Qualitative Analysis for Planning & Policy

Beyond the Numbers

John Gaber, Sharon Gaber

• Publisher: Routledge
• Year: 2018
• ISBN:
  9781351177894
• Edition: 1
• Topic: Architecture
• Subtopic: Urban Planning & Landscaping

This book explains how to use and adapt these techniques and how to integrate these methods with more traditional qualitative research. Chapters offer step-by-step guidance to setting up various kinds of qualitative research projects, collecting data, organizing data, and analyzing data. Case studies show how a mix of qualitative and quantitative research can help planners build consensus and tackle large, complicated projects.

Chapter
1
Introduction

The Need for Qualitative Planning Research

Qualitative research can alter the course of planning in a city. This was the case 25 years ago in New York City. William Whyte's qualitative study of urban spaces in New York helped to redefine the city's zoning code to better accommodate public open spaces.¹ Unfortunately, today's planners are inadequately trained in how to recognize, access, organize, and integrate qualitative observations—which yield data in the form of words and images—into their research projects. This lack of qualitative research skills limits planners to asking questions that can only be answered with quantitative methods, which deal only with numbers. A better knowledge of qualitative research techniques will expand a planner's repertoire of researchable topics beyond what is available through quantitative methods.

This hands-on book for practicing planners, planning students, planning researchers, and policy makers examines how to use qualitative research to see the data beyond the numbers. Planning research is only one part of the range of influences (zoning codes, city ordinances, comprehensive plans, and politics, for example) that go into making a plan. Planners are already well versed in quantitative research methods as prescribed by the American Institute of Certified Planners exam, the Planning Accreditation Board for American planning schools, and existing planning research methods books. But as evidenced by William Whyte, planners cannot afford to ignore qualitative research strategies when they are confronted with problems that cannot be quantified.

Planners do research when they need more data to make intelligent decisions. They know that planning and policy decisions are not based solely on the numbers, but because qualitative research is often deemed too costly in terms of time or money, planners are not well trained in qualitative research strategies. They commonly mistake qualitative data as either "politics," "common sense," or "street smarts," or worse yet, file it away as outside the purview of the research project, never allowing their observational insights to make a significant impact in the planning process. Many times planners cannot see the qualitative data through the lines of previously generated quantitative data. This book is a road map to help planners access qualitative data and integrate it into their planning investigations.

To respond to the oft-cited criticism that qualitative research is too resource intensive, we offer the old adage: Pay now or pay later. The large-scale public housing projects built in the 1950s and 1960s are classic examples. Professionals now widely recognize that these projects were a bad idea. However, at the time, quantitative planning research pointed toward high rates of poverty and the high cost of housing. The housing projects were seen as the solution: move thousands of low-income residents into huge, high-density residential facilities that can accommodate thousands of individuals (but which, as we know now, show little consideration for basic community life needs).

The Pruitt-Igoe housing project in St. Louis had to be demolished because it did not work for its residents, even though it had won an AIA (American Institute of Architects) design award. If a qualitative inquiry had been done beforehand, the planners would have found that socially isolated, high-density residential dwelling units for low-income families do not produce a positive neighborhood environment. In this classic example, the expense of a qualitative research strategy at the onset could have potentially saved millions of dollars spent on housing projects and spared thousands of low-income residents from being trapped in socially isolated towers in the park.

To better acquaint planning researchers with qualitative research methods and data, we first look at the overall research process or "research act."² In the first section of this introductory chapter, we discuss the research process, which includes data steps, how data relate to research methods, how to choose a method, and how to know whether the data is valid and reliable. In the second section, we provide an overview of the organization of the book. Here, we briefly discuss the focus of the remaining chapters and explain how they come together to form a planner's approach to qualitative research. Throughout the book, we provide real planning applications and examples.

Data Steps

The foundation of planning research is the "research act,"³ the process of constructing research activities. We divide this process into five sequential steps in relation to data: establishing the question (need for data), accessing data (methodology), organizing and analyzing data for its observations of reality (analysis), testing the significance and reliability of data (confidence and reliability), and presenting the research results.

An example of a planner in a mid-sized southern city shows how these research activities work in concert. The planner is interested in changing a two-way street in the industrial section of town to a one-way street to better accommodate large trucks and their deliveries to local area businesses, but is confronted with a series of questions and needs data to answer them. The first step in the research act is establishing the research question. One of the possible questions is: What do local business owners think about this proposal? Having established this question, the planner then moves on to the second step and chooses a survey methodology to access data about what the business owners think. The planner drafts a survey and asks peers and superiors for input, then mails the survey to the businesses immediately impacted by the proposed one-way street.

After a month of receiving completed surveys, the planner moves on to the third step in the investigation—organizing and analyzing the data. Here, the researcher inputs all the survey responses into a spreadsheet, then analyzes the data to see how they speak to the larger issue of changing the two-way street to a one-way street to better accommodate truck traffic. In analyzing the data, the planner learns that local business owners support widening the street but are concerned that modifications made to the street would hamper public access to their buildings. After the analysis is complete, the planner goes to the fourth step and tests the significance, validity, and reliability of the data to check its veracity. At this point the planner checks the validity of the observations ("Am I drawing the right conclusions about the data?") and the reliability of the research method ("Did my survey instrument work properly?").

Once confident that the observations are accurate and that the research instrument was reliable, the planner presents the research findings to colleagues so results can be integrated in the final planning proposal. The research was successful in determining the business owners' perspective and in providing insights on how the widening of the street could affect customer access to the businesses. There will be more to this analysis later.

Once you are sure that you are asking the correct question, the most significant step in the research act is determining what type of research methodology (method to access data) is needed to answer the question. Asking the right question but accessing the wrong data leads to, at best, partially answered questions; at worst, inconclusive data.

To understand why accessing methods are important, we first need to understand data. Sidestepping the philosophical debate of empirical reality, we assume that researchable reality is multifaceted and complex. Some parts of reality can be understood as being governed by laws. For example, Isaac Newton's law of gravity illustrates an understanding that gravity controls everything on earth: What goes up must come down. This "Newtonian" understanding of reality supports traditional scientific quantitative research. Other parts of reality are not governed by laws but are constituted in relationships and experiences. John Dewey's "experiential" understanding supports a more exploratory and naturalistic approach to research.

Empirical reality is not static or single-faceted. Instead, we better understand it as dynamic with a kaleidoscope of characteristics. As planning and policy researchers, we cannot understand all aspects of reality on a particular topic. But we can, with the right research method, collect data that provide a very good image of some of the reality we are studying. A clear distinction exists between reality and data.

Research Methodology

One way to visually illustrate empirical reality in relation to data is to imagine reality as a multifaceted cake and research methods as techniques that take observational slices out of the empirical cake in the form of data. Data give planning researchers insight into what reality is all about. In fact, we like to think of data as "data slices."⁴ (See Figure 1.1.)

Each data slice we take out of the empirical cake gives us one particular insight into reality. The methods we use to generate data slices determine what we know about empirical reality. The complex characteristics of reality can be loosely organized into the two types of data we discussed earlier: quantitative and qualitative. Quantitative data require research methods that are good at capturing data on shared population characteristics and general patterns for an entire community.⁵ Qualitative data require research methods that allow the researcher to ask exploratory and descriptive questions. (See Table 1.1 for a detailed distinction between the two data sets.) Qualitative data fit in the "experiential" understanding of data.

Figure 1.1 Empirical reality composed of various data slices

To illustrate how different questions are more in tune with different slices of data, look at the differences in the questions asked by two community planning research approaches. A quantitative research approach asks questions to access numeric data sets that work well with a more precise research technique. For example, how many people live

TABLE 1.1 DISTINGUISHING CHARACTERISTICS BETWEEN QUANTITATIVE AND QUALITATIVE DATA

QUANTITATIVE	QUALITATIVE
Positivist in orientation, seeking objective facts about and causes of social phenomena with little or no reference to subjective states of individuals	Phenomenological in orientation, seeking to understand human behavior from the social actor’s own frame of reference
Obtrusive and controlled measurement	Naturalistic and uncontrolled observation
Objective	Subjective
Removed from the data: the “outsider” perspective	Close to the data: the “insider” perspective
Verification-oriented, inferential, confirmatory, and hypothesis-testing	Discovery-oriented, descriptive, exploratory, inductive
Outcome-oriented	Process-oriented
Reliable, “hard,” and replicable data	Valid, “real,” “rich,” and “deep” data
Generalizable; multiple case studies	Tends to be ungeneralizable; single case studies
Particularistic	Holistic
Assumes a stable reality	Assumes a dynamic reality

in the community? What is the demographic profile of the community, such as the number of unemployed and youth? How have these numbers changed over time? All of these questions look at the external characteristics of the empirical cake. How tall is the cake? How wide is the cake? How much does the cake weigh? These data slices give precise quantitative insights about the cake but tell us nothing on what is going on inside of it.

On the other hand, a qualitative research approach will ask less precise research questions and more accurate questions that focus on process and how things work on the inside. For example, how do members of the community spend their free time? How do low-income residents make ends meet? How does race impact where people live and send their kids to school? Qualitative research questions look at the internal characteristics of the cake. What type of cake is inside? Is it marbled? If so, what is it marbled with? Does the type of icing work well with the cake? This type of data slice provides a more accurate qualitative insight about what is going on inside t...

Table of contents

• Cover
• Title
• Copyright
• Contents
• Tables
• Figures
• Acknowledgments
• Chapter 1. Introduction
• Chapter 2. Field Research
• Chapter 3. Photographic Research
• Chapter 4. Focus Group Research
• Chapter 5. Content Analysis and Meta-Analysis
• Chapter 6. Getting the Big Picture
• References
• Index
• Questions for Discussion