Basic Quantitative Research Methods for Urban Planners - 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