Structure of the Book

As a comprehensive book of quantitative research methods for notice researchers, this book starts with key definitions and concepts in general social science research (Chapter 1). As writing is the essential communication tool for both planning practitioners and scholars, Chapter 2 presents writing skills and techniques. Three fundamental approaches to research—qualitative, quantitative, and mixed inquiries—are introduced in Chapter 3. Chapter 4 introduces various types of planning data and sources. The following chapters deal with basic concepts in quantitative research—conceptual frameworks (Chapter 5) and validity and reliability (Chapter 6).

One of two types of statistics—descriptive statistics—is presented in Chapter 7, in addition to some data visualization examples. Chapters 8 to 13 are about inferential statistics, from the simplest to the more complex. This is the core of the book. Chapter 14 presents the rationale and mechanics of quasi-experimental research.

Structure

Most chapters have a recurring structure to help readers understand each of the methods more easily, albeit varied among chapters. Each section answers specific questions.

• • Overview: What is the essence of the method? What do we need to know before applying the method?
• • Purpose: How is this method used and what is it used for?
• • History: When was it developed? Who developed the method?
• • Mechanics: What are the components of the method? What assumptions must be met, or under what conditions would you use this method? What are the limitations of the method?
• • Interpreting results: How can we understand the results? How can we know if the results are valid and reliable?
• • Step by step: How can we utilize the method using SPSS or R (or another appropriate software package)? How can we apply the method to real planning data?
• • Planning examples: What are two interesting studies from the planning literature that use the method?
• • Conclusion: How can we summarize the method and assess its usefulness.

Actual examples from peer-reviewed journal articles are provided to show how planning scholars have used the particular method to analyze a real planning problem (s). This part makes our books distinguishable from other textbooks in other fields, by connecting the methods to our readers.

Datasets

Step-by-step is another section that makes our books different from earlier planning texts. These books provide not only theoretical understanding but also seek to foster understanding of how to apply each method using real planning data. Both datasets are available online through the publisher’s website.

UZA Dataset

One dataset used in this book, the urbanized area (UZA) dataset, allows us to test the theory' of induced travel demand (highways generate their own demand) and also test theories about how the built environment is related to highway demand. The highway data come from the Federal Highway Administration’s (FHWA) Highway Statistics. Our transit data come from the National Transit Database. Other data come from other sources. For spatial units, we used FHWA’s urbanized area boundaries. We limited our sample to large urbanized areas with populations of 200,000 or more for which all variables in Table 1.2 could be estimated. Of the 173 urbanized areas meeting the population criterion, some cases were lost for lack of other data (e.g., compactness metrics, transit data, or fuel price data). Our final sample consists of 157 urbanized areas.

The variables in our models are defined in Table 1.2. They are as follows:

• • Our dependent variables: daily VMT per capita and annual traffic delay per capita.
• • Our independent variables: The independent variables of primary interest are lane miles of highway capacity and population density and other measures of compactness. Control variables include population size, per capita income, and metropolitan average fuel price.

Table 1.2 Variables in UZA Dataset (FHWA Urbanized Areas in 2010)

Table 1.2 (Continued)

Note: When you open the dataset, you will notice that there are some variables starting with "In." It represents log-transformed variables.

All variables are presented both in linear and logged forms (the latter transformed by taking natural logarithms). The use of logarithms has two advantages (see Chapter 12). First, it makes relationships among our variables more nearly linear and reduces the influence of outliers (such as New York and Los Angeles). Second, it allows us to interpret parameter estimates as elasticities (the ratio of the percentage change in a dependent variable associated with the percentage change in an independent variable), which summarize relationships in an understandable and transferable form.

This database has been used in three published articles (Ewing et al., 2014, 2017, 2018) which relate daily VMT per capita or annual delay per capita for urbanized areas to regional compactness, highway capacity, transit service, average fuel price, and other covariates. More studies using this database are coming.

• [1] The job-population index measures balance between employment and resident population within a buffer. Index ranges from 0, where only jobs or residents are present within a buffer, not both, to 1. where the ratio of jobs to residents is optimal from the standpoint of trip generation. Values are intermediate when buffers have both jobs and residents, but one predominates, jobpop = 1 - [ABS(employment -Depopulation)/ (employment + Depopulation) ]. ABS is the absolute value of the expression in parentheses. The value 0.2, representing a balance of employment and population, was found through trial and error to maximize the explanatory power of the variable. 2 The entropy index measures balance between three different land uses. The index ranges from 0, where all land is in a single use, to 1 where land is evenly divided among the three uses. Values are intermediate when buffers have more than one use but one use predominates. The entropy calculation is: entropy = - [residential share*ln (residential share) + commercial share*ln (commercial share) + public share*ln(public share)]/hi(3), where hi is the natural logarithm of the value in parentheses and the shares are measured in terms of total parcel land areas.