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研究生:鄭仲婷
研究生(外文):Chung-Ting Cheng
論文名稱:兒童通學方式對社會網絡之影響—新北市國小學童之實證分析
論文名稱(外文):The Influence of Children’s School Travel Patterns on Social Networks: Empirical Analysis of Elementary School Students in New Taipei City
指導教授:林楨家林楨家引用關係
指導教授(外文):Jen-Jia Lin
口試委員:汪進財溫在弘
口試日期:2013-06-07
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:地理環境資源學研究所
學門:社會及行為科學學門
學類:地理學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:214
中文關鍵詞:通學方式社會網絡兒童
外文關鍵詞:School travel patternSocial networkChild
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兒童通學由同儕陪同,可以在過程中與同儕互動、分享學校生活、個人經驗或分享秘密,以達到增進彼此的關係,對兒童的社會發展有很大助益,並強化兒童的社會網絡。過去關於兒童通學之旅運研究,主要著眼於通學安全及運具選擇對於兒童的生理健康影響,但鮮少研究關注通學陪同方式對於兒童的社會層面的影響。鑒此,本研究欲釐清兒童通學陪同方式與社會網絡之理論關係。
根據文獻回顧與訪談結果,歸納出影響兒童社會網絡的重要因素,包括生理、心理、外在表現、家庭因素、社經地位等五大面向,做為研究之控制變數。在社會網絡資料蒐集方面,social-cognitive map (SCM) 被廣泛運用在社會網絡研究中,藉由詢問兒童的好朋友是誰,並請他們提名,進而建立網絡關係。社會網絡的指標選擇size (網絡大小)、in degree (被提名數)、eigenvector centrality、core/periphery (核心/邊陲)及自編P_F值,做為依變數,分別從基本描述網絡的大小,到利用in degree與eigenvector centrality捕捉兒童的受歡迎程度,以及位於網絡中的重要性等。P_F值則是表現通學陪同名單中,同時為好朋友的比例,意在表示通學互動的關係強度。
研究對象來自新北市板橋區H國小的六年級學童,使用問卷進行調查,並使用Tobit model、次序羅吉特,與二項羅吉特進行模式校估。根據實證結果,發現兒童通學陪同方式對於社會網絡有顯著影響,與同儕相處時間越長、同儕陪同規模越大,對於社會網絡有正向影響,成年人的陪同卻有負向影響。然而,並非所有與同儕相關之變數對於社會網絡皆有助益。上學旅次的同儕規模越大,對於社會網絡卻呈負向影響,推測與樣本特性有關,尚待後續研究釐清原因。
本研究釐清通學陪同方式對於社會網絡的影響,具有推進理論連結之貢獻。根據實證結果,通學由同儕陪同對於兒童社交生活有許多正向影響,建議學校可推行相關策略,鼓勵兒童多與同儕通學,以增進同儕關係,並培養社交能力。



Commuting with peers during school travel creates social benefits to children. With peer companies, children can share school life, personal feelings and even secrets with each other; therefore, it might strengthen children’s social networks. Previous researches of school travel focused on safety and physical health of children, but studies of social domain were quite few. This study tried to link the relationships between school travel patterns and social networks of children.
Based on literature review and interviews, this study selected important influential variables to a child’s social networks as control variables, which include physical, psychological, appearance/performance, family relationship, and socioeconomic statuses. In social network studies, social-cognitive map (SCM) is a well-used method to find who children’s best friends are, and then build the network. Social network indicators like size, in degree, eigenvector centrality and core/periphery were used as dependent variables.
Sixth grade students were selected as study sample from an elementary school in Banciao District, New Taipei City. The samples were collected by a questionnaire survey and analyzed by ordered logit model, Tobit model, and binary logit model. The empirical evidence shows that school travel patterns do influence children’s social networks. Generally, the more time and larger scale with peers are beneficial to social networks, but accompanied by adults has negative influence to social networks. However, it is not for certain, we can see some exceptions in the results that peer scale’s effect on social networks is negative in to-school travel. These expectations could be related to characteristics of samples, and need further researches to clarify their reasons.
The contribution of this research lies in clarifying the influences of school travel patterns on social networks, which bridges the gap of previous researches. According to the results, accompanied by peers has a lot positive influences to child’s social life. Therefore, this study recommends that school authorities can promote a policy like “go to school with friends more” to encourage children to develop peer relationships and social capability during school travels.


目錄
口試委員審定書……………………………………………i
謝誌…………………………………………………………ii
中文摘要……………………………………………………iii
英文摘要……………………………………………………iv
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究範疇 4
第三節 研究流程 8
第四節 研究方法 13
第二章 文獻回顧 15
第一節 兒童社會網絡 15
第二節 通學方式對兒童發展的影響 44
第三節 交通運輸與社會網絡 60
第四節 綜合評述 63
第三章 研究設計 67
第一節 課題研析 68
第二節 假說研提 85
第三節 分析方法 98
第四章 資料調查與分析 116
第一節 問卷調查與樣本資料蒐集 116
第二節 敘述統計分析 124
第三節 相關分析 137
第五章 實證分析 155
第一節 模式校估 155
第二節 假說驗證 163
第三節 討論與應用 168
第六章 結論與建議 176
第一節 結論 176
第二節 建議 179
參考文獻 182
附錄一 訪談記錄 192
附錄二 「國小兒童自我概念量表」使用同意書 207
附錄三 國立台灣大學行為與社會科學研究倫理委員會 審查核可證明 208
附錄四 問卷內容 國小學童通學方式與社會網絡研究問卷 209
附錄五 研究參與者知情同意書 214

圖目錄
圖1-2-1 板橋區H國小學區示意圖 7
圖1-3-1 研究流程圖 9
圖2-1-1 文獻回顧架構圖 15
圖2-2-1 通學方式、運具選擇與通學陪同關係示意圖 44
圖2-2-2 通學方式中的運具選擇與生理健康的影響關係 45
圖2-2-3 通學方式中的通學陪同與獨立性的關係 46
圖2-2-4 通學方式影響兒童的面向 48
圖2-3-1 交通運輸與社會網絡關係圖 60
圖2-3-2 通學方式與社會網絡關係圖 62
圖3-1-1 研究設計架構圖 67
圖3-1-2 課題研析架構圖 69
圖3-1-3 節點連結示意圖 71
圖3-1-4 具有核心/邊緣結構之網絡R (引自Borgatti, 1999) 72
圖3-1-5 網絡R與理想網絡I的相鄰矩陣(引自Borgatti, 1999) 72
圖3-1-6 兒童通學方式構想圖 76
圖3-1-7 影響兒童社會網絡的控制變數 78
圖3-1-8 資料蒐集構想圖 80
圖3-1-9 研究分析構想圖 83
圖3-2-1 研究假說關係圖 96
圖3-3-1 分析架構圖 99
圖3-3-2 Tobit model (censored distribution)示意圖 101
圖3-3-3 Normal distribution、truncated distribution、censored distribution 比較圖 106
圖3-3-4 次序羅吉特模式示意圖(μ0=0) 111
圖4-1 樣本資料調查與分析架構圖 116
圖4-2-3 步行陪同狀況人數分配長條圖 126
圖4-2-4 機車陪同狀況人數分配長條圖 127
圖4-2-5 汽車陪同狀況人數分配長條圖 127
圖4-2-6 上學公車陪同狀況人數分配長條圖 128
圖4-2-7 放學補習班交通車陪同狀況人數分配長條圖 128

表目錄
表1-2-1 實證研究學校基本資料表 7
表2-1-1 兒童社會網絡研究文獻整理 21
表2-1-2 相關研究地區歸納表 33
表2-1-3 研究對象歸納表 33
表2-1-4 各文獻樣本數歸納表 33
表2-1-5 資料類型歸納表 34
表2-1-6 社會網絡資料蒐集方法歸納表 34
表2-1-7 社會網絡資料分析方法歸納表 35
表2-1-8 以兒童社會網絡為依變數歸納表 36
表2-1-9 影響社會網絡的自變數歸納表 41
表2-1-10 研究結果歸納表 42
表2-2-1 通學方式對兒童發展影響之文獻整理 49
表2-2-2 相關研究地區歸納表 54
表2-2-3 研究對象歸納表 54
表2-2-4 各文獻樣本數歸納表 55
表2-2-5 資料類型歸納表 55
表2-2-6 資料蒐集方法歸納表 55
表2-2-7 資料分析方法歸納表 56
表2-2-8 兒童通學方式影響的變項歸納 57
表2-2-9 各文獻所使用之自變數歸納表 58
表2-2-10 文獻實證結果歸納表 59
表3-1-1 依變數型態分類與分析模式、模式校估整理表 82
表3-2-1 通學陪同對於社會網絡的因果關係推測表 87
表3-2-3 非通學方式因素與社會網絡的因果關係表 95
表3-3-1 各模式依變數與自變數定義表 106
表4-1-1 資料蒐集方式分類表 117
表4-1-2 莊耀嘉、李雯娣(2001)五大性格與侵略行為的字詞一覽表 121
表4-1-3 問卷調查結果統計表 123
表4-2-1 上學與放學運具選擇次數分配表 124
表4-2-2 上學與放學通學陪同次數分配表 125
表4-2-3 陪同規模、陪同時間、P_F值 敘述統計表 129
表4-2-4 社會網絡指標-size/n、in degree/n、eigenvector centrality敘述統計表 129
表4-2-5 各班core/periphery 次數分配與百分比表 130
表4-2-6 BMI與月考平均成績之敘述統計表 131
表4-2-7 家庭結構型態、家戶年所得次數分配表 131
表4-2-8 體味、外出自由度、管教態度、多才多藝 敘述統計表 132
表4-2-9 五大性格與侵略行為 敘述統計表 133
表4-2-10 國小兒童自我概念量表結果 敘述統計表 135
表4-3-1 相關係數強度與意義說明表 137
表4-3-2 連續型態自變數間的相關係數整理表 139
表4-3-3 連續變數分類表-自變數 141
表4-3-4 連續變數與類別變數之卡方值整理表-自變數 143
表4-3-5 類別變數與類別變數之卡方值整理表-自變數 145
表4-3-7 社會網絡指標size/n之卡方檢定表 147
表4-3-8 社會網絡指標in degree/n之卡方檢定表 148
表4-3-9 社會網絡指標eigenvector centrality之卡方檢定表 149
表4-3-10 社會網絡指標core/periphery之卡方檢定表 150
表4-3-11 P_F值之卡方檢定表 151
表4-3-12 自變數相關分析結果整理表 153
表4-3-13 自變數與依變數相關分析結果整理表 153
表5-1-1 size/n之Tobit模式校估結果表 156
表5-1-2 in degree/n之tobit模式校估結果表 158
表5-1-3 eigenvector centrality之Tobit模式校估結果表 159
表5-1-4 P_F值次序羅吉特模式校估結果表 160
表5-1-5 core/periphery之二項羅吉特模式校估結果表 161
表5-1-6 各依變數之上學與放學模式-達顯著(α=0.05)之通學變數比較整理表 162
表5-2-1 研究假說檢核表 166
表5-3-1通學方式變數影響關係整理表 169
表5-3-2非通學方式變數影響關係整理表 172



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