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3 article(s) found.
Kuan-cheng Lee, PhD student, Department of Political Science, National Chengchi University.
Tsung-wei Liu, Associate Professor, Department of Political Science, National Chung Cheng University.
An Empirical Analysis of the M+1 Rule and the Number of Effective Candidates: The Case of the Legislative Yuan Elections in Taiwan from 1989 to 2004 (in Chinese) Download
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According to ”the M+1 rule” proposed by Gary Cox, under the SNTV system the number of effective candidates tends to be limited into M+1 (M stands for district magnitudes). In fact, empirical studies show that the votes are not necessarily concentrated on M+1 candidates, since assumptions of the M+1 rule may be not always true in the real world. Parties can nominate candidates rationally and distribute the votes equally under certain circumstances, but they sometimes can not overcome the problems of coordination. Voters tend to vote strategically as long as they have perfect information. But the information is by no means costless in the real world. Therefore, when the deviations between the theoretical expectations and the empirical observations occur, it does not necessarily mean that the theory or model is false. The theory still stands true if the deviations can be explained systematically.

The dependent variable of the study is the difference between the numbers of effective candidates and the numbers predicted by ”the M+1 rule.” Using the aggregate data of 167 districts of the Legislative Yuan Elections in Taiwan from 1989 to 2004, this paper finds that the district magnitude, party nomination strategies, successful vote distributions within parties' candidates, the quality of voters’ information and the learning effects are systematically correlated with the extent to which the effective numbers of candidates deviate from the M+1 rule. Overall, although the numbers of effective candidates are equal to M+1 in only a quarter of districts, the M+1 rule is supported by the empirical evidence of Taiwan.
Kuang-hui Chen, PhD candidate, Department of Political Science, University of California, Santa Barbara.
Tsung-wei Liu, Associate Professor, Department of Political Science, National Chung-Cheng University
The Examination of Taiwan's Election and Democratization Study Panel Data (in Chinese) Download
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TEDS conducted two waves of panel studies. These panel data can be used to describe the dynamics of Taiwanese voters and to develop related causal models. However, because of panel attrition and panel effect, there may be problems of internal and external validity. The examination of panel data shows that the panel attrition did not occur randomly. There are significant differences between those respondents who participated in the second interview and those who dropped out in terms of demographic characteristics, but no significant difference was found in terms of political attitudes.

Both TEDS 2003 and 2004P were composed of panel samples and independent samples. Panel samples are those respondents who were interviewed in TEDS 2001 and independent samples are those respondents who were never interviewed before. To be interviewed by academic research staff is a special experience, so the respondents may be intrigued to access more political information and become more willing to participate in political activities afterwards. Therefore, the three TEDS surveys could be treated as a quasi-experiment. While the panel samples is treatment group, the independent samples is control group, and the interview is the treatment. This quasi-experiment demonstrates that panel effect did change the respondents' political attitudes and increase their political participation. To sum up the consequences of panel attrition and panel effect, TEDS panel data are biased. Researchers who analyze this data set should be attentive to the issue of biased sample and think about the methods to correct the bias before drawing conclusions or making inferences.
Tsung-wei Liu, Assistant Professor, Department of Political Science, National Chung-Cheng University.
Kuang-hui Chen, Ph.D. candidate, Department of Political Science, University of California, Santa Barbara.
Is Weighting a Routine or Something that Needs to be Justified? (in English) Download
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Survey research as a method of collecting sample data is supposed to produce sample statistics which can estimate the corresponding population parameters if the sampling design is appropriate. However, for reasons such as unit non-response, survey data is usually weighted by the institutes that collect the data or by researchers who analyse the data in order to correct or diminish the discrepancies between sample and population. Sample statistics based on weighted data are more representative of the population parameters than unweighted data in terms of some demographic characteristics.Therefore, to some extent, it seems legitimate to weight data and this manipulation has become a routine when dealing with survey data.

It is true that to weight data could be helpful, but this manipulation needs justifications. This paper therefore tries to argue that to weight data is no panacea and should not be taken for granted when considering the examples in Taiwan’s Election and Democratization Studies (TEDS) surveys. The first section discusses why weighted data is not necessarily representative of the population. As the TEDS surveys show, the turnout, the vote shares of parties, and marital status become more deviant from the population parameters after weighting the data.

If the focus is the relationships between variables, the correlations may be changed by weighting the data in bivariate or multivariate analysis. However, it is not clear whether we manufacture relationships which do not exist or if weighting the data actually helps us approximate the relationships that already exist in the population. Besides, it should be noted that to weight data set as a whole only deals with the problem of unit non-response, but does not solve the problem of item non-response.

The third section discusses why most efforts should be devoted to examining and improving questionnaires, sampling designs, and interviewerm straining and supervision, instead of simply appealing to post-weighting. If everything necessary has been tried, weighting data may be the last resort to improve the estimates. But the justifications for the selection of auxiliary variables and the methods of calculating weight factors should be provided rather than doing it without any explicit considerations. It is also important to consider whether the consequence of weighting is positive or negative.