Basic quantitative research methods for urban planners
著者
書誌事項
Basic quantitative research methods for urban planners
(APA planning essentials)
Routledge, 2020
- : hbk
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
In most planning practice and research, planners work with quantitative data. By summarizing, analyzing, and presenting data, planners create stories and narratives that explain various planning issues. Particularly, in the era of big data and data mining, there is a stronger demand in planning practice and research to increase capacity for data-driven storytelling.
Basic Quantitative Research Methods for Urban Planners provides readers with comprehensive knowledge and hands-on techniques for a variety of quantitative research studies, from descriptive statistics to commonly used inferential statistics. It covers statistical methods from chi-square through logistic regression and also quasi-experimental studies. At the same time, the book provides fundamental knowledge about research in general, such as planning data sources and uses, conceptual frameworks, and technical writing. The book presents relatively complex material in the simplest and clearest way possible, and through the use of real world planning examples, makes the theoretical and abstract content of each chapter as tangible as possible.
It will be invaluable to students and novice researchers from planning programs, intermediate researchers who want to branch out methodologically, practicing planners who need to conduct basic analyses with planning data, and anyone who consumes the research of others and needs to judge its validity and reliability.
目次
1. Introduction 2. Technical Writing 3. Types of Research 4. Planning Data and Analysis 5. Conceptual Frameworks 6. Validity and Reliability 7. Descriptive Statistics and Visualizing Data 8. Chi-Square 9. Correlation 10. Difference of Means Tests (T-Tests) 11. Analysis of Variance (ANOVA) 12. Linear Regression 13. Logistic Regression 14. Quasi-Experimental Research
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