DSpace Collection:
https://ah.lib.nccu.edu.tw/handle/140.119/102
2024-03-19T04:04:57Z2016年至2020年「臺灣選舉與民主化調查」四年期研究計畫結案報告
https://ah.lib.nccu.edu.tw/handle/140.119/135641
題名: 2016年至2020年「臺灣選舉與民主化調查」四年期研究計畫結案報告
摘要: 「2012年至2016年「選舉與民主化調查」四年期研究規劃」研究案在2012年就已擬定,於2012年4月向國科會人文處提出申請,並於2012年 6月底獲核定通過。擬訂就2012-2016年間臺灣的民主議題及重大的主要選舉進行面訪調查,藉此對臺灣選民的投票行為提供一個充分,有系統的描述與解 釋,並經由嚴謹且周延的分析,提供一個有關臺灣選民的全貌性之瞭解,在堅實的個體資料上,我們將可進一步展望,分析臺灣總體政治與地方政治的可能發展,尤 其是民主治理與民主品質的相關議題。 第一年期計畫為「2013年大規模基點調查面訪案」(以下簡稱TEDS2013),完成有效樣本2,292份,並擬於2016年針對2013年成功的 2,292位受訪者,進行定群追蹤訪問。並於2014年3月22日舉辦國際學術研討會,發表研究成果。這2,292位受訪者提供的資料,除了有助於我們瞭 解民眾對連任總統各項施政表現的評估,也將作為本次四年期研究計畫後續年度研究的重要參考基準。接下來的第二年期計畫除完成TEDS2013面訪計畫執行 與後續作業以外,主要工作為持續建立長期電話調查、網路調查、焦點團體資料庫。 本次第三年期計畫,主要為執行「2014年九合一選舉面訪案」(以下簡稱TEDS2014),針對地方公職人員九合一選舉進行研究調查。由於地方層級 的行政首長與民意代表全面改選,在政府治理與地方發展上將面對更多民眾的需求與環境的挑戰,有些議題不僅攸關民眾的福祉,也將更進一步對於我國民主品質有 重要的影響。民眾對於這些重要議題所反映出來的意見與想法為何?都相當值得探討。 TEDS2014原以新北市、臺中市、高雄市作為訪問區,惟三個城市於本次直轄市長選舉中,皆有現任市長參選連任的情形,而臺北市因時任市長任期屆 滿,參選人確認為全新候選人。因此TEDS第122次委員會中決議將原訪問地區的新北市改為臺北市,與四年前針對2010年直轄市公職人員選舉所進行的 TEDS2010C面訪一樣選擇了臺北市、臺中市、高雄市三都進行訪問。原預計執行之臺北市選前電話訪問案,則改為新北市選前電話訪問。TEDS2014 預定成功樣本數為臺北市、臺中市、高雄市各完成1,100份,總問卷完成份數預計為3,300份。另外,為了解TEDS2014面訪案問卷之效度與信 度,TEDS2014於獨立樣本完成後,分別在臺北市、臺中市與高雄市的成功樣本中,以行政區為單位隨機抽取百分之二十的樣本進行再測訪問。 最後,TEDS2014完成數為,臺北市獨立樣本完成1,133份、臺中市完成1,141份、高雄市完成1,174份;再測樣本臺北市完成228份、 臺中市完成235份、高雄市完成241份。TEDS2014面訪,除了深入了解地方選舉各項議題與民眾感受外,也進一步檢定問卷效度與信度,提供學界更多 關於地方選舉之研究參考資訊。2021-06-08T07:11:22Z貫通微觀與宏觀數據:民調與區位資料之空間整合與多層分析 2/2
https://ah.lib.nccu.edu.tw/handle/140.119/133375
題名: 貫通微觀與宏觀數據:民調與區位資料之空間整合與多層分析 2/2; Micro and Macro Data Linkage: Spatially Integrating Surveys with Area-Based Open Data for Multilevel Analyses
摘要: 本計畫之目的是建立一套通用之地理區編碼系統(geocoding system),打通微觀與宏觀數據之任督二脈,讓個體民調資料與集 體數據相互整合,跨越並超越層次、全盤掌握脈絡,洞悉政治現象 之全貌。本計畫首先以地理資訊系統(geographic information system, GIS)為基礎,建立一套地理區編碼系統,作為串連民意調 查資料中的個別(微觀)受訪者與其居住地的集體(宏觀)數據資 料的基準。其次,本計畫以前述開發之地理區系統對微觀與宏觀數 據進行地理編碼,透過地理空間作為串接民調個體資料與區位變數 的骨幹,串接微觀與宏觀數據,建構與選區空間單位相同的多層模 型。此種「整合空間」(spatially integrated)的民調資料不但 可以分析傳統上選民特徵、政黨偏好、政治態度等微觀因素對政治 行為的影響,也能檢證學理上社經環境、鄰近效應等空間因素的作 用。接著,本計畫以地理空間頗為多樣異質之新北市為例,於 2018年地方公職人員選舉後進行電話訪問,蒐集個體選民微觀資料 。並根據2018年地方選舉時的重要議題,蒐集以地理區塊為單位的 新北市社會經濟及選舉等集體資料。最後,本計畫整合上述個體民 調資料與集體資料(aggregate data),建立多層模型 (multilevel model)加以分析,將蒐集之資料應用至選舉投票研 究,分析新北市選民在2018年市長、市議員選舉中之一致與分裂投 票。; This research project plans to develop a unified geocoding system so as to spatially integrate micro-level survey data with macro-level area (or ecological) data. The purpose of this micro-macro data linkage through geocoded survey data is to emancipate investigators from given data constraints and stimulate context-rich and spatially integrated multilevel analyses. Survey research is undoubtedly a major approach to observe and measure individuals’ attitudes, opinions, and behavior. In his seminal article, Robinson (1950) warned the possibility of erroneously drawing conclusions about individuals solely from the data of groups. On the other hand, however, an overly strict limitation of analysis at individual level may well lead to another fallacy of ignoring contextual effects. Recent decades have witnessed the growing awareness of the importance merging individual- and aggregate-level data into contextual analyses. Yet the fact that most survey data sets remain limited to individual respondents’ records often hinders analysts from reaching out relevant higher-level data, which are typically scattered in different archives and based on cross-cutting geographical boundaries. Thus, this two-year research project has two objectives: 1. In terms of methods: This project will develop a unified geocoding system for Taiwan so as to spatially integrate micro-level survey data with macro-level area data based on the geographic information system (GIS). This geocoding system will serve as the key in linking respondents in survey data with the related area-based social, economic and electoral data released by the various government agencies (including the General Accounting Office, the Ministry of Interior, and the Central Election Commission) as well as academic data archives (such as the Taiwan’s Political Geographic Information System, TPGIS). 2. In terms of applied research: This project will apply multilevel model to the spatially integrated and geocoded survey data to incorporate local contextual effects into traditional analysis of voting behavior with political attitudes, party identification and demographic variables. Geocoding will first be applied to the planned TEDS 2017 face-to-face survey. The same geocoding system will then be extended to a telephone interview to be conducted after the 2018 local elections of the New Taipei City. Furthermore, small area estimation and spatial microsimulation methods will also be applied while constructing electoral districts’ social and economic indicators. 2020-12-23T07:53:42Z貫通微觀與宏觀數據:民調與區位資料之空間整合與多層分析 1/2
https://ah.lib.nccu.edu.tw/handle/140.119/133374
題名: 貫通微觀與宏觀數據:民調與區位資料之空間整合與多層分析 1/2; Micro and Macro Data Linkage: Spatially Integrating Surveys with Area-Based Open Data for Multilevel Analyses
摘要: 本計畫之目的是建立一套通用之地理區編碼系統(geocoding system),打通微觀與宏觀數據之任督二脈,讓個體民調資料與集 體數據相互整合,跨越並超越層次、全盤掌握脈絡,洞悉政治現象 之全貌。本計畫首先以地理資訊系統(geographic information system, GIS)為基礎,建立一套地理區編碼系統,作為串連民意調 查資料中的個別(微觀)受訪者與其居住地的集體(宏觀)數據資 料的基準。其次,本計畫以前述開發之地理區系統對微觀與宏觀數 據進行地理編碼,透過地理空間作為串接民調個體資料與區位變數 的骨幹,串接微觀與宏觀數據,建構與選區空間單位相同的多層模 型。此種「整合空間」(spatially integrated)的民調資料不但 可以分析傳統上選民特徵、政黨偏好、政治態度等微觀因素對政治 行為的影響,也能檢證學理上社經環境、鄰近效應等空間因素的作 用。接著,本計畫以地理空間頗為多樣異質之新北市為例,於 2018年地方公職人員選舉後進行電話訪問,蒐集個體選民微觀資料 。並根據2018年地方選舉時的重要議題,蒐集以地理區塊為單位的 新北市社會經濟及選舉等集體資料。最後,本計畫整合上述個體民 調資料與集體資料(aggregate data),建立多層模型 (multilevel model)加以分析,將蒐集之資料應用至選舉投票研 究,分析新北市選民在2018年市長、市議員選舉中之一致與分裂投 票。; This research project plans to develop a unified geocoding system so as to spatially integrate micro-level survey data with macro-level area (or ecological) data. The purpose of this micro-macro data linkage through geocoded survey data is to emancipate investigators from given data constraints and stimulate context-rich and spatially integrated multilevel analyses. Survey research is undoubtedly a major approach to observe and measure individuals’ attitudes, opinions, and behavior. In his seminal article, Robinson (1950) warned the possibility of erroneously drawing conclusions about individuals solely from the data of groups. On the other hand, however, an overly strict limitation of analysis at individual level may well lead to another fallacy of ignoring contextual effects. Recent decades have witnessed the growing awareness of the importance merging individual- and aggregate-level data into contextual analyses. Yet the fact that most survey data sets remain limited to individual respondents’ records often hinders analysts from reaching out relevant higher-level data, which are typically scattered in different archives and based on cross-cutting geographical boundaries. Thus, this two-year research project has two objectives: 1. In terms of methods: This project will develop a unified geocoding system for Taiwan so as to spatially integrate micro-level survey data with macro-level area data based on the geographic information system (GIS). This geocoding system will serve as the key in linking respondents in survey data with the related area-based social, economic and electoral data released by the various government agencies (including the General Accounting Office, the Ministry of Interior, and the Central Election Commission) as well as academic data archives (such as the Taiwan’s Political Geographic Information System, TPGIS). 2. In terms of applied research: This project will apply multilevel model to the spatially integrated and geocoded survey data to incorporate local contextual effects into traditional analysis of voting behavior with political attitudes, party identification and demographic variables. Geocoding will first be applied to the planned TEDS 2017 face-to-face survey. The same geocoding system will then be extended to a telephone interview to be conducted after the 2018 local elections of the New Taipei City. Furthermore, small area estimation and spatial microsimulation methods will also be applied while constructing electoral districts’ social and economic indicators. 2020-12-23T07:53:28Z「類別依變數模型中之內因自變數問題:方法論之探討與經濟投票研究之應用」結案報告
https://ah.lib.nccu.edu.tw/handle/140.119/120476
題名: 「類別依變數模型中之內因自變數問題:方法論之探討與經濟投票研究之應用」結案報告
摘要: In empirical political studies, researchers often intend to estimate and test the effects of independent variables on dependent variables. Among various analysis methods, the regression model is the most commonly used one. If an independent variable is an exogenous variable that fits the assumption of conditional independence, then the unbiased coefficients can be estimated with ordinary regression analysis. However, in cases where an independent variable and a dependent variable are simultaneously affected by other variables not included in the model, the independent variable will become correlated to the error terms. This would violate the assumption of conditional independence, as the estimated value of its regression coefficient would contain \"omitted variable bias.\" This type of independent variables is often called the \"endogenous explanatory variable.\" In example, the economic voting theory suggests that voters` retrospective and prospective evaluations of the economy will determine their voting decisions, whether to reward or punish the incumbent and the ruling party. In recent years, however, some scholars have challenged this theory and argued that voters` economic evaluations are often affected by their party identifications. As voters tend to favor their preferred political party, their economic evaluation should not be considered as an exogenous variable. This kind of debate involves variables` relative positions in theory, and its empirical analysis cannot be resolved by adding control variables to regression model. Although the literature contains many studies on how to handle endogenous explanatory variables, they mostly target continuous dependent variables. Among these methods, the commonly noted \"instrumental variables\" (IV) and \"two-stage least square\" (2SLS) are not applicable to the categorical dependent variables and their nonlinear regression models that are most frequently encountered in politics. In view of the above arguments, this two-year research project has two objectives: 1. In terms of methods: This project will combine the basis of generalized linear models (GLM) with mediation analysis and structural equation model (SEM) to develop an endogenous explanatory variable model that is applicable to categorical dependent variables. Not only will the model be able to consistently estimate the regression coefficient, but it can also correctly interpret the probability of occurrence for each category of dependent variables. This can avoid misinterpretations caused by coefficient-rescaling in nonlinear models. 2. In terms of applied research: This project will apply the \"categorical dependent variable`s endogenous explanatory variable model\" to economic voting research. It will be able to solve the traditional problem of applying IV and 2SLS from linear models to categorical dependent variable models, as well as verify numerous plausible debates in theory. For example: do voters` party identifications influence their retrospective and prospective evaluations of the economy before their economic evaluations affect the voting decisions, or do the economic evaluations reflect objective economic conditions independent of voters` party preferences.; 政治學的經驗研究中,往往想估計並檢驗自變數對依變數的影響,而迴歸模型是最常應用的分析方法。如果該自變數為外因變數(exogenous variable),符合條件獨立(conditional independence)的假定,則一般的迴歸分析便可估計不偏之係數。不過棘手的是,若有其他未納入模型的變數同時影響到該自變數與依變數,使該自變數與誤差項產生相關,便違反了條件獨立的假定,其迴歸係數估計值有「忽略變數偏差」(omitted variable bias),這類自變數常稱為「內因解釋變數」(endogenous explanatory variable)。例如經濟投票的學理,認為選民對經濟的回顧或前瞻評估,會決定其投票的抉擇,獎勵或懲罰現任者及執政黨。不過近年也有學者挑戰此一理論,認為選民的經濟評估其實常受政黨認同的左右,偏袒自己喜歡的政黨,因此並非外因變數。這類辯論因涉及變數在學理上的相對位置,其經驗檢證無法以單一迴歸式中加入控制變數(control variables)來解決。儘管文獻中對內因解釋變數的處理方式討論甚多,但大多是針對連續(continuous)依變數,其中一般熟悉之「工具變數」(instrumental variable, IV)及「兩階段最小平方法」(two-stage least square, 2SLS),並不宜套用至政治學中最常遭遇之類別(categorical)依變數及其非線性迴歸模型。 有鑑於此,本兩年期研究計畫的目的有二: 一、 在方法方面:以廣義線性迴歸模型(generalized linear models, GLM)為基礎,結合中介分析(mediation analysis)與結構方程式模型(structural equation model, SEM),發展適用於類別依變數的內因解釋變數模型,不僅能一致估計迴歸係數,且能正確解讀依變數的各類別發生的機率,避免非線性模型中「係數受變異數尺度大小牽動」(coefficient- rescaling)引起之錯誤解讀。 二、 在應用研究方面:將「類別依變數的內因解釋變數模型」應用至經濟投票研究,既可克服傳統上將線性模型之IV 及2SLS 逕行套用至類別依變數模型的問題,又可檢證學理上數種言之成理的觀點論辯:例如選民的政黨認同是否先影響其回顧與前瞻經濟評估,然後經濟評價才影響投票抉擇;還是經濟評估反映客觀的經濟榮枯,不會受政黨偏好的左右。2018-10-09T08:11:43Z