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5 article(s) found.
Professor, Dept. of Civic Education and Leadership, National Taiwan Normal University.
Critical Citizens in Taiwan Revisited: 2008-2016 Download
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From a typological perspective, this paper addresses Taiwanese citizens’ attitudes to democracy and applies the modernization paradigm and the ‘election losers’ explanation to clarify the origins of critical citizens. By examining the TEDS survey data conducted after the three presidential elections held from
2008 to 2016, we find that, first, the proportion of critical citizens remained stable over time, while those of the other types changed along with the election results. Second, the comparison across different types of democratic attitudes suggests that critical citizens in Taiwan hold some critical traits, but they are older citizens rather than representatives of the young generation as expected. Also, the explanation that ‘election losers tend to be critical citizens’ can be confirmed only when the green camp of voters is on the losing side; when the blue camp loses the election, voters on the losing side are inclined to be democratic alienators, carrying negative implications for democratic legitimacy. Overall, the critical citizens in Taiwan originate from the combination of the modernization paradigm and the election-losers explanation. The findings of this paper imply that democratic legitimacy in Taiwan continues to deepen after two elections resulting in a change in the ruling party; however, the young generation’s view and electoral competition, as shown, may result in different meanings.
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.
Yi-ching Shiao, Ph. D. student, Department of Political Science, National Chengchi University.
Analysis of Test-Retest Reliability in Taiwan's Election and Democratization Study (in Chinese) Download
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The major purpose of this article is to analyze the test-retest reliability in the series of Taiwan's Election and Democratization Study (2001, 2003 and 2004P), and explore what factors influencing the questionnaire reliability. This research finds that TEDS has consistent questionnaire reliability although the degree of reliability varies with subjects. The ”voting intention” has the highest reliability, followed by ”party identification”. Attitude for the ”position of independence/unification” is the third, ahead of the ”Taiwanese/Chinese identity” and the ”presidential candidate image”. Attitudes about democratic evaluation, including the ”degree of satisfiaction about the practice of democracy in Taiwan” and ”comparing the DPP vs. KMT government” and ”democratic governing” are the least reliable measurements. It is also found that there is no significant difference in reliability regarding the same subject across different datasets of TEDS.

The respondent's education is the important factor of questionnaire reliability. The high-educated respondent has more consistent response for the same question in different interview than others. Besides, female respondents has more stable political attitude than male in TEDS 2001 and TEDS 2004P. The shorter the period between the first interview to the second one, the more stable attitude respondents show, especially in TEDS 2001 and TEDS 2003. Last, no matter who the interviewer is, test-retest reliability is the same. In other words, the TEDS interviewers obey ”standard interview principle” during the surveys.
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.
Cih Huang, Professor of Political Science, National Churg-Chen University.
Yu-tzung Chang, Assistant Professor of Political Science, National Taiwan University.
On Minimum-Discrimination-Information (MDI) Method of Weighting: an Application to the 2001 Taiwan's Election and Democratization Study(TEDS) (in Chinese) Download
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Goodness-of-fit tests allow us to examine if the sample at hand is representative enough of the population to ensure accurate statistical inferences of parameters. When the sample fails the tests, survey researchers often appeal to reweighting as a remedy. Post-stratification and raking are perhaps the two most popular weighting methods. However, post-stratification requires the knowledge of multivariate joint distribution of the population when more than one post-stratifying variable is considered. Without such detailed information, raking comes as a rescue since it requires only the knowledge of marginal distributions of selected variables. Popular as it may be, raking takes no account of associations among post-stratifying variables. Furthermore, it relies heavily on Chi-squared tests and a pre-selected p-value (usually 0.5) as the stopping rule of iteration, an ad hoc rule justified only by convenience.

This article proposes a third way of Weighting, which we call it the minimum-discrimination-information (MDI) method. MDI approach finds optimal (in terms of minimum cross-entropy) relative weights by treating sample joint distribution as prior and known population marginal distributions as constraints. We first explain the rationale behind this proposed MDI method and then use TEDS 2001 survey data to compare the estimates of raking and MDI weights. We find that nearly 70 percent of the latter indeed replicate the Census 2000 population joint distribution better than the former. We thus conclude that MDI method is an approach worth further theoretical investigation.