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Abstract

A survey is designed to explore the participants’ opinions, attitudes and actions towards certain topics. The amount of information possessed by participants is not the only factor that influences their willingness to participate; question types and options design also influence participants’ responses. In reality, given cost constraints and questionnaire length, it is not feasible to provide a multiple survey design for a single concept, or to verify participants' response mode under different survey designs. This study used an experimental design to measure political knowledge from Taiwan’s Election and Democratization Study (TEDS) as an example, based on (1) an “open-ended vs. close-ended” question design; (2) whether it provides “non-response” as an option, to design four different types of surveys. The study uses a posttest-only control group design with university students as participants. We randomly released the questionnaires to participants and had 1,110 valid questionnaires.
The study found that question type and non-response design affects the participant response mode; a close-ended questionnaire design does increase the correct response ratio from participants, but it also produces a higher proportion of incorrect answers than an open-ended questionnaire. An openended questionnaire design does not have options as reference, and so it could lower the willingness of participants to take part in the survey, and it therefore resulted in a higher non-response ratio. From the composite design of question type and non-response option, we were able to precisely estimate types of participants as in Mondak (1999), but the combinations of different types of participants vary significantly as results from the level of difficulties in a questionnaire designed to measure political knowledge.

Abstract

By using the following three variables: candidate image evaluation, expected capability in solving problem, and voter's party preference in predicting in 1996 presidential, this study demonstrates that the findings have been highly close to election result, with the difference falling within three percent. The model has also been proved stable in terms of its minor variable in prediction throughout the three different testing time frame. The major findings of this study can be summed up as following: first of all, a no-response voter could vote for the candidate who was ranked first in image evaluation; secondary, problem-solving capability will be the second electoral decisive factor for a no-response voter, if he/she could not decide which candidate scores highest; thirdly, if a no-response repondent can decide which candidate could be the most capable one, he/she would vote accordingly, otherwise he/she tends to vote for the candidate with the same party affiliation; fourthly, those could not be screened out through the preceding process are, to an extent, inclined to vote for the DPP's candidates than for the counterparts of the rest two parties.

Abstract

Opinion poll has been the most widely used way to conduct election prediction. However, recently prediction market has become another widely applied prediction mechanism, attracting the literature to compare the accuracy of the two prediction methods. According to trading data of the Exchange of Future Events and opinion polls collected by this study, this paper analyzes the prediction results of the 2009 magistrate and mayoral election in Taiwan, and compares the prediction accuracies on this election between prediction markets and poll institutions. What this paper finds are: for prediction contracts on election winners, the weighted average prices of prediction markets are positive and statistically significant on the ratio of winning elections and can be regarded as the candidates' probability of winning elections. In addition, based upon five indicators of correctness rate, precision rate, hit rate, false alarm rate and Kuipers score, predictive power of prediction markets on election winners is obviously higher than that of poll institutions. For prediction on vote shares, predictive power of prediction markets is higher than that of poll institutions within 20 days before the election, and prediction accuracy of prediction markets is getting higher along with approaching the expiration of the contracts. Nevertheless, we also agree that opinion survey can help researchers conduct covariance analysis, which can be used together with prediction market to reinforce the findings of each other.