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Basic Quantitative Research Methods for Urban Planners
Overview
Quantitative Methods in Planning
What’s Different?
Definitions and Concepts You Should Know
Data and Measurements
Conceptual Framework
Statistics
Structure of the Book
Structure
Datasets
UZA Dataset
Household Dataset
Computer Software Used in This Book
Works Cited
Technical Writing
Overview
Purpose
Technical Writing
Public Scholarship
Technical Versus Non-Technical Writing
Planning and JAPA
Research You Can Use
Preliminaries
Audience
Scope
Objectives
Learn Everything Practical
Example
Mechanics
Words
Sentences
Paragraphs
Cohesion Within and Between Paragraphs
Tone and Voice
Organization
Rewriting, Editing, and Polishing
Literature Reviews
Style
Scope
Accuracy
Analysis
Connection to Planning
Planning Examples
JPER Winner
JAPA Winner
Conclusion
Works Cited
Types of Research
Overview
History
General Concepts
Data Type
Deductive Versus Inductive Logic
Timeframe
Research Design
Triangulation
Qualitative Methods
Quantitative Methods
Mixed Methods
Which Method to Use?
Conclusion
Works Cited
Planning Data and Analysis
Overview
Planning Data Types and Structures/Formats
Sampling and Bias
Tabular Data
Qualitative Data
Structured and Unstructured Data
Spatial Data
Data Processing and Management
Planning Data Sources
Demographic Data
Economic Data
Housing Data
Transportation Data
Public Health Data
Environmental Data
The Emergence of Big Data
Challenges in Using Big Data
Open Data
Machine Learning and Data Mining
Use of Machine Learning in Urban Planning
Types of Machine Learning
Planning Example
Summary
Note
Works Cited
Conceptual Frameworks
Overview
Purpose
Mechanics
Step by Step
Conclusion
Works Cited
Validity and Reliability
Overview
Reliability
Inter-Rater Reliability
Equivalency Reliability
Internal Consistency
Validity
Face Validity
Construct Validity
Internal Validity
External Validity
Planning Examples
Security of Public Spaces
Urban Sprawl and Air Quality
Conclusion
Works Cited
Descriptive Statistics and Visualizing Data
Overview
Purpose
History
Mechanics
Frequency Distribution
Central Tendency
Dispersion
Data Visualization
Data Matrix
Frequency Table
Cross Tabulation
Informative Graphs
Step by Step
Frequency Table
Central Tendency and Dispersion
Cross Tabulation
Informative Graphs
In R
Planning Examples
Residential Water Use
Urban Parks
Conclusion
Works Cited
Chi-Square
Overview
Purpose
History
Mechanics
Types of Data
Assumptions
Hypothesis in Chi-Square Test
Calculate the Test Statistic
Determine the Degrees of Freedom and a Critical Value
Strength Test for the Chi-Square
Step by Step
In R
Planning Examples
Sustainable Development Policies
Attitudes Toward Growth Management
Conclusion
Works Cited
Correlation
Overview
Purpose
History
Mechanics
Pearson Correlation Coefficient
Spearman Correlation Coefficient
Intraclass Correlation Coefficient
Step by Step
Pearson Correlation Coefficient
Partial Correlation Coefficient
Spearman Correlation Coefficient
Intraclass Correlation Coefficient
In R
Planning Examples
Environment Equity
Urban Design Qualities
Conclusion
Works Cited
Difference of Means Tests (T-Tests)
Overview
Purpose
History
Mechanics
Independent Samples T-Test
Standard Error
Determining Significance
Dependent Samples T-Test
Step by Step
Check Assumptions
Null Hypothesis
Independent Samples T-Test
Results
In R
Planning Examples
Transit and VMT
Shrinking Cities
Conclusions
Works Cited
Analysis of Variance (ANOVA)
Overview
Purpose
History
Mechanics
Assumptions
Interpreting Results
Step by Step
Check Assumptions
Null Hypothesis
One-Way ANOVA Test
Results
Post-Hoc Test
In R
Planning Examples
Urban Heat Islands
Urban Form and Travel Behavior
Conclusion
Works Cited
Linear Regression
Overview
Purpose
Explanation
Prediction
Hypothesis Testing
Wise Use of Regression
History
Mechanics
Simple Linear Regression
Interpreting Simple Linear Regression Results
R-squared: Goodness of Fit
F-statistic
t-Statistic(s)
Multiple Linear Regression
Step by Step
Simple Regression
Multiple Regression
Assumption Behind the Model
Data-Related Problems
Nonlinearity and Outliers
Log-transformation
Multicollinearity
Residual Error-Related Problems
In R
Planning Examples
Role of the Arts in Neighborhood Change
Residential Yard Management and Crime
Conclusion
Causality
Conceptual Frameworks
When Other Models Are More Appropriate
Works Cited
Logistic Regression
Overview
Purpose
History
Mechanics
Terminology and Transformations
Goodness of Fit
Step by Step
Multinomial Logistic Regression
In R
Planning Examples
Smart Growth Policies and Automobile Dependence
Mobility Disability and the Urban Built Environment
Conclusion
Works Cited
Quasi-Experimental Research
Overview
Purpose
History
Mechanics
Two-Group Pretest-Posttest Design
Regression to the Mean
Propensity Score Matching
Regression Discontinuity (RD)
Planning Examples
Light Rail Transit Expansion
Assessing Bids
Conclusion
Works Cited
Contributors
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