NO PLAGIARISM DUE THURSDAY NOVEMBER 21, 2019. ATTACHED ARE RESOURCES THAT MUST BE USED.

ATTACHED ARE RESOURCES THAT MUST BE USED NO EXCEPTIONS

TOPIC:  GROUNDED THEORY

 

To prepare for this discussion, read the instructor guidance, Chapter 12 by Levitt (2016), and Sections 3.13.2, “Pros and Cons of Observational Research” and “Types of Observational Research” in Section 3.4 of the Newman (2016) textbook. View the following videos: Different Qualitative Approaches (Links to an external site.) and When to Use a Qualitative Research Design? Four Things to Consider (Links to an external site.).

Then, determine from the list below your assigned qualitative research design based on the first letter of your last name:

  • G-L: Grounded theory

Using the Research Methods research guide’s list of suggested articles, look for information about your assigned qualitative research design. You may also search the Library databases for articles about the research design. In your initial post:

  • Evaluate the features of the design and what kinds of research topics it is suitable for.
  • Explain the data collection and data analysis methods used in the design.
  • Cite at least one scholarly/peer-reviewed article about the design and one published research study that used the design, for a total of at least two scholarly/peer-reviewed journal articles.

    3 Descriptive Designs— Observing Behavior

    Alexander Macfarlane/Axiom Photographic/Design Pics/Superstock

    Learning Outcomes

    By the end of this chapter, you should be able to:

    • Explain the distinguishing features of qualitative research. • Distinguish the key features, pros, and cons of case studies. • Distinguish the key features, pros, and cons of archival research. • Distinguish the key features, pros, and cons of observational research. • Outline best practices for describing data, both graphically and numerically.

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    In the fall of 2009, Phoebe Prince and her family relocated from Ireland to South Hadley, Massachusetts. Phoebe was immediately singled out by bullies at her new high school and subjected to physical threats, insults about her Irish heritage, and harassing posts on her Facebook page. This relentless bullying continued until January of 2010, ending only because Phoebe elected to take her own life in order to escape her tormentors (“Report of plea deal,” 2011). Tragic stories like this one are all too common, and it should come as no surprise that the Centers for Disease Control and Prevention (2012) has identified bullying as a serious problem facing our nation’s children and adolescents.

    Scientific research on bullying began in Norway in the late 1970s in response to a wave of teen suicides. Work begun by psychologist Dan Olweus—and since continued by many others— has documented both the frequency and the consequences of bullying in the school system. Thus, we know that approximately one third of children are victims of bullying at some point during development, with between 5% and 10% bullied on a regular basis (Griffin & Gross, 2004; Nansel et al., 2001). Victimization by bullies has been linked with a wide range of emo- tional and behavioral problems, including depression, anxiety, self-reported health problems, and an increased risk of both violent behavior and suicide (for a detailed review, see Griffin & Gross, 2004). Recent research even suggests that bullying during adolescence may have a lasting impact on the body’s physiological stress response (Hamilton, Newman, Delville, & Delville, 2008).

    Nevertheless, most of this research has a common limitation: It has studied the phenomenon of bullying using self-report survey measures. That is, researchers typically ask students and teachers to describe the extent of bullying in the schools. In many studies, researchers will also have students fill out a collection of survey measures, describing both bullying experiences and psychological functioning in their own words. These studies are conducted rigorously, and the measures they use certainly meet the criteria of reliability and validity discussed in Chapter 2 (2.2). However, as Wendy Craig, Professor of Psychology at Queen’s University, and Debra Pepler, a Distinguished Professor at York University, suggested in a 1997 article, this questionnaire approach cannot capture the full context of bullying behaviors. As we have already discussed, self-report measures are fully dependent on people’s ability and willing- ness to answer honestly and accurately. It is easy to imagine scenarios in which reports of bullying experiences might be downplayed out of fear, or perhaps misremembered simply due to the stress of the experience itself.

    To address this limitation, Craig and Pepler (1997) decided to observe bullying behaviors as they occurred naturally on the playground. Among other things, the researchers found that acts of bullying occurred approximately every 7 minutes, lasted only about 38 seconds, and tended to occur within 120 feet of the school building. They also found that peers intervened to try to stop the bullying more than twice as often as adults did (11% versus 4%, respec- tively). These findings add significantly to scientific understanding of when and how bully- ing occurs. For our purposes, the most notable thing about them is that none of the findings could have been documented without directly observing and recording bullying behaviors on the playground. By using this technique, the researchers were able to gain a more thorough understanding of the phenomenon of bullying and, as a result, to provide real-world advice to teachers and parents.

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    Increasing Control . . .Increasing Control . . .

    • Case Study • Archival Research • Observation

    Descriptive Methods

    • Survey Research

    Predictive Methods

    • Quasi-experiments • “True” Experiments

    Experimental Methods

    Section 3.1 Qualitative and Quantitative Methods

    One recurring theme in this book is that it is absolutely critical for researchers to pick the right research design to address their hypothesis. The next three chapters will discuss three specific categories of research designs, proceeding in order of increasing control over elements of the design (see Figure 3.1). This chapter focuses on descriptive research designs, in which the pri- mary goal is to describe attitudes or behavior. We will begin by contrasting qualitative and quantitative approaches to description. We will then discuss three approaches to descriptive designs—studying single cases, mining existing archives, and observing behavior directly— covering the basic concept and the pros and cons of each. Finally, the chapter concludes with a discussion of guidelines for presenting descriptive data in both graphical and numeric form.

    Figure 3.1: Descriptive designs on the continuum of control

    Increasing Control . . .Increasing Control . . .

    • Case Study • Archival Research • Observation

    Descriptive Methods

    • Survey Research

    Predictive Methods

    • Quasi-experiments • “True” Experiments

    Experimental Methods

    3.1 Qualitative and Quantitative Methods

    Chapter 1 explained that researchers generally take one of two broad approaches to answer- ing their research questions. Quantitative research is a systematic and empirical approach that attempts to generalize results to other contexts, whereas qualitative research is a more descriptive approach that attempts to gain a deep understanding of particular cases and con- texts. Before we discuss specific examples of descriptive designs, it is important to under- stand that these can represent either quantitative or qualitative perspectives. This section contrasts the two approaches in more detail.

    Chapter 1 used the analogy of studying traffic patterns to contrast qualitative and quantita- tive methods—a qualitative researcher would likely study a single busy intersection in detail. This example illustrates a key point about this approach: Qualitative researchers are focused on interpreting and making sense out of what they observe rather than trying to simplify and quantify these observations. In general, qualitative research involves a collection of inter- views and observations made in a natural setting. Regardless of the overall approach (quali- tative or quantitative), data collection in the real world results in less control and structure than it does in a laboratory setting. But whereas quantitative researchers might view reduced control as a threat to reliability and validity, qualitative researchers view it as a strength of the study. Conducting observations in a natural setting makes it possible to capture people’s natural and unfiltered responses.

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    Section 3.1 Qualitative and Quantitative Methods

    As an example, consider two studies of the ways people respond to traumatic events. In a 1993 paper, psychologists James Pennebaker and Kent Harber took a quantitative approach to examining the community-wide impact of the 1989 Loma Prieta earthquake (near San Francisco). These researchers conducted phone surveys of 789 area residents, asking people to indicate, using a 10-point scale, how often they “thought about” and “talked about” the earthquake during the three-month period after its occurrence. In analyzing these data, Pen- nebaker and Harber discovered that people tend to stop talking about traumatic events about two weeks after they occurred but keep thinking about the event for approximately four more weeks. That is, the event is still on people’s minds, but they decide to stop discussing it with other people. In a follow-up study using the 1991 Gulf War, the same researchers found that this conflict leads to an increased risk of illness, measured via an increase in visits to the doctor (Pennebaker & Harber, 1993). The goal of the study was to gather data in a controlled manner and test a set of hypotheses about community responses to trauma.

    Contrast Pennebaker and Harber’s approach with the more qualitative one taken by the developmental psychologist Paul Miller and colleagues (2012), who used a qualitative approach to study the ways that parents model coping behavior for their children. These researchers conducted semistructured interviews of 24 parents whose families had been evacuated following the 2007 wildfires in San Diego County and an additional 32 parents whose families had been evacuated following a 2008 series of deadly tornadoes in Tennessee. Because of a lack of prior research on how parents teach their children to cope with trauma, Miller and colleagues approached their interviews with the goal of “documenting and describ- ing” (p. 8) these processes. That is, rather than attempt to impose structure and test a strict hypothesis, the researchers focused on learning from these interviews and letting the inter- viewees’ perspectives drive the acquisition of knowledge.

    Qualitative and quantitative methods also differ quite strikingly in how they approach analyses of the data. Because all quantitative methods have the goal of discovering findings that can be generalized—that apply across differ- ent contexts—all quantitative stud- ies must translate phenomena into numerical values and conduct statis- tical analyses. So, for example, Pen- nebaker and Harber’s (1993) study of coping with trauma measured the con- crete value of “visits to the doctor,” and then compared changes in this number over time. In contrast, because qualita- tive methods have the goal of learning and interpreting phenomena from the ground up, qualitative studies focus on discovering the underlying meaning of

    phenomena in their own right. So, for example, Miller and colleagues’ 2012 study of cop- ing focused on “documenting and describing” the ways that parents teach children to cope and learning from a critical evaluation of the interview content. At risk of oversimplifying:

    Lisafx/iStock/Thinkstock

    Paul Miller’s research, which involved a series of semi-structured, qualitative interviews, attempted to document and describe a phenomenon rather than test a theory.

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    Section 3.2 Case Studies

    Quantitative methods gloss over some of the richness of experience in order to discover knowledge that can be generalized, while qualitative methods sacrifice the ability to general- ize in order to capture the richness of experience.

    As one final example of this contrast, consider the way that each approach would analyze the content of an interview. Interviewing people can be a very effective way to understand their experiences and can form the basis for many of the descriptive designs we cover in this chap- ter. A qualitative researcher would likely conduct a smaller number of interviews (perhaps only one, for a case study), due to the time required for analysis. The researcher would read each interview in depth and then start to identify themes that appeared across the entire set. These themes would serve as the basis for understanding people’s experiences. (For an excellent deep dive into different theoretical approaches to interview analysis, see Smith [2008].) By comparison, a quantitative researcher would conduct a larger number of inter- views, because quantitative text analysis can be very fast. Rather than read each interview, the researcher could input the text of these interviews into a software program, which could count and categorize the overall sentiment of the language people used. These counts and cat- egories would then serve as the basis for quantifying people’s experiences on a larger scale.

    The following three sections examine three specific examples of descriptive designs—case studies, archival research, and observational research. Because each of these methods has the goal of describing attitudes, feelings, and behaviors, each one can be used from either a quan- titative or a qualitative perspective. In other words, qualitative and quantitative researchers use many of the same general methods but do so with different goals. To illustrate this flex- ibility, each section concludes with a paragraph that contrasts qualitative and quantitative uses of the particular method.

    3.2 Case Studies

    At the 1996 meeting of the American Psychological Association, James Pennebaker—now chair of the psychology department at the University of Texas—delivered an invited address, describing his research on the benefits of therapeutic writing. Rather than follow the nor- mal approach to an academic conference presentation, showing graphs and statistical tests to support his arguments, Pennebaker told a story. In the mid-1980s, when Pennebaker’s lab was starting to study the effects of structured writing on physical and psychological health, one study participant was an American soldier who had served in the Vietnam War. Like many others, this soldier experienced difficulty adjusting to what had happened during the war and consequent trouble reintegrating into “normal” life. In Pennebaker’s study, he was asked to simply spend 15 minutes per day, over the course of a week, writing about a traumatic experience—in this case, his tour of duty in Vietnam. At the end of this week, as might have been expected, this veteran felt awful, revisiting unpleasant memories over a decade old. But over the next few weeks, amazing things started to happen: The soldier slept better; he made fewer visits to his doctor; he even reconnected with his wife after a long separation.

    Pennebaker’s presentation was a case study, which provides a detailed, in-depth analysis of one person over a period of time. Although this case study was collected as part of a larger quantitative experiment, case studies are usually conducted in a therapeutic setting and

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    Section 3.2 Case Studies

    involve a series of interviews. An interviewer will typically study the subject in detail, record- ing everything from direct quotes and observations to his or her own interpretations. We encountered this technique briefly in Chapter 2 (2.1), in discussing Oliver Sacks’s case studies of individuals learning to live with neurological impairments.

    Pros and Cons of Case Studies

    Case studies in psychology are a form of qualitative research and represent the lowest point on our continuum of control. Because they involve one person at a time, without a control group, case studies are often unsystematic. That is, the participants are chosen, rather than selected randomly, because they tell a compelling story or because they represent an unusual set of circumstances. Studying these individuals allows for a great deal of exploration, which can often inspire future research. However, it is nearly impossible to generalize from one case study to the larger population. In addition, because the case study includes both direct obser- vation and the researcher’s interpretation, a researcher’s biases run the risk of influencing the interpretations. For example, Pennebaker’s personal investment in demonstrating that writing has health benefits could have led to more positive interpretations of the Vietnam vet- eran’s outcomes. However, in this particular case study, the benefits of writing mirror those seen in hundreds of controlled experimental studies involving thousands of people, so we can feel confident in the conclusions from the single case.

    Case studies have two distinct advantages over other forms of research. First is the simple fact that anecdotes are persuasive. Despite Pennebaker’s nontraditional approach to a sci- entific talk, the audience came away utterly convinced of the benefits of therapeutic writing. And although Oliver Sacks studied one neurological patient at a time, the collection of stories in his books sheds very convincing light on the ability of humans to adapt to their circum- stances. Second, case studies provide a useful way to study rare populations and individuals with rare conditions. For example, from a scientific point of view, the ideal might be to gather a random sample of individuals living with severe memory impairment due to alcohol abuse and conduct some sort of controlled study in a laboratory environment. This approach could allow us to make causal statements about the results, as Chapter 5 (5.4) will discuss. But from a practical point of view, such a study would be nearly impossible to conduct, making case studies such as Sacks’s interviews with William Thompson the best strategy for understand- ing this condition in depth.

    Examples of Case Studies

    Throughout the history of psychology, case studies have been used to address a number of important questions and to provide a starting point for controlled quantitative studies. For example, in developing his theories of cognitive development, the Swiss psychologist Jean Piaget first studied the way that his own children developed and changed their thinking styles. Piaget proposed that children would progress through a series of four stages in the way that they approached the world—sensorimotor, preoperational, concrete operational, and formal operational—with each stage involving more sophisticated cognitive skills than the previous

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    Section 3.2 Case Studies

    stage. By observing his own children, Piaget noticed preliminary support for this theory and later was able to conduct more controlled research with larger populations.

    Perhaps one of the most famous case studies in psychology is that of Phineas Gage, a 19th- century railroad worker who suffered severe brain damage. In September of 1848, Gage was working with a team to blast large sections of rock to make way for new rail lines. After a large hole was drilled into a section of rock, Gage’s job was to pack the hole with gunpowder, sand, and a fuse and then tamp it down with a long cylindrical iron rod (known as a “tamping rod”). On this particular occasion, it seems Gage forgot to pack in the sand. So, when the iron rod struck gunpowder, the powder exploded, sending the 3-foot long iron rod through his face, behind his left eye, and out the top of his head. Against all odds, Gage survived this incident with relatively few physical side effects. However, everyone around him noticed that his per- sonality had changed—Gage became more impulsive, violent, and argumentative. Gage’s phy- sician, John Harlow, reported the details of this case in an 1868 article. The following passage offers a strong example of the rich detail that is often characteristic of case studies:

    He is fitful, irreverent, indulging at times in the grossest profanity (which was not previously his custom), manifesting but little deference for his fellows, impatient of restraint or advice when it conflicts with his desires. A child in his intellectual capacity and manifestations, he has the animal passions of a strong man. Previous to his injury, although untrained in the schools, he possessed a well-balanced mind, and was looked upon by those who knew him as a shrewd, smart businessman, very energetic and persistent in executing all his plans of operation. In this regard his mind was radically changed, so decidedly that his friends and acquaintances said he was “no longer Gage.” (pp. 339–342)

    Gage’s transformation ultimately inspired a large body of work in psychology and neurosci- ence that attempts to understand the connections between brain areas and personality. The area of his brain destroyed by the tamping rod is known as the frontal lobe, now understood to play a critical role in impulse control, planning, and other high-level thought processes. Gage’s story is a perfect illustration of the pros and cons of case studies: On the one hand, it is difficult to determine exactly how much the brain injury affected his behavior because he is only one per- son. On the other hand, Gage’s tragedy inspired researchers to think about the connections among mind, brain, and personality. As a result, we now have a vast—and still growing—understand- ing of the brain. The story illustrates a key point about case studies: Although individual cases provide only limited knowledge about people in general, these cases often lead researchers to conduct additional work that does lead to generalizable knowledge.

    Everett Collection

    Various views show an iron rod embedded in Phineas Gage’s (1823–1860) skull.

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    Section 3.3 Archival Research

    Qualitative versus Quantitative Approaches

    Case studies tend to be qualitative more often than not: The goal of this method is to study a particular case in depth, as a way to learn more about a rare phenomenon. In both Pennebak- er’s study of the Vietnam veteran and Harlow’s study of Phineas Gage, the researcher approached the interview process as a way to gather information and learn from the bottom up about the interviewee’s experience. However, a case study can certainly represent quanti- tative research. This is often the case when researchers conduct a series of case studies, learn- ing from the first one or the first few and then developing hypotheses to test on future cases. For example, a researcher could use the case of Phineas Gage as a starting point for hypotheses about frontal lobe injury, perhaps predicting that other cases would show similar changes in personality. Another way in which case studies can add a quantitative element is for research- ers to conduct analyses within a single subject. For example, a researcher could study a patient with brain damage for several years following an injury, tracking the association between deterioration of brain regions with changes in personality and emotional responses. At the end of the day, though, these examples would still suffer the primary drawback of case studies: Because they examine a single individual, researchers find it difficult to generalize findings.

    Research: Thinking Critically

    Analyzing Acupuncture

    Follow the link below to a press release from the Peninsula College of Medicine and Dentistry. This short article reviews recent research from the college, suggesting that acupuncture treatment might be of benefit to patients suffering from “unexplained” symptoms. As you read the article, consider what you have learned so far about the research process, and then respond to the questions below.

    http://www.sciencedaily.com/releases/2011/05/110530080513.htm

    Think about it:

    1. In this study, researchers interviewed acupuncture patients using open-ended questions and recorded their verbal responses, which is a common qualitative research technique. What advantages does this approach have over administering a quantitative questionnaire with multiple-choice items?

    2. What are some advantages of adding a qualitative element to a controlled medical trial like this?

    3. What would be some disadvantages of relying exclusively on this approach?

    3.3 Archival Research

    Slightly further along the continuum of control is archival research, which involves drawing conclusions by analyzing existing sources of data, including both public and private records. Sociologist David Phillips (1977) hypothesized that media coverage of suicides would lead to “copycat” suicides. He tested this hypothesis by gathering archival data from two sources: front-page newspaper articles devoted to high-profile suicides and the number of fatalities

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    Section 3.3 Archival Research

    in the 11-day period following coverage of the suicide. By examining these patterns of data, Phillips found support for his hypothesis. Specifically, fatalities appeared to peak three days after coverage of a suicide, and a greater degree of publicity was associated with a greater peak in fatalities.

    Pros and Cons of Archival Research

    It is difficult to imagine a better way to test Phillips’s hypothesis about copycat suicides. A researcher could never randomly assign people to learn about suicides and then wait to see whether they killed themselves. Nor could someone interview people right before they commit suicide to determine whether they were inspired by media coverage. Archival research provides a test of the hypothesis by examining data that already exist and, thereby, avoids most of the ethical and practical problems of other research designs. One key element of archival research is that it neatly sidesteps issues of partici- pant reactivity, or the tendency of people to behave differently when they are aware of being observed. Any time research is conducted in a laboratory, par- ticipants know they are part of a study and may not behave in a completely natural manner. In contrast, archival data involves making use of records of peo- ple’s natural behaviors. The subjects of Phillips’s study of copycat suicides were individuals who decided to kill themselves, who had no awareness that they would be part of a research study.

    Archival research is also an excellent strategy for examining trends and changes over time. For exam- ple, much of the evidence for global warming comes from observing upward trends in recorded temper- atures around the globe. To gather this evidence, researchers dig into existing archives of weather patterns and conduct statistical tests of the changes over time. Psychologists and other social scientists also make use of this approach to examine population-level changes in everything from suicide rates to voting patterns over time. These comparisons can sometimes involve a blend of archival and current data. For example, a great deal of social-psychology research has been dedicated to understanding peo- ple’s stereotypes about other groups. In a classic series of studies known as the “Princeton Trilogy,” researchers documented the stereotypes held by Princeton students for 25 years (1933 to 1969). Social psychologist Stephanie Madon and her colleagues (2001) collected a new round of data but also conducted a new analysis of the previous archival data. These new analyses suggested that, over time, people have become more willing stereotype other groups, even as the stereotypes themselves have become less negative.

    AP Photo

    Copycat suicides often peak 3 days after media coverage of a high profile suicide, such as when Nirvana’s Kurt Cobain killed himself in 1994.

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    Section 3.3 Archival Research

    One final advantage of archival research is that once a researcher gains access to the relevant archives, it requires relatively few resources. The typical laboratory experiment involves one participant at a time, sometimes requiring the dedicated attention of more than one research assistant for an hour or more. After researchers assemble data from the archives, though, con- ducting statistical analyses is a relatively simple matter. In a 2001 article, the psychologists Shannon Stirman and James Pennebaker used a text-analysis computer program to compare the language of poets who committed suicide (e.g., Sylvia Plath) with the language of simi- lar poets who had not committed suicide (e.g. Denise Levertov). In total, these researchers examined 300 poems from 20 poets, half of whom had committed suicide. Consistent with Durkheim’s theory of suicide as a form of “social disengagement,” Stirman and Pennebaker (2001) found that suicidal poets used more self-references and fewer references to other people in their poems. The impressive part of the study is this: Once they had assembled their archive of poems, their computer program took only seconds to analyze the language and generate a statistical profile of each poet.

    Overall, however, archival research is still relatively low on the continuum of control. Research- ers have to accept the archival data in whatever form they exist, with no control over the way they were collected. For instance, in Stephanie Madon’s (2001) reanalysis of the “Princeton Trilogy” data, she had to trust that the original researchers had collected the data in a reason- able and unbiased way. In addition, because archival data often represent natural behavior, it can be difficult to categorize and organize responses in a meaningful and quantitative way. The upshot is that archival research often requires some creativity on the researcher’s part— such as analyzing poetry using a text-analysis program. In many cases, as we discuss next, the process of analyzing archives involves developing a coding strategy for extracting the most relevant information.

    Content Analysis—Analyzing Archives

    In most examples so far, the data come in a straightforward, ready-to-analyze form. That is, it is relatively simple to count the number of suicides, track the average temperature, or com- pare responses to questionnaires about stereotyping over time. In other cases, the data can come as a sloppy, disorganized mass of information. How does someone who wants to ana- lyze literature, media images, or changes in race relations on television accomplish the analy- sis? These types of data can yield incredibly useful information, provided the researcher can develop a strategy for extracting it.

    Mark Frank and Tom Gilovich—both psychologists at Cornell University—were interested in whether cultural associations with the color black affected behavior. In virtually all cultures, the term “black” is associated with evil—the bad guys wear black hats; people have a “black day” when things turn sour; and some are excluded from social groups by being “blacklisted” or “blackballed.” These associations appear to be independent of any culture-specific preju- dices regarding race or skin color. Frank and Gilovich (1988) wondered whether “a cue as subtle as the color of a person’s clothing” (p. 74) would influence aggressive behavior. To test this hypothesis, they examined aggressive behaviors in professional football and hockey games, comparing teams whose uniforms were black to teams who wore other colors. Imag- ine for a moment being a researcher for this study. Professional sporting events contain a

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    Section 3.3 Archival Research

    wealth of behaviors and events. How would information about the relationship between uni- form color and aggressive behavior be extracted?

    Frank and Gilovich (1988) solved this problem by examining public records of penalty yards (football) and penalty minutes (hockey) because these represent instances of punishment for excessively aggressive behavior, as recognized by the referees. In addition, in both sports, the size of the penalty increases according to the degree of aggression. These penalty records were obtained from the central offices of both leagues, covering the period from 1970 to 1986. Consistent with the researchers’ hypothesis, teams with black uniforms were “uncom- monly aggressive” (p. 76). Most strikingly, two NHL hockey teams changed their uniforms to black during the period under study and showed a marked increase in penalty minutes with the new uniforms. One equally compelling alternative explanation is that, rather than the teams acting more aggressive in black uniforms, referees perceived them to be more aggres- sive while wearing black uniforms. Both explanations are consistent with the idea that cul- tural associations can affect behavior.

    Even this analysis, however, is relatively straightforward because it involved data that were already in quantitative form (penalty yards and minutes). In many cases, the starting point is a jumbled mess of human behavior. In a pair of journal articles, psychologist Russell Weigel and colleagues (1980; 1995) examined the portrayal of race relations on prime-time televi- sion. To do so, they had to make several critical decisions about what to analyze and how to quantify it. The process of systematically extracting and analyzing the contents of a collection of information is known as content analysis. In essence, content analysis involves developing a plan to code and record specific behaviors and events in a consistent way. We can break this plan down into a three-step process.

    Step 1—Identify Relevant Archives Before we develop our coding scheme, we have to start by finding the most appropriate source of data. Sometimes the choice is fairly obvious: To compare temperature trends, the most relevant archives will be weather records. To track changes in stereotyping over time, the most relevant archive is questionnaire data assessing people’s attitudes. In other cases, this decision involves careful consideration of both the research question and practical concerns. Frank and Gilovich decided to study penalties in professional sports because these data were both readily available (from the central league offices) and highly relevant to their hypothesis about aggression and uniform color.

    Because these penalty records were publicly available, the researchers were able to access them easily. But if the research question involved sensitive or personal information—such as hospital records or personal correspondence—researchers would need to obtain permis- sion from a responsible party. Say we wanted to analyze the love letters written by soldiers serving overseas and then try to predict relationship stability. Given the personal, even inti- mate nature of these letters, we would need permission from each person involved before proceeding with the study. However researchers manage to obtain access to private records, protecting the privacy and anonymity of the people involved is paramount. This would mean, for example, using pseudonyms and/or removing names and other identifiers from published excerpts of personal letters.

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    Section 3.3 Archival Research

    Step 2—Sample From the Archives In Weigel’s research on race relations, the most obvious choice of archives comprised snip- pets of both television programming and commercials. Yet this decision was only the first step of the process. Should the researchers examine every second of every program ever aired on television? Naturally not; instead, their approach was to take a smaller sample of televi- sion programming. Chapter 4 (4.3) will discuss sampling in more detail, but the basic pro- cess involves taking a smaller, representative collection of the broader population to conserve resources. Weigel and colleagues (1980) decided to sample one week’s worth of prime-time programming from 1978, assembling videotapes of everything broadcast by the three major networks at the time (CBS, NBC, and ABC). The research team narrowed its sample by elim- inating news, sports, and documentary programming because the hypotheses centered on portrayals of fictional characters of different races.

    Step 3—Code and Analyze the Archives Content analysis’ third and most involved step is to develop a system for coding and analyzing the archival data. Even a sample of one week’s worth of prime-time programming contains a near-infinite amount of information. In the race-relations studies, Weigel et al. elected to code four key variables: (1) the “total human appearance time,” or time during which people were onscreen; (2) the “Black appearance time,” in which Black characters appeared onscreen; (3) the “cross-racial appearance time,” in which characters of two races were onscreen at the same time; and (4) the “cross-racial interaction time,” in which cross-racial characters inter- acted. In the original (1980) paper, these authors reported that Black characters were shown only 9% of the time, and cross-racial interactions only 2% of the time. Fortunately, by the time of their 1995 follow-up study, the rate of Black appearances had doubled, and the rate of cross-racial interactions had more than tripled. However, depressingly little change occurred in some of the qualitative dimensions that they measured, including the degree of emotional connection between characters of different races.

    This study also highlights the variety of options for coding complex behaviors. The four key ratings of “appearance time” consist of simply recording the amount of time that each person or group is onscreen. In addition, the researchers assessed several abstract qualities of inter- action using judges’ ratings. The degree of emotional connection, for instance, was measured by having judges rate the “extent to which cross-racial interactions were characterized by conditions promoting mutual respect and understanding” (Weigel et al., 1980, p. 888). As Chapter 2 (2.2) explained, any time researchers use judges’ ratings, it is important to collect ratings from more than one rater and to make sure they agree in their assessments.

    A researcher’s goal is to find a systematic way to record the observations most relevant to the hypothesis. This is particularly true for quantitative research, where the key is to start with clear operational definitions that capture the variables of interest. This involves both deciding the most appropriate variables and the best way to measure these variables. For example, if someone who analyzes written communication might decide to compare words, sentences, characters, or themes across the sample. A study of newspaper coverage might code the amount of space or number of stories dedicated to a topic, while a study of television news might code the amount of airtime given to different positions. The best strategy in each case will be the one that best represents the variables of interest.

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    Qualitative versus Quantitative Approaches

    Archival research can represent either qualitative or quantitative research, depending on the researcher’s approach to the archives. Most of the examples in this section represent the quan- titative approach: Frank and Gilovich (1988) counted penalties to test their hypothesis about aggression, and Stirman and Pennebaker (2001) counted words to test their hypothesis about suicide. However, the race-relations work by Weigel and colleagues (1980; 1995) represents a nice mix of qualitative and quantitative research. In the initial 1980 study, their primary goal was to document the portrayal of race relations on prime-time television, learning from the ground up (i.e., qualitative). In the 1995 follow-up study, though, the researchers primarily wanted to determine whether these portrayals had changed over a 15-year period. That is, they tested the hypothesis that race relations were portrayed in a more positive light (i.e., quantita- tive). Another way in which archival research can be qualitative is to study open-ended nar- ratives, without attempting to impose structure upon them. This approach is commonly used to study free-flowing text, such as personal correspondence or letters to the editor in a news- paper. A researcher approaching these from a qualitative perspective would attempt to learn from these narratives, without attempting to impose structure via the use of content analyses.

    3.4 Observational Research

    Moving further along the continuum of control, we come to the descriptive design with the greatest amount of researcher control. Observational research involves studies that directly observe behavior and record these observations in an objective and systematic way. Your previous psychology courses may have explored the concept of attachment theory, which argues that an infant’s bond with his or her primary caregiver has implications for later social and emotional development. Mary Ainsworth, a Canadian developmental psychologist, and John Bowlby, a British psychologist and psychiatrist, articulated this theory in the 1960s. They argued that children can form either “secure” or a variety of “insecure” attachments with their caregivers (Ainsworth & Bell, 1970; Bowlby, 1963).

    To assess these classifications, Ainsworth and Bell developed an observational technique called the “strange situation.” Mothers would arrive at their laboratory with their children for a series of struc- tured interactions, including having the mother play with the infant, leave him alone with a stranger, and then return to the room after a brief absence. The researchers were most interested in coding the ways in which the infant responded to these vari- ous episodes (eight in total). One group of infants, for example, was curious when the mother left but then returned to playing with toys, trusting that she would return. Another group showed immedi- ate distress when the mother left and clung to her nervously upon her return. Based on these and other behavioral observations, Ainsworth and colleagues classified these groups of infants as “securely” and “insecurely” attached to their mothers, respectively.

    Rayes/Photodisc/Thinkstock

    Observational research can be used to measure an infant’s attachment to a caregiver.

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    Pros and Cons of Observational Research

    Observational designs are well suited to a wide range of research questions, provided the questions can be addressed through directly observable behaviors and events. For example, researchers can observe parent–child interactions, or nonverbal cues to emotion, or even crowd behavior. However, if they are interested in studying thought processes—such as how close mothers feel to their children—then observation will not suffice. This point harkens back to the discussion of behavioral measures in Chapter 2 (2.2): In exchange for giving up access to internal processes, researchers gain access to unfiltered behavioral responses.

    Research: Making an Impact

    Harry Harlow

    In the 1950s, U.S. psychologist Harry Harlow conducted a landmark series of studies on the mother–infant bond using rhesus monkeys. Although contemporary standards would consider his research unethical, the results of his work revealed the importance of affection, attachment, and love on healthy childhood development.

    Prior to Harlow’s findings, it was believed that infants attached to their mothers as a part of a drive to fulfill exclusively biological needs, in this case obtaining food and water and avoiding pain (Herman, 2007; van der Horst & van der Veer, 2008). In an effort to clarify the reasons that infants so clearly need maternal care, Harlow removed rhesus monkeys from their natural mothers several hours after birth, giving the young monkeys a choice between two surrogate “mothers.” Both mothers were made of wire, but one was bare and one was covered in terry cloth. Although the wire mother provided food via an attached bottle, the monkeys preferred the softer, terry-cloth mother, even though the latter provided no food (Harlow & Zimmerman, 1958; Herman, 2007).

    Further research with the terry-cloth mothers contributed to the understanding of healthy attachment and childhood development (van der Horst & van der Veer, 2008). When the young monkeys were given the option to explore a room with their terry-cloth mothers and had the cloth mothers in the room with them, they used the mothers as a safe base. Similarly, when exposed to novel stimuli such as a loud noise, the monkeys would seek comfort from the cloth-covered surrogate (Harlow & Zimmerman, 1958). However, when the monkeys were left in the room without their cloth mothers, they reacted poorly—freezing up, crouching, crying, and screaming.

    A control group of monkeys who were never exposed to either their real mothers or one of the surrogates revealed stunted forms of attachment and affection. They were left incapable of forming lasting emotional attachments with other monkeys (Herman, 2007). Based on this research, Harlow discovered the importance of proper emotional attachment, stressing the importance of physical and emotional bonding between infants and mothers (Harlow & Zimmerman, 1958; Herman, 2007).

    Harlow’s influential research led to improved understanding of maternal bonding and child development (Herman, 2007). His research paved the way for improvements in infant and child care and in helping children cope with separation from their mothers (Bretherton, 1992; Du Plessis, 2009). In addition, Harlow’s work contributed to the improved treatment of children in orphanages, hospitals, day care centers, and schools (Herman, 2007; van der Horst & van der Veer, 2008).

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    To capture these unfiltered behaviors, it is vital for the researcher to be as unobtrusive as pos- sible. As we have already discussed, people have a tendency to change their behavior when they are being observed. In the bullying study by Craig and Pepler (1997) discussed at the beginning of this chapter, the researchers used video cameras to record children’s behavior unobtrusively. Imagine how (artificially) low the occurrence of bullying might be if the play- ground had been surrounded by researchers with clipboards!

    If researchers conduct an observational study in a laboratory setting, they have no way to hide the fact that people are being observed, but the use of one-way mirrors and video recordings can help people to become comfortable with the setting. Researchers who conduct an obser- vational study out in the real world have even more possibilities for blending into the back- ground, including using observers who are literally hidden. For example, someone hypoth- esizes that people are more likely to pick up garbage when the weather is nicer. Rather than station an observer with a clipboard by the trash can, the researcher could place someone out of sight behind a tree, or perhaps sitting on a park bench pretending to read a magazine. In both cases, people would be less conscious of being observed and therefore more likely to behave naturally.

    One extremely clever strategy for blending in comes from a study by the social psychologist Muzafer Sherif et al. (1954), involving observations of cooperative and competitive behaviors among boys at a summer camp. For Sherif, it was particularly important to make observations in this context without the boys realizing they were part of a research study. Sherif took on the role of camp janitor, which allowed him to be a presence in nearly all of the camp activities. The boys never paid enough attention to the “janitor” to realize his omnipresence—or his dis- creet note-taking. The brilliance of this idea is that it takes advantage of the fact that people tend to blend into the background once we become used to their presence.

    Types of Observational Research

    Several variations of observational research exist, according to the amount of control that a researcher has over the data collection process. Structured observation involves creating a standard situation in a controlled setting and then observing participants’ responses to a predetermined set of events. The “strange situation” studies of parent–child attachment (discussed above) are a good example of structured observation—mothers and infants are subjected to a series of eight structured episodes, and researchers systematically observe and record the infants’ reactions. Even though these types of studies are conducted in a labora- tory, they differ from experimental studies in an important way: Rather than systematically manipulate a variable to make comparisons, researchers present the same set of conditions to all participants.

    Another example of structured observation comes from the research of John Gottman, a psy- chologist at the University of Washington. For nearly three decades, Gottman and his col- leagues have conducted research on the interaction styles of married couples. Couples who take part in this research are invited for a three-hour session in a laboratory that closely resembles a living room. Gottman’s goal is to make couples feel reasonably comfortable and natural in the setting to get them talking as they might do at home. After allowing them to set- tle in, Gottman adds the structured element by asking the couple to discuss an “ongoing issue or problem” in their marriage. The researchers then sit back to watch the sparks fly, recording

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    Section 3.4 Observational Research

    everything from verbal and nonverbal communication to measures of heart rate and blood pressure. Gottman has observed and tracked so many couples over the decades that he is able to predict, with remarkable accuracy, which couples will divorce in the 18 months following the lab visit (Gottman & Levenson, 1992).

    Naturalistic observation, meanwhile, involves observing and systematically recording behavior in the real world. This can be conducted in two broad ways—with or without inter- vention on the part of the researcher. Intervention in this context means that the researcher manipulates some aspect of the environment and then observes people’s responses. For example, a researcher might leave a shopping cart just a few feet away from the cart-return area and track whether people move the cart. (Given the number of carts that are abandoned just inches away from their proper destination, someone must be doing this research all the time.) Recall an example from Chapter 1 (the discussion of ethical dilemmas in section 1.5) in which Harari et al. (1995) used naturalistic observation to study whether people would help in emergency situations. In brief, these researchers staged what appeared to be an attempted rape in a public park and then observed whether groups or individual males were more likely to rush to the victim’s aid.

    The ABC network has developed a hit reality show that mimics this type of research. The show, What Would You Do?, sets up provocative situations in public settings and videotapes people’s reactions (full episodes are available online at http://abcnews.go.com/WhatWouldYouDo/). An unwitting participant in one of these episodes might witness a customer stealing tips from a restaurant table, or a son berating his father for being gay, or a man proposing to his girl- friend who minutes earlier had been kissing another man at the bar. Of course, these observa- tion “studies” are more interested in shock value than data collection (or Institutional Review Board [IRB] approval; see Section 1.5), but the overall approach can be a useful strategy to assess people’s reactions to various situations. In fact, some of the scenarios on the show are based on classic studies in social psychology, such as the well-documented phenomenon that people are reluctant to take responsibility for helping in emergencies.

    Alternatively, naturalistic studies can involve simply recording ongoing behavior without any attempt by the researchers to intervene or influence the situation. In these cases, the goal is to observe and record behavior in a completely natural setting. For example, researchers might station themselves at a liquor store and observe the numbers of men and women who buy beer versus wine. Or, they might observe the numbers of people who give money to the Salvation Army bell-ringers during the holiday season. A researcher can use this approach to compare different conditions, provided the differences occur naturally. That is, researchers could observe whether people donate more money to the Salvation Army on sunny or snowy days, or compare donation rates when the bell ringers are different genders or races. Do peo- ple give more money when the bell-ringer is an attractive female? Or do they give more to someone who looks needier? These are all research questions that could be addressed using a well-designed naturalistic observation study.

    Finally, participant observation involves having the researcher(s) conduct observations while engaging in the same activities as the participants. The goal is to interact with these participants to gain better access and insight into their behaviors. In one famous example, the psychologist David Rosenhan (1973) was interested in the experience of people hospital- ized for mental illness. To study these experiences, he had eight perfectly sane people gain admission to different mental hospitals. These fake patients were instructed to give accurate

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    life histories to a doctor but lie about one diagnostic symptom. They all claimed to hear an occasional voice saying the words “empty,” “hollow,” and “thud.” Such auditory hallucinations are a symptom of schizophrenia, and Rosenhan chose these words to vaguely suggest an exis- tential crisis.

    Once admitted, these “patients” behaved in a normal and cooperative manner, with instruc- tions to convince hospital staff that they were healthy enough to be released. In the meantime, they observed life in the hospital and took notes on their experiences—a behavior that many doctors interpreted as “paranoid note-taking.” The main finding of this study was that hospi- tal staff tended to view all patient behaviors through the lens of their initial diagnoses. Despite immediately acting “normally,” these fake patients were hospitalized an average of 19 days (with a range from 7 to 52) before being released. All but one was diagnosed with “schizo- phrenia in remission” upon release. Rosenhan’s other striking finding was that treatment was generally depersonalized, with staff spending little time with individual patients.

    In another example of participant observation, Festinger, Riecken, and Schachter (1956) decided to join a doomsday cult to test their new theory of cognitive dissonance. Briefly, this theory argues that people are moti- vated to maintain a sense of consis- tency among their various thoughts and behaviors. So, for example, a per- son who smokes a cigarette despite being aware of the health risks might rationalize smoking by convincing herself that lung-cancer risk is really just genetic. In this case, Festinger and colleagues stumbled upon the case of a woman named Mrs. Keach, who was predicting the end of the world, via alien invasion, at 11 p.m. on a specific date six months in the future. What would happen, they wondered, when this prophecy failed to come true? (One can only imag- ine how shocked they would have been had the prophecy turned out to be correct.)

    To answer this question, the researchers pretended to be new converts and joined the cult, living among the members and observing them as they made their preparations for dooms- day. Sure enough, the day came, and 11 p.m. came and went without the world ending. Mrs. Keach first declared that she had forgotten to account for a time-zone difference, but as sun- rise started to approach, the group members became restless. Finally, after a short absence to communicate with the aliens, Mrs. Keach returned with some good news: The aliens were so impressed with the devotion of the group that they decided to postpone their invasion. The group members rejoiced, rallying around this brilliant piece of rationalizing, and quickly began a new campaign to recruit new members.

    As these examples illustrate, participant observation can provide access to amazing and one- of-a-kind data, including insights into group members’ thoughts and feelings. This approach

    RENARD/BSIP/Superstock

    Psychologists David Rosenhan’s study of staff and patients in a mental hospital found that patients tended to be treated based on their diagnosis, not on their actual behavior.

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    also provides access to groups that might be reluctant to allow outside observers. However, the participant approach has two clear disadvantages over other types of observation. The first problem is ethical; data are collected from individuals who do not have the opportunity to give informed consent. Indeed, the whole point of the technique is to observe people with- out their knowledge. Before an IRB can approve this kind of study, researchers must show an extremely compelling reason to ignore informed consent, as well as extremely rigorous measures to protect identities. The second problem is methodological; the approach provides ample opportunity for the objectivity of observations to be compromised by the close contact between researcher and participant. Because the researchers are a part of the group, they can change the dynamics in subtle ways, possibly leading the group to confirm their hypothesis. In addition, the group can shape the researchers’ interpretations in subtle ways, leading them to miss important details.

    Another spin on participant observation is called ethnography, or the scientific study of the customs of people and cultures. This is very much a qualitative method that focuses on observing people in the real world and learning about a culture from the perspective of the person being studied—that is, learning from the ground up rather than testing hypotheses. Ethnography is used primarily in other social-science fields, such as anthropology. In one famous example, the cultural anthropologist Margaret Mead (1928) used this approach to shed light on differences in social norms around adolescence between American and Samoan societies. Mead’s conclusions were based on interviews she conducted over a six-month period, observing and living alongside a group of 68 young women. Mead concluded from these interviews that Samoan children and adolescents are largely ignored until they reach the age of 16 and become full members of society. Among her more provocative claims was the idea that Samoan adolescents were much more liberal in their sexual attitudes and behav- iors than American adolescents.

    Mead’s work has been the subject of criticism by a handful other anthropologists, one of whom has even suggested that Mead was taken in by an elaborate joke played by the group of young girls. Still others have come to Mead’s rescue and challenged the critics’ interpretations. The nature of this debate between Mead’s critics and her supporters highlights a distinctive char- acteristic of qualitative methods: “Winning” the argument is based on challenging interpreta- tions of the original interviews and observations. In contrast, disagreements around quanti- tative methods are generally based on examining statistical results from hypothesis testing. While quantitative methods may lose much of the richness of people’s experiences, they do offer an arguably more objective way of settling theoretical disputes.

    Steps in Observational Research

    One of the major strengths of observational research is its high degree of ecological validity; that is, the research can be conducted in situations that closely resemble the real world. Think of the chapter examples so far—married couples observed in a living-room-like laboratory; doomsday cults observed from within; bullying behaviors on the school playground. In every case, people’s behaviors are observed in the natural environment or something very close to it. However, this ecological validity comes at a price; the real world is a jumble of information, some relevant, some not so much. The challenge for researchers, then, is to decide on a sys- tem that provides the best test of their hypothesis, one that can sort out the signal from the

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    noise. This section discusses a three-step process for conducting observational research. The key point to note right away is that most of this process involves making decisions ahead of time so that the process of data collection is smooth, simple, and systematic.

    Step 1—Develop a Hypothesis For research to be systematic, it is important to impose structure by having a clear research question, and, in the case of quantitative research, a clear hypothesis as well. Other chapters have covered hypotheses in detail, but the main points bear repeating: A hypothesis must be testable and falsifiable, meaning that it must be framed in such a way that it can be addressed through empirical data and might be disconfirmed by these data. In the example involving Salvation Army donations, we predicted that people might donate more money to an attractive bell-ringer. This hypothesis could easily be tested empirically and could just as easily be discon- firmed by the right set of data—say, if attractive bell-ringers brought in the fewest donations.

    This particular example also highlights an additional important feature of observational hypotheses; namely, they must be based on observable behaviors. That is, we can safely make predictions about the amount of money people will donate because we can directly observe it. We are, nonetheless, unable to make predictions in this context about the reasons for dona- tions. We would have no way to observe, say, that people donate more to attractive bell-ring- ers because they are trying to impress them. In sum, one limitation of observing behavior in the real world is that it prevents researchers from delving into the cognitive and motivational reasons behind the behaviors.

    Step 2—Decide What and How to Sample Once a researcher has developed a hypothesis that is testable, falsifiable, and observable, the next step is to decide what kind of information to gather from the environment to test this hypothesis. The simple fact is that the world is too complex to sample every- thing. Imagine that someone wanted to observe the dinner rush at a restaurant. A nearly infinite list of possibilities for observation presents itself: What time does the restaurant get crowded? How often do people send their food back to the kitchen? What are the most popular dishes? How often do people get in arguments with the wait staff? To simplify the process of observing behavior, the researcher will need to take a sample, or a smaller portion of the population, that is relevant to the hypothesis. That is, rather than observing “dinner at the restaurant,” the researcher’s goal is to narrow his or her focus to something as specific as “the number of people waiting in line for a table at 6 p.m. versus 9 p.m.”

    The choice of what and how to sample will ulti- mately depend on the best fit for the hypothesis.

    Steve Mason/Photodisc/Thinkstock

    The dinner scene at a busy restaurant offers a wide variety of behaviors to observe. In order to simplify the obser- vation process, researchers should narrow the focus by taking a sample.

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    The context of observational research offers three strategies for sampling behaviors and events. The first strategy, time sampling, involves comparing behaviors during different time intervals. For example, to test the hypothesis that football teams make more mistakes when they start to get tired, researchers could count the number of penalties in the first five minutes and the last five minutes of the game. This data would allow researchers to compare mistakes at one time interval with mistakes at another time interval. In the case of Festinger’s (1956) study of a doomsday cult, time sampling was used to compare how the group mem- bers behaved before and after their prophecy failed to come true.

    The second strategy, individual sampling, involves collecting data by observing one person at a time to test hypotheses about individual behaviors. Many of the examples already dis- cussed involve individual sampling: Ainsworth and colleagues (1970) tested their hypotheses about attachment behaviors by observing individual infants, while Gottman (1992) tests his hypotheses about romantic relationships by observing one married couple at a time. These types of data allow researchers to examine behavior at the individual level and test hypoth- eses about the kinds of things people do—from the way they argue with their spouses to whether they wear team colors to a football game.

    The third strategy, event sampling, involves observing and recording behaviors that occur throughout an event. For example, we could track the number of fights that break out during an event such as a football game, or the number of times people leave the restaurant without paying the check. This strategy allows for testing hypotheses about the types of behaviors that occur in a particular environment or setting. For instance, a researcher might compare the number of fights that break out in a professional football versus a professional hockey game. Or, the next time we host a party, we could count the number of wine bottles versus beer bottles that end up in the recycling bin. The distinguishing feature of this strategy is its focus on occurrence of behaviors more than on the individuals performing these behaviors.

    Step 3—Record and Code Behavior Having formulated a hypothesis and decided on the best sampling strategy, researchers must perform one final and critical step before beginning data collection. Namely, they have to develop good operational definitions of the variables by translating the underlying concepts into measurable variables. Gottman’s research turns the concept of marital interactions into a range of measurable variables, such as the number of dismissive comments and passive- aggressive sighing—all things that can be observed and counted objectively. Rosenhan’s 1973 study involving fake schizophrenic patients turned the concept of patient experience into mea- sureable variables such as the amount of time staff members spent with each patient—again, something very straightforward to observe.

    It is vital that researchers decide up front what kinds and categories of behavior they will be observing and recording. In the last section, we narrowed down our observation of dinner at the restaurant to the number of people in line at 6 p.m. versus the number of people in line at 9 p.m. But how can we be sure of an accurate count? What if two people are waiting by the door while the other two members of the group are sitting at the bar? Are those at the bar waiting for a table or simply having drinks? One possibility might be to count the number of individuals who walk through the door in different time periods, although our count could be inflated by those who give up on waiting or who only enter to sneak in and out of the restroom.

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    In short, observing behavior in the real world can be messy. The best way to deal with this mess is to develop a clear and consistent categorization scheme and stick with it. That is, in testing a hypothesis about the most crowded time at a restaurant, researchers would choose one method of counting people and use it for the duration of the study. In part, this choice of a method is a judgment call, but researchers’ judgment should be informed by three criteria. First, they should consider practical issues, such as whether their categories can be directly observed. A researcher can observe the number of people who leave the restaurant but can- not observe whether they got impatient. Second, they should consider theoretical issues, such as how well the categories represent the underlying theory. Why did researchers decide to study the most crowded time at the restaurant? Perhaps this particular restaurant is in a new, up-and-coming neighborhood, and they expect the restaurant to become crowded over the course of the evening. The time would also lead researchers to include people sitting both at tables and at the bar—because this crowd may come to the restaurant with the sole inten- tion of staying at the bar. Finally, researchers should consider previous research in choosing their categories. Have other researchers studied dining patterns in restaurants? What kinds of behaviors did they observe? If these categories make sense for the project, researchers may feel free to re-use them—no need to reinvent the wheel.

    Last but not least, a researcher should take a step back and evaluate both the validity and the reliability of the coding system. (See Section 2.2 for a review of these terms.) Validity in this case means making sure the categories capture the underlying variables in the hypothesis (i.e., construct validity; see Section 2.2). For example, in Gottman’s studies of marital interactions, some of the most important variables are the emotions expressed by both partners. One way to observe emotions would be to count the number of times a person smiles. However, we would have to think carefully about the validity of this measure, because smiling could indi- cate either genuine happiness or condescension. As a general rule, the better and more spe- cific researchers’ operational definitions, the more valid their measures will be (Chapter 2).

    Reliability in this context means making sure data are collected in a consistent way. If research involves more than one observer using the same system, their data should look roughly the same (i.e., interrater reliability). This reliability is accomplished in part by making the obser- vation task simple and straightforward—for example, having trained assistants use a check- list to record behaviors rather than depending on open-ended notes. The other key to improv- ing reliability is careful training of the observers, giving them detailed instructions and ample opportunities to practice the rating system.

    Observation Examples

    To explain how all of this comes together, we will explore a pair of examples, from research question to data collection.

    Example 1—Theater Restroom Usage First, imagine, for the sake of this example, that someone is interested in whether people are more likely to use the restroom before or after watching a movie. Such a research question could provide valuable information for theater owners in planning employee schedules (i.e., when are bathrooms most likely to need cleaning). Thus, studying patterns of human behav- ior results in valuable applied knowledge.

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    The first step is to develop a specific, testable, and observable hypothesis. In this case, we might predict that people are more likely to use the restroom after the movie, as a result of consum- ing those 64-ounce sodas during the movie. Just for fun, we will also compare the restroom usage of men and women. Perhaps men are more likely to wait until after the movie, whereas women are just as likely to go before as after? This pattern of data might look something like the percentages in Table 3.1. That is, men make 80% of their restroom visits after the movie and 20% before the movie, while women make about 50% of their restroom visits at each time.

    The next step is to decide on the best sam- pling strategy to test this hypothesis. Of the three sampling strategies discussed— individual, event, and time—which one seems most relevant here? The best option would probably be time sampling because the hypothesis involves compar- ing the number of restroom visitors in

    two time periods (before versus after the movie). So, in this case, we would need to define a time interval for collecting data. We could limit our observations to the 10 minutes before the previews begin and the 10 minutes after the credits end. The potential problem here, of course, is that some people might use either the previews or the end credits as a chance to use the restroom. Another complication arises in trying to determine which movie people are watching; in a giant multiplex theater, movies start just as others are finishing. One possible solution, then, would be to narrow the sample to movie theaters that show only one movie at a time and to define the sampling times based on the actual movie start- and end-times.

    Having determined a sampling strategy, the next step is to identify the types of behaviors we want to record. This particular hypothesis poses a challenge because it deals with a rather private behavior. To faithfully record people “using the restroom,” we would need to station researchers in both men’s and women’s restrooms to verify that people actually, well, “use” the restroom while they are in it. However, this strategy poses the potential downside that the researcher’s presence (standing in the corner of the restroom) will affect people’s behavior. Another, less intrusive option would be to stand outside the restroom and simply count “the number of people who enter.” The downside to that, of course, is that we technically do not know why people are going into the restroom. But sometimes research involves making these sorts of compromises—in this case, we chose to sacrifice a bit of precision in favor of a less- intrusive measurement. This compromise would also serve to reduce ethical issues with observing people in the restroom.

    So, in sum, we started with the hypoth- esis that men are more likely to use the restroom after a movie, while women use the restroom equally before and after. We then decided that the best sampling strat- egy would be to identify a movie theater showing only one movie and to sample from the 10-minute periods before and after the actual movie’s running time. Finally, we decided that the best strategy for recording behavior would be to station observ- ers outside the restrooms and count the number of people who enter. Now, say we conduct these observations every evening for one week and collect the data in Table 3.2.

    Table 3.1: Hypothesized restroom visits

    Gender Men Women

    Before movie 20% 50%

    After movie 80% 50%

    Total 100% 100%

    Table 3.2: Findings from observing restroom visits

    Gender Men Women

    Before movie 75 (25%) 300 (60%)

    After movie 225 (75%) 200 (40%)

    Total 300 (100%) 500 (100%)

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    Section 3.4 Observational Research

    Notice that more women (N = 500) than men (N = 300) attended the movie theater during our week of sampling. The real test of our hypothesis, however, comes from examining the per- centages within gender groups. That is, of the 300 men who went into the restroom, what per- centage of them did so before the movie and what percentage of them did so after the movie? In this dataset, women used the restroom with relatively equal frequency before (60%) and after (40%) the movie. Men, in contrast, were three times as likely to use the restroom after (75%) than before (25%) the movie. In other words, our hypothesis appears to be confirmed by examining these percentages.

    Example 2—Cell Phone Usage While Driving Imagine that we are interested in patterns of cell phone usage among drivers. Several recent studies have reported that drivers using cell phones are as impaired as drunk drivers, mak- ing this an important public safety issue. Thus, if we could understand the contexts in which people are most likely to use cell phones, it would provide valuable information for develop- ing guidelines for safe and legal use of these devices. So, this study might count the number of drivers using cell phones in two settings: while navigating rush-hour traffic and while moving on the freeway.

    The first step is to develop a specific, testable, and observable hypothesis. In this case, we might predict that people are more likely to use cell phones when they are bored in the car. So, we hypothesize that we will see more drivers using cell phones while stuck in rush-hour traffic than while moving on the freeway.

    The next step is to decide on the best sampling strategy to test this hypothesis. Of the three sampling strategies discussed—individual, event, and time—which one seems most relevant here? The best option would probably be individual sampling because we are interested in the cell phone usage of individual drivers. That is, for each individual car we see during the observation period, we want to know whether the driver is using a cell phone. One strategy for collecting these observations would be to station observers along a fast-moving stretch of freeway, as well as along a stretch of road that is clogged during rush hour. These observers would keep a record of each passing car and note whether the driver is on the phone.

    After selecting a sampling strategy, we next must decide the types of behaviors to record. One challenge this study presents is how broadly to define cell phone usage. Should we include both talking and text messaging? Given our interest in distraction and public safety, we prob- ably want to include text messaging. Several states have recently banned this practice while driving, often in response to tragic accidents. Because we will be observing moving vehicles, the most reliable approach might be to simply note whether drivers have a cell phone in their hand. As with the restroom study, we sacrifice a little bit of precision (i.e., knowing what the driver is using the cell phone for) to capture behaviors that are easier to record.

    To sum up, we started with the hypothesis that drivers would be more likely to use cell phones when stuck in traffic. We then decided that the best sampling strategy would be to station observers along two stretches of road who would note whether drivers were using cell phones. Finally, we decided that the cell phone usage would be defined as each driver holding a cell phone. Now, suppose we conducted these observations over a 24-hour period and collected the data in Table 3.3.

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    Section 3.4 Observational Research

    The results show that more cars passed by on the highway (N = 300) than on the street during the rush-hour stretch (N = 100). The real test of our hypoth- esis, though, comes from examining the percentages within each stretch. That is, of the 100 people observed during rush hour and the 300 observed on the highway, what percentage was using cell

    phones? In this data set, 30% of those in rush hour were using cell phones, compared with 67% of those on the highway. In other words, the data did not confirm our hypothesis. Drivers in rush hour were less than half as likely to be using cell phones. The next step in this research program would be to speculate on the reasons the data contradicted the hypothesis.

    Qualitative versus Quantitative Approaches

    The general method of observation lends itself equally well to qualitative and quantitative approaches, although some types of observation fit one approach better than the other. For example, structured observation tends to focus on hypothesis testing and quantification of responses. In Mary Ainsworth’s (1970) “strange situation” research (described previously), the primary goal was to expose children to a predetermined script of events and to test hypotheses about how children with secure and insecure attachments would respond to these events. In contrast, naturalistic observation—and, to a greater extent, participant obser- vation—tends to focus on learning from events as they occur naturally. In Leon Festinger’s “doomsday cult” study, the researchers joined the group to observe the ways members reacted when their prophecy failed to come true. Margaret Mead (1928) spent several months living with Samoan adolescents to understand social norms around coming of age.

    Research: Thinking Critically

    “Irritable Heart” Syndrome in Civil War Veterans

    Follow the link below to an article by science writer and editor K. Kris Hirst. In this article, Hirst reviews compelling research from health psychologist Roxanne Cohen Silver and her colleagues at the University of California, Irvine. Cohen Silver and her colleagues reviewed the service records of 15,027 Civil War veterans, finding an astounding rate of mental illness—long before post-traumatic stress disorder was recognized. As you read the article, consider what you have learned so far about the research process, and then respond to the questions below.

    http://psychology.about.com/od/ptsd/a/irritableheart.htm

    Think about it:

    1. What hypotheses are the researchers testing in this study? 2. How did the researchers quantify trauma experienced by Civil War soldiers? Do you

    think this is a valid way to operationalize trauma? Explain why or why not. 3. Would this research be best described as case studies, archival research, or natural

    observation? Does the study involve elements of more than one type? Explain.

    Table 3.3: Findings from observing cell phone usage

    Rush Hour Highway

    Cell Phone 30 (30%) 200 (67%)

    No Cell Phone 70 (70%) 100 (33%)

    Total 100 300

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    Section 3.5 Describing Your Data

    3.5 Describing Your Data

    Before we move on from descriptive research designs, this last section discusses the process of presenting descriptive data in both graphical and numeric form. No matter how the researcher presents data, a good description is accurate, concise, and easy to understand. In other words, researchers have to represent the data accurately and in the most efficient way possible so that their audience can understand it. Another, more eloquent way to think of these principles is to take the advice of Edward Tufte, a statistician and expert in the display of visual informa- tion. Tufte (2001) suggests that when people view visual displays, they should spend time on content-reasoning rather than design-decoding. The sole purpose of designing visual presenta- tions is to communicate information. So, the audience should spend time thinking about the information being presented, not trying to puzzle through the display itself. The following sec- tions explain guidelines for accomplishing this goal in both numeric and visual form.

    Table 3.4 presents hypothetical data from a sample of 20 participants. In this example, we have asked people to report their gender and ethnicity, as well as answer questions about their overall life satisfaction and daily stress. Each row in this table represents one participant

    Table 3.4: Raw data from a sample of 20 individuals

    Subject ID Gender Ethnicity Life satisfaction Daily stress

    1 Male White 40 10

    2 Male White 47 9

    3 Female Asian 29 8

    4 Male White 32 9

    5 Female Hispanic 25 3

    6 Female Hispanic 35 3

    7 Female White 28 8

    8 Male Hispanic 40 9

    9 Male Asian 37 10

    10 Female African-American 30 10

    11 Male White 43 8

    12 Male Asian 40 4

    13 Male White 48 7

    14 Female African-American 30 4

    15 Female White 37 7

    16 Male Hispanic 40 1

    17 Female White 36 1

    18 Male African-American 45 8

    19 Female White 42 8

    20 Female African-American 38 7

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    Section 3.5 Describing Your Data

    in the study, and each column represents one of the variables for which data were collected. This chapter focuses on ways to describe the sample characteristics. Later chapters will return to these principles in discussing graphs that display the relationship between two or more variables.

    Numeric Descriptions

    Because psychology is a scientific discipline, it often expresses preference for presenting data in number form. These numbers provide a metric that can be used to compare findings from one study to another, to evaluate the overall consistency of whatever phenomenon is being studied. Following is a brief overview of some common numeric descriptors for data.

    Frequency Tables Often, a good first step in approaching a data set is to obtain a sense of the fre- quencies for demographic variables—in this example, gender and ethnicity. The frequency tables shown in Table 3.5 are designed to present the number and per- centage of the sample that fall into each of a set of categories. As this pair of tables shows, the sample consisted of an equal number of men and women (i.e., 50% for each gender). The majority of participants were White (45%), with the remainder divided almost equally between African- American (20%), Asian (15%), and His- panic (20%) ethnicities.

    Researchers can gain a lot of information from numerical summaries of data. In fact, numeric descriptors form the start- ing point for doing inferential statistics and testing hypotheses. A statistics course explores these statistics in detail, but for now it is important to understand that two numeric descrip- tors can provide a wealth of information about a data set: measures of central tendency and measures of dispersion.

    Measures of Central Tendency The first number we need to describe our data is a measure of central tendency, which rep- resents the most typical case in our data set. Central tendency is a single number that pro- vides an overall sense of all the numbers. Think of what happens when colors are mixed: Add- ing yellow to blue creates green, so green gives us an overall sense of the combination of the two colors. In the same way, think of a household where one parent has a high salary, another has a moderate salary, and a teenager makes minimum wage. Taking the average of all three gives us an overall sense of the income for the entire household.

    Table 3.5: Frequency table summarizing ethnicity and sex distribution

    Gender Frequency Percentage

    Female 10 50.0

    Male 10 50.0

    Total 20 100.0

    Ethnicity Frequency Percentage

    African-American 4 20.0

    Asian 3 15.0

    Hispanic 4 20.0

    White 9 45.0

    Total 20 100.0

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    Section 3.5 Describing Your Data

    Central tendency can be represented by these three indices:

    The mean is the mathematical average of a data set, calculated by adding up all the scores in the data set and then dividing this total by the number of scores in the data set. Because we are adding and dividing our scores, the mean can only be calculated using interval or ratio data (see Chapter 2 for a review of the four scales of measurement).

    The median, another measure of central tendency, represents the number in the middle of a dataset, with 50% of scores both above and below it. The median is identified by placing the list of values in ascending numeric order, then selecting the number in the middle. This measure of central tendency can be used for ordinal, interval, or ratio data because it does not require mathematical manipulation to obtain.

    The final measure of central tendency, the mode, represents the most frequent score in a data set and is obtained either by visual inspection of the values or by consulting a frequency table like in the one in Table 3.5. Because the mode represents a simple frequency count, it can be used with any of the four scales of measurement. In addition, it is the only measure of central tendency that is valid for use with nominal data—that is, those that do not have a numerical value—since the numbers assigned to these data are arbitrary.

    One important takeaway is that the scale of measurement largely dictates the choice between measures of central tendency—nominal scales can only use the mode, and only interval or ratio scales can use the mean. (For a review of these scales of measurement, see Chapter 2, Section 2.3.) The other piece of the puzzle is to consider which measure best represents the data. Remember that the central tendency is a way to represent the “typical” case using a single number, so the goal is to settle on the most representative number. The examples in Table 3.6 illustrate this process.

    Table 3.6: Comparing the mean, median, and mode

    Data Mean Median Mode Discussion

    1,2,3,4,5,11,11 5.29 4 11 • Both the mean and the median seem to repre- sent the data fairly well.

    • The mean is a slightly better choice because it hints at the higher scores.

    • The mode is not representative—two people seem to have higher scores than everyone else.

    1,1,1,5,10,10,100 18.29 5 1 • The mean is inflated by the atypical score of 100 and therefore does not represent the data accurately.

    • The mode is also not representative because it ignores the higher values.

    • In this case, the median is the most representa- tive value to describe this dataset.

    Measures of Dispersion The second measure used to describe a dataset is a measure of dispersion, or the spread of scores around the central tendency—also referred to as measures of “variability.” Measures of dispersion tell us just how typical the typical score is. If the dispersion is low, then scores are

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    Low Amount of Dispersion Around the Mean (red dotted line)

    High Amount of Dispersion Around the Mean (red dotted line)

    Section 3.5 Describing Your Data

    clustered tightly around the central tendency; if dispersion is higher, then the scores stretch out farther from the central tendency. Figure 3.2 presents a conceptual illustration of disper- sion. The graph on the left has a low amount of dispersion because the scores (i.e., the yellow curve) cluster tightly around the average value (i.e., the red dotted line). The graph on the right shows a high amount of dispersion because the scores (yellow curve) spread out widely from the average value (red dotted line). The graph on the right might represent the earlier example of household income: The average income represents all three family members, but between the high-earning parent and the minimum-wage-earning teenager is a fairly large spread.

    One of the most straightforward mea- sures of dispersion is the range, which is the difference between the highest and lowest scores. In Table 3.6, the range of the first dataset would be found by simply subtracting the lowest value (1) from the highest value (11), to get a range of 10. The range is useful in giving a general idea of the spread of scores, although it does not say much about how tightly these scores cluster around the mean.

    The most common measures of dispersion are the variance and standard deviation, both of which represent the average difference between the mean and each individual score. The variance is calculated by subtracting each score from the mean to obtain a deviation score, squaring and summing these individual deviation scores, and then dividing by the sample size. The more scores are spread out around the mean, the higher the sum of these deviation scores will be, and therefore the higher the variance will be. Another common measure, the standard deviation (SD), is calculated as the square root of the variance.

    Once we know the central tendency and the dispersion of variables, we have a good sense of what the sample looks like. These numbers also provide a valuable part in calculating the inferential statistics that we ultimately use to test our hypotheses.

    Standard Scores So far we have discussed ways to describe a particular sample in numeric terms. What do we do when we want to compare results from different samples—or from studies using differ- ent scales? Say we want to compare the anxiety levels of two people; unfortunately, in this example, these people were measured using different anxiety scales:

    Joe scored 25 on the ABC Anxiety Scale, which has a mean of 15 and a stan- dard deviation of 2.

    Deb scored 40 on the XYZ Anxiety Scale, which has a mean of 30 and a stan- dard deviation of 10.

    Figure 3.2: Two distributions with a low versus high amount of dispersion

    Low Amount of Dispersion Around the Mean (red dotted line)

    High Amount of Dispersion Around the Mean (red dotted line)

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    Section 3.5 Describing Your Data

    At first glance, Deb’s anxiety score appears higher, but note that the scales have different properties: The ABC scale has an average score of 15, while the XYZ scale has a higher average score of 30. The dispersion of these scales is also different; scores on the ABC scale cluster more tightly around the mean (i.e., the standard deviation is 2 compared to 10 on the XYZ scale).

    The solution for comparing these scores is to convert both of them to standard scores (often expressed as z scores), which represent the distance of each score from the sample mean, expressed in standard deviation units. Standard scores let researchers translate raw scores into distributions with a predefined mean and standard deviation for easier interpretation. For example, scores on IQ tests are converted (i.e., standardized) onto a scale that has a mean of 100 and a standard deviation of 15. This tells us that a person with an IQ score of 100 is right at the average for the population, while someone with a score of 130 is two standard deviations above average.

    The formula for a z score is worth examining in greater detail, as a way to understand the broader concept. Memorizing or using the formula in this research methods course is not required. The formula for a z score is:

    z = (x – M)/SD

    This formula subtracts the mean (M) from the individual score (x) and then divides this dif- ference by the standard deviation of the sample (SD). To compare Joe’s score with Deb’s score, we simply substitute the appropriate numbers, using the mean and standard deviation from the scale that each one completed. This enables us to place scores from very different distri- butions on the same scale, making them easier to compare with one another. So, in this case:

    Joe: z = (x – M)/SD = (25 – 15)/2 = 10/2 = 5

    Deb: z = (x – M)/SD = (40 – 30)/10 = 10/10 = 1

    The resulting scores represent each person’s score in standard deviation terms: Joe is 5 stan- dard deviations above the mean of the ABC scale, while Deb is only 1 standard deviation above the mean of the XYZ scale. Or, in plain English, Joe is considerably more anxious than Deb.

    To understand just how anxious Joe is, it is helpful to know a bit about why this technique works. Anyone who has taken a statistics class will have encountered the concept of the nor- mal distribution (or “bell curve”), a symmetric distribution with an equal number of scores on either side of the mean, as Figure 3.3 illustrates.

    It turns out that many variables in the social and behavioral sciences fit this normal distribu- tion, provided the sample sizes are large enough. A normal distribution is useful because it has a consistent set of properties, such as having the same value for mean, median, and mode. In addition, if the distribution is normal, each standard deviation cuts off a known percentage of the curve, as illustrated in Figure 3.3. That is, 68% of scores will fall within ±1 standard deviation of the mean; 95% of scores will fall within ± two standard deviations; and 99.7% of scores will fall within ± three standard deviations.

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    Low –3SD –2SD –1SD +1SD +2SD +3SDMean

    Score

    68%

    95%

    99.7%

    High

    Fr eq

    u en

    cy

    Section 3.5 Describing Your Data

    These percentages allow us to understand individual data points in even more useful ways, because we can easily move back and forth between z scores, percentages, and standard deviations. Take the example of Joe and Deb’s anxiety scores: Deb has a z score of 1, which means her anxiety is 1 standard deviation above the mean. Furthermore, as we can see by consulting the normal distribution (Figure 3.3), her anxiety level is higher than 84% of the population. Joe has a z score of 5, which means his anxiety is 5 standard deviations above the mean. This also means that his anxiety is higher than 99.999% of the population. (For a handy online calculator that converts between z scores and percentages, see: http://www .measuringusability.com/pcalcz.php.)

    Discussions of intelligence test scores also commonly use the relationship between z scores and percentiles. Tests that purport to measure IQ are converted to a scale that has a mean of 100 and a standard deviation of 15. Because IQ is normally distributed, we can move easily back and forth between z scores and percentages. For example, someone who has an IQ test score of 130 falls 2 standard deviations above the mean and falls in the upper 2.5% of the population. A person with an IQ test score of 70 is 2 standard deviations below the mean and thus falls in the bottom 2.5% of the population.

    Ultimately, the use of standard scores allows us to take data that have been collected on dif- ferent scales—perhaps in different laboratories and different countries—and place them on the same metric for comparison. As we have discussed in several contexts, science is all about the accumulation of knowledge one study at a time. The best support for an idea comes when data from different researchers, using different measures to capture the same concept, back the idea. The ability to convert these different measures back to the same metric is an invalu- able tool for researchers who want to compare research results.

    Figure 3.3: Standard deviations and the normal distribution

    Low –3SD –2SD –1SD +1SD +2SD +3SDMean

    Score

    68%

    95%

    99.7%

    High

    Fr eq

    u en

    cy

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    Fr eq

    u en

    cy

    10

    White Asian Hispanic African–American

    Ethnicity

    9

    8

    7

    6

    5

    4

    3

    1

    2

    0

    Section 3.5 Describing Your Data

    Visual Descriptions

    Displaying data in visual form is often one of the most effective ways to communicate find- ings—as the cliché goes, a picture is worth a thousand words. What sort of visual should a researcher use? The choice of graphs is guided by two criteria: the scale of measurement and the best fit for the results. This section introduces some of the most common visual displays, based on hypothetical data used in Table 3.4.

    Displaying Frequencies One common type of graph is the bar graph, which also summarizes the frequency of data by category. Figure 3.4a depicts a bar graph, showing four categories of ethnicity along the hori- zontal axis and the number of people falling into each category indicated by the height of the bars. So, for example, this sample contains nine White participants and four Hispanic partici- pants. Notice that these bar graphs contain exactly the same information as the frequency table in Table 3.5. When reporting results in a paper, a researcher would, of course, use only one of these methods. More often than not, graphical displays are the most effective way to communicate information.

    Figure 3.4a: Bar graph displaying frequency by ethnicity

    Fr eq

    u en

    cy

    10

    White Asian Hispanic African–American

    Ethnicity

    9

    8

    7

    6

    5

    4

    3

    1

    2

    0

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    Fr eq

    u en

    cy

    6

    White Asian Hispanic African–American

    Gender

    Male

    Female

    5

    4

    3

    2

    1

    0

    Section 3.5 Describing Your Data

    Keep in mind that bar graphs are used for qualitative, or nominal, categories. We could just as easily have listed Caucasian participants second, third, or fourth along the axis because ethnicity is measured on a nominal scale.

    When we want to present quantitative data—that is, those values measured on an ordinal, interval, or ratio scale—we use a different kind of graph called a histogram. As Figure 3.5a shows, histograms are drawn with the bars touching one another to indicate that the cat- egories are quantitative and on a continuous scale. This figure has broken down the “life- satisfaction” values into three categories (less than 30, 31–40, and 41–50) and displayed the frequencies for each category in numerical order. For example, six people had life satisfaction scores falling between 31 and 40.

    Finally, all of our bar graphs and histograms so far have displayed data that have been split into categories. However, as Figure 3.5b illustrates, histograms can also present data on a con- tinuous scale. Figure 3.5b also has an additional new feature—a curved line overlaid on the graph. This curve represents a normal distribution and allows us to gauge visually how close our sample data are to being normally distributed.

    Figure 3.4b shows another variation on the bar graph, the clustered bar graph, which sum- marizes the frequency by two categories at one time. In this case, the bar graph displays infor- mation about both gender and ethnicity. As in the previous graph, categories of ethnicity are displayed along the horizontal axis. But this time, we have divided the total number of each ethnicity by the gender of respondents—indicated using different colored bars. For example, notice that the nine White participants are divided into five males and four females. Similarly, the four African-American participants are divided into one male and three females.

    Figure 3.4b: Clustered bar graph displaying frequency by ethnicity and gender

    Fr eq

    u en

    cy

    6

    White Asian Hispanic African–American

    Gender

    Male

    Female

    5

    4

    3

    2

    1

    0

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    Fr eq

    u en

    cy

    10

    Less than 31 31–40 41–50

    Life Satisfaction

    9

    8

    7

    6

    5

    4

    3

    1

    2

    0

    Fr eq

    u en

    cy

    4

    3

    2

    1

    0 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

    Life Satisfaction

    Section 3.5 Describing Your Data

    Figure 3.5b: Histogram showing life satisfaction scores on a continuous scale

    Fr eq

    u en

    cy

    4

    3

    2

    1

    0 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

    Life Satisfaction

    Figure 3.5a: Histogram showing frequencies by life satisfaction (quantitative) categories

    Fr eq

    u en

    cy

    10

    Less than 31 31–40 41–50

    Life Satisfaction

    9

    8

    7

    6

    5

    4

    3

    1

    2

    0

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    50

    African–American Asian Hispanic White

    45

    40

    35

    30

    25

    20

    15

    5

    10

    0

    Male

    Female

    Section 3.5 Describing Your Data

    Displaying Central Tendency Graphs are also commonly used to display numeric descriptors in an easy-to-understand visual format. Referring back to the sample data in Table 3.4 provides information about eth- nicity and gender but also about reports of daily stress and life satisfaction. Thus, a natural question is whether there are gender or ethnic differences in these two variables. Figure 3.6 displays a clustered bar graph showing the mean level of life satisfaction in each group of participants. Of note is that males appear to report more life satisfaction than females, as revealed by the fact that the red bars are always higher than the gold bars. We can also see some variation in satisfaction levels by ethnicity: African-American males (45) appear to report slightly more satisfaction than White males (42).

    Figure 3.6: Clustered bar graph displaying life satisfaction scores by gender and ethnicity

    50

    African–American Asian Hispanic White

    45

    40

    35

    30

    25

    20

    15

    5

    10

    0

    Male

    Female

    These particular data are fictional, of course, but even if our graph depicted real data, we would want to be cautious in interpreting them. One reason for caution is that the data rep- resent a descriptive study. We might be able to state which demographic groups report more life satisfaction, but we would be unable to determine the reasons for the difference. Another, more important, reason for caution is that visual presentations can be misleading, and we would need to conduct statistical analyses to discover the real patterns of differences.

    The best way to appreciate this latter point is to notice what happens when we tweak the graph a little bit. The original graph in Figure 3.6 is a fair representation of the data: The scale starts at zero, and the y-axis on the left side increases by reasonable intervals. However, if we were trying to win an argument about gender differences in happiness, we could always alter the scale, as Figure 3.7 shows. These bars represent the same set of means, but we have com- pressed the y-axis to show only a small part of the range of the scale. That is, rather than rang- ing from 0 to 50, this misleading graph ranges from 28 to 45, in increments of 1. To the uncrit- ical eye, the graph appears to show an enormous gender difference in life satisfaction; to the trained eye, it shows an obvious attempt to make the findings seem more interesting. Anytime we encounter a bar graph used to support a particular argument, we must always pay close

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    Summary and Resources

    attention to the scale of the results: Does it represent the actual range of the data, or is it com- pressed to exaggerate the difference? Likewise, any time researchers create a graph to display results, they have a responsibility to ensure that the graph is an accurate representation of the data.

    Figure 3.7: Clustered bar graph altered to exaggerate the differences

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    Summary and Resources

    Chapter Summary This chapter has focused on descriptive designs, the first of three specific research designs the text will discuss. As the name implies, the primary goal of descriptive designs is to describe attitudes and behavior, without any pretense of making causal claims. One common feature of all descriptive designs is that they are able to assess behaviors in their natural environ- ment, or at least in something very close to it. The chapter covered three types of descriptive research: case studies, archival research, and observational research. Because each of these methods has the goal of describing attitudes, feelings, and behaviors, each one can be used from either a quantitative or a qualitative perspective.

    In a case study, the researcher studies one person in great detail over a period of time. This approach is often used to study special populations and to gather detailed information about rare phenomena. On the one hand, case studies represent the lowest point on the continuum of control because of the lack of a comparison group and the difficulty of generalizing from a single case. On the other hand, case studies are a valuable tool for beginning to study a phenomenon in depth. We discussed the example of Phineas Gage, who suffered severe brain damage and showed drastic changes in his personality and cognitive skills. Although it is diffi- cult to generalize from the specifics of Gage’s experience, this case has helped to inspire more than a century’s worth of research into the connections among mind, brain, and behavior.

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    Summary and Resources

    Archival research involves drawing new conclusions by analyzing existing sources of data. This approach is often used to track changes over time or to study things that would be impos- sible to measure in a laboratory setting. For example, we discussed Phillips’s study of copy- cat suicides, which he conducted by matching newspaper coverage of suicides to subsequent spikes in fatality rates. There would be no practical or ethical way to study these connections other than examining the patterns as they occurred naturally. Archival studies are still rela- tively low on the continuum of control, primarily because the researcher does not have much control over how the data are collected. In many cases, analyzing archives involves a process known as content analysis, or developing a coding strategy to extract relevant information from a broader collection of content. Content analysis involves a three-step process: identify- ing the most relevant archives, sampling from these archives, and finally coding and recording behaviors. For example, Weigel and colleagues studied race relations on television by sam- pling a week’s worth of prime-time programming and recording the screen time dedicated to portraying interactions between characters of different races.

    Lastly, observational research involves directly observing behavior and recording observa- tions in a systematic way. This approach is well suited to a wide variety of research questions, provided the variables can be directly observed. That is, researchers can observe what people do but not why they do it. In exchange for giving up access to internal processes, researchers gain access to unfiltered behavioral responses—especially when they find ways to observe people unobtrusively. We discussed three main types of observational research. Structured observation involves creating a standardized situation, often in a laboratory setting, and tracking people’s responses. Naturalistic observation involves observing behavior as it occurs naturally, often in its natural context. Participant observation involves having the researcher take part in the same activities as the participants in order to gain greater insight into their private behaviors. All three of these variations go through a similar three-step process as archival research: choose a hypothesis, choose a sampling strategy, and then code and record behaviors.

    Finally, this chapter discussed principles for describing data in both visual and numeric form. To move toward conducting statistical analyses, summarizing data in numeric form is also useful. We discussed two categories of numeric summaries, central tendency and dis- persion. Measures of central tendency (i.e., mean, median, and mode) provide information about the “typical” score in a dataset, while measures of dispersion (i.e., range, variance, and standard deviation) provide information about the distribution of scores around the central tendency—that is, they tell us how typical the typical score is. Finally, the chapter described the process of translating scores into standard scores (aka, z scores), which express indi- vidual scores in terms of standard deviations. This technique is useful for comparing results from different studies and using different measures. The chapter also discussed guidelines for visual presentation. Remember that the sole purpose of visual information is to communicate research findings to an audience. Thus, a researcher’s descriptions should always be accurate, concise, and easy to understand. The most common visual displays for summarizing data are bar graphs (for nominal data) and histograms (for quantitative data). Regardless of the choice of visual display, it should represent the data accurately; it is especially important to make sure that the y-axis accurately represents the range of data.

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    Summary and Resources

    Key Terms

    archival research A descriptive design that involves drawing conclusions by analyzing existing sources of data, including both pub- lic and private records.

    bar graph A visual display that summarizes the frequency of data by category; used to display nominal data.

    case study A descriptive design that pro- vides a detailed, in-depth analysis of one person over a period of time.

    central tendency A numeric descriptor that represents the most typical case in a data set.

    clustered bar graph A visual display that summarizes the frequency by two categories at one time; used to display nominal data.

    content analysis The process of systemati- cally extracting and analyzing the contents of a collection of information.

    deviation score The difference between an individual score and the sample mean, obtained by subtracting each score from the mean.

    dispersion A numeric descriptor that represents the spread of scores around the central tendency.

    ecological validity The extent to which the research setting resembles conditions in the real world.

    ethnography A qualitative methodology involving the scientific study of the customs of people and cultures.

    event sampling In observational research, a technique that involves observing and recording behaviors that occur throughout an entire event.

    frequency tables Summary tables that present the number and percentage of the sample that fall into each of a set of categories.

    histogram A variation of a bar graph used to display ordinal, interval, or ratio data; his- tograms are drawn with the bars touching one another to indicate that the categories are quantitative.

    individual sampling In observational research, a technique that involves collect- ing data by observing one person at a time in order to test hypotheses about individual behaviors.

    mean A measure of central tendency that represents the mathematical average of a data set; calculated by adding all the scores together and then dividing by the number of scores.

    median A measure of central tendency that represents the number in the middle of a data set, with 50% of scores both above and below it.

    mode A measure of central tendency that represents the most frequent score in a data set, obtained either by visual inspection of the values or by consulting a frequency table.

    naturalistic observation A type of obser- vational study that involves observing and systematically recording behavior in the real world; can be done with or without inter- vention by the researcher.

    normal distribution (or “bell curve”) A symmetric distribution with an equal num- ber of scores on either side of the mean; has the same value for mean, median, and mode.

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    HIGHLOW

    Summary and Resources

    Apply Your Knowledge

    1. Compare and contrast the sets of the following terms. Your answers should demon- strate that you understand each term. a. individual sampling versus event sampling b. participant observation versus naturalistic observation c. mean versus median versus mode d. variance versus standard deviation e. bar graph versus histogram

    2. Place each of the three research methods we have discussed in this chapter (listed below) on the continuum of control.

    observational research A descriptive design that involves directly observing behavior and recording these observations in an objective and systematic way.

    participant observation A type of obser- vational study that involves having the researcher(s) conduct observations while engaging in the same activities as the par- ticipants; the goal is to interact with partici- pants to gain access and insight into their behaviors.

    participant reactivity The tendency of people to behave differently when they are aware of being observed.

    range A measure of dispersion that repre- sents the difference between the highest and lowest scores.

    sample A subset, or smaller portion, of the population, members of which are represen- tative of the larger population.

    standard scores (or z scores) Scores that represent the distance of each score from the sample mean, expressed in standard deviation units; calculated by subtracting a score from the mean, then dividing by the standard deviation.

    structured observation A type of observa- tional study that involves creating a stan- dard situation in a controlled setting and then observing participants’ responses.

    time sampling In observational research, a technique that involves comparing behav- iors during different time intervals.

    variance A measure of dispersion that rep- resents the average difference between the mean and each individual score; calculated by subtracting each score from the mean to obtain a deviation score, squaring and sum- ming these individual deviation scores, and dividing by the sample size.

    archival research case study naturalistic observation

    HIGHLOW

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    Summary and Resources

    3. For each of the following research methods, list one advantage and one disadvantage. a. archival research

    advantage:

    disadvantage:

    b. case studies

    advantage:

    disadvantage:

    c. observation studies

    advantage:

    disadvantage:

    4. For each of the following data sets, compute the mean, median, mode, and standard deviation. Once you have determined all three measures of central tendency, decide which one best represents the data. a. 2, 2, 4, 5 b. 10, 13, 15, 100

    5. Mike scores an 80 on a math test that has a mean of 100 and a standard deviation of 20. Convert Mike’s test score into a z score.

    6. For each of the following relationships, state the best way to present it graphically (bar graph, clustered bar graph, or histogram). a. average income by years of school completed (ratio scale) b. average income based on category of school completed (high school, some col-

    lege, college degree, master’s degree, and doctoral degree) c. average income based on gender and category of school completed

    7. For each of the following questions, state how you would test them using an observa- tional design. a. Are people who own red cars more likely to drive recklessly?

    (1) What would your hypothesis be? (2) Where would you acquire your sample and how (i.e., which type)? (3) What categories of behavior would you record? How would you define them?

    b. Are men more likely than women to “lose control” at a party? (1) What would your hypothesis be? (2) Where would you acquire your sample and how (i.e., which type)? (3) What categories of behavior would you record? How would you define them?

    c. How many fights break out in an average NHL (hockey) game? (1) What would your hypothesis be? (2) Where would you acquire your sample and how (i.e., which type)? (3) What categories of behavior would you record? How would you define them?

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    Summary and Resources

    Critical Thinking Questions

    1. Explain the tradeoffs involved in taking a qualitative versus a quantitative approach to a research question. What are the pros and cons of each one?

    2. What are the advantages and disadvantages of conducting participant observation?

    Research Scenarios: Try It What makes a teacher great? This a question that has dogged U.S. educators, parents, and politicians for decades. Common sense suggests the importance of specialized training, expe- rience, and personal characteristics such as energy and passion. But some researchers, such as Bob Pianta, the dean of the University of Virginia’s Curry School of Education, are taking a more empirical approach to the question by observing teachers—”the good and the not so good”—in action. (You can read more about this research here: http://curry.virginia.edu /academics/directory/robert-c.-pianta.)

    Imagine that you are an educational psychologist interested in understanding what makes an effective high school science teacher. In past research you have found that certain teach- ers—whom you call “super-teachers”—consistently inspire high numbers of students to go on to pursue technological careers. You set out to answer the question “What makes these super-teachers so great?”

    1. Which of the following would be the best method to begin exploring what qualities make super-teachers so effective? a. A case study of one or two super-teachers, based on classroom observation and

    qualitative interviews b. An archival analysis of school records c. A structured observation of super-teacher interactions with students d. A controlled experiment evaluating whether a specific trait accounts for the effec-

    tiveness of super-teachers

    2. Suppose that after completing your initial study, you are struck by the frequency and quality of interactions between super-teachers and their students. These super- teachers are constantly engaging students by giving them individualized feedback during class. You decide to design an observational study around this idea. The first step of observational research is to develop a testable hypothesis. With respect to your initial observations about super-teacher feedback, which of the following would be an appropriate hypothesis to test using observational methods? a. Students perceive super-teachers as being more supportive than other teachers. b. Super-teachers believe in the importance of feedback more than other teachers. c. Super-teachers give more individual feedback in class than other teachers. d. Super-teachers give more feedback in class than other teachers because they care

    more about students.

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    Summary and Resources

    3. The second step of observational research involves deciding what and how to sam- ple. You arrange to video record several months of high-school science classes taught by both super-teachers and non-super-teachers. Suppose you decide to code the recordings with regard to whether and how teachers interact directly with individual students. What type of sampling is this? a. Event sampling b. Individual sampling c. Time sampling d. Random sampling

    4. The next step is to operationalize your variable. What might be a good way of opera- tionalizing the frequency of teacher feedback in class? a. Number of times the teacher answers a student’s question b. Rating the quality of each teacher-student interaction c. Number of times the teacher speaks directly to a particular student d. Number of times the teacher responds to a particular student about class

    material

    5. The approach of coding recorded classes is a type of naturalistic observation. Iden- tify one advantage and one disadvantage of using such methodology to test your hypothesis: a. advantage: high ecological validity; disadvantage: cannot infer causality b. advantage: high construct validity; disadvantage: cannot infer causality c. advantage: high ecological validity; disadvantage: not systematic d. advantage: high construct validity; disadvantage: not systematic

    See Appendix A for answers to Research Scenarios: Try It questions.

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    PSY 326 Research Methods Week 3 Guidance

    Welcome to Week 3 of Research Methods! This week, you will learn about a few of the most popular qualitative research designs. Required resources are sections 3.1, 3.2, and the parts of section 3.4 about “Pros and Cons of Observational Research” and “Types of Observational Research” in the Newman (2016) textbook, an ebook chapter by Levitt (2016), and two videos about qualitative research. The videos are linked in the Course Materials and the discussion prompt.

    Assignments for the week include a discussion, an interactive learning activity and quiz, and a written assignment. To see how your assignments will be graded, look at the rubrics accessible through a link on the screen for each discussion or assignment.

    The Week 3 discussion is Pros and Cons of Qualitative Research. Your initial post is due by Day 3, and all replies are due by Day 7. To prepare for the discussion, read the sections of the Newman (2016) textbook listed above, the Levitt (2016) book chapter, and the lecture portion of this instructor guidance. Also, view the videos  Different Qualitative Approaches  (Kawulich, 2013) and  When to Use a Qualitative Research Design: Four Things to Consider  (Zhang, 2017), which are linked in the Course Materials and the discussion prompt.

    This week’s discussion assignment is a jigsaw puzzle. Instead of having the entire class read and report on four different qualitative research designs, each person will research and report on one specific design. Designs are assigned based on the first letter of your last name. When you determine your assigned design, use the Research Methods research guide and the databases in the Ashford University Library to find at least one scholarly/peer-reviewed article about the research design AND at least one published research study that used the design. Then, explain the characteristics and features of the research design and what kinds of topics it can be used for, describe the data collection and data analysis methods used in the design, and discuss the published study you found. Document your sources in APA style.

    At least three replies to the initial posts of classmates will be required for this discussion, because you must read and respond to at least one post about each of the other three qualitative research designs. As the expert on your assigned design, you will also be expected to respond to some of the questions posted on your thread by others. See the discussion prompt for complete details.

    After you have learned about qualitative research from the assigned readings and participating in the discussion, you will be ready to do the interactive activity and take the quiz called Qualitative Research Fundamentals, due by Day 6. In the first part of the learning activity, match terms related to qualitative research with their definitions. In the scenarios presented in the second part of the activity, you must select the most appropriate qualitative research design for the situation. After mastering the interactive activity, take the graded quiz. As with all quizzes in this course, you may retake it as many times as you wish until the end of the course to improve your score. Your highest score will be retained.

    The written assignment is a Qualitative Research Critique paper, which is due on Day 7. Review the assigned readings, videos, and discussion forum posts. The assignment prompt also provides links to Writing Center and Library resources on how to read a scholarly article and write a critique, which will be helpful to view before starting the assignment. Your instructor will post an announcement with the reference for the assigned article to be critiqued. Retrieve the article from the Ashford University Library, and also download the Qualitative Research Critique Template provided in the Course Materials and the assignment prompt. The template is set up in APA format with a series of questions to answer about the assigned study. Submit your completed template form to Waypoint.

    After completing this instructional unit, you will be able to:

    · Explain the distinguishing features of qualitative research.

    · Identify the key features, pros, and cons of selected qualitative research designs.

    · Critique a qualitative research study.

    Keep these objectives in mind as you go through this week’s learning activities.

    Qualitative research is not an experiment. It does not involve manipulating anything or controlling extraneous variables in a laboratory setting. Qualitative research is holistic. You may have heard the centuries-old story about a group of blind men trying to describe an elephant. They all felt different parts of the elephant. The one who felt the elephant’s trunk concluded that an elephant was like a thick snake. Another, who felt the elephant’s side, said that an elephant was like a wall. A man who felt the elephant’s ear was sure that an elephant was like a fan. One who felt the tusk stated that the elephant was like a spear, and so on.

    Each of the blind men only perceived one part or aspect of the elephant, and they argued about which one of them was right. They were all partially correct, but none of them really understood what an elephant was because they did not have the whole picture. In a way, this piecemeal approach is like quantitative research, which parses information about thoughts, feelings, behaviors, and objects into segments called variables. If all the relevant variables are not included in a quantitative study, the results might not present the whole truth. In contrast, qualitative research tries to consider the whole phenomenon in its context.

    Qualitative research focuses on “what” and “how” types of questions. It is not about finding out the right answer, but about understanding the perceptions and perspectives of other people, as individuals or in groups. One of the features of the qualitative approach is that researchers acknowledge that they see the world from behind a lens composed of their upbringing, culture, language, and experiences. Because everyone has such a lens, yet every individual’s lens is unique, it is important for researchers to be aware of how their lenses color their perception and understanding of what they see and hear from the participants. The effort to recognize one’s own lens and biases is referred to as reflexivity (Levitt, 2016; Roberts, 2009; Tickle, 2017). Making an effort to identify and set aside potential biases is called bracketing (Levitt, 2016). A person’s worldview, which includes the cultural lens, beliefs about the nature of reality, and beliefs about the nature of knowledge, is called a paradigm. Certain paradigms are associated with particular qualitative research approaches. For more information about paradigms, see the article by Ponterotto (2013) or other sources about the philosophy behind qualitative research.

    Sampling in qualitative research tends to be purposive instead of random or by convenience. Participants are selected because they have knowledge or personal experience with the topic of the study, and are willing and able to communicate in depth about it. Sample sizes are smaller than in quantitative studies. It is common not to set a desired sample size ahead of time in qualitative research. Often, researchers rely on data saturation to determine when the sample is large enough. Data saturation is the point at which no new information or insight is added from additional interviews or observations. One sampling strategy used when the topic is sensitive and locating qualified participants may be difficult, is called snowball sampling. This involves asking each participant to refer someone they know who is qualified and might be interested in participating.

     

    A qualitative researcher’s goal is to describe observed phenomena, behaviors, or situations in rich detail in words or pictures. This is called thick description. While quantitative research uses deductive reasoning, qualitative research typically uses inductive reasoning, which goes from the specific to the general. In induction, the researcher starts with pieces of data, then finds how they are connected in patterns. Another feature of qualitative data analysis is constant comparison. Instead of collecting all of the data before beginning analysis (as must be done in quantitative research), data from each individual source is analyzed as soon as possible after collection, and the findings are compared with and added to findings from the other sources in the study. The process of data analysis involves at least three steps: coding, categorizing, and generating themes. Codes are labels for significant statements or observations found in the raw data. Categories are clusters of related codes. Themes are meanings or insights that go across codes and categories.

    In some qualitative studies, the researcher may send the analysis or findings to participants to get their feedback on the accuracy of the researcher’s understanding of the data. This is called member checking or respondent validation. Trustworthiness is a term usually used in qualitative research instead of the quantitative terms validity and reliability. The qualitative concept of trustworthiness includes the components of credibility (comparable to internal validity), transferability (comparable to external validity), and dependability (comparable to reliability). Trustworthiness is supported by thick description, reflexivity, bracketing, and member checking. Four of the most popular qualitative research approaches are ethnography, grounded theory, narrative research, and phenomenology.

    Ethnography focuses on a culture-sharing group and how the group works, including core values and beliefs. The researcher collects data over an extended period of time, with a combination of observation, interviews, and document analysis. There are different types of observation, including non-participant and participant observation (Roberts, 2009). Non-participant observation is unobtrusive observation without interaction between the research and the people being observed. Participant observation is when the researcher not only observes the behavior and activities of participants, but also joins in the activities as part of the group. Data analysis for ethnography includes description of what was observed; analysis of the observations, documents, and interviews with key informants to determine the rules and patterns of the culture; and interpretation to form a word picture of the culture as a whole.

    Grounded theory is a qualitative approach that aims to generate a theory based on data collected (Levitt, 2016). It is usually used to study a process or the way in which something happens. Qualitative research studies do not start with a hypothesis, but a grounded theory study might formulate a hypothesis as its final product, to be tested later in a quantitative study. Grounded theory uses multiple forms of data collection (Marjan, 2017), including interviews with individuals, focus groups, observation, and content analysis of documents. Data analysis usually uses at least three kinds of coding: open coding (codes, categories, and themes), axial coding (causal conditions, strategies, intervening conditions, and/or consequences), and selective coding (developing hypotheses).

    Narrative research focuses on the story of one individual. Most data collection is from in-depth interviews, but observation can also be added. The researcher gets the participant’s life story or the participant’s experiences related to a specific topic, directly from the participant (Levitt, 2016). During data analysis, one or more epiphanies are identified and situated in context.

    Phenomenology is similar to narrative research, except that it involves more than one individual participant. In phenomenology, a small number of people who have experience with the topic of the study are interviewed individually. The aim of phenomenological research is to get the insider’s perspective, the lived experience, or the worldview of a person in the situation of interest (Levitt, 2016). Bracketing is essential, as the researcher must consciously set aside his or her own perspective to be able to see and understand the perspective of the participant.

    If you have any questions about this week’s readings or assignments, email your instructor or post your question on the “Ask Your Instructor” forum. Remember, use the forum only for questions that may concern the whole class. For personal issues, use email.

    References

    Kawulich, B. (2013).  Different qualitative approaches (Links to an external site.). Retrieved from https://www.youtube.com/watch?v=vXJjxh5Ed0A

    Levitt, H. M. (2016). Chapter 12: Qualitative methods (Links to an external site.). In Norcross, J. C., VandenBos, G. R., Freedheim, D. K., & Olatunji, B. O. (Eds.). APA Handbook of Clinical Psychology: Vol. 2 Theory and Research. American Psychological Association. DOI: http://dx.doi.org/10.1037/14773-012

    Marjan, M. (2017). A comparative analysis of two qualitative methods: Deciding between grounded theory and phenomenology for your research (Links to an external site.)Vocational Training: Research & Realities, 28(1), 23-40. DOI: https://doi.org/10.2478/vtrr-2018-0003

    Newman, M. (2016). Research methods in psychology (2nd ed.). San Diego, CA: Bridgepoint Education, Inc.

    Ponterotto, J. G. (2013). Qualitative research in multicultural psychology: Philosophical underpinnings, popular approaches, and ethical considerations. Qualitative Psychology, 1(S), 19-32. doi:10.1037/2326-3598.1.S.19.

    Roberts, T. (2009). Understanding ethnography. British Journal of Midwifery, 17(5), 291-294.

    Tickle, S. (2017). Ethnographic research with young people: Methods and rapport. Qualitative Research Journal, 17(2), 66-76.

    Zhang, R. [Ranywayz Random]. (2017, March 31).  When to use a qualitative research design? 4 things to consider (Links to an external site.). [Video File]. Retrieved from https://www.youtube.com/watch?v=4FJPNStnTvA

 
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