Basic Quantitative Research Methods for Urban Planners (eBook, ePUB)

Redaktion: Ewing, Reid; Park, Keunhyun

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 342
• Erscheinungstermin: 24. Februar 2020
• Englisch
• ISBN-13: 9781000769357
• Artikelnr.: 58712694

Autorenporträt

Reid Ewing, Ph.D., is Distinguished Professor of City and Metropolitan Planning at the University of Utah, associate editor of the Journal of the American Planning Association and Cities, and columnist for Planning magazine, writing the column Research You Can Use. He directs the Metropolitan Research Center at the University of Utah. He holds master's degrees in Engineering and City Planning from Harvard University and a Ph.D. in Urban Planning and Transportation Systems from the Massachusetts Institute of Technology. A recent citation analysis found that Ewing, with 24,000 citations, is the 6th most highly cited among 1,100 planning academic planners in North America. Keunhyun Park, Ph.D., is an Assistant Professor in the Department of Landscape Architecture and Environmental Planning at Utah State University. He holds master's degrees in Landscape Architecture from Seoul National University and a Ph.D. in Metropolitan Planning, Policy, Design from the University of Utah. His research interests include technology-driven behavioral research (e.g., drone, VR/AR, sensor, etc.), behavioral outcomes of smart growth, and active living.

Inhaltsangabe

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