pollsters could interview fewer respondents and have the same degree of accuracy if they used Samplemiser.
Forget this nonsense and just show me a picture of the results.Other example datasets: Click here to view another example dataset, 50 quarterly CBS/New York Times poll readings of party identification in California, 1981-1995. Entries are the proportion of all respondents who self-identify as a Republican. Since the number of Californians in national surveys is often small, filtering provides a useful means for charting partisan change, net of sampling fluctuations. Click here to view another example dataset, this one tracking Democratic partisanship among African-Americans from 1972-1996 (General Social Survey). Click here to view a dataset tracking Democratic partisanship among Southerners from 1952-1994 (American National Election Studies). Click here to view a dataset tracking Bill Clinton's 1993-1999 approval ratings, as charted by the Gallup Polls.
If your web browser returns an error complaining that the 'form contains no data,' look back over all of the input windows to ensure that you've entered your data correctly. Likely suspects: (1) you entered an inconsistent number of data points (e.g., 3 sets of poll results but only 2 sets of sample sizes), (2) you've entered some kind of non-numeric data, (3) you have inadvertently entered a carriage return in one of the fields; press the reset button, and reenter the data; (4) you are using an outdated web browser; (5) your percentage input is in decimal form; or (6) your percentage input is, due to small sample size, at the boundary of 0% or 100% (aggregate or discard these surveys to avoid this problem). If trouble persists, send me an email (see below) and explain the difficulty.
Note that, in contrast to previous versions of Samplemiser, the standard errors for the filtered and smoothed estimates in the current version of this program take into account the uncertainty in the autoregressive and error variance parameters. See James D. Hamilton (1986) "A Standard Error for the Estimated State Vector of a State-Space Model" Journal of Econometrics 33:398-97.
See also Green, Donald P., Alan S. Gerber, and Suzanna L. De Boef. 1999. "Tracking Opinion over Time: A Method for Reducing Sampling Error." Public Opinion Quarterly. 63:178-92.
Run into problems? Need help interpreting the output? Want to learn how
Samplemiser can be used to help design more efficient surveys? Send me an
email... Don Green
Revised: September, 2000
Copyright © Yale University, 1998, 1999, 2000, 2001, 2002
