1 The Nature and Tools of
Research
In virtually every subject area, our collective knowledge about the world is incomplete: Certain
questions remain unanswered, and certain problems remain unsolved. Systematic research provides
many powerful tools—not only physical tools but also mental and social tools—that can help us
discover possible answers and identify possible solutions.
Learning Outcomes
1. 1.1 Distinguish between (a) common uses of the term research that reflect misconceptions about what research
involves and (b) the true nature of research in academic settings.
2. 1.2 Describe the cyclical, iterative nature of research, including the steps that a genuine research project
involves.
3. 1.3 Distinguish among positivism, postpositivism, constructivism, and pragmatism/realism as philosophical
underpinnings of a research project.
4. 1.4 Identify examples of how six general research tools can play significant roles in a research project: (a) the
library and its resources, (b) computer technology, (c) measurement, (d) statistics, (e) language, and (f) the
human mind.
5. 1.5 Describe steps you might take to explore research in your field.
6. In everyday speech, the word research is often used loosely to refer to a variety of activities. In some situations
the word connotes simply finding a piece of information or taking notes and then writing a so-called “research
paper.” In other situations it refers to the act of informing oneself about what one does not know, perhaps by
rummaging through available sources to locate a few tidbits of information. Such uses of the term can create
considerable confusion for university students, who must learn to use it in a narrower, more precise sense.
Yet when used in its true sense—as a systematic process that leads to new knowledge and understandings—the
word research can suggest a mystical activity that is somehow removed from everyday life. Many people
imagine researchers to be aloof individuals who seclude themselves in laboratories, scholarly libraries, or the
ivory towers of large universities. In fact, research is often a practical enterprise that—given appropriate
tools—any rational, conscientious individual can conduct. In this chapter we lay out the nature of true research
and describe the general tools that make it possible.
7.
What Research is Not
Following are three statements that describe what research is not. Accompanying each statement is an example
that illustrates a common misconception about research.
a.
Research is not merely gathering information. A sixth grader comes home from school and tells her parents,
“The teacher sent us to the library today to do research, and I learned a lot about black holes.” For this student,
research means going to the library to find a few facts. This might be information discovery, or it might be
learning reference skills. But it certainly is not, as the teacher labeled it, research.
b. Research is not merely rummaging around for hard-to-locate information. The house across the street is for
sale. You consider buying it and call your realtor to find out how much someone else might pay you for your
current home. “I’ll have to do some research to determine the fair market value of your property,” the realtor
tells you. What the realtor calls doing “some research” means, of course, reviewing information about recent
sales of properties comparable to yours; this information will help the realtor zero in on a reasonable asking
price for your own home. Such an activity involves little more than searching through various files or websites
to discover what the realtor previously did not know. Rummaging—whether through records in one’s own
office, at a library, or on the Internet—is not research. It is more accurately called an exercise in selfenlightenment.
c.
Research is not merely transporting facts from one location to another. A college student reads several
articles about the mysterious Dark Lady in William Shakespeare’s sonnets and then writes a “research paper”
describing various scholars’ suggestions of who the lady might have been. Although the student does, indeed,
go through certain activities associated with formal research—such as collecting information, organizing it in
a certain way for presentation to others, supporting statements with documentation, and referencing
statements properly—these activities do not add up to true research. The student has missed the essence of
research: the interpretation of data. Nowhere in the paper does the student say, in effect, “These facts I have
gathered seem to indicate such-and-such about the Dark Lady.” Nowhere does the student interpret and draw
conclusions from the facts. This student is approaching genuine research; however, the mere compilation of
facts, presented with reference citations and arranged in a logical sequence—no matter how polished and
appealing the format—misses genuine research by a hair. Such activity might more realistically be called fact
transcription, fact documentation, fact organization, or fact summarization.Going a little further, this student
would have traveled from one world to another: from the world of mere transportation of facts to the world of
interpretation of facts. The difference between the two worlds is the distinction between transference of
information and genuine research—a distinction that is critical for novice researchers to understand.
What Research Is
Research is a systematic process of collecting, analyzing, and interpreting information—data—in order to
increase our understanding of a phenomenon about which we are interested or concerned.1 People often use a
systematic approach when they collect and interpret information to solve the small problems of daily living.
Here, however, we focus on formal research, research in which we intentionally set out to enhance our
understanding of a phenomenon and expect to communicate what we discover to the larger scientific
community.
1 Some people in academia use the term research more broadly to include deriving new equations or abstract
principles from existing equations or principles through a sequence of mathematically logical and valid steps.
Such an activity can be quite intellectually challenging, of course, and is often at the heart of doctoral
dissertations and scholarly journal articles in mathematics, physics, and related disciplines. In this book,
however, we use the term research more narrowly to refer to empirical research—research that involves the
collection and analysis of new data.
Although research projects vary in complexity and duration, in general research involves seven distinct steps,
shown in Figure 1.1. We now look at each of these steps more closely.
FIGURE 1.1
▪ The Research Cycle
i.
The researcher begins with a problem—an unanswered question. Everywhere we look, we see things that
cause us to wonder, to speculate, to ask questions. And by asking questions, we strike a spark that ignites a
chain reaction leading to the research process. An inquisitive mind is the beginning impetus for research; as
one popular tabloid puts it, “Inquiring minds want to know!”Look around you. Consider unresolved
situations that evoke these questions: What is such-and-such a situation like? Why does such-and-such a
phenomenon occur? What does it all mean? With questions like these, research begins.
ii.
The researcher clearly and specifically articulates the goal of the research endeavor. A clear, unambiguous
statement of the problem one will address is critical. This statement is an exercise in intellectual honesty: The
ultimate goal of the research must be set forth in a grammatically complete sentence that specifically and
precisely answers the question, “What problem do you intend to solve?” When you describe your objective in
clear, concrete terms, you have a good idea of what you need to accomplish and can direct your efforts
accordingly.
iii.
The researcher often divides the principal problem into more manageable subproblems. From a design
standpoint, it is often helpful to break a main research problem into several subproblems that, when solved,
can resolve the main problem.Breaking down principal problems into small, easily solvable subproblems is a
strategy we use in everyday living. For example, suppose you want to drive from your hometown to a town
many miles or kilometers away. Your principal goal is to get from one location to the other as expeditiously
as possible. You soon realize, however, that the problem involves several subproblems:
Main problem:
How do I get from Town A to Town B?
Subproblems: i.
1. What route appears to be the most direct one?
ii.
2. Is the most direct one also the quickest one? If not, what route might take the least amount of time?
iii.
3. Which is more important to me: minimizing my travel time or minimizing my energy consumption?
iv.
4. At what critical junctions in my chosen route must I turn right or left?
What seems like a single question can be divided into several smaller questions that must be addressed before
the principal question can be resolved.So it is with most research problems. By closely inspecting the
principal problem, the researcher often uncovers important subproblems. By addressing each of the
subproblems, the researcher can more easily address the main problem. If a researcher doesn’t take the time
or trouble to isolate the lesser problems within the major problem, the overall research project can become
cumbersome and difficult to manage.Identifying and clearly articulating the problem and its subproblems are
the essential starting points for formal research. Accordingly, we discuss these processes in depth in Chapter 2.
iv.
The researcher identifies hypotheses and assumptions that underlie the research effort. Having stated the
problem and its attendant subproblems, the researcher sometimes forms one or more hypotheses about what
he or she may discover. A hypothesis is a logical supposition, a reasonable guess, an educated conjecture. It
provides a tentative explanation for a phenomenon under investigation. It may direct your thinking to
possible sources of information that will aid in resolving one or more subproblems and, as a result, may also
help you resolve the principal research problem.Hypotheses are certainly not unique to research. In your
everyday life, if something happens, you immediately try to account for its cause by making some reasonable
conjectures. For example, imagine that you come home after dark, open your front door, and reach inside for
the switch that turns on a nearby table lamp. Your fingers find the switch. You flip it. No light. At this point,
you identify several hypotheses regarding the lamp’s failure:
.
Hypothesis 1: A recent storm has disrupted your access to electrical power.
i.
Hypothesis 2: The bulb has burned out.
ii.
Hypothesis 3: The lamp isn’t securely plugged into the wall outlet.
iii.
Hypothesis 4: The wire from the lamp to the wall outlet is defective.
iv.
Hypothesis 5: You forgot to pay your electric bill.
Each of these hypotheses hints at a strategy for acquiring information that may resolve the nonfunctioninglamp problem. For instance, to test Hypothesis 1, you might look outside to see whether your neighbors have
lights, and to test Hypothesis 2, you might replace the current light bulb with a new one.Hypotheses in a
research project are as tentative as those for a nonfunctioning table lamp. For example, a biologist might
speculate that certain human-made chemical compounds increase the frequency of birth defects in frogs. A
psychologist might speculate that certain personality traits lead people to show predominantly liberal or
conservative voting patterns. A marketing researcher might speculate that humor in a television commercial
will capture viewers’ attention and thereby will increase the odds that viewers buy the advertised product.
Notice the word speculate in all of these examples. Good researchers always begin a project with open minds
about what they may—or may not—discover in their data.Hypotheses—predictions—are an essential
ingredient in certain kinds of research, especially experimental research (see Chapter 7). To a lesser degree,
they might guide other forms
v.
of research as well, but they are intentionally not identified in the early stages of some kinds of qualitative
research (e.g., see the discussion of grounded theory studies in Chapter 9).Whereas a hypothesis involves a
prediction that may or may not be supported by the data, an assumption is a condition that is taken for
granted, without which the research project would be pointless. Careful researchers—certainly those
conducting research in an academic environment—set forth a statement of their assumptions as the bedrock
upon which their study rests. For example, imagine that your problem is to investigate whether students learn
the unique grammatical structures of a language more quickly by studying only one foreign language at a
time or by studying two foreign languages concurrently. What assumptions would underlie such a problem?
At a minimum, you must assume that
i.
The teachers used in the study are competent to teach the language or languages in question and have
mastered the grammatical structures of the language(s) they are teaching.
ii.
The students taking part in the research are capable of mastering the unique grammatical structures of any
language(s) they are studying.
iii.
The languages selected for the study have sufficiently different grammatical structures that students might
reasonably learn to distinguish between them.
Aside from such basic ideas as these, however, careful researchers state their assumptions, so that other
people inspecting the research project can evaluate it in accordance with their own assumptions. For the
beginning researcher, it is better to be overly explicit than to take too much for granted.
vi.
The researcher develops a specific plan for addressing the problem and its subproblems. Research is not a
blind excursion into the unknown, with the hope that the data necessary to address the research problem will
magically emerge. It is, instead, a carefully planned itinerary of the route you intend to take in order to reach
your final destination—your research goal. Consider the title of this text: Practical Research: Planning and
Design. The last three words—Planning and Design—are especially important ones. Researchers plan their
overall research design and specific research methods in a purposeful way so that they can acquire data
relevant to their research problem and subproblems. Depending on the research question, different designs
and methods are more or less appropriate.In the formative stages of a research project, much can be decided:
Are any existing data directly relevant to the research problem? If so, where are they, and are you likely to
have access to them? If the needed data don’t currently exist, how might you generate them? And later, after
you have acquired the data you need, what will you do with them?2 Such questions merely hint at the fact
that planning and design cannot be postponed. Each of the questions just listed—and many more—must
have an answer early in the research process. In Chapter 4, we discuss several general issues related to
research planning. Then, beginning in Chapter 6, we describe strategies related to various research
methodologies.2 As should be apparent in the questions posed in this paragraph, we are using the
word data as a plural noun; for instance, we ask “Where are the data?” rather than “Where is the data?”
Contrary to popular usage of the term as a singular noun, data (which has its origins in Latin) refers to two
or more pieces of information. A single piece of information is known as a datum, or sometimes as a data
point.
vii.
The researcher collects, organizes, and analyzes data related to the problem and its subproblems. After a
researcher has isolated the problem, divided it into appropriate subproblems, identified hypotheses and
assumptions, and chosen a suitable design and methodology, the next step is to collect whatever data might
be relevant to the problem and to organize and analyze them in meaningful ways.The data collected in
research studies take one of two general forms. Quantitative research involves looking at amounts,
or quantities, of one or more variables of interest. A quantitative researcher typically tries to measure
variables in some numerical way, perhaps by using commonly accepted measures of the physical world (e.g.,
rulers, thermometers, oscilloscopes) or carefully designed measures of psychological characteristics or
behaviors (e.g., tests, questionnaires, rating scales).In contrast, qualitative research involves looking at
characteristics, or qualities, that cannot be entirely reduced to numerical values. A qualitative researcher
typically aims to examine the many nuances and complexities of a particular phenomenon. You are most
likely to see qualitative research in studies of complex human situations (e.g., people’s in-depth perspectives
about a particular issue, the behaviors and values of a particular cultural group) or complex human creations
(e.g., television commercials, works of art). Qualitative research is not limited to research problems
involving human beings, however. For instance, some biologists study, in a distinctly qualitative manner, the
complex social behaviors of other animal species; Dian Fossey’s work with gorillas and Jane Goodall’s
studies of chimpanzees are two well-known examples (e.g., see Fossey, 1983; Goodall, 1986).The two kinds
of data—quantitative and qualitative—often require distinctly different research methods and data analysis
strategies. Accordingly, three of the book’s subsequent chapters focus predominantly on quantitative
techniques (see Chapters 6, 7, and 8) and three others focus largely on qualitative techniques
(see Chapters 9, 10, and 11). Nevertheless, we urge you not to think of the quantitative–qualitative distinction
as a mutually exclusive, it-has-to-be-one-thing-or-the-other dichotomy. Many researchers collect both
quantitative and qualitative data in a single research project—an approach sometimes known as mixedmethods research (see Chapter 12). Good researchers tend to be eclectic researchers who draw from diverse
methodologies and data sources in order to best address their research problems and questions (e.g., see
Gorard, 2010; Onwuegbuzie & Leech, 2005).
viii.
The researcher interprets the meaning of the data as they relate to the problem and its
subproblems. Quantitative and qualitative data are, in and of themselves, only data—nothing more. The
significance of the data depends on how the researcher extracts meaning from them. In research,
uninterpreted data are worthless: They can never help us answer the questions we have posed.Yet
researchers must recognize and come to terms with the subjective and dynamic nature of interpretation.
Consider, for example, the many books written on the assassination of U.S. President John F. Kennedy.
Different historians have studied the same events: One may interpret them one way, and another may arrive
at a very different conclusion. Which one is right? Perhaps they both are; perhaps neither is. Both may have
merely posed new problems for other historians to try to resolve. Different minds often find different
meanings in the same set of facts.Once we believed that clocks measured time and that yardsticks measured
space. In one sense, they still do. We further assumed that time and space were two different entities. Then
along came Einstein’s theory of relativity, and time and space became locked into one concept: the time–
space continuum. What’s the difference between the old perspective and the new one? It’s the way we think
about, or interpret, the same information. The realities of time and space have not changed; the way we
interpret them has.Data demand interpretation. But no rule, formula, or algorithm can lead the researcher
unerringly to a correct interpretation. Interpretation is inevitably a somewhat subjective process that depends
on the researcher’s hypotheses, assumptions, and logical reasoning processes.Now think about how we
began this chapter. We suggested that certain activities cannot accurately be called research. At this point
you can understand why. None of those activities demands that the researcher draw any conclusions or make
any interpretations of the data.
We must emphasize two important points related to the seven-step process just described. First, the process
isiterative: A researcher sometimes needs to move back and forth between two or more steps along the way.
For example, while developing a specific plan for a project (Step 5), a researcher might realize that a genuine
resolution of the research problem requires addressing a subproblem not previously identified (Step 3). And
while interpreting the collected data (Step 7), a researcher may decide that additional data are needed to fully
resolve the problem (Step 6).
Second, the process is cyclical. The final step in the process depicted in Figure 1.1— interpretation of the
data—is not really the final step at all. Only rarely is a research project a one-shot effort that completely
resolves a problem. For instance, even with the best of data, hypotheses in a research project are rarely
proved or disproved—and thus research questions are rarely answered—beyond a shadow of a doubt. Instead,
hypotheses are either supported or not supported by the data. If the data are consistent with a particular
hypothesis, the researcher can make a case that the hypothesis probably has some merit and should be taken
seriously. In contrast, if the data run contrary to a hypothesis, the researcher rejects the hypothesis and turns
to other hypotheses as being more likely explanations of the phenomenon in question. In either case, one or
more additional, follow-up studies are called for.
Ultimately, then, most research studies don’t bring total closure to a research problem. There is no obvious
end point—no point at which a researcher can say “Voila! I’ve completely answered the question about
which I’m concerned.” Instead, research typically involves a cycle—or more accurately, a helix (spiral)—in
which one study spawns additional, follow-up studies. In exploring a topic, one comes across additional
problems that need resolving, and so the process must begin anew. Research begets more research.
To view research in this way is to invest it with a dynamic quality that is its true nature—a far cry from the
conventional view, which sees research as a one-time undertaking that is static, self-contained, an end in
itself. Here we see another difference between true research and the nonexamples of research presented
earlier in the chapter. Every researcher soon learns that genuine research is likely to yield as many problems
as it resolves. Such is the nature of the acquisition of knowledge.
Philosophical Assumptions Underlying
Research Methodologies
Let’s return to Step 4 in the research process: The researcher identifies hypotheses and assumptions that
underlie the research effort. The assumptions underlying a research project are sometimes so seemingly selfevident that a researcher may think it unnecessary to mention them. In fact, the researcher may not even be
consciously aware of them! For example, two general assumptions underlie many research studies:
ix.
▪ The phenomenon under investigation is somewhat lawful and predictable; it is not comprised of completely
random events.
x.
▪ Cause-and-effect relationships can account for certain patterns observed in the phenomenon.
But are such assumptions justified? Is the world a lawful place, with some things definitely causing or
influencing others? Or are definitive laws and cause-and-effect relationships nothing more than figments of
our fertile human imaginations?
As we consider such questions, it is helpful to distinguish among different philosophical orientations3 that
point researchers in somewhat different directions in their quests to make sense of our physical, social, and
psychological worlds. Historically, a good deal of research in the natural sciences has been driven by a
perspective known as positivism. Positivists believe that, with appropriate measurement tools, scientists can
objectively uncover absolute, undeniable truths about cause-and-effect relationships within the physical
world and human experience.
3 Some writers use terms such as worldviews, epistemologies, or paradigms instead of the term philosophical
orientations.
In the social sciences, most researchers have been less self-assured and more tentative, especially within the
past few decades. Some social scientists take a perspective known as postpositivism, believing that true
objectivity in seeking absolute truths can be an elusive goal. Although researchers might strive for objectivity
in their collection and interpretation of data, they inevitably bring certain biases to their investigations—
perhaps biases regarding the best ways to measure certain variables or the most logical inferences to draw
from patterns within the data. From a postpositivist perspective, progress toward genuine understandings of
physical, social, and psychological phenomena tends to be gradual and probabilistic. For example, recall the
earlier discussion of hypotheses being either supported or not supported by data. Postpositivists don’t say,
“I’ve just proven such-and-such.” Rather, they’re more likely to say, “This increases the probability that
such-and-such is true.”
Still other researchers have abandoned any idea that absolute truths are somewhere “out there” in the world,
waiting to be discovered. In this perspective, known as constructivism, the “realities” researchers identify are
nothing more than human creations that can be helpful in finding subjective meanings within the data
collected. Constructivists not only acknowledge that they bring certain biases to their research endeavors but
also try to be as upfront as possible about these biases. The emphasis on subjectivity and bias—rather than
objectivity—applies to the phenomena that constructivist researchers study as well. By and large,
constructivists focus their inquiries on people’s perceptions and interpretations of various phenomena,
including individuals’ behaviors, group processes, and cultural practices.
Many of the quantitative methodologies described in this book have postpositivist, probabilistic
underpinnings—a fact that becomes especially evident in the discussion of statistics in Chapter 8. In contrast,
some qualitative methodologies have a distinctly constructivist bent, with a focus on ascertaining
people’s beliefs about truth, rather than trying to pin down absolute, objective truths that might not exist at
all.
Yet once again we urge you not to think of quantitative research and qualitative research as reflecting a
mutually exclusive, either-this-or-that dichotomy. For instance, some quantitative researchers approach a
research problem from a constructivist framework, and some qualitative researchers tend to think in a
postpositivist manner. Many researchers acknowledge both that (a) absolute truths regarding various
phenomena may actually exist—even if they are exceedingly difficult to discover—and (b) human beings’
self-constructed beliefs about those phenomena are legitimate objects of study in their own right. You might
see the labels pragmatism and realism used in reference to such a philosophical orientation (e.g., see R. B.
Johnson & Onwuegbuzie, 2004; Maxwell & Mittapalli, 2010).
Tools of Research
Every professional needs specialized tools in order to work effectively. Without hammer and saw, the
carpenter is out of business; without scalpel or forceps, the surgeon cannot practice. Researchers, likewise,
have their own set of tools to carry out their plans.
The tools that researchers use to achieve their research goals can vary considerably depending on the
discipline. A microbiologist needs a microscope and culture media; an attorney needs a library of legal
decisions and statute law. By and large, we do not discuss such discipline-specific tools in this book. Rather,
our concern here is with general tools of research that the great majority of researchers of all disciplines need
in order to collect data and derive meaningful conclusions.
We should be careful not to equate the tools of research with the methodology of research. A research tool is
a specific mechanism or strategy the researcher uses to collect, manipulate, or interpret data. The research
methodology is the general approach the researcher takes in carrying out the research project; to some extent,
this approach dictates the particular tools the researcher selects.
Confusion between the tool and the research method is immediately recognizable. Such phrases as “library
research” and “statistical research” are telltale signs and largely meaningless terms. They suggest a failure to
understand the nature of formal research, as well as a failure to differentiate between tool and method. The
library is merely a place for locating or discovering certain data that will be analyzed and interpreted at some
point in the research process. Likewise, statistics merely provide ways to summarize and analyze data,
thereby allowing us to see patterns within the data more clearly.
Six general tools of research are these:
xi.
The library and its resources
xii.
Computer technology
xiii.
Measurement
xiv.
Statistics
xv.
Language
xvi.
The human mind
In the following sections, we look more closely at each of these general tools.
The Library and Its Resources
Historically, many literate human societies used libraries to assemble and store their collective knowledge.
For example, in the seventh century B.C., the ancient Assyrians’ Library of Nineveh contained 20,000 to
30,000 tablets, and in the second century A.D., the Romans’ Library of Celsus in Ephesus housed more than
12,000 papyrus scrolls and, in later years, parchment books as well.4
4 Many academic scholars would instead say “seventh century BCE” and “second century CE” in this
sentence, referring to the more religiously neutral terms Before Common Era and Common Era. However, we
suspect that some of our readers are unfamiliar with these terms, hence our use of the more traditional ones.
Until the past few decades, libraries were primarily repositories of concrete, physical representations of
knowledge—clay tablets, scrolls, manuscripts, books, journals, films, and the like. For the most part, any
society’s collective knowledge expanded rather slowly and could seemingly be contained within masonry
walls. But by the latter half of the 20th century, people’s knowledge about their physical and social worlds
began to increase many times over, and at the present time it continues to increase at an astounding rate. In
response, libraries have evolved in important ways. First, they have made use of many emerging technologies
(e.g., microforms, CDs, DVDs, online databases) to store information in more compact forms. Second, they
have provided increasingly fast and efficient means of locating and accessing information on virtually any
topic. And third, many of them have made catalogs of their holdings available on the Internet. The libraries of
today—especially university libraries—extend far beyond their local, physical boundaries.
We explore efficient use of a library and its resources in depth in Chapter 3. For now, we simply want to stress
that the library is—and must be—one of the most valuable tools in any researcher’s toolbox.
Computer Technology
As a research tool, the personal computer is now commonplace. Personal computers have become
increasingly compact and portable—first in the form of laptops and more recently in the forms of iPads, other
tablet computers, and smartphones. In addition, computer software packages and applications have become
increasingly user friendly, such that novice researchers can easily take advantage of them. But like any tool—
no matter how powerful—computer technology has its limitations. Yes, computers can certainly calculate,
compare, search, retrieve, sort, and organize data more efficiently and accurately than you can. But in their
present stage of development, they depend largely on people to give them directions about what to do.
A computer is not a miracle worker—it cannot do your thinking for you. It can, however, be a fast and
faithful assistant. When told exactly what to do, it is one of the researcher’s best friends. Table 1.1 provides
suggestions for how you might use computer technology as a research tool.
USING TECHNOLOGY
Measurement
Especially when conducting quantitative research, a researcher needs a systematic way of measuring the
phenomena under investigation. Some common, everyday measurement instruments—rulers, scales,
stopwatches—can occasionally be helpful for measuring easily observable variables, such as length, weight,
or time. But in most cases, a researcher needs one or more specialized instruments. For example, an
astronomer might need a high-powered telescope to detect patterns of light in the night sky, and a
neurophysiologist might need a magnetic resonance imaging (MRI) machine to detect and measure neural
activity in the brain.
TABLE 1.1
▪ The Computer as a Research Tool
Part of the Study
Planning the study
Relevant Technological Support Tools
xvii.
Brainstorming assistance—software used to help generate and organize ideas related to the res
research strategies, or both.
xviii.
Outlining assistance—software used to help structure various aspects of the study and focus w
xix.
Project management assistance—software used to schedule and coordinate varied tasks that m
timely manner.
xx.
Budget assistance—spreadsheet software used to help in outlining, estimating, and monitoring
costs involved in the research effort.
Literature review
xxi.
Literature identification assistance—online databases used to help identify relevant research s
considered during the formative stages of the research endeavor.
xxii.
Communication assistance—computer technology used to communicate with other researcher
pursuing similar topics (e.g., e-mail, Skype, electronic bulletin boards, list servers).
xxiii.
Writing assistance—software used to facilitate the writing, editing, formatting, and citation m
the literature review.
Study implementationxxiv.
and
Materials production assistance—software used to develop instructional materials, visual disp
data gathering
simulations, or other stimuli to be used in experimental interventions.
Part of the Study
Relevant Technological Support Tools
xxv.
Experimental control assistance—software used to physically control the effects of specific va
minimize the influence of potentially confounding variables.
xxvi.
Survey distribution assistance—databases and word processing software used in combination
communications to a targeted population.
xxvii.
Online data collection assistance—websites used to conduct surveys and certain other types o
Internet.
xxviii.
Data collection assistance—software used to take field notes or to monitor specific types of re
by participants in a study.
Analysis and interpretation
xxix.
Organizational assistance—software used to assemble, categorize, code, integrate, and search
data sets (such as qualitative interview data or open-ended responses to survey questions).
xxx.
Conceptual assistance—software used to write and store ongoing reflections about data or to c
theories that integrate research findings.
xxxi.
Statistical assistance—statistical and spreadsheet software packages used to categorize and an
types of data sets.
Reporting
xxxii.
Graphic production assistance—software used to depict data in graphic form to facilitate inter
xxxiii.
Communication assistance—telecommunication software used to distribute and discuss resear
initial interpretations with colleagues and to receive their comments and feedback.
xxxiv.
Writing and editing assistance—word processing software used to write and edit successive d
report.
xxxv.
Dissemination assistance—desktop publishing software and poster creation software used to p
professional-looking documents and posters that can be displayed or distributed at conference
xxxvi.
Presentation graphics assistance—presentation software used to create static and animated slid
conference presentations.
xxxvii.
Networking assistance—blogs, social networking sites, and other Internet-based mechanisms
communicate one’s findings to a wider audience and to generate discussion for follow-up stud
the field.
In quantitative research, social and psychological phenomena require measurement as well, even though they
have no concrete, easily observable basis in the physical world. For example, an economist might use the
Dow-Jones Industrial Average or NASDAQ index to track economic growth over time, a sociologist might
use a questionnaire to assess people’s attitudes about marriage and divorce, and an educational researcher
might use an achievement test to measure the extent to which school children have learned something.
Finding or developing appropriate measurement instruments for social and psychological phenomena can
sometimes be quite a challenge. Thus, we explore measurement strategies in some depth when we discuss the
research planning process in Chapter 4.
Statistics
Statistics tend to be more useful in some academic disciplines than in others. For instance, researchers use
them quite often in such fields as psychology, medicine, and business; they use statistics less frequently in
such fields as history, musicology, and literature.
Statistics have two principal functions: to help a researcher (a) describe quantitative data and (b) draw
inferences from these data. Descriptive statistics summarize the general nature of the data obtained—for
instance, how certain measured characteristics appear to be “on average,” how much variability exists within
a data set, and how closely two or more characteristics are associated with one another. In contrast, inferential
statistics help the researcher make decisions about the data. For example, they might help a researcher decide
whether the differences observed between two experimental groups are large enough to be attributed to the
differing experimental interventions rather than to a once-in-a-blue-moon fluke. Both of these functions of
statistics ultimately involve summarizing the data in some way.
In the process of summarizing data, statistical analyses often create entities that have no counterpart in
reality. Let’s take a simple example: Four students have part-time jobs on campus. One student works 24
hours a week in the library, a second works 22 hours a week in the campus bookstore, a third works 12 hours
a week in the parking lot, and the fourth works 16 hours a week in the cafeteria. One way of summarizing the
students’ work hours is to calculate the arithmetic mean.5 By doing so, we find that the students work, “on
average,” 18.5 hours a week. Although we have learned something about these four students and their
working hours, to some extent we have learned a myth: None of these students has worked exactly 18.5 hours
a week. That figure represents absolutely no fact in the real world.
5 When the word arithmetic is used as an adjective, as it is here, it is pronounced with emphasis on the third
syllable (“ar-ith-MET-ic”).
If statistics offer only an unreality, then why use them? Why create myth out of hard, demonstrable data? The
answer lies in the nature of the human mind. Human beings can cognitively think about only a very limited
amount of information at any single point in time.6 Statistics help condense an overwhelming body of data
into an amount of information that the mind can more readily comprehend and deal with. In the process, they
can help a researcher detect patterns and relationships in the data that might otherwise go unnoticed. More
generally, statistics help the human mind comprehend disparate data as an organized whole.
6 If you have some background in human memory and cognition, you may realize that we are talking about
the limited capacity of working memory here (e.g., see Cowan, 2010; G. A. Miller, 1956).
Any researcher who uses statistics must remember that calculating statistical values is not—and must not
be—the final step in a research endeavor. The ultimate question in research is, What do the data
indicate? Statistics yield information about data, but conscientious researchers are not satisfied until they
determine the meaning of this information.
Although a book such as this one cannot provide all of the nitty-gritty details of statistical analysis, we give
you an overview of potentially useful statistical techniques in Chapter 8.
Language
One of humankind’s greatest achievements is language. Not only does it allow us to communicate with one
another but it also enables us to think more effectively. People can often think more clearly and efficiently
about a topic when they can represent their thoughts in their heads with specific words and phrases.
For example, imagine that you’re driving along a country road. In a field to your left, you see an object with
the following characteristics:
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xxxix.
▪ Black and white in color, in a splotchy pattern
▪ Covered with a short, bristly substance
xl.
▪ Appended at one end by something similar in appearance to a paintbrush
xli.
▪ Appended at the other end by a lumpy thing with four smaller things coming out of its top (two soft and
floppy; two hard, curved, and pointed)
xlii.
▪ Held up from the ground by four spindly sticks, two at each end
Unless you have spent most of your life living under a rock, you would almost certainly identify this object as
a cow.
Words—even those as simple as cow—and the concepts that the words represent enhance our thinking in
several ways (J. E. Ormrod, 2012; also see Jaccard & Jacoby, 2010):
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Words reduce the world’s complexity. Classifying similar objects and events into categories and assigning
specific words to those categories can make our experiences easier to make sense of. For instance, it’s much
easier to think to yourself, “I see a herd of cows,” than to think, “There is a brown object, covered with
bristly stuff, appended by a paintbrush and a lumpy thing, and held up by four sticks. Ah, yes, and I also see
a black-and-white spotted object, covered with bristly stuff, appended by a paintbrush and a lumpy thing,
and held up by four sticks. And over there is a brown-and-white object . . . .”
xliv.
Words allow abstraction of the environment. An object that has bristly stuff, a paintbrush at one end, a
lumpy thing at the other, and four spindly sticks at the bottom is a concrete entity. The
concept cow, however, is more abstract: It connotes such characteristics as female, supplier of milk, and, to
the farmer or rancher, economic asset. Concepts and the labels associated with them allow us to think about
our experiences without necessarily having to consider all of their discrete, concrete characteristics.
xlv.
Words enhance the power of thought. When you are thinking about an object covered with bristly stuff,
appended by a paintbrush and a lumpy thing, held up by four sticks, and so on, you can think of little else (as
mentioned earlier, human beings can think about only a very limited amount of information at any one time).
In contrast, when you simply think cow, you can easily think about other ideas at the same time and perhaps
form connections and interrelationships among them in ways you hadn’t previously considered.
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Words facilitate generalization and inference drawing in new situations. When we learn a new concept, we
associate certain characteristics with it. Then, when we encounter a new instance of the concept, we can
draw on our knowledge of associated characteristics to make assumptions and inferences about the new
instance. For instance, if you see a herd of cattle as you drive through the countryside, you can infer that you
are passing through either dairy or beef country, depending on whether you see large udders hanging down
between two of the spindly sticks.
Just as cow helps us categorize certain experiences into a single idea, so, too, does the terminology of your
discipline help you interpret and understand your observations. The words tempo, timbre, and perfect
pitch are useful to the musicologist. Such terms as central business district, folded mountain, and distance to
k have special meaning for the geographer. The terms lesson plan, portfolio, and charter school communicate
a great deal to the educator. Learning the specialized terminology of your field is indispensable to conducting
a research study, grounding it in prior theories and research, and communicating your results to others.
Two outward manifestations of language usage are also helpful to the researcher: (a) knowing two or more
languages and (b) writing one’s thoughts either on paper or in electronic form.
The Benefits of Knowing Two or More
Languages
It should go without saying that not all important research is reported in a researcher’s native tongue.
Accordingly, many doctoral programs require that students demonstrate reading competency in one or two
foreign languages in addition to their own language. The choice of these languages is usually linked to the
area of proposed research.
The language requirement is a reasonable one. Research is and always has been a worldwide endeavor. For
example, researchers in Japan have made gigantic strides in electronics and robotics. And two of the most
influential theorists in child development today—Jean Piaget and Lev Vygotsky—wrote in French and
Russian, respectively. Many new discoveries are first reported in a researcher’s native language.
Knowing two or more languages has a second benefit as well: Words in a second language may capture
the meaning of certain phenomenon in ways that one’s native tongue may not. For example, the German
word Gestalt—which roughly means “organized whole”—has no direct equivvalent in English. Thus, many
English-speaking psychologists use this word when describing the nature of human perception, because
people often perceive organized patterns and structures in visual data that, in the objective physical world,
are not organized. Likewise, the Zulu word ubuntu defies an easy translation into English. This word—which
reflects the belief that people become fully human largely through regularly caring for others and
contributing to the common good—can help anthropologists and other social scientists capture a cultural
worldview quite different from the more self-centered perspective so prevalent in mainstream Western
culture.
The Importance of Writing
To be generally accessible to the larger scientific community and ultimately to society as a whole, all
research must eventually be presented as a written document—a research report—either on paper or in
electronic form. A basic requirement for writing such a report is the ability to use language in a clear,
coherent manner.
Although a good deal of conventional wisdom tells us that clear thinking precedes clear writing, in fact
writing can be a productive form of thinking in and of itself. When you write your ideas down on paper, you
do several things:
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▪ You must identify the specific ideas you do and do not know about your topic.
xlviii.
▪ You must clarify and organize your thoughts sufficiently to communicate them to your readers.
xlix.
▪ You may detect gaps and logical flaws in your thinking.
Perhaps it isn’t surprising, then, that writing about a topic actually enhances the writer’s understanding of the
topic (e.g., Kellogg, 1994; Shanahan, 2004).
If you wait until all your thoughts are clear before you start writing, you may never begin. Thus, we
recommend that you start writing parts of your research proposal or report as soon as possible. Begin with a
title and a purpose statement for your study. Commit your title to paper; keep it in plain sight as you focus
your ideas. Although you may very well change the title later as your research proceeds, creating a working
title in the early stages can provide both focus and direction. And when you can draft a clear and concise
statement that begins, “The purpose of this study is . . .,” you are well on your way to planning a focused
research study.
PRACTICAL APPLICATION Communicating
Effectively Through Writing
In our own experiences, we authors have found that most students have a great deal to learn about what good
writing entails. Yet we also know that with effort, practice, mentoring, and regular feedback,
students can learn to write more effectively. Subsequent chapters present specific strategies for writing
literature reviews (Chapter 3), research proposals (Chapter 5), and research reports (Chapter 13). Here we offer
general strategies for writing in ways that can help you clearly communicate your ideas and reasoning to
others. We also offer suggestions for making the best use of word processing software.
GUIDELINES Writing to Communicate
The following guidelines are based on techniques often seen in effective writing. Furthermore, such
techniques have consistently been shown to facilitate readers’ comprehension of what people have written
(e.g., see J. E. Ormrod, 2012).
l.
Be specific and precise. Precision is of utmost importance in all aspects of a research endeavor, including
writing. Choose your words and phrases carefully so that you communicate your exact meaning, not some
vague approximation. Many books and online resources offer suggestions for writing clear, concise
sentences and combining them into unified and coherent paragraphs (e.g., see the sources in the “For Further
Reading” list at the end of the chapter).
li.
Continually keep in mind your primary objective in writing your paper, and focus your discussion
accordingly. All too often, novice researchers try to include everything they have learned—both from their
literature review and from their data analysis—in their research reports. But ultimately, everything you say
should relate either directly or indirectly to your research problem. If you can’t think of how something
relates, leave it out! You will undoubtedly have enough things to write about as it is.
lii.
Provide an overview of what you will be talking about in upcoming pages. Your readers can more effectively
read your work when they know what to expect as they read. Providing an overview of what topics you will
discuss and in what order—and possibly also showing how the various topics interrelate—is known as
an advance organizer. As an example, Dinah Jackson, a doctoral student in educational psychology, was
interested in the possible effects of self-questioning—asking oneself questions about material one is
studying—on college students’ note taking. Jackson began her dissertation’s “Review of the Literature” with
the following advance organizer:The first part of this review will examine the theories, frameworks, and
experimental research behind the research on adjunct questioning. Part two will investigate the transition of
adjunct questioning to self-generated questioning. Specific models of self-generated questioning will be
explored, starting with the historical research on question position [and progressing] to the more
contemporary research on individual differences in self-questioning. Part three will explore some basic
research on note taking and tie note taking theory with the research on self-generated questioning. (Jackson,
1996, p. 17)
liii.
Organize your ideas into general and more specific categories, and use headings and subheadings to guide
your readers through your discussion of these categories. We authors have read many student research
reports that seem to wander aimlessly and unpredictably from one thought to another, without any obvious
organizational structure directing the flow of ideas. Using headings and subheadings is one simple way to
provide an organizational structure for your writing and to make that structure crystal clear to others.
liv.
Use concrete examples to make abstract ideas more understandable. There’s a fine line between being
abstract and being vague. Even as scholars who have worked in our respective academic disciplines for
many years, we authors still find that we can more easily understand something when the writer gives us a
concrete example to illustrate an abstract idea. As an example, we return to Jackson’s dissertation on selfquestioning and class note taking. Jackson made the point that how a researcher evaluates, or codes, the
content of students’ class notes will affect what the researcher discovers about those notes. More
specifically, she argued that only a superficial coding scheme (e.g., counting the number of main ideas
included in notes) would fail to capture the true quality of the notes. She clarified her point with a concrete
example:For example, while listening to the same lecture, Student A may record only an outline of the
lecture, whereas Student B may record an outline, examples, definitions, and mnemonics. If a researcher
only considered the number of main ideas that students included in their notes, then both sets of notes might
be considered equivalent, despite the fact that the two sets differ considerably in the type of material
recorded. (Jackson, 1996, p. 9)
lv.
Use figures and tables to help you more effectively present or organize your ideas and findings.Use figures
and tables to help you more effectively present or organize your ideas and findings. Although the bulk of
your research proposal or report will almost certainly be prose, in many cases it might be helpful to present
some information in figure or table form. For example, as you read this book, look at the variety of
mechanisms we use to accompany our prose, including art, diagrams, graphs, and summarizing tables. We
hope you will agree that these mechanisms help you understand and organize some of the ideas we present.
lvi.
At the conclusion of a chapter or major section, summarize what you have said. You will probably be
presenting a great deal of information in any research proposal or report that you write. Summarizing what
you have said in preceding paragraphs or pages helps your readers identify the things that are, in your mind,
the most important things for them to remember. For example, in a dissertation that examined children’s
beliefs about the mental processes involved in reading, Debby Zambo summarized a lengthy discussion
about the children’s understanding of what it means to pay attention:In sum, the students understand
attention to be a mental process. They know their attention is inconsistent and affected by emotions and
interest. They also realize that the right level of material, amount of information, and length of time helps
their attention. The stillness of reading is difficult for some of the students but calming for others, and they
appear to know this, and to know when reading will be difficult and when it will be calming. This idea is
contrary to what has been written in the literature about struggling readers. (Zambo, 2003, p. 68)
lvii.
Anticipate that you will almost certainly have to write multiple drafts. All too often, we authors have had
students submit research proposals, theses, or dissertations with the assumption that they have finished their
task. Such students have invariably been disappointed— sometimes even outraged—when we have asked
them to revise their work, usually several times. The need to write multiple drafts applies not only to novice
researchers but to experienced scholars as well. For instance, we would hate to count the number of times
this book has undergone revision—certainly far more often than the label “eleventh edition” indicates!
Multiple revisions enable you to reflect on and critically evaluate your own writing, revise and refocus
awkward passages, get feedback from peers and advisors who can point out where a manuscript has gaps or
lacks clarity, and in other ways ensure that the final version is as clear and precise as possible.
lviii.
Fastidiously check to be sure that your final draft uses appropriate grammar and punctuation, and check
your spelling. Appropriate grammar, punctuation, and spelling are not just bothersome formalities. On the
contrary, they help you better communicate your meanings. For example, a colon announces that what
follows it explains the immediately preceding statement; a semicolon communicates that a sentence includes
two independent clauses (as the semicolon in this sentence does!).Correct grammar, punctuation, and
spelling are important for another reason as well: They communicate to others that you are a careful and
disciplined scholar whose thoughts and work are worth reading about. If, instead, you mispel menny of yur
words—as we our doing in this sentance—your reeders may quikly discredit you as a sloppy resercher who
shuldn’t be taken seriusly!Many style manuals, such as those in the “For Further Reading” list at the end of
this chapter, have sections dealing with correct punctuation and grammar. In addition, dictionaries and word
processing spell-check functions can obviously assist you in your spelling.
USING TECHNOLOGY
GUIDELINES Using the Tools in Word Processing
Software
Most of our readers know the basics of using word processing software—for instance, how to “copy,”
“paste,” and “save”; how to choose a particular font and font size; and how to format text
as italicized, underlined, or boldface. Following are specific features and tools that you may not have
routinely used in previous writing projects but that can be quite useful in writing research reports:
lix.
▪ Outlining. An “outlining” feature lets you create bullets and subbullets to organize your thoughts. (In
Microsoft Word, you can find this tool under the “View” pull-down menu at the top of the screen.)
lx.
▪ Setting headers and footers. A “header” is a line or two at the top of the page that appears on every page; a
“footer” appears at the bottom of each page. For example, using the “insert date” function, you might create
a header that includes the specific date on which you are writing a particular draft. And using an “insert page
number” function will add appropriate numbers to the tops or bottoms of successive pages.
lxi.
▪ Creating tables. Using a “table” feature, you can create a table with the number of rows and columns you
need. You can easily adjust the widths of various columns; format the text within each table cell; add new
rows or tables; and merge two or more cells into a single, larger cell. Usually an “autoformat” option will
give you many possible table formats from which to choose.
lxii.
▪ Inserting graphics. You are likely to find a variety of options under an “Insert” pull-down menu. Some of
these options enable you to insert diagrams, photographs, charts, and other visuals you have created
elsewhere. (For instance, in Microsoft Word, you might explore the possibilities within the “insert picture”
and “insert object” options.)
lxiii.
▪ Creating footnotes. Footnotes are easy to create using an “insert footnote” feature. Typically you can
choose the symbols to be used in designating footnotes—perhaps 1, 2, 3, . . ., a, b, c, . . ., or special symbols
such as * and †.
lxiv.
▪ Using international alphabets and characters. Computers and computer software sold in English-speaking
countries have the English alphabet as the default alphabet, but often either your word processing software
or your “system preferences” on your computer’s operating system will let you choose a different alphabet
(e.g., Turkish, as in the surname Kagˇitçibas i) or certain characters (e.g., in Chinese or Japanese) for
particular words or sections of text.
lxv.
▪ Tracking changes. A “track changes” feature enables you to keep a running record of specific edits you
have made to a document; you can later go back and either “accept” or “reject” each change. This feature is
especially useful when two or more researchers are coauthoring a report: It keeps track of who made which
changes and the date on which each change was made.
We offer three general recommendations for using a word processor effectively.
lxvi.
Save and back up your document frequently. We authors can recall a number of personal horror stories we
have heard (and in some cases experienced ourselves) about losing data, research materials, and other
valuable information. Every computer user eventually encounters some type of glitch that causes problems
in information retrieval. Whether the electricity goes out before you can save a file, a misguided keystroke
leads to a system error, or your personal computer inexplicably crashes, things you have written sometimes
get lost. It’s imperative that you get in the habit of regularly saving your work. Save multiple copies so that
if something goes awry in one place, you will always have a backup in a safe location. Here are a few things
to think about:
.
Save your work-in-progress frequently, perhaps every 5 to 10 minutes. Many software programs will do this
for you automatically if you give them instructions about whether and how often to do it.
i.
Save at least two copies of important files, and save them in different places—perhaps one file at home and
another at the office, at a relative’s house, in a safe deposit box, or somewhere in cyberspace. One option is
to save documents on a flash drive or external hard drive. Another is to copy them to an electronic dropbox,
iCloud (for Macintosh), or other Internet-based storage mechanism. One of us authors uses a flash drive to
back up much of her past work (including several book manuscripts) and any in-progress work; she keeps
this flash drive in her purse and takes it everywhere she goes. Also, she occasionally sends herself inprogress documents as attachments to self-addressed e-mail messages—giving her an almost-current backup
version of the documents in the event that an unintended keystroke somehow wreaks havoc on what she has
written.
ii.
Save various versions of your work with titles that help you identify each version—for instance, by
including the date on which you completed each file.
iii.
If your computer completely dies—seemingly beyond resuscitation—some software programs (e.g., Norton
Utilities) may be able to fix the damage and retrieve some or all of the lost material. And service
departments at computer retailers can often retrieve documents from the hard drives of otherwise “dead”
machines.
lxvii.
Use such features as the spell checker and grammar checker to look for errors, but do not rely on them
exclusively. Although computers are marvelous machines, their “thinking” capabilities have not yet begun to
approach those of the human mind. For instance, although a computer can detect spelling errors, it does so
by comparing each word against its internal “dictionary” of correctly-spelled words. Not every word in the
English language will be included in the dictionary; for instance, proper nouns (e.g., surnames like Leedy
and Ormrod) will not be. Furthermore, it may assume that abut is spelled correctly when the word you really
had in mind was about, and it may very well not know that there should actually be their or they’re.
lxviii.
Print out a paper copy for final proofreading and editing. One of us authors once had a student who turned
in a dissertation draft chock-full of spelling and grammatical errors—and this from a student who was,
ironically, teaching a college-level English composition course at the time. A critical and chastising e-mail
message to the student made her irate; she had checked her document quite thoroughly before submitting it,
she replied, and was convinced that it was virtually error-free. When her paper draft was returned to her
almost bloodshot with spelling and grammatical corrections, she was quite contrite. “I don’t know how I
missed them all!” she said. When asked if she had ever edited a printed copy of the draft, she replied that she
had not, figuring that she could read her work just as easily on her computer monitor and thereby save a tree
or two. But in our own experience, it is always a good idea to read a printed version of what you have
written. For some reason, reading a paper copy often alerts us to errors we have previously overlooked on
the computer screen.
The Human Mind
The research tools discussed so far—the library, computer technology, measurement, statistics, and
language—are effective only to the extent that another critical tool also comes into play. The human mind is
undoubtedly the most important tool in the researcher’s toolbox. Nothing equals its powers of
comprehension, integrative reasoning, and insight.
Over the past few millennia, human beings have developed several general strategies through which they can
more effectively reason about and better understand worldly phenomena. Key among these strategies are
critical thinking, deductive logic, inductive reasoning, scientific method, theory building, and collaboration
with other minds.
Critical Thinking
Before beginning a research project, effective researchers typically look at research reports and theoretical
discussions related to their topic of interest. But they don’t just accept research findings and theories at face
value; instead, they scrutinize those findings and theories for faulty assumptions, questionable logic,
weaknesses in methodologies, and unwarranted conclusions. And, of course, effective researchers scrutinize
their own work for the same kinds of flaws. In other words, good researchers engage in critical thinking.
In general, critical thinking involves evaluating the accuracy, credibility, and worth of information and lines
of reasoning. Critical thinking is reflective, logical, and evidence-based. It also has a purposeful quality to
it—that is, the researcher thinks critically in order to achieve a particular goal.
Critical thinking can take a variety of forms, depending on the context. For instance, it may involve any one
or more of the following (Halpern, 1998, 2008; Nussbaum, 2008):
lxix.
▪ Verbal reasoning. Understanding and evaluating persuasive techniques found in oral and written language.
lxx.
▪ Argument analysis. Discriminating between reasons that do and do not support a particular conclusion.
lxxi.
▪ Probabilistic reasoning. Determining the likelihood and uncertainties associated with various events.
lxxii.
▪ Decision making. Identifying and evaluating several alternatives and selecting the alternative most likely to
lead to a successful outcome.
lxxiii.
▪ Hypothesis testing. Judging the value of data and research results in terms of the methods used to obtain
them and their potential relevance to certain conclusions. When hypothesis testing includes critical thinking,
it involves considering questions such as these:
.
Was an appropriate method used to measure a particular outcome?
i.
Are the data and results derived from a relatively large number of people, objects, or events?
ii.
Have other possible explanations or conclusions been eliminated?
iii.
Can the results obtained in one situation be reasonably generalized to other situations?
To some degree, different fields of study require different kinds of critical thinking. In history, critical
thinking might involve scrutinizing various historical documents and looking for clues as to whether
things definitely happened a particular way or only maybe happened that way. In psychology, it might involve
critically evaluating the way in which a particular psychological characteristic (e.g., intelligence, personality)
is being measured. In anthropology, it might involve observing people’s behaviors over an extended period of
time and speculating about what those behaviors indicate about the cultural group being studied.
Deductive Logic
Deductive logic begins with one or more premises. These premises are statements or assumptions that the
researcher initially takes to be true. Reasoning then proceeds logically from these premises toward
conclusions that—if the premises are indeed true—must also be true. For example,
lxxiv.
If all tulips are plants, (Premise 1)
lxxv.
And if all plants produce energy through photosynthesis, (Premise 2)
lxxvi.
Then all tulips must produce energy through photosynthesis. (Conclusion)
To the extent that the premises are false, the conclusions may also be false. For example,
lxxvii.
If all tulips are platypuses, (Premise 1)
lxxviii.
And if all platypuses produce energy through spontaneous combustion, (Premise 2)
lxxix.
Then all tulips must produce energy through spontaneous combustion. (Conclusion)
The if-this-then-that logic is the same in both examples. We reach an erroneous conclusion in the second
example—we conclude that tulips are apt to burst into flames at unpredictable times—only because both of
our premises are erroneous.
Let’s look back more than 500 years to Christopher Columbus’s first voyage to the New World. At the time,
people held many beliefs about the world that, to them, were irrefutable facts: People are mortal, the Earth is
flat, the universe is finite and relatively small. The terror that gripped Columbus’s sailors as they crossed the
Atlantic was a fear supported by deductive logic. If the Earth is flat (premise) and the universe finite and
small (premise), the Earth’s flat surface must stop at some point. Therefore, a ship that continues to travel
into uncharted territory must eventually come to the Earth’s edge and fall off, and its passengers (who are
mortal—another premise) will meet their deaths. The logic was sound; the conclusions were valid. Where the
reasoning fell short was in two faulty premises: that the Earth is flat and relatively small.Deductive logic
provides the basis for mathematical proofs in mathematics, physics, and related disciplines. It is also
extremely valuable for generating research hypotheses and testing theories. As an example, let’s look one
more time at doctoral student Dinah Jackson’s dissertation project about the possible effects of selfquestioning during studying. Jackson knew from well-established theories about human learning that forming
mental associations among two or more pieces of information results in more effective learning than does
trying to learn each piece of information separately from the others. She also found a body of research
literature indicating that the kinds of questions students ask themselves (mentally) and try to answer as they
listen to a lecture or read a textbook influence both what they learn and how effectively they remember it.
(For instance, a student who is trying to answer the question, “What do I need to remember for the test?”
might learn very differently from the student who is considering the question, “How might I apply this
information to my own life?”) From such findings, Jackson generated several key premises and drew a
logical conclusion from them:
lxxx.
If learning information in an associative, integrative manner is more effective than learning information in a
fact-by-fact, piecemeal manner, (Premise 1)
lxxxi.
If the kinds of questions students ask themselves during a learning activity influence how they learn,
(Premise 2)
lxxxii.
If training in self-questioning techniques influences the kinds of questions that students ask themselves,
(Premise 3)
lxxxiii.
And if learning is reflected in the kinds of notes that students take during class, (Premise 4)
lxxxiv.
Then teaching students to ask themselves integrative questions as they study class material should lead to
better-integrated class notes and higher-quality learning. (Conclusion)
Such reasoning led Jackson to form and test several hypotheses, including this one:
Students who have formal training in integrative self-questioning will take more integrative notes than
students who have not had any formal training. (Jackson, 1996, p. 12)
The data Jackson collected in her dissertation research supported this hypothesis.
Inductive Reasoning
Inductive reasoning begins not with a preestablished truth or assumption but instead with an observation. For
instance, as a baby in a high chair many years ago, you may have observed that if you held a cracker in front
of you and then let go of it, it fell to the floor. “Hmmm,” you may have thought, “what happens if I do that
again?” So you grabbed another cracker, held it out, and released it. It, too, fell to the floor. You followed the
same procedure with several more crackers, and the result was always the same: The cracker traveled in a
downward direction. Eventually you may have performed the same actions on other things—blocks, rattles,
peas, milk—and invariably observed the same result. Eventually you drew the conclusion that all things fall
when dropped—your first inkling about a force called gravity. (You may also have concluded that dropping
things from your high chair greatly annoyed your parents, but that is another matter.)
In inductive reasoning, people use specific instances or occurrences to draw conclusions about entire classes
of objects or events. In other words, they observe a sample and then draw conclusions about the
larger population from which the sample has been taken. For instance, an anthropologist might draw
conclusions about a certain culture after studying a certain community within that culture. A professor of
special education might use a few case studies in which a particular instructional approach is effective with
students who have dyslexia to recommend that teachers use the instructional approach with other students
with dyslexia. A sociologist might conduct three surveys (one each in 1995, 2005, and 2015) asking 1,000
people to describe their beliefs about AIDS and then drawing conclusions about how society’s attitudes
toward AIDS have changed over the 20-year period.
Figure 1.2
graphically depicts the nature of inductive reasoning. Let’s look at an example of how this
representation applies to an actual research project. Neurologists Silverman, Masland, Saunders, and Schwab
(1970) sought the answer to a problem in medicine: How long can a person have a “flat EEG” (i.e., an
absence of measurable electrical activity in the brain, typically indicative of cerebral death) and still recover?
Silverman and his colleagues observed 2,650 actual cases. They noted that, in all cases in which the flat EEG
persisted for 24 hours or more, not a single recovery occurred. All of the data pointed to the same
conclusion: People who exhibit flat EEGs for 24 hours or longer will not recover. We cannot, of course, rule
out the unexplored cases, but from the data observed, the conclusion reached was that recovery is impossible.
The EEG line from every case led to that one conclusion.
FIGURE 1.2
▪ The Inductive Process
Scientific Method
During the Renaissance, people found that when they systematically collected and analyzed data, new
insights and understandings might emerge. Thus was the scientific method born; the words literally mean
“the method that searches after knowledge” (scientia is Latin for “knowledge” and derives from scire, “to
know”). The scientific method gained momentum during the 16th century with such men as Paracelsus,
Copernicus, Vesalius, and Galileo.
Traditionally, the term scientific method has referred to an approach in which a researcher (a) identifies a
problem that defines the goal of one’s quest; (b) posits a hypothesis that, if confirmed, resolves the problem;
(c) gathers data relevant to the hypothesis; and (d) analyzes and interprets the data to see whether they
support the hypothesis and resolve the question that instigated the research. In recent years, however, the
term has been a controversial one, because not all researchers follow the steps just listed in a rigid, lock-step
manner; in fact, as noted earlier, some researchers shy away from forming any hypotheses about what they
might find. Some of the controversy revolves around which article to use in front of the term—more
specifically, whether to say “the scientific method” or “a scientific method.” If we are speaking generally
about the importance of collecting and analyzing data systematically rather than haphazardly, then saying
“the scientific method” makes sense. If, instead, we are speaking about a specific methodology—say,
experimental research or ethnographic research (described in Chapter 7 and Chapter 9, respectively), it is
probably better to say “a scientific method.” In any event, we are talking about a somewhat flexible—
although certainly also rigorous—process.
As you may already have realized, application of a scientific method usually involves both deductive logic
and inductive reasoning. Researchers might develop a hypothesis either from a theory (deductive logic) or
from observations of specific events (inductive reasoning). Using deductive logic, they might make
predictions about the patterns they are likely to see in their data if a hypothesis is true. And they often use
inductive reasoning to generalize about a large population from which they have drawn a small sample.
Theory Building
Psychologists are increasingly realizing that the human mind is a very constructive mind. People don’t just
passively absorb and remember a large body of unorganized facts about the world. Instead, they pull together
the things they see and hear to form well-organized and integrated understandings about a wide variety of
physical and social events. Human beings, then, seem to have a natural tendency to develop theories about
the world around them (e.g., see Bransford, Brown, & Cocking, 2000; J. E. Ormrod, 2012).
In general, a theory is an organized body of concepts and principles intended to explain a particular
phenomenon. Even as young children, human beings are inclined to form their own, personal theories about
various physical and social phenomena—for instance, why the sun “goes down” at night, where babies come
from, and why certain individuals behave in particular ways. People’s everyday, informal theories about the
world aren’t always accurate. For example, imagine that an airplane drops a large metal ball as it travels
forward through the air. What kind of path will the ball take as it falls downward? The answer, of course, is
that it will fall downward at an increasingly fast rate (thanks to gravity) but will also continue to travel
forward (thanks to inertia). Thus, its path will have the shape of a parabolic arc. Yet many college students
erroneously believe that the ball (a) will fall straight down, (b) will take a straight diagonal path downward,
or (c) will actually move backward from the airplane as it falls down (McCloskey, 1983).
What characterizes the theory building of a good researcher is the fact that it is supported by welldocumented findings—rather than by naive beliefs and subjective impressions of the world—and by logically
defensible reasoning. Thus, the theory-building process involves thinking actively and intentionally about a
phenomenon under investigation. Beginning with the facts known about the phenomenon, the researcher
brainstorms ideas about plausible and, ideally, best explanations—a process that is sometimes
called abduction (e.g., Jaccard & Jacoby, 2010; Walton, 2003). Such explanations are apt to involve an
interrelated set of concepts and propositions that, taken together, can reasonably account for the phenomenon
being studied.
After one or more researchers have developed a theory to explain a phenomenon of interest, the theory is apt
to drive further research, in part by posing new questions that require answers and in part by suggesting
hypotheses about the likely outcomes of particular investigations. For example, one common way of testing a
theory is to use deductive reasoning to make a prediction (hypothesis) about what should occur if the theory
is a viable explanation of the phenomenon being examined. As an example, let’s consider Albert Einstein’s
theory of relativity, first proposed in 1915. Within the context of his theory, Einstein hypothesized that light
passes through space as photons—tiny masses of spectral energy. If light has mass, Einstein reasoned, it
should be subject to the pull of a gravitational field. A year later, Karl Schwarzschild predicted that, based on
Einstein’s reasoning, the gravitational field of the sun should bend light rays considerably more than Isaac
Newton had predicted many years earlier. In 1919 a group of English astronomers traveled to Brazil and
North Africa to observe how the sun’s gravity distorted the light of a distant star now visible due to a solar
eclipse. After the data were analyzed and interpreted, the results clearly supported the Einstein–
Schwarzschild hypothesis—and therefore also supported Einstein’s theory of relativity.
As new data emerge that either do or do not support particular hypotheses, a researcher may continue to
revise a theory, reworking parts to better account for research findings, filling in gaps with additional
concepts or propositions, extending the theory to apply to additional situations, and relating the theory to
other theories regarding overlapping phenomena (Steiner, 1988; K. R. Thompson, 2006). Occasionally, when
an existing theory cannot adequately account for a growing body of evidence, a good researcher casts it aside
and begins to formulate an alternative theory that better explains the data.
Theory building tends to be a relatively slow process, with any particular theory continuing to evolve over a
period of years, decades, or centuries. Often, many researchers contribute to the theory-building effort,
testing hypotheses that the theory suggests, suggesting additional concepts and propositions to include in the
theory, and conducting additional investigations to test one or more aspects of the theory in its current state.
This last point brings us to yet another strategy for effectively using the human mind: collaborating
with other minds.
Collaboration with Other Minds
As an old saying goes, two heads are better than one. Three or more heads can be even better. Any single
researcher is apt to have certain perspectives, assumptions, and theoretical biases—not to mention gaps in his
or her knowledge about the subject matter—that will limit how he or she approaches a research project. By
bringing one or more professional colleagues into a research project—ideally, colleagues who have
perspectives, backgrounds, and areas of expertise somewhat different from the researcher’s own—the
researcher brings many more cognitive resources to bear on how to tackle the research problem and how to
find meaning in the data obtained (e.g., see Nichols, 1998).
Sometimes these colleagues enter the picture as equal partners. At other times they may simply offer
suggestions and advice. For example, when a graduate student conducts research for a master’s thesis or
doctoral dissertation, the student is, of course, the key player in the endeavor. Yet the student typically has
considerable guidance from an advisor and, especially in the case of a doctoral dissertation, from a faculty
committee. The prudent student selects an advisor and committee members who have the expertise to help
shape the research project into a form that will truly address the research question and—more importantly—
will make a genuine contribution to the student’s topic of study.
USING TECHNOLOGY
As a general rule, productive researchers keep in regular communication with others who conduct similar
research in their field, exchanging ideas, critiquing one another’s work, and directing one another to
potentially helpful resources. Such ongoing communication is also a form of collaboration—albeit a less
systematic one—in that everyone can benefit from and build on what others are thinking and finding.
Increasingly, computer technology is playing a central role in this cross-communication and crossfertilization. For example, some researchers maintain professional web pages that describe their research
programs and include links to relevant research reports; often you can find these web pages by going to the
websites of the researchers’ universities or other home institutions. Also of value are list servers, which
provide a mechanism for electronic discussion groups. A list server is essentially a mailing list, and any email message sent to it is distributed to everyone who has subscribed to the list.
As the preceding sections should make clear, we human beings are—or at least have the potential to be—
logical, reasoning beings. But despite our incredible intellectual capabilities—which almost certainly surpass
those of all other species on the planet—we don’t always reason as logically or objectively as we might. For
example, sometimes we “discover” what we expect to discover, to the point where we don’t look objectively
at the data we collect. And sometimes we are so emotionally attached to particular perspectives or theories
about a phenomenon that we can’t abandon them when mountains of evidence indicate that we
should. Figure 1.3 describes some common pitfalls in human reasoning—pitfalls we urge you to be on the
lookout for and try to overcome. Good researchers are reflective researchers who regularly and critically
examine not only their research designs and data but also their own thinking processes.
We human beings often fall short of the reasoning capacities with which Mother Nature has
endowed us. Following are seven common pitfalls to watch for in your own thinking as a researcher.
lxxxv.
Confusing what must logically be true with what seems to be true in the world as we know it—a
potential pitfall in deductive reasoning. Our usual downfall in deductive reasoning is failing to
separate logic from everyday experience. For example, consider Isaac Newton’s second law of
motion: Force equals mass times acceleration (F = ma). According to this basic principle of
Newtonian physics, any force applied to an object results in acceleration of the object. Using simple
algebra—deductive reasoning at its finest—we can conclude that a = F ÷ m and therefore that if
there is no acceleration (a = 0), then there is no force (F = 0). This deduction makes no sense to
anyone who has ever tried to push a heavy object across the floor: The object may not move at all,
let alone accelerate. What explains the object’s stubbornness, of course, is that other forces,
especially friction with and resistance from the floor, are counteracting any force that the pusher
may be applying.
lxxxvi.
Making generalizations about members of a category after having encountered only a restricted
subset of that category—a potential pitfall in inductive reasoning. The main weakness of inductive
reasoning is that, even if all of our specific observations about a particular set of objects or events
are correct, our generalizations about the category as a whole may not be correct. For example, if
the only tulips we ever see are red ones, we may erroneously conclude that tulips can only be red.
And if we conduct research about the political or religious beliefs of people who live in a particular
location—say, people who live in Chicago—we may draw conclusions that don’t necessarily apply
to the human race as a whole. Inductive reasoning, then, is most likely to fall short when we gather
data from only a small, limited sample.
lxxxvii.
Looking only for evidence that supports our hypotheses, without also looking for evidence that
would disconfirm our hypotheses. We humans seem to be predisposed to look for confirming
evidence rather than disconfirming evidence— a phenomenon known as confirmation bias. For
many everyday practical matters, this approach serves us well. For example, if we flip a light switch
and fail to get any light, we might immediately think, “The light bulb probably burned out.” We
unscrew the existing light bulb and replace it with a new one—and voila! We now have light.
Hypothesis confirmed, problem solved, case closed. However, truly objective researchers don’t just
look for evidence that confirms what they believe to be true. They also look for evidence that
might disprove their hypotheses. They hope that they don’t find such evidence, of course, but they
look for it nevertheless.
lxxxviii.
Confirming expectations even in the face of contradictory evidence. Another aspect of our
confirmation bias is that we tend to ignore or discredit any contradictory evidence that comes our
way. For example, consider the topic of global climate change. Convincing evidence continues to
mount to support the ideas that (a) the Earth’s average temperature is gradually rising and (b) this
temperature rise is at least partly the result of carbon emissions and other human activities. Yet
some folks have great difficulty looking at the evidence objectively—perhaps the researchers
incorrectly analyzed the data, they say, or perhaps the scientific community has a hidden agenda
and so is not giving us the straight scoop.
lxxxix.
Mistaking dogma for fact. Although we might be inclined to view some sources of information with
a skeptical, critical eye, we might accept others without question. For example, many of us willingly
accept whatever an esteemed researcher, scholarly book, or other authority source says to be true.
In general, we may uncritically accept anything said or written by individuals or groups we hold in
high esteem. Not all authority figures and works of literature are reliable sources of information
and guidance, however, and blind, unquestioning acceptance of them can be worrisome.
xc.
Letting emotion override logic and objectivity. We humans are emotional beings, and our emotions
often infiltrate our efforts to reason and think critically. We are apt to think quite rationally and
objectively when dealing with topics we don’t feel strongly about and yet think in decidedly
irrational ways about emotionally charged issues—issues we find upsetting, infuriating, or
personally threatening.
xci.
Mistaking correlation for causation. In our efforts to make sense of our world, we human beings are
often eager to figure out what causes what. But in our eagerness to identify cause-and-effect
relationships, we sometimes “see” them when all we really have is two events that just happen to
occur at the same time and place. Even when the two events are consistently observed together—in
other words, when they are correlated—one of them does not necessarily cause the other. The
ability for a researcher to distinguish between causation and correlation is a critical one, as you
will discover in Chapter 6.
FIGURE 1.3
▪ Common Pitfalls in Human Reasoning
List of pitfalls based on Chapter 8, “Common Sense Isn’t Always Sensible: Reasoning and Critical Thinking”
in Our Minds, Our Memories by J. E. Ormrod, 2011, pp. 151–183. Copyright by Pearson E Reflections on
Noteworthy Research
The time: February 13, 1929. The place: St. Mary’s Hospital, London. The occasion: the reading of a paper
before the Medical Research Club. The speaker: a member of the hospital staff in the Department of
Microbiology. Such was the setting for the presentation of one of the most significant research reports of the
early 20th century. The report was about a discovery that has transformed the practice of medicine. Dr.
Alexander Fleming presented to his colleagues his research on penicillin. The group was apathetic. No one
showed any enthusiasm for Fleming’s paper. Great research has frequently been presented to those who are
imaginatively both blind and deaf.
Despite the lukewarm reception, Fleming knew the value of what he had done. The first public announcement
of the discovery of penicillin appeared in the British Journal of Experimental Pathology in 1929. It is a
readable report—one that André Maurois (1959) called “a triumph of clarity, sobriety, and precision.” Get it;
read it. You will be reliving one of the great moments in 20th-century medical research.
Soon after the publication of Fleming’s paper, two other names became associated with the development of
penicillin: Ernst B. Chain and Howard W. Florey (Chain et al., 1940; also see Abraham et al., 1941).
Together they developed a pure strain of penicillin. Florey was especially instrumental in initiating its mass
production and its use as an antibiotic for wounded soldiers in World War II (Coghill, 1944; also see Coghill
& Koch, 1945). Reading these reports takes you back to the days when the medical urgency of dying people
called for a massive research effort to make a newly discovered antibiotic available for immediate use.
On October 25, 1945, the Nobel Prize in medicine was awarded to Fleming, Chain, and Florey.
If you want to learn more about the discovery of penicillin, read André Maurois’s The Life of Sir Alexander
Fleming (1959), the definitive biography done at the behest of Fleming’s widow. The book will give you an
insight into the way great research comes into being.
The procedures used in groundbreaking research are identical to those every student follows in completing a
dissertation, thesis, or other research project. All research begins with a problem, an observation, a question.
Curiosity is the germinal seed. Assumptions are made. Hypotheses might be formulated. Data are gathered.
Conclusions are reached. What you do in a research project is the same as what many others have done
before you, including those who have pushed back the barriers of ignorance and made discoveries that have
greatly benefited humankind.Exploring Research in Your FieldEarly in the chapter we mentioned that
academic research is popularly seen as an activity far removed from everyday living. Even graduate students
working on theses or dissertations may consider their task to be meaningless busywork that has little or no
relevance to the world beyond the university campus. This “busywork” conception of an academic program’s
research requirement is simply not accurate. Conducting the research required to write an acceptable thesis or
dissertation is one of the most valuable educational experiences a person can have. Furthermore, a good
research project adds to our knowledge about our physical and social worlds and so can ultimately promote
the welfare and well-being of ourselves as a species and of the planet as a whole.
Even if you plan to become a practitioner rather than a researcher—say, a nurse, social worker, or school
principal—knowledge of strong research methodologies and legitimate ways to collect and analyze data is
essential for keeping up with advances in your field. The alternative— not being well versed in sound
research practices—can lead you to base important professional decisions on faulty data, inappropriate
interpretations and conclusions, or unsubstantiated personal intuitions. Truly competent and effective
practitioners base their day-to-day decisions and long-term priorities on solid research findings in their field.
As a way of getting your feet wet in the world of research, take some time to read articles in research journals
in your academic discipline. You can do so by spending an hour or two in the periodicals section of your
local college or university library or, alternatively, making use of your library website’s online databases to
download and read a number of articles at home.
Your professors should have suggestions about journals that are especially relevant to your discipline.
Reference librarians can be helpful as well. If you are shy about asking other people for advice, you can get
insights about important journals by scanning the reference lists in some of your textbooks.
Keep in mind that the quality of research you find in your explorations may vary considerably. One rough
indicator of the quality of a research study is whether the research report has been juried or nonjuried.
A juried (or refereed) research report has been judged by respected colleagues in one’s field and deemed to
be of sufficient quality and importance to warrant publication. For instance, the editors of many academic
journals send submitted manuscripts to one or more reviewers who pass judgment on the manuscripts, and
only manuscripts that meet certain criteria are published in the journal. A nonjuried (or nonrefereed) report is
one that appears in a journal or on the Internet without first being screened by one or more experts. Some
nonjuried reports are excellent, but others may not be.
PRACTICAL APPLICATION Identifying Important Tools in Your Discipline
We have introduced several key research tools in the preceding pages, and we describe many more specific
ones in subsequent chapters. Some of the tools you learn about in this book may be somewhat new to you.
How will you learn when, how, and why you should use them? One effective means of learning about
important tools in your discipline is to work closely with an expert researcher in your field.
Take the time to find a person who has completed a few research projects—perhaps someone who teaches a
research methods class, someone who has published in prestigious journals, someone who has successfully
obtained research grants, or even someone who has recently finished a dissertation. Ideally this individual
should be someone in your own field of study. Ask the questions listed in the following checklist and, if
possible, observe the person as he or she goes about research work. If you can’t locate anyone locally, it may
be possible to recruit one or more willing individuals through e-mail.
CHECKLIST Interviewing an Expert Researcher
1. How do you start a research project?
2. What specific tools do you use (e.g., library resources, computer software, forms of measurement,
statistics)?
3. How did you gain your expertise with the various tools you use?
4. What are some important experiences you suggest for a novice researcher?
5. If I wanted to learn how to become a competent researcher, what specific tools would you suggest
I work with?
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