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How does a researcher determine how many research questions and hypotheses are needed for a particular study? Do all research studies require hypotheses?

How does a researcher determine how many research questions and hypotheses are needed for a particular study? Do all research studies require hypotheses?

Discussion on this Learning Activity
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W6DQ1: How do research questions frame and guide studies?
W6DQ2: What type of research questions would lead to a qualitative study? To a quantitative one? How would the wording differ? Discuss these questions relative to your field of study.
W6DQ3: How does a researcher determine how many research questions and hypotheses are needed for a particular study? Do all research studies require hypotheses?
W6DQ4: Why might a hypothesis be inappropriate for a qualitative study?
W6DQ5:* Is a quantitative study that has a research question but no hypothesis weaker than one with a hypothesis? Why or why not?
W6DQ6: How are research questions different from survey and/or interview questions?
Surveyors and sailors measure distances between objects by taking observations from multiple positions. By observing the object from several different angles or viewpoints, the surveyors and sailors can obtain a good fix on an object’s true location (see Figure 6.1). Social researchers employ a similar process of triangulation. In social research, we build on the principle that we learn more by observing from multiple perspectives than by looking from only a single perspective.
The idea that looking at something from multiple points of view improves accuracy.
Social researchers use several types of triangulation (see Expansion Box 6.1, Example of Four Types of Triangulation). The most common type is triangulation of measure, meaning that we take multiple measures of the same phenomena. For example, you want to learn about a person’s health. First, you ask the person to complete a questionnaire with multiple-choice answers. Next you conduct an open-ended informal interview. You also ask a live-in partner/caregiver about the person’s health. You interview the individual’s physician and together examine his or her medical records and lab test results. Your confidence that you have an accurate picture grows from the multiple measures you used compared to relying on just one, especially if each measure offers a similar picture. Differences you see among the measures stimulates questions as well.
figure 6.1 Triangulation: Observing from Different Viewpoints
expansion box 6.1 Example of Four Types of Triangulation
The amount of violence in popular American films
Measures: Create three quantitative measures of violence: the frequency (e.g., number of killings, punches), intensity (e.g., volume and length of time screaming, amount of pain shown in face or body movement), and level of explicit, graphic display (e.g., showing a corpse with blood flowing, amputated body parts, close-ups of injury) in films.
Observers: Have five different people independently watch, evaluate, and record the forms and degrees of violence in a set of ten highly popular American films.
Theory: Compare how a feminist, a functional, and a symbolic interaction theory explains the forms, causes, and societal results of violence that is in popular films.
Method: Conduct a content analysis of a set of ten popular films, as an experiment to measure the responses of experimental subjects to violence in each film, to survey attitudes toward film violence among the movie-going public, and to make field observations on audience behavior during and immediately after showing the films.
Triangulation of observers is a variation on the first type. In many studies, we conduct interviews or are the lone observer of events and behavior. Any limitations of a single observer (e.g., lack of skill in an area, a biased view on an issue, inattention to certain details) become restrictions of the study. Multiple observers bring alternative perspectives, backgrounds, and social characteristics. They thereby reduce the limitations. For example, two people interact with and observe the behavior of ten 5-year-old children at a child care center. One of the observers is a 60-year-old White male pediatrician with 25 years of experience working in a large city hospital. The other is a 31-year-old Hispanic female mother of two children who has 6 years of experience as an elementary school teacher in a small town. Each observer may notice and record different data. Combining what both see and experience will produce a fuller picture than relying on either one alone.
Triangulation of theory requires using multiple theoretical perspectives to plan a study or interpret the data. Each theoretical perspective has assumptions and concepts. They operate as a lens through which to view the social world. For example, a study of work relations in a bank could use conflict theory with its emphasis on power differences and inequality. The study could highlight the pay and working condition inequalities based on positions of authority (e.g., manager versus teller). The study reveals relevant differences in social backgrounds: a middle-aged White male manager with an MBA and a young African American female teller with an associate’s degree. Next, rational choice theory is applied to focus on decision-making and rational strategies individuals use to maximize personal benefits. This perspective highlights how the bank manager varies the time/effort he devotes to various customers depending on their loan or savings account size. It also presents a better picture of how the teller invests her time and energy differently with various supervisors, depending on whether she believes they might help her get a promotion. Each perspective guides the study: It identifies relevant data, provides a set of concepts, and helps to interpret the meaning and significance of the data.
Triangulation of method mixes the qualitative and quantitative research approaches and data. Most researchers develop an expertise in one approach, but the approaches have complementary strengths. A study that combines both tends to be richer and more comprehensive. Mixing them occurs in several ways:1 by using the approaches sequentially, first one and then the other, or by using them in parallel or simultaneously. In the study that opened this chapter, Klinenberg mixed a statistical analysis of quantitative data on deaths with interviews and document analysis. (seeExample Box 6.1, A Multimethod Study on page 166).
In all research, we strive to collect empirical data systematically and to examine data patterns so we can better understand and explain social life, yet differences between research approaches can create miscommunication and misunderstandings. They are mutually intelligible; grasping both approaches and seeing how each complements the other simply takes more time and effort. Next we will look at some sources of differences.
A first difference originates in the nature of the data itself. Soft data (i.e., words, sentences, photos, symbols) dictate qualitative research strategies and data collection techniques that differ from hard data (in the form of numbers) for which quantitative approaches are used. Such differences may make the tools for a quantitative study inappropriate or irrelevant for a qualitative study and vice versa.
Another difference between qualitative and quantitative research originates in principles about the research process and assumptions about social life. Qualitative and quantitative research principles give rise to different “languages of research” with different emphases. In a quantitative study, we rely more on positivist principles and use a language of variables and hypotheses. Our emphasis is on precisely measuring variables and test hypotheses. In a qualitative study, we rely more on the principles from interpretive or critical social science. We speak a language of “cases and contexts” and of cultural meaning. Our emphasis is on conducting detailed examinations of specific cases that arise in the natural flow of social life. Interestingly, more female than male social researchers adopt the qualitative approach.2
example box 6.1 A Multimethod Study
Lee and Bean (2007) mixed quantitative and qualitative research approaches in a study of multiracial identity in the United States. They observed that social diversity has increased because of growing immigration since 1970, and for the first time in 2000, the United States census offered the option of classifying oneself as multiracial. The new diversity contrasts to the long history of single-race categories and a dominant White-Black dichotomous racial division. Lee and Bean asked whether multiracial people feel free or highly constrained when they pick a single racial-ethnic or multiracial identity. They also asked whether selecting a multiracial category on the census form is a symbolic action or a reflection of a person’s multiracial daily existence. In the quantitative part of the study, the authors statistically analyzed 2000 census data on the numbers and mixes of people who classified themselves as multiracial. In the qualitative part of the study, they conducted forty-six in-depth semi-structured interviews with multiracial adults from northern and southern California. In the interviews, Lee and Bean asked how and why a person chose to identify herself or himself as she or he did, whether that identity changed over time or by context, and about language use and other practices associated with race and ethnicity. They interviewed adults of various mixtures of Asian, White, Latino, and Black races. Based on the interviews, Lee and Bean found that multiracial Blacks were less likely to call themselves multiracial than people of other mixed race categories. This restriction is consistent with the U.S. historical pattern of the public identifying a person with only some Black heritage as being Black. Persons of mixed White and Asian or Latino or Latino-Asian heritage had more flexibility. Some mixed Asian-White or Latino-White people self-identified as White because of public perceptions and a narrow stereotypical definition of proper Asian or Latino appearance. Other White-Asian and White-Latino people said that they are proud of their mixed heritage even if it made little difference in their daily encounters. People did not stick with one label but claimed different racial-ethnic backgrounds in different situations. Pulling together the quantitative and qualitative findings, Lee and Bean suggested that racial-ethnic group boundaries are fading faster for Latinos and Asians than for Blacks. They concluded that a new Black versus non-Black divide is emerging to replace the old White-Black division but that Blacks are still in a disadvantaged position relative to all racial categories.
A third difference between qualitative and quantitative research lies in what we try to accomplish in a study. “The heart of good work”—whether it is quantitative or qualitative—“is a puzzle and an idea” (Abbott, 2003:xi). In all studies, we try to solve a puzzle or answer a question, but depending on the approach, we do this in different ways. In the heat wave study that opened this chapter, Klinenberg (2002) asked why so many people died. But he also asked how they died, and why some categories of people were greatly affected but others were not. In a quantitative study, we usually try to verify or falsify a relationship or hypothesis we already have in mind. We focus on an outcome or effect found across numerous cases. The test of a hypothesis may be more than a simple true or false answer; frequently it includes learning that a hypothesis is true for some cases or under certain conditions but not others. In the heat wave study, Klinenberg asked whether a person’s social class influenced an outcome: being likely to die during the heat wave. Using quantitative data, he tested the relationship between class and death rate by comparing the social class of the roughly 700 who died with thousands who did not.
In many qualitative studies, we often generate new hypotheses and describe details of the causal mechanism or process for a narrow set of cases. Returning to the heat wave study, Klinenberg (2002) tested existing hypotheses about class and death rates. He also developed several new hypotheses as he looked closely into the mechanism that caused some to die but not others. He learned that high death rates occurred in poverty- and crime-ridden neighborhoods. More males than females died, and more African Americans died than Latinos or Whites. By walking around in different low-income neighborhoods and interviewing many people firsthand, he identified the mechanisms of urban isolation that accounted for very different heat wave survival rates among people of the same social class. He examined the social situations of older African American men and discovered the local social environment to be the critical causal mechanism. He also looked at larger forces that created the social situations and local environments in Chicago in the mid-1990s.
A fourth difference between quantitative and qualitative studies is that each has a distinct “logic” and path of conducting research. In a quantitative study, we employ a logic that is systematic and follows a linear research path. In a qualitative study, the logic arises from ongoing practice and we follow a nonlinear research path. In the next section, we examine the logics and paths of research.
Reconstructed Logic and Logic in Practice
How we learn and discuss research tends to follow one of two logics.3 The logics summarize the degree to which our research strategy is explicit, codified, and standardized. In specific studies, we often mix the two logics, but the proportion of each varies widely by study.
A reconstructed logic emphasizes using an explicit research process. Reconstructed logic has been “reconstructed” or restated from the many messy details of doing a real-life study into an idealized, formal set of steps with standard practices and consistent principles, terms, and rules. You can think of it as a “cleansed model” of how best to do a high-quality study. Following this logic is like cooking by exactly following a printed recipe. Thus, the way to conduct a simple random sample (discussed in Chapter 7) is straightforward and follows a clear step-by-step procedure.
Reconstructed logic
A logic of research based on reorganizing, standardizing, and codifying research knowledge and practices into explicit rules, formal procedures, and techniques; it is characteristic of quantitative research.
The logic in practice is messy and closer to the concrete practice of doing research. Logic in practice includes advice that comes from the practical activities of doing specific real-life studies more than a set of restated, ideal rules. This logic relies heavily on “judgment calls” and “tricks of the trade” that active, experienced researchers share. We learn it best by reading many studies and being an apprentice researcher and from the folk wisdom that passes informally among experienced researchers. It is like cooking without a written recipe—adding a pinch of an ingredient here, stirring until something “looks right,” and adjusting while cooking until we reach a certain smell or taste.
Logic in practice
A logic of research based on an apprenticeship model and the sharing of implicit knowledge about practical concerns and specific experiences; it is characteristic of qualitative research.
You can see the reconstructed logic in the distinct research methods section of a quantitative research report. In contrast, in qualitative research reports, you may not see the research method (common for historical-comparative research) discussed or find it mixed with a personal autobiographical account of a particular study (common for field research). The absence of a standard method does not make qualitative study less valid; however, it often requires more time and a different style of thinking for the newcomer to master.
Linear and Nonlinear Paths
The path is a metaphor for a sequence of things to do: what you finish first or where you have been and what comes next. You can follow a straight, well-worn, and marked path that has clear signposts and is where many others have trod before. Alternatively, you may follow a path that meanders into unknown territory where few others have gone. The path has few signs, so you move forward, veer off to the side, and sometimes backtrack a little before going forward again.
When using the linear research path, we follow a fixed sequence of steps that are like a staircase that leads upward in one direction. By following a linear path, we move in a direct, narrow, and straight way toward a conclusion. This pathway toward task completion is the dominant approach in western European and North American cultures. It is most widely used in quantitative research. By contrast, a nonlinear research path requires us to make successive passes through the steps. We may move forward, backward, and sideways before advancing again. It is more of a spiral than a straight staircase. We move upward but slowly and indirectly. With each cycle or repetition, we may collect new data and gain new insights.
Nonlinear research path
Research that proceeds in a cyclical, iterative, or back-and-forth pattern and is often used in qualitative research.
Linear research path
Research that proceeds in a clear, logical, step-by-step straight line; often used in quantitative research.
People who are accustomed to a direct, linear approach often become impatient with a less direct cyclical path. Although a nonlinear path is not disorganized, undefined chaos, the cyclical path appears inefficient and without rigor. People who are used to a nonlinear path often feel stifled and “boxed in” by a linear approach. To them, a linear path feels artificial or rigid. They believe that this approach prevents them from being naturally creative and spontaneous.
Each path has its strengths. The linear path is logical, easy to follow, and efficient. The nonlinear path can be highly effective in creating an authentic feeling for understanding an entire setting, for grasping subtle shades of meaning, for integrating divergent bits of information, and for switching perspectives. Each path has its own discipline and rigor. The linear path borrows from the natural sciences with their emphasis on logic and precision. A nonlinear path borrows devices from the humanities (e.g., metaphor, analogy, theme, motif, and irony) and is suited for tasks such as translating languages, a process in which delicate shades of meaning, subtle connotations, or contextual distinctions can be important (see Figure 6.2 for a graphic representation of each path).
Objectivity and Integrity
We try to be fair, honest, truthful, and unbiased in our research activity, yet, we also have opportunities to be biased, dishonest, or unethical in all knowledge production including social research. The two major research approaches address the issue of reducing difficulties and ensuring honest, truthful studies in different ways.
In qualitative research, we often try to acquire intimate, firsthand knowledge of the research setting. Thus, we do not want to distance ourselves from the people or events we are studying. Acquiring an intimate understanding of a setting does not mean that we can arbitrarily interject personal opinion, be sloppy about data collection, or use evidence selectively to support our prejudices. Rather, we take maximum advantage of personal insight, inner feelings, and life perspective to understand social life. We “walk a fine line” between intimacy and detachment and place personal integrity and honesty at the forefront. Some techniques may help us walk a fine line. One technique is to become highly sensitive to our own views, preconceptions, and prior assumptions and then “bracket” them, or put them aside, so we can see beyond them better. Instead of trying to bury or deny our assumptions, viewpoints, and values, we find that acknowledging them and being open about them is best. We can then recognize how they might influence us. We try to be forthright and candid in our involvement in the research setting, in dealing with the people in the study, and with any relevant issues that arise. We do this in the way that we conduct the study and report on the findings.
Personal openness and integrity by the individual researcher are central to a qualitative study. By contrast, in a quantitative study, we stress neutrality and objectivity. In a quantitative study, we rely on the principle of replication, adhere to standardized procedures, measure with numbers, and analyze the data with statistics.4 In a sense, we try to minimize or eliminate the subjective human factor in a quantitative study. As Porter (1995:7, 74) has argued,
• Ideally, expertise should be mechanized and objectified … grounded in specific techniques…. This ideal of objectivity is a political as well as scientific one. Objectivity means rule of law, not of men. It implies the subordination of personal interests and prejudices to public standards.
figure 6.2 Graphic Representation of Linear and Nonlinear Paths
The issue of integrity in quantitative research mirrors the natural science approach. It relies on using an explicit and objective technology, such as making statements in precise neutral terms, using well-documented standard techniques, and making replicable, objective numerical measures.
Quantitative social research shares the hallmarks of natural science validation: explicit, standard procedures; precise numerical measurement; and replication. By contrast, validation in qualitative research relies more on a dependable, credible researcher and her or his personal integrity, self-discipline, and trustworthiness.5 Four other forms of validation in qualitative research somewhat parallel the objective procedures found in quantitative studies.6

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