Qualitative Analysis for Planning & Policy

Beyond the Numbers

John Gaber

• Publisher: Routledge
• Year: 2020
• eBook ISBN: 9781000055511
• Edition: 2
• Topic: Architecture
• Subtopic: Urban Planning & Landscaping

The revised and updated second edition of Qualitative Analysis for Planning & Policy is a roadmap to help planners access qualitative data and integrate it into their planning investigations. Planning and policy decisions are not based solely on numbers, and this book equips planners with a how-to guide to see what has been missing "between the lines" of quantitative data and make good decisions using the best possible information.

Each chapter offers step-by-step instruction on how to set up and enact diverse types of qualitative research, and case studies demonstrate how qualitative research techniques can be combined with quantitative methods to tackle complex real-world projects.

For over a decade Qualitative Analysis for Planning & Policy has been an indispensable resource for students and researchers, experienced and novice planners. The revised second edition offers myriad tools to help twenty-first-century planners make intelligent decisions, including new qualitative research techniques, technological innovations, and contemporary case studies.

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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, and planning researchers examines how to use qualitative planning research to see what has been missing “between the lines” of quantitative data. 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, 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 nonquantifiable problems.

Planners do research when they need more data to make intelligent decisions and know that planning decisions are not based solely on the numbers. Because qualitative research is often deemed too costly in terms of time or money, ­planning researchers are not well trained in qualitative research strategies. They commonly mistake qualitative data as either politics, “common sense,” “street smarts,” or worse yet, file them 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 them into their planning investigations.

To respond to the oft-cited criticism that qualitative research is too resource-intensive, I offer the old adage: Pay now or pay later. A classic example is the large-scale public housing projects built in the 1950s and 1960s. Professionals now widely recognize that these projects were a bad idea. However, at the time, the 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 did 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 in 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, I first look at the overall research process or “research act.” [2] The first section of this introductory chapter discusses the research process, which includes data steps, how to develop a researchable question, how data relate to research methods, how to choose a method, and how to know whether the data are valid and reliable. The second section provides an overview of the organization of the book. Here, I 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, I provide real planning applications and examples.

DATA STEPS

The foundation of planning research is the “research act,” [3] the process of constructing research activities. The research act is divided 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 questions is: What do local business owners think about this proposal? Having established this research question, the planner then moves on to the second step in the research process and chooses a survey methodology to access the data about what the business owners think. The planner drafts a survey, asks that peers and superiors provide 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 the survey data 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 having a one-way 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 in the research process and tests the significance, validity, and reliability of the data to check on 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 planning researcher moves on to the last step. The research project concludes when the planner reports and presents the research findings to planning 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 changes to the street should consider customer access to the businesses. There will be more to this analysis later.

DEVELOPING A RESEARCHABLE QUESTION

The quality of a researchable question is based on its ability to guide the planning researcher to efficiently obtain needed data. According to Patrick White in his book Developing Research Questions (2009), good researchable questions have four key parts: (1) Clear picture of needed answer, (2) accurate location of needed data (“population of interest”), (3) good understanding of research variables, and (4) realistic assessment of the research project. (See Figure 1.1.) All investigations begin with a simple question: “what do I need to know to be able to make an informed planning or policy decision?” As obvious as it sounds, “if you do not know exactly what kind of evidence is required by your research question, you will be unable to choose the most appropriate research design and methods of data collection and analysis.” [4] Knowing what answer you are looking for does not mean you know which direction your data will point to during the data analysis stage of the investigation. For example, in a transit-oriented development research project where a community housing planner is searching for the answer to the question; “where are people coming from to access the local subway stop?”, she does not know where people are coming from at the start of her research project.

Figure 1.1 Four Parts to a Doable Research Question

It is important to know the location of your needed data. Data are everywhere and include people, buildings, images, research reports, artifacts, and meeting transcripts that can be found in multiple locations. There will most likely be several different locations/sources that will have part or all of your data. For example, in the City of Greenville, South Carolina, open space data (undeveloped land) can be found in the Departments of Community Development, GIS, Parks and Recreation, Parking, Planning and Zoning, and Public Works. Part of the function of research methods is to help you locate data that you need to answer your research question.

In developing your research question, the more you know about what is going on in the community, the more precisely you will be able to identify key issues (variables) that are impacting the community. For example, a community development planner needing information on how she can develop more housing opportunities for low- and moderate-income families will first need to have a basic land use understanding on the location of low- and moderate-income neighborhoods, schools, primary employers, public infrastructure, parks, and existing zoned land uses.

Planners and policy makers conduct applied research projects that commonly operate within tight time constraints and on limited budgets. “The time and money you have at your disposal will have fundamental implications for the kind of research questions that you can realistically address.” [5] You need to make sure that the research question you ask can be answered with a research technique that fits your timeframe and budget. Some research techniques can take months to complete (e.g. focus group investigations) while other techniques can be completed in an afternoon (e.g. photographic research).

Of the five steps in the research act, the most significant is determining what type of research methodology (methods 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, you first need to understand data. Sidestepping the philosophical debate of empirical reality, in this book I 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 researchers, we cannot understand all of the reality on a particular topic. But we can, with the right research method, collect data providing 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, I like to think of data as “data slices.” [6] (See Figure 1.2.)

Each data slice we take out of the empirical cake gives us one particular insight into reality. The type of methods we use to generate data slices determines what we know about empirical reality. The complex characteristics of reality can be loosely organized into the two types of data I 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. [7] (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 1.2 Empirical Reality Composed of Various Data Slices

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	Assume...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• List of Tables
• List of Figures
• Acknowledgements
• 1. Introduction
• 2. Field Research
• 3. Photographic Research
• 4. Focus Group Research
• 5. Content Analysis and Meta-Analysis
• 6. Getting the Big Picture
• 7. Applying Qualitative Techniques in Citizen Participation
• Index