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Abstract

This study looks into issues evolving from dual-frame telephone surveys carried out in Taiwan. Some issues have been resolved, while others are still being explored. Directions of future studies are also suggested.
This study employs indicators of election prediction errors to evaluate the effectiveness and pros and cons of various estimation procedures for combining data sets collected from dual-frame telephone surveys.
There are basically three types of combining dual-frame survey data, namely, all landline survey data plus those of cell-phone only; all cell-phone survey data plus those of landline only, and combing both sets of data according to their coverage proportions in the population. This study shows that the landline phone survey samples differ systematically from the cell-phone survey samples in many demographical characteristics. Using a total predicting error indicator to compare the results of various estimation procedures in three election results, namely, the 2016 presidential election, the 2018 referendum on the same-sex marriage law, and the 2020 presidential election, this study finds the following: (1) The original data collected from the landline survey fares the best. (2) Adjusting data by applying weights derived from government household registration data enlarges the prediction errors for the landline phone survey but performs slightly better for the cell-phone survey. (3) Due to selection bias from cell-phone survey samples, the prediction errors tend to be further away from the true election results. The damage done does not redeem its good intention for correcting the coverage shortcomings caused by the traditional landline phone survey.
The study also suggests several issues to be explored in future studies: (1) Investigate further the characteristics of using both landline and cell-phone samples for landline and cell-phone surveys. (2) Investigate the mode effects on landline and cell-phone surveys. (3) Investigate the performance of dual-frame telephone surveys in areas other than electoral studies.

Abstract

Due to undervotes, misvotes, or switchvotes bias, many polling data users felt frustrated in using the past polling outcome to forecast the new election. It is commonplace for voters to note an early frontrunner in polls will be doomed to fall in the real election outcome. A beta-binominal distribution is suggested to model the accuracy of early poll outcome which strategically influences the polling data users such as political parties, candidates, and mass media in implementing the election campaign. We demonstrate the advantages of probabilistic distribution and Bayesian reasoning, and how to estimate the parameters from past data, in modifying the accuracy of prior poll outcomes. In comparison with the traditional frequency approach, beta-binominal mixture distribution imposes a statistical-adjusting framework with ability to proportionate a coherent mechanism that synthesizes the performances of prior votes. The empirical data sets include the 2004 US presidential election in Atlas Web and TVBS polls in 2006 Kaohsiung mayor election and 2008 presidential election in Taiwan. This paper describes the general fitting of beta-binomial distribution on both datasets and discusses fruitful avenues for future research.

Abstract

The advancement of information and communication technologies has greatly changed the lifestyle of people while using landline surveys in soliciting precise public opinion is becoming limited. As people use a variety of devices such as cellphones, internet phones and APPs in daily communication, problems of insufficient population coverage arise from relying only on landline phones to reach respondents. Therefore, a daunting task in the telephone polling industry is to ensure sample representation for obtaining precise population parameters. To achieve such an objective, a common practice by pollsters in Taiwan is to use household data as
weighting statistics. Many cases, however, have shown this practice to be inappropriate.
To solve the above-mentioned problem, this study proposes an estimation method based on a dual frame survey that combines landline phones and cellphones. We further use data from the 2016 presidential election to compare different estimations based on a dual frame survey. Our results demonstrate that a “landline survey supplemented by cellphoneonly” is the best combination, considering sample coverage and estimation error. The second-best alternatives are “cellphone survey supplemented by landline-only” and “use both landline and cellphone.” In other words, “the most economical and efficient” strategy of a dual frame survey is to conduct a traditional landline survey and incorporating cellphone-only respondents. The data collected in such combination not only reflect the characteristics of the population, but also cost much less than other strategies.