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Data Collection
in Geography
Overview
Learning Objectives:
- What is the distinction between primary and secondary data sources?
- What are the five major types of data collection in geography?
- What are some of the ways geographers and others have made a distinction between quantitative and qualitative methods, and how do they relate to scientific and humanistic approaches in geography?
I
n the previous chapter, we explained that the empirical part of scientific research involves systematically observing cases in order to record measurements of vari- ables that reflect properties of those cases. Researchers analyze the resulting set of data (usually numbers) graphically, verbally, and mathematically in order to learn something about the properties of the cases. Data collection efforts do not generally go on continuously but are grouped into periods of activity focused on particular research issues or questions. Such a focused period of data collection and analysis is a study (in Chapter 7, we learn that there are two major categories of scientific studies, experimental and nonexperimental). In this chapter, we introduce some basic characteristics of data collection in geography, including the distinction between primary and secondary data sources, the five major types of data collec- tion, and the distinction between quantitative and qualitative methods.
CHAPTER 3
Primary and Secondary Data Sources
One way to characterize data in geography concerns whether they were collected specifically for the purpose of a researcher’s particular study. If so, we call the data primary. An example would be a geographer who interviews people about their attitudes toward bioengineered agriculture. If, instead, the data have been collected for another purpose, usually by someone other than the researcher, we call it sec- ondary. An example of that would be a geographer who uses Landsat imagery to study landslides on the California coast. The imagery was not collected by that researcher, and it was not collected primarily so he or she could study landslides. The major asset of primary data is that they are collected in a way specifically tai- lored to a particular research question, which means they are probably the data best suited to answering that question. In our attitude example above, the geographer would design the survey specifically to address the issue of attitudes toward bio- engineering and agriculture, including customizing it to fit the people answering the survey and the place where they live. But all of this takes considerable time and effort to do well. In contrast, the major asset of secondary data is that they are sometimes the only data available to address a particular research question that are even moderately suited to that question. Also, secondary data are almost always less expensive than primary data (in terms of money, time, and effort). In our landslide example, the geographer gets a very large amount of free data obtainable in some- thing like an hour or less, depending on the geographer’s units of analysis, but that geographer has to accept the way the Landsat satellite collects imagery. This includes the extent of earth surface coverage, the time the satellite passes over, the spatial resolution of the imagery, and the spectral bands recorded. Some geographers use mostly primary data, whereas others use mostly sec- ondary data. This depends mostly on the geographer’s topical area of research. However, compared to many other scientific disciplines, both human and physical geographers use a great deal of secondary data. This is probably because they so often study phenomena at large spatial and temporal scales, where it is typically so difficult and costly to collect data that a single study does not warrant it. The fact that secondary data are not tailored to the geographer’s specific research question influences the nature of many geographers’ research. Problems addressed by census data, for example, are the subject of more geographic research than is necessarily warranted from an intellectual or applied perspective. Especially characteristic of much geographic research in this respect is that researchers study problems at the analysis scale of the available data set, which is often not exactly the scale at which the phenomena operate (see Chapter 2). We consider the characteristically geo- graphic problem that results from this “data-driven” approach to science several times in the rest of the text but especially in Chapter 9.
Types of Data Collection in Geography
We can characterize data in geography more precisely than just distinguishing pri- mary from secondary. Geographers collect and analyze many different kinds of data
36 ——AN INTRODUCTION TO SCIENTIFIC RESEARCH METHODS IN GEOGRAPHY
order to produce usable data; for this reason, we discuss them in Chapter 5 along with behavioral observations. The next type of data collection is quite popular in human geography. Explicit reports are beliefs people express about things—about themselves or other people, about places or events, about activities or objects. Actually, explicit reports are also observations of behavior; answering a question on a survey is behaving, for instance. But we distinguish reports as distinct types of data collection because they always involve explicit recognition by people that researchers are studying them, and because research participants’ explicit beliefs and choices determine the data collected with explicit reports. Explicit reports such as surveys and interviews often consist of questions that have no right or wrong answers, or at least the correctness of the answer is not of chief interest to the researcher. When the explicit report consists of questions that do have right or wrong answers, and the correctness of answers is of interest to the researcher, we call the explicit report a test. That is, whereas many types of explicit reports are used to study opinions, attitudes, and preferences, tests are used to study knowledge. These measures are called “explicit” reports because people responding to them know they are responding to a request for information by a researcher. This turns out to be both an important strength and an important limitation of explicit reports, as we discuss in detail in Chapter 6. The fifth and final type of data collection is computational modeling , applied in both physical and human geography. In Chapter 2, we defined models as simplified representations of portions of reality. We noted that models can be realized in con- ceptual, physical, graphical, or computational form. Understood in this broad way, models and modeling are pervasive in geography and other sciences. We refer to them frequently in this book, in several different chapters. For instance, in Chapter 9 we discuss statistical models, and in Chapter 10 we discuss graphical models (maps are models). We consider conceptual models in several different chapters, at least implicitly. As a unique approach to data collection, computational modeling is modeling that evaluates theoretical structures and processes expressed mathemati- cally, typically in a computer. We discuss computational modeling in detail in Chapter 7, which covers research designs, because we believe it makes sense to think about modeling as an alternative to standard experimental and nonexperimental approaches. We see in Chapter 7 that we evaluate how well models fit portions of reality by comparing outputs of the model to measurements made on the reality to which the model refers. Alternatively, models are sometimes created and thought about as if they were creations of new realities rather than simulations of existing realities. We consider how this creation of “artificial realities” may or may not be thought of as scientific research in Chapter 7.
An Introduction to Quantitative
and Qualitative Methods
Geographers, and other natural and social scientists, have been collecting and analyzing all of the types of data we have just discussed for well over a century
38 ——AN INTRODUCTION TO SCIENTIFIC RESEARCH METHODS IN GEOGRAPHY
(of course, many specific techniques and procedures are regularly introduced). Besides geographers, these scientists have included geologists, biologists, oceanog- raphers, hydrologists, atmospheric scientists, anthropologists, psychologists, soci- ologists, economists, political scientists, and others. Many of these early scientists incorporated a variety of data collection techniques and a variety of data types in order to understand their phenomena of interest. In other words, early scientists of the earth and its people were unabashedly heterogeneous in their empir- ical methods, using whatever they thought provided insight into their problem domain. We enthusiastically believe that this heterogeneous approach is still the best approach. During the middle and latter part of the 20th century, characteristics of the var- ied methodological approaches applied in the sciences, particularly the social and behavioral sciences, were summarized in terms of a distinction between quantita- tive and qualitative methods. Like our definition of science in Chapter 1, the quan- titative/qualitative distinction is difficult to define in a precise way. The distinction reflects a continuum as much as two sharp categories. There are clear examples of each but also examples that are more-or-less quantitative or qualitative. A few different factors have been identified that distinguish quantitative and qualitative methods. One concerns the nature of the data recorded and analyzed in a research study. Quantitative data consist of numerical values, measured on at least an ordinal level but more likely a metric level. Qualitative data are nonnumerical, or, as in nominal data, numerical values that have no quantitative meaning. They consist of words (in natural language), drawings, photographs, and so on. However, the distinction between quantitative and qualitative methods is not just whether a researcher uses numbers or not. Another factor distinguishing the two emphasizes the data collection technique used to create the data, rather than the data itself. According to this, quantitative methods are those that impose a rel- atively great amount of prior structure on collected data. That is, such methods involve a prior choice of constructs to study, a prior choice of variables with which to measure those constructs, and prior numerical categories with which to express the measured values of those variables. Qualitative methods, in contrast, involve less prior structure on data collection. Data collection that is very clearly qualitative might start with little more than a topic area or a broad research question. The con- structs, variables, and especially the measurement values for the variables are deter- mined as observations are made or even afterward. For example, a survey that asks respondents to pick one of a finite number of predetermined categories as a way to measure their attitudes about highway construction would be relatively quantita- tive in this sense; an interview that asks respondents “how they feel” about highway construction, without any constraints on what they can give for an answer, would be relatively qualitative. Importantly, these examples also show that a single type of data collection, in this case explicit reports (Chapter 6), may be used in a relatively quantitative or qualitative way. Still another factor in differentiating quantitative and qualitative methods focuses on the analysis of data. Either methodological approach may start with relatively unstructured and open-ended responses, such as oral responses in an interview. These can be treated quantitatively, however, by rigorously coding the
Data Collection in Geography—— 39
human activity and society, and especially between geographers who focus on humans versus those who study the natural earth (unfortunately, the word “versus” sometimes fits all too well). We believe both of these positions are too extreme. Apparently unlike every- thing else in the world, humans and some other animals have agency (will)—to a degree, they determine when and how they act. Furthermore, because of brain evolution and cultural developments (including language and mathematics), human beings are, in part, semantic and semiotic entities—meaning and experi- ence, often expressed in symbolic representations, partially guide their activities and explain their geography. Not all human geographers are required to incorpo- rate such constructs in their work, by any means, but anyone who denies their rel- evance to geography is mistaken. We see no reason that scientific geographers have to ignore meaning and experience, although these constructs certainly create spe- cial intellectual and methodological challenges that biophysical scientists do not face. At the same time, there are unequal and unjust power relationships among subsets of people, cultural variations in conceptual structures, and idiosyncratic motivations among individual scientists for doing science. These do not, in our opinion, invalidate scientific approaches to understanding humans, although they certainly have implications for understanding how science should work and does work.
Review Questions
Primary and Secondary Data Sources
- What is the difference between primary and secondary data sources?
- How are primary and secondary data sources used in geographic research?
Types of Data Collection in Geography
- What are the following types of data collection in geography: physical mea- surement, observation of behavior, archives, explicit reports, computational modeling?
Quantitative and Qualitative Methods
- What are different factors that distinguish between quantitative and qualita- tive methods?
- What are some strengths and weaknesses of quantitative methods and of qualitative methods, and how do we recommend incorporating the two approaches into research?
- What role have ideas about quantitative and qualitative methods played in the 20th-century history of geography as an academic discipline?
Data Collection in Geography—— 41
Key Terms
agency: property of humans and some other animals of having at least partial self- determination of when and how to act
archives: type of data collection in which existing records that have been collected by others primarily for nonresearch purposes are analyzed, often after coding behavior: the overt and potentially observable actions or activities of individuals or groups of people, or other animals
behavioral observation: type of data collection in which ongoing behaviors are recorded and analyzed, often after coding coding: the process of categorizing qualitative records (such as behavioral record- ings, archival records, and open-ended explicit reports) in order to turn them into analyzable data
computational modeling: type of data collection involving the output of a compu- tational model, a model of theoretical structures and processes expressed in mathematical form, typically in a computer explicit reports: type of data collection in which people’s expression of their beliefs about themselves, other people, places, events, activities, or objects are recorded
physical measurement: type of data collection in which physical properties of the earth or its inhabitants are measured and analyzed primary data: data collected specifically for the purpose of a researcher’s particular study
qualitative methods: broad term referring to scientific methods that incorporate some combination of collecting nonnumerical data such as verbal or pictorial records, collecting data using relatively unstructured and open-ended appro- aches and formats, and analyzing data with nonnumerical and nonstatistical approaches; commonly used only in human geography quantitative methods: broad term referring to scientific methods that incorporate some combination of collecting numerical data such as metric-level measure- ments, collecting data using relatively structured and closed-ended approaches and formats, and analyzing data with numerical and statistical approaches; commonly used in both physical and human geography secondary data: data not collected specifically for the purpose of a researcher’s particular study but for another research or nonresearch purpose semantic: concerning meaning semiotic: concerning entities or properties that represent or stand for other entities or properties, including signs, codes, symbols, models, and so on study: unit of data collection and analysis activity focused on addressing a specific question or hypothesis
42 ——AN INTRODUCTION TO SCIENTIFIC RESEARCH METHODS IN GEOGRAPHY
