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chapter 3

Qualitative and Descriptive Designs—Observing Behavior

Chapter Contents

• Qualitative and Descriptive Research Designs • Qualitative Research Interviews • Critiquing a Qualitative Study • Writing the Qualitative Research Proposal • Describing Data in Descriptive Research

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CHAPTER 3Introduction

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 (United Press International, 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 (CDC) have iden- tified bullying as a serious problem facing our nation’s children and adolescents (Centers for Disease Control and Prevention [CDC], 2012).

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 bul- lying 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 to a wide range of emotional 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 et al., 2008).

But most of this research has a common limitation: It has studied the phenomenon of bul- lying using self-report survey measures. That is, researchers typically ask students and teachers to describe the extent of bullying in the schools or have students fill out a col- lection of survey measures, describing in their own words both bullying experiences and psychological functioning. These studies are conducted rigorously, and the measures they use certainly meet the criteria of reliability and validity that we discussed in Chapter 2 (Section 2.2, Reliability and Validity). 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 is unable to capture the full con- text of bullying behaviors. And, as we have already discussed, self-report measures are fully dependent on people’s ability to answer honestly and accurately.

In order to address this limitation, Craig and Pepler (1997) decided to observe bully- ing 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%, respectively). These findings add significantly to scientific understanding of when and how bullying occurs. And for our purposes, the most nota- ble 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 phe- nomenon of bullying and thus able to provide real-world advice to teachers and parents. Qualitative research is valuable when the nature of a phenomenon such as bullying, its signs, symptoms, dynamics, and emotional consequences are not well understood.

One recurring theme in this book is that it is absolutely critical to pick the right research design to address your hypothesis. Over the next three chapters, we will be discussing

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three specific categories of research designs, proceeding in order of increasing control over elements of the design: descriptive designs, quasi-experimental designs, and true experimental designs. This chapter will also focus on qualitative research designs that have similar levels of control as the case study, in which the primary goal is to examine phenomena of interest in great detail. We will begin by discussing qualitative designs, including ethnography study, phenomenological study, and grounded theory study. We will then discuss three prominent examples of descriptive designs that can be used in either qualitative or quantitative approaches—case studies, archival research, and obser- vational research—covering the basic concepts, the pros and cons, and contrasting quali- tative and quantitative approaches of each design (see Figure 3.1). We go on to discuss interview techniques and then offer guidelines for presenting descriptive data in graphi- cal, numerical, and narrative form. Finally, we show how to critique a study and write a proposal for qualitative research projects.

Figure 3.1: Qualitative and descriptive research on the continuum of control

3.1 Qualitative and Descriptive Research Designs

We learned in Chapter 1 that researchers generally take one of two broad approaches to answering their research questions. Quantitative research is a systematic, empirical approach that attempts to generalize results to other con- texts, whereas qualitative research is a more descriptive approach that attempts to gain a deep understanding of particular cases and contexts. Before we discuss specific examples of both qualitative and descriptive designs, it is important to understand that descriptive designs can represent either quantitative or qualitative perspectives, whereas qualitative designs represent only qualitative perspectives. In this section, we examine the qualitative and descriptive approaches in more detail.

In Chapter 1, we used the analogy of studying traffic patterns to contrast qualitative and quantitative methods—a quantitative researcher would do a “flyover” and perform a sta- tistical analysis, whereas a qualitative researcher would likely study a single busy inter- section in detail. This illustrates a key point about the latter approach. All qualitative approaches have two characteristics in common: (1) Focusing on phenomena that occur in natural or real-world settings; and (2) studying those phenomena in their complexity.

Increasing Control . . .Increasing Control . . .

• Ethnographic Study • Phenomenological Study • Grounded Theory Study • Case Study • Archival Research • Observational Research

Qualitative and Descriptive Methods

• Survey Research

Predictive Methods

• Pre-experiments • Quasi-experiments • “True” Experiments

Experimental Methods

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Qualitative researchers focus on interpreting and making sense out of what they observe rather than trying to simplify and quantify these observations. In general, qualitative research involves collecting interviews, recordings, and observations made in a natural setting. Regardless of the overall approach (qualitative or quantitative), however, collect- ing data in the real world results in less control and structure than does collecting data 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 because the phenomenon of interest is being studied in its natural environment. By conducting observations in a natural setting, it is possible to capture people’s natural and unfiltered responses. The concepts of reliability and validity for both qualitative and quantitative approaches are discussed further in Chapter 5.

As an example, consider two studies on 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 (centered in the San Francisco Bay Area). 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 3-month period after its occurrence. In analyzing these data, Pennebaker and Harber discovered that people tend to stop talking about traumatic events about 2 weeks after they occur but keep thinking about the event for approximately 4 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, these researchers found that this conflict between thoughts and their verbalization leads to an increased risk of illness (Pennebaker & Harber, 1993). Thus, 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 this approach with the more qualitative one taken by the developmental psy- chologist 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. Owing to a lack of prior research on how parents teach their children to cope with trauma, Miller and col- leagues approached their interviews with the goal of “documenting and describing” (p. 8) these processes. That is, rather than attempt to impose structure and test a strict hypoth- esis, the researchers focused on learning from these interviews and letting the interview- ees’ perspectives drive the acquisition of knowledge.

Qualitative research is undertaken in many academic disciplines, including, psychology, sociology, anthropology, biology, education, history, and medicine (Leedy & Ormrod, 2010). Although once frowned upon in the fields of psychology and education, due to their subjective nature, qualitative techniques have gained wide acceptance as legitimate research. In fact, many researchers argue that qualitative research is the beginning step to all types of inquiry. Thus, qualitative research can explore unknown topics, unknown variables, and inadequate theory bases and thereby assist in the generating of hypotheses for future quantitative studies.

Unlike quantitative studies, qualitative studies do not allow the researcher to iden- tify cause-and-effect relationships among variables. Rather, the focus is on describing,

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interpreting, verifying, and evaluating phenomena, such as personal experiences, events, and behaviors, in their natural environment. The most common forms of qualitative data collection techniques are observations, interviews, videotapes, focus groups, and docu- ment review. Creswell (2009) lists the following characteristics as generally present in most types of qualitative research:

• Data collection occurs in the natural or real-world setting where participants experience the issue or problem being investigated.

• The researcher is the key instrument used to collect data through means of exam- ining documents, observing behavior, or interviewing participants.

• Multiple sources of data are collected and reviewed. • As discussed in Chapter 1, qualitative researchers use inductive data analysis

and build patterns and themes from the bottom up. • Focus is on understanding the participants’ experiences, not on what the

researcher believes those experiences mean. • The research process is emergent and can change after the researcher enters the

field and begins collecting data. • Researchers as well as participants and readers interpret what they see, hear, and

understand. This results in multiple views of the problem. • Researchers attempt to develop a complex picture of the problem under investi-

gation, utilizing multiple methods of data collection.

Descriptive research does not fit neatly into the categories of either qualitative or quanti- tative methodologies; instead, it can utilize qualitative, quantitative, or a mixture of both methods to describe and interpret events, conditions, behaviors, feelings, and situations. In all cases, descriptive research investigates situations as they are, and similar to quali- tative designs, does not involve changing (controlling) the situation under investigation or attempting to determine cause-and-effect relationships. However, unlike qualitative designs, descriptive designs usually yield quantitative data that can be analyzed using statistical analyses. That is, descriptive research gathers data that describe events and then organizes, tabulates, depicts, and describes the collected data, often using visual aids such as graphs, tables, and charts.

Collecting data for descriptive research can be done with a single method or a variety of methods, depending upon the research questions. The most common data collection methods utilized in descriptive research include surveys, interviews, observations, and portfolios. In general, descriptive research often yields rich data that can lead to important recommendations and findings.

In the following six sections, we examine six specific examples of qualitative and descrip- tive designs: ethnography, phenomenological studies, grounded theory studies, case studies, archival research, and observational research. The sections on ethnography, phe- nomenological studies, and grounded theory studies will focus specifically on the quali- tative uses of these methods, since these are qualitative-only research methods. Because case studies, archival research, and observational research share the goals of describing attitudes, feelings, and behaviors, each one can be undertaken from either a quantitative or a qualitative perspective. In other words, qualitative and quantitative researchers use many of the same general methods but do so with different ends in mind. To illustrate this flexibility, we will end these three sections with a paragraph that contrasts qualitative and quantitative uses of the particular method.

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Ethnography Study (Qualitative Design)

Ethnographies were first developed by anthropologists to examine human society and various cultural groups but are now frequently used in the sociology, psychology, and education fields. In fact, today ethnographies are probably the most widely used qualita- tive method for researching social and cultural conditions. Unlike case studies (which will be discussed later in this chapter) that examine a particular person or event, ethnogra- phies focus on an entire cultural group or a group that shares a common culture. Although culture has various definitions, it usually refers to “the beliefs, values and attitudes that shape the behavior of a particular group of people” (Merriam & Associates, 2002, p. 8). The concept of what a culture is has also changed over time. Recently, more research has

focused on smaller groups, such as classrooms and work offices, than on larger groups, such as northwest Alaskan Natives.

Regardless of whether the cultural group is a classroom or an entire ethnic group in a particu- lar region of the world, ethnographic research involves studying an entire community in order to obtain a holistic picture of it. For example, in addition to studying behaviors, researchers will examine the economic, social, and cultural con- texts that shape the community or were formed by the community.

In order to thoroughly study a particular cultural group, researchers will often immerse themselves in the community. That is, the researcher will live in the study community for a prolonged period and participate in the daily routine and activities of those being studied. This is called participant observation. Such prolonged involvement is nec- essary in order to observe and record processes that occur over time. Participant observation is an important data collection procedure in ethno- graphic research; thus, it is imperative that the researcher establish rapport and build trusting

relationships with the individuals he or she is studying (Hennink, Hutter, & Bailey, 2011). Establishing trusting relationships can be a quite lengthy process, which is why ethno- graphic studies usually span long periods of time.

Steps in Ethnographic Research Several steps are involved in conducting site-based research and data collection. First, the researcher must select a site or community that will address the research questions being asked. Because researchers should not have any expectations regarding the outcome of the study, it is best if the researcher selects a site that he or she is not affiliated with. Select- ing sites that the researcher is acquainted with may make it difficult for him or her to study the group in an unbiased manner.

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Employees who are part of an office culture are an example of those who might be studied in an ethnography.

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The next step involves gaining entry into the site. This can be a difficult task, as some researchers may not be well received. Therefore, a successful entrance into a site requires having access to a gatekeeper, an individual “who can provide a smooth entrance into the site” (Leedy & Ormrod, 2010, p. 139). Gatekeepers may include a principal of a school, a leader of a community, a director of a company, a tribal shaman, or any other well- respected leader of a particular cultural group.

Once inside the site, the researcher must take several delicate steps, including establishing rapport with individuals and forming trusting relationships. As mentioned previously, establishing rapport is one of the most critical aspects of participant observation and pro- vides a foundation for the quality and quantity of data that will be collected. Initially, establishing trust will involve interacting with everyone. At some point, however, the researcher will generally select key “informants” who can assist him or her in collecting the data. Finally, similar to all types of research, the researcher will need to inform indi- viduals about why he or she is there and the purpose of the study.

As with case studies, data collection and data analysis tend to occur simultaneously. Data collection may include making observations, obtaining recordings, conducting inter- views, and/or collecting records from the group. As the information is being collected, the researcher will read through it in great detail to obtain a general sense of what has been collected and to reflect on what all the data mean.

The next step is to organize the data based on events, issues, opinions, behaviors, and other factors and begin to analyze it by sorting the data into categories. The categorized information will allow the researcher to observe any potential patterns or commonalities that may exist, as well as to identify any key or critical events.

In addition to categorizing and observing patterns, the researcher will generally develop thick descriptions of the data, which “involves reading the data and delving deeper into each issue by exploring its context, meaning, and the nuances that surround it” (Hennink, Hutter, & Bailey, 2011, p. 239). For example, thick descriptions answer questions about the data such as, What is the issue? Why does it occur? When does it occur? What are the perceptions about the issue? What are some explanations about the issue? and, Is the issue related to other data? Thick descriptions provide additional information on potential con- nections and relationships that will be useful during data interpretation.

Pros and Cons of Ethnography Through extensive and expansive investigation that is often personally involving for the researcher, ethnography allows the examination of a particular cultural group in great detail. This method provides a holistic picture and understanding of the group as well as diverse aspects of it. It also allows great flexibility in the types of data collection methods that can be used. However, as we have seen, ethnographic research requires a long process of obtaining data and, therefore, can be quite expensive and time consuming. In addi- tion, if one is not familiar with the various data collection methods, immersing oneself into a group without a clear idea of how to collect data from it can be overwhelming and distracting.

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As with all forms of participant observation, researcher bias and participant-expectancy bias (or the participant-observer effect) should be considered when examining the results of ethnographic research, and in all qualitative research for that matter. Researcher bias occurs when the researcher influences the results in order to portray a certain outcome. This type of bias can influence how the data are collected, as well as how it is analyzed and interpreted. It can also impact what type of data is collected, how the data are catego- rized, and what types of conclusions are drawn from the data analysis. For example, if a researcher is not able to lay aside his or her beliefs or assumptions, the type of data col- lected and the conclusions that are drawn could be biased or misleading. Also, we must take into account the influence that the researcher has on the participants’ behaviors and actions. Human nature being what it is, participants sometimes alter their normal behav- iors to be consistent with what they think the researcher is expecting from them or act differently simply because they are being observed.

Phenomenological Study (Qualitative Design)

In the same way that ethnography focuses on cultural groups and their behaviors and expe- riences, a phenomenological study focuses on the person’s perceptions and understand- ings of an experience. A phenomeno- logical study is one that attempts to understand the inner experiences of an event, such as a person’s percep- tions, perspectives, and understand- ings (Leedy & Ormrod, 2010). Phe- nomenological studies are concerned primarily with understanding what it is like to experience certain events. For example, researchers might be interested in studying the experi- ences of military spouses who have spouses deployed, wounded sol- diers coming back from war, juvenile offenders’ perceptions of the thera- peutic relationship in counseling, or elderly individuals being placed into a nursing home. In any situation, the idea is to better understand the sub- jective or personal perspectives of different people as they experience a particular event.

Some researchers conduct phenomenologicalstudies to obtain a more thorough under- standing of an experience that they have personally gone through. Looking at an experi- ence or phenomenon from multiple perspectives can allow them to generalize about what it is like to experience that phenomenon. However, regardless of the reason for wanting to conduct the research, it is important that the researcher set aside his or her personal beliefs and attitudes toward the experience in order to see and fully understand the essence of the phenomenon being studied (Merriam & Associates, 2002).

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Phenomenological studies attempt to under-stand what it is like to experience a certain event, such as returning home from war.

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Steps in Phenomenological Research Phenomenological research is generally conducted through in-depth, unstructured, and recorded interviews with a select participant sample (see Section 3.2, Qualitative Research Interviews). The sample size is usually between 5 and 25 participants who have directly experienced the phenomenon being studied (Creswell, 1998). Unstructured interviews are conducted individually with each participant, which allows the researcher to follow the participant’s experiences thoroughly and ask spontaneous questions based on what is being discussed. Generally, unstructured interviews do not contain any predetermined questions, although some researchers develop a few questions to guide the interview, which is acceptable in phenomenological research. Thus, a typical phenomenological interview is more like an informal conversation, although the participant does most of the talking and the researcher does most of the listening. In addition to listening, the researcher should also note any meaningful facial expressions or body language, as these can provide additional information regarding the intensity of a feeling or thought.

In phenomenological studies, data are usually analyzed by identifying common themes across people’s experiences. Themes are created by first transcribing the information from the interview in full and then editing to remove any unnecessary content. The next step is to group common statements from the interviews into categories that reflect the vari- ous aspects of the experience as well as to examine any divergent perspectives among subjects. The final step is to develop an overall description of how people experience the phenomenon (Leedy & Ormrod, 2010).

Pros and Cons of Phenomenological Studies Phenomenological studies give researchers a comprehensive view of a particular phenom- enon, which is experienced by many but illumined by studying the subjective responses of a few. Unstructured interviews provide a wealth of data while allowing participants to describe their experiences in their own way and under their own terms. Phenomenologi- cal studies are rich in personal experiences and provide a more complete or holistic view of what people experience.

Phenomenological studies can also be flawed if the interviews veer off topic or commu- nication misunderstandings crop up. For example, some recorded information may be difficult to understand. In addition, interviews, data analysis, and data interpretation can be influenced by researcher bias regarding the experience. As mentioned previously, if a researcher has personally experienced the phenomenon being studied (rape would be an emotionally charged example), it is possible that he or she may bring preconceived notions or prejudices to the study, which will in turn influence how the data are collected and interpreted.

Grounded Theory Study (Qualitative Design)

Unlike most qualitative research, grounded theory does not begin from a theoretical perspective or theory but rather utilizes data that are collected to develop new theories or hypotheses. According to Smith and Davis (2010), “A grounded theory is one that is uncovered, developed, and conditionally confirmed through collecting and making sense of data related to the issue at hand” (p. 54). Thus, theories are built from “grounded”

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data that have been systematically analyzed and reanalyzed. Grounded theory is typi- cally used in qualitative research; however, grounded theory can utilize either qualitative or quantitative data (Glaser, 2008), or a mixture of the two. As Glaser posits, grounded theory is not only considered a qualitative method but a general method in research. For example, you may use grounded theory as the only method for your qualitative study, or you may choose to use it as the first step toward identifying constructs and generat- ing hypotheses about their relationships to one another. You may then want to employ a quantitative, cause-and-effect design to further test your hypotheses that were developed from your grounded theory study.

Grounded theory is especially useful for exploring the relationships and behaviors of groups that either have not been previously studied or have been inadequately studied. Grounded theory has been used to study a wide variety of topics, such as stress manage- ment in Olympic champions (Fletcher & Sakar, 2012), the role of leaders in knowledge management (Lakshman, 2007), reflections of therapists during role-playing sessions (Rober, Elliot, Buysse, Loots, & Corte, 2008), normalizing risky sexual behaviors in female adolescents (Weiss, Jampol, Lievano, Smith, & Wurster, 2008), and team leadership dur- ing trauma resuscitation (Xiao, Seagull, Mackenzie, & Klein, 2004), to name a few. When choosing to utilize the grounded theory approach, the idea is to select a topic that has been minimally explored.

Steps in Grounded Theory Research In grounded theory research, data are simultaneously collected, coded, and analyzed. This procedure differs from quantitative methods because during the research process, data collection and analysis do not occur sequentially. Rather, in grounded theory, data analysis begins almost immediately when data collection starts. Grounded theory can utilize a variety of data collection techniques, including interviews, observations, focus groups, historical records, videotapes, diaries, news reports, and any other form of data that is relevant to the research question (Leedy & Ormrod, 2010), although in-depth inter- views are the most commonly used method.

One of the most widely used approaches to data analysis in grounded theory is the one suggested by Strauss and Corbin (1990). In this approach, data analysis begins by developing categories to classify the data. This process, called open coding, involves the researcher labeling and organizing the data into categories or themes and smaller sub- categories that describe the phenomenon being investigated. In this step, initial coding is generally guided by some of the literature review, as well as by topic guides developed by the researcher that direct the coding of themes and categories, based upon the study’s research questions. Glaser (1978) suggests three questions to be used in generating and identifying open codes:

1. What is this data a study of? 2. What category does this incident indicate? 3. What is actually happening in the data?

The next step in data analysis is axial coding, which involves finding connections or rela- tionships between the categories and subcategories (Smith & Davis, 2010). Strauss (1987) indicates that axial coding should involve the examination of antecedent conditions, interactions among subjects, strategies, tactics, and consequences. The idea here is to fit

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together all the pieces, similar to a jigsaw puzzle. Strauss and Corbin (1990) further sug- gest that axial coding focus on asking the questions Who, When, Where, Why, How, and With what consequences. As new data are collected, the researcher will move constantly between data collection, open coding, and axial coding to refine the categories. Also dur- ing this process, hypotheses are generated and continually tested, based on new data coming in. Data collection and analysis continue until the categories are completely satu- rated. Saturation occurs when no additional supporting or disconfirming data are being found to develop a category. Thus, saturation occurs when we have learned everything that we can about a category.

The final step, selective coding, involves the researcher combining the categories and their interrelationships into theoretical constructs or a “story line that describes what happens in the phenomenon being studied” (Leedy & Ormrod, 2010, p. 143). In other words, the researcher is integrating and refining the categories so that the categories can be related to the core categories, or categories that lie at the core of the theory being generated. It is from this process that theories are generated.

To illustrate the process of grounded theory research, consider an investigation of the active or passive roles played by companions who accompany patients to their dental appoint- ments. To examine how these companions affect the interactions between the patient and the dental provider, we could begin collecting data using field notes and audio recordings. Next we would compare the interactions among companions, patients, and dentists by assessing their similarities and differences. We would then identify codes from the initial data collected, and develop categories to organize the codes. The following step would be to develop hypotheses about the patterns we observed. Next, we would continue to collect and analyze data for an extended period to test those hypotheses and develop more patterns. We would continue collecting data and refining hypotheses until we were able to account for and explain all examples (the saturation point). We would then gener- ate a theory from the data regarding the roles that companions play when attending den- tal appointments with patients.

Pros and Cons of Grounded Theory Studies Grounded theory gives the researcher significant flexibility with respect to the types of data collection methods and the ability to readjust the investigation as new data are being collected (Houser, 2009). Grounded theory also provides a thorough analysis of the data, which can lead to fairly solid theories or hypotheses about a particular phenomenon. Additionally, through systematic data collection and analysis procedures, the researcher is able to explore the complexity of the problem, which often produces richer and more informative results.

Despite the advantages of being able to develop theories from data collected, there are some disadvantages to grounded theory. Probably the biggest disadvantage involves the difficulty in managing large amounts of data. Since there are no standard guidelines regarding how to identify categories, the novice researcher may have difficulty devel- oping categories and analyzing the data appropriately. Identifying when a category has become saturated and when a theory has been completely formed can also be difficult and requires some experience. Additionally, grounded theory research can be very time consuming and tedious.

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Case Studies (Qualitative or Descriptive Design)

At the 1996 meeting of the American Psychological Association (APA), James Pennebaker— chair of psychology at the University of Texas at Austin—delivered an invited address, describing his research on the benefits of therapeutic writing. Rather than follow the expected route of 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 partici- pant was an American soldier who had served in the Vietnam War. Like many others, this soldier had had difficulty adjusting to what had happened during the war and con- sequent trouble reintegrating into “normal” civilian 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 you might expect, this veteran felt awful; these were unpleasant memories that he had not relived in over a decade. But during the next few weeks, amazing things started to happen. He slept better, he made fewer visits to his doctor, and he even reconnected with his wife after a long separation.

Pennebaker’s presentation is an example of a case study that 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 therapeu- tic setting and involve a series of interviews. An interviewer will typically study the sub- ject in detail, recording everything from direct quotes and observations to his or her own interpretations. We encountered this technique briefly in Chapter 2 (Section 2.1, Overview of Research Designs), in discussing Oliver Sacks’s case studies of individuals learning to live with neurological impairments.

Pros and Cons of Case Studies In psychology, case studies are a form of qualitative research; thus, they represent the low- est 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 because they tell a compelling story or because they represent an unusual set of circum- stances rather than being selected randomly. Studying these individuals allows for a great deal of exploration, which can often inspire future research. However, it is nearly impos- sible to generalize from one case study to the larger population. In addition, because the case study includes both direct observation and the researcher’s interpretation, there is a risk that a researcher’s biases might influence the interpretations. For example, Pen- nebaker’s investment in demonstrating that writing has health benefits could have led to more positive interpretations of the Vietnam veteran’s outcomes. However, in this par- ticular case study, Pennebaker’s hypothesis about the benefits of writing was supported because his findings mirror those seen in hundreds of controlled experimental studies that involved thousands of people. This body of work allows us to feel confident about 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 scientific talk, the audience came away utterly convinced of the benefits of therapeutic

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writing. And, despite the fact that Oliver Sacks studied one neurological patient at a time, the stories in his books shed very convincing light on the ability of humans to adapt to their circumstances and have a wide appeal to the lay reader. Second, case studies pro- vide 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 we will discuss in Chapter 5 (Section 5.4, Experimental Designs). However, from a practical point of view, this study would be nearly impossible to conduct, making case studies such as Sacks’s interviews with William Thompson the best strategy for understanding 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 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 opera- tional, and formal operational—with each stage involving more sophisticated cognitive skills than the previous 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 the story 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 personality had changed—Gage became more impulsive, violent, and argumentative. Gage’s physician, John Harlow, reported the details of this case in an 1868 article. The following passage is a great 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 fel- lows, 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 per- sistent 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.” (Harlow, 1868, pp. 339–342)

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Gage’s transformation ultimately inspired a large body of work in psychology and neuroscience that attempts to understand the con- nections 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 stud- ies. On the one hand, it is difficult to determine exactly how much the brain injury affected his behavior because he is only one person. 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—under- standing of the brain. This illustrates a key point about case studies: Although individual cases provide limited knowledge about people in general, they often lead researchers to conduct additional work that does lead to generalizable knowledge.

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 Pennebaker’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, it is certainly possible for a case study to represent quantitative research. This is often the case when research- ers conduct a series of case studies, learning from the first one of the initial 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 researchers to conduct analyses within a single subject. For example, a researcher could study a patient with brain dam- age 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 from the primary downside of case studies: Because they study a single individual, it is difficult to generalize their findings.

Courtesy Everett Collection

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

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Research: Thinking Critically

Acupuncture of Benefit to Those with Unexplained Symptoms

By the Peninsula College of Medicine and Dentistry, Exeter, UK

Attending frequently with medically unexplained symptoms is distressing for both patient and doc- tor. In these settings, effective treatment or management options are limited: One in five patients has symptoms that remain unexplained by conventional medicine. Studies have shown that the cost to the National Health Service (NHS, United Kingdom) of managing the treatment of a patient with medically unexplained symptoms can be twice that of a patient with a diagnosis.

A research team from the Institute of Health Services Research, Peninsula Medical School, University of Exeter, has carried out a randomised control trial and a linked interview study regarding 80 such patients from GP (General Practitioner) practices across London to investigate their experiences of having five-element acupuncture added to their usual care. This is the first trial of traditional acu- puncture for people with unexplained symptoms.

The results of the research are published in the British Journal of General Practice. They reveal that acupuncture had a significant and sustained benefit for these patients and, consequently, acupunc- ture could be safely added to the therapies used by practitioners when treating frequently attending patients with medically unexplained symptoms.

The patient group was made up of 80 adults, 80% female, with an average age of 50 years and from a variety of ethnic backgrounds who had consulted their GP at least eight times in the past year. Nearly 60% reported musculoskeletal health problems, of which almost two thirds had been pres- ent for a year.

In the 3 months before taking part in the study, the 80 patients had accounted for the following NHS experiences: 21 patient in-days; 106 outpatient clinic visits; 52 hospital clinic visits (for treatments such as physiotherapy, chiropody, and counselling); 44 hospital visits for investigations (including 10 magnetic resonance imaging [MRI]scans); and 75 visits to non-NHS practitioners such as opticians, dentists, and complementary therapists.

The patients were randomly divided into an acupuncture group and a control group. Eight acupunc- turists administered individual five-element acupuncture to the acupuncture group immediately, up to 12 sessions over 26 weeks. The same numbers of treatments were made available to the control group after 26 weeks.

At 26 weeks, the patients were asked to complete a number of questionnaires including the individu- alized health status questionnaire “Measure Yourself Medical Outcome Profile.”

The acupuncture group registered a significantly improved overall score when compared with the control group. They also recorded improved well-being but did not show any change in GP and other clinical visits and the number of medications they were taking. Between 26 and 52 weeks, the acu- puncture group maintained their improvement and the control group, now receiving their acupunc- ture treatments, showed a “catch-up” improvement.

The associated qualitative study, which focused on the patients’ experiences, supported the quan- titative work. This element identified that the participating patients had a variety of long-standing symptoms and disability, including chronic pain, fatigue, and emotional problems, which affected their ability to work, socialize, and carry out everyday tasks. A lack of a convincing diagnosis to explain their symptoms led to frustration, worry, and low mood.

(continued)

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Research: Thinking Critically (continued) Participating patients reported that their acupuncture consultations became increasingly valuable. They appreciated the amount of time they had with each acupuncturist and the interactive and holis- tic nature of the sessions—there was a sense that the practitioners were listening to their concerns and, via therapy, doing something positive about them.

As a result, many patients were encouraged to take an active role in their treatment, resulting in cognitive and behavioural lifestyle changes, such as a new self-awareness about what caused stress in their lives, and a subsequent ability to deal with stress more effectively, and taking their own initia- tives based on advice from the acupuncturists about diet, exercise, relaxation, and social activities.

Comments from participating patients included: “The energy is the main thing I have noticed. You know, yeah, it’s marvellous! Where I was going out and cutting my grass, now I’m going out and cut- ting my neighbour’s after because he’s elderly”; “I had to reduce my medication. That’s the big help actually, because medication was giving me more trouble . . . side effects”; and “It kind of boosts you, somehow or another.”

Dr. Charlotte Paterson, who managed the randomised control trial and the longitudinal study of patients’ experiences, commented: “Our research indicates that the addition of up to 12 five-element acupuncture consultations to the usual care experienced by the patients in the trial was feasible and acceptable and resulted in improved overall well-being that was sustained for up to a year.

This is the first trial to investigate the effectiveness of acupuncture treatment to those with unex- plained symptoms, and the next development will be to carry out a cost-effectiveness study with a longer follow-up period. While further studies are required, this particular study suggests that GPs may recommend a series of five-element acupuncture consultations to patients with unexplained symptoms as a safe and potentially effective intervention.

Paterson added: “Such intervention could not only result in potential resource savings for the NHS, but would also improve the quality of life for a group of patients for whom traditional biomedicine has little in the way of effective diagnosis and treatment.”

Peninsula College of Medicine and Dentistry. (2011, May 27). Acupuncture and those with unexplained symptoms. From Paterson, C., Taylor, R., Griffiths, P., Britten, N., Rugg, S., Bridges, J., McCallum, B., Kite, G. (2011). Acupuncture for ‘frequent attenders’ with medically unexplained symptoms: a randomised controlled trial (CACTUS Study). British Journal of General Practice, Volume 61, Number 587, June 2011 , pp. e295-e305(11) and Rugg, S. , Paterson, C., Britten, N., Bridges, J., Griffiths, P. (2011). Traditional acupuncture for people with medically unexplained symptoms: a longitudinal qualitative study of patients’ experiences. British Journal of General Practice, Volume 61, Number 587, June 2011 , pp. e306-e315(10).

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?

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Archival Research (Qualitative or Descriptive Design)

Moving slightly further along the continuum of control, we come to archival research, which involves drawing conclusions by analyzing existing sources of data, including both public and private records. Sociologist David Phillips (1997) hypothesized that media cov- erage 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 sui- cides and the number of fatalities in the 11-day period following coverage of the suicide. By examining these patterns of data, Phillips found support for his hypothesis. Specifi- cally, fatalities appeared to peak 3 days after coverage of a suicide, and increased 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. You could never randomly assign people to learn about suicides and then wait to see whether they killed themselves. Nor could you interview people right before they com- mitted suicide to determine whether they were being inspired by media coverage. Archi- val research provides a way to test the hypothesis by examining existing data and thereby avoids most of the ethical and practical problems of other research designs. Related to this point, archival research also neatly sidesteps issues of participant reactivity, or the ten- dency of people to behave differently when they are aware of being observed. Any time you conduct research in a laboratory, participants are aware that they are in a research study and may not behave in a completely natural manner. In contrast, archival data involve making use of records of people’s natural (unstudied) behaviors. The subjects of Phillips’s study of copycat suicides were individuals who decided to kill themselves and 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 example, much of the evidence for global warming comes from observing upward trends in recorded temperatures around the globe. To gather this evidence, researchers dig into existing archives of weather patterns and conduct statistical tests on 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 people’s stereotypes about other groups. In a classic series of stud- ies known as the “Princeton Trilogy,” researchers documented the stereotypes held by Princeton students over several decades (1933 to 1969). Social psychologist Stephanie Madon and her colleagues (2001) collected a new round of data but also conducted a new analysis of this archival data. These new analyses suggested that, over time, people have become more willing to use stereotypes about other groups, even as stereotypes themselves have become less negative.

One final advantage of archival research is that once you manage to gain 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 over a period of an hour or more. But once you have assem- bled your data from the archives, it is a relatively simple matter to conduct statistical

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analyses. 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 sui- cide (e.g., Sylvia Plath) with the language of similar 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 Émile 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. But here’s the impressive part: Once they had assembled their archive of poems, it took only seconds for their computer program to analyze the language and generate a statistical profile of each poet’s verbal output.

Overall, however, archival research is still relatively low on our continuum of control. As a researcher, you 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 research- ers had collected the data in a reasonable and unbiased way. In addition, because archi- val 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 of our examples so far, the data have come in a straightforward, ready-to-analyze form. That is, it is relatively simple to count the number of suicides, track the average tem- perature, or compare responses to questionnaires about stereotyping over time. In other cases, the data can come in a sloppy, disorganized mass of information. What do you do if you want to analyze literature, media images, or changes in race relations on television? These types of data can yield incredibly useful information, provided you can develop a strategy for extracting it.

Mark Frank and Tom Gilovich—both psychologists at Cornell University—were inter- ested in whether cultural associations with the color black would have an effect on behav- ior. In virtually all cultures, black is associated with evil—the bad guys wear black hats; we have a “black day” when things turn sour; and we are excluded from social groups by being blacklisted or blackballed. 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 with teams who wore other colors. Imagine for a moment that this was your research study. Professional sporting events contain a wealth of behaviors and events. How would you extract information on the relationship between uniform color and aggressive behavior?

Frank and Gilovich (1988) solved this problem by examining public records of penalty yards (football) and penalty minutes (hockey) because these represent instances of pun- ishment for excessively aggressive behavior, as recognized by the referees. And, in both sports, the size of the penalty increases according to the degree of aggression. These pen- alty records were obtained from the central offices of both leagues, covering the period from 1970 to 1986. Consistent with their hypothesis, teams with black uniforms were

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“uncommonly 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 pen- alty minutes while sporting the new uniforms!

But even this analysis is relatively straightforward in that it involved data that were already in quantitative form (penalty yards and minutes). In many cases, the starting point is a messy collection of human behavior. In a pair of journal articles, psychologist Russell Wei- gel and his colleagues (1980; 1995) examined the portrayal of race relations on prime-time television. In order to do this, 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 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. If you want to compare temperature trends, the most relevant archives will be weather records. If you want to track changes in stereotyping over time, the most relevant archive will comprise questionnaire data assessing people’s attitudes. In other cases, this decision involves careful con- sideration of both your 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 your research question involved sen- sitive or personal information—such as hospital records or personal correspondence—you would need to obtain permission from a responsible party. Let’s say you wanted to analyze the love letters written by soldiers serving overseas and then try to predict relationship stability. Because these letters would be personal, perhaps rather intimate, you would need permission from each person involved before proceeding with the study. Or, say you wanted to analyze the correlation between the length of a person’s hospital stay and the number of visitors he or she receives. This would most likely require permission from both hospital administrators, doctors, and the patients themselves. How- ever you manage to obtain access to private records, it is absolutely essential to protect the privacy and anonymity of the people involved. This would mean, for example, using pseudonyms and/or removing names and other identifiers from published excerpts of personal letters.

iStockphoto/Thinkstock

A personal letter is an example of a data source that a researcher would need to obtain permission to use.

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Step 2—Sample From the Archives

In Weigel’s research on race relations, the most obvious choice of archives consisted of snippets of both television programming and commercials. But this decision was only the first step of the process. Should they examine every second of every program ever aired on television? Naturally not; instead, their approach was to take a smaller sample of television programming. We will discuss sampling in more detail in Chapter 4 (Sec- tion 4.3, Sampling From the Population), but the basic process involves taking a smaller, representative collection of the broader population in order 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). They narrowed their sample by eliminating news, sports, and documentary programming because their hypotheses were centered on portrayals of fictional characters of different races.

Step 3—Code and Analyze the Archives

The 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 on-screen; (2) the Black appearance time, in which Black characters appeared on-screen; (3) the cross-racial appearance time, in which characters of two races were on- screen at the same time; and (4) the cross-racial interaction time, in which cross-racial characters interacted. In the original (1980) paper, these authors reported that Black char- acters 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, there was discouragingly little change in some of the qualitative dimensions that they mea- sured, 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 on-screen. In addition, the researchers assessed several abstract quali- ties of interaction 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 you’ll remember from Chapter 2 (Section 2.2, Reliability and Validity), any time you use judges’ ratings, it is important to collect ratings from more than one rater and to make sure they agree in their assessments.

Your goal as an archival researcher is to find a systematic way to record the variables most relevant to your hypothesis. As with any research design, 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 you analyze written communication, you might decide to compare words, sentences, characters, or themes across a sample. A study of newspaper coverage might code the amount of space or number of stories dedicated to a topic. Also, a study of television news might code the amount of airtime given to different points of view. 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 our examples in this section represent the quantitative approach: Frank and Gilovich (1988) counted penalties to test their hypoth- esis about aggression; and Stirman and Pennebaker (2001) counted self-referential words in poetry to test their hypothesis about suicide. But the race-relations work by Weigel and colleagues (1980; 1995) represents a nice mix of qualitative and quantitative research. In their initial 1980 study, the primary goal was to document the portrayal of race relations on prime-time television (i.e., qualitative). But in the 1995 follow-up study, the primary goal was 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., quantitative). Another way in which archival research can be qualitative is to study open-ended narratives 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 newspaper. A researcher approaching these from a qualitative perspective would attempt to learn from these narratives but without imposing structure via the use of content analyses.

Observational Research (Qualitative or Descriptive Design)

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. In previous psychology courses, you may have encountered the concept of attach- ment 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 early 1960s, arguing that children can form either “secure” or a variety of “insecure” attachments with their caregivers (Ainsworth & Bell, 1970; Bowlby, 1963).

In order to assess these classifications, Ainsworth and Bell (1970) developed an observa- tional technique called the “strange situation.” Mothers would arrive at their laboratory with their children for a series of structured interactions, including having the mother play with the infant, leave him or her 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 the various episodes (eight in total). One group of infants, for example, showed curiosity when the mother left but then returned to playing with their toys, trusting that she would return. Another group showed immediate 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.

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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 exam- ple, if the researcher is able to observe parent–child interactions, nonverbal cues to emo- tion, or even crowd behavior. However, if a researcher is interested in studying thought processes—such as how mothers interpret their interactions—then observation will not suffice. This harkens back to our discussion of behavioral measures in Chapter 2 (Section 2.2, Reliability and Validity): In exchange for giving up access to internal processes, you gain access to unfiltered behavioral responses.

To capture these unfiltered behaviors, it is vital for the researcher to be as unobtrusive as possible. 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

Research: Making an Impact

Harry Harlow

In the 1950s, U.S. psychologist Harry Harlow conducted a landmark series of studies with rhesus monkeys on the mother–infant bond. While his research would be considered unethical by contem- porary standards, 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 to avoid 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 pro- vided 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 attach- ment 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 sur- rogates 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 develop- ment (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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at the beginning of this chapter, the researchers used video cameras to record children’s behavior unobtrusively; otherwise, the occurrence of bullying might have been artificially low. If you conduct an observational study in a laboratory setting, there is no way to hide the fact that people are being observed, but the use of one-way mirrors and video record- ings can help people to become comfortable with the setting (versus having an experi- menter staring at them across the table). If you conduct an observational study out in the real world, there are even more possibilities for blending into the background, includ- ing using observers who are literally hidden. For example, let’s say you hypothesize 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, you could place someone out of sight, standing 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 psychol- ogist Muzafer Sherif, involving observations of cooperative and competitive behaviors among boys at a summer camp (Sherif et al., 1954). You can imagine that it was particu- larly 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, allowing him to be a pres- ence in nearly all of the camp activities. The boys never paid enough attention to the “jani- tor” to realize his omnipresence—or his discreet 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 There are several variations on observational research, according to the amount of control that a researcher has over the data collection process.

Structured Observation

Structured observation involves creating a standard situation in a controlled setting and then observing participants’ responses to a predetermined set of events. The “strange sit- uation” studies of attachment (discussed previously) 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 laboratory, they differ from experimental studies in an important way: Rather than systematically manipulate a variable to make compari- sons, researchers present the same set of conditions to all participants.

Another example of structured observation comes from the research of John Gottman, a psychologist at the University of Washington. For nearly three decades, Gottman and his colleagues have conducted research on the interaction styles of married couples. Couples who take part in this research are invited for a 3-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, in order to get them talking as they might do at home. After allowing them to settle 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 everything from verbal and nonverbal

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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 remark- able accuracy, which couples will divorce in the 18 months following the lab visit (Gottman & Levenson, 1992).

Naturalistic Observation

Naturalistic observation involves observing and systematically recording behavior out in the real world. This can be done in two broad ways—with or without intervention on the part of the researcher. Naturalistic studies that involve researcher intervention consist of manipulating some aspect of the environment and then observing responses. For exam- ple, you might leave a shopping cart just a few feet away from the cart return area and measure 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. . . .) In another example you may remember from Chapter 1 (in our discussion of ethical dilemmas in Section 1.7, Ethics in Research), Harari and associates (1995) used this approach 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 illustrates this type of research. The show What Would You Do? sets up provocative settings in public and videotapes peo- ple’s reactions; full episodes are available online at http://abcnews.go.com/WhatWould YouDo/. If you were an unwitting participant in one of these episodes, you might see a customer stealing tips from a restaurant table or a son berating his father for being gay or a man proposing to his girlfriend who minutes earlier had been kissing another man at the bar. Of course, these observation “studies” are more interested in shock value than data collection (or IRB approval; see Section 1.6), 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 psychol- ogy, such as the well-documented phenomenon that people are reluc- tant 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, you might station your- self at a liquor store and observe the numbers of men and women who buy beer versus wine. Or, you might observe the numbers of people who give money to the Salvation Army

James D. Smith/Associated Press

Naturalistic studies involve observing and recording behavior in the real world. One example might be noting what type of people give money to charities and under what conditions they donate.

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bell ringers during the holiday season. You can use this approach to make comparisons of different conditions, provided the differences occur naturally. That is, you 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 of a different gender or race. Do people 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.

Participant Observation

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 par- ticipants in order to gain better access and insight into their behaviors. In one famous example, the psychologist David Rosenhan (1973) was interested in the experience of peo- ple hospitalized 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 life histories to a doctor except for lying about one diagnostic symptom; they all supposedly heard voices occasionally, a symptom of schizophrenia.

Once admitted, these “patients” behaved in a normal and cooperative manner, with instructions 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 hospital staff tended to see 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. And all but one was given a diagnosis of “schizophrenia in remission” upon release. The other striking finding was that treatment was generally depersonalized, with staff spending little time with individual patients.

In another great 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 motivated to maintain a sense of consistency among their various thoughts and behaviors. So, for example, if you find yourself smok- ing a cigarette despite being aware of the health risks, you might rationalize your smoking by convincing yourself 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 predict- ing the end of the world, via alien invasion, at 11 p.m. on a specific date 6 months in the future. What would happen, they wondered, when this prophecy failed to come true?

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 doomsday. 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 the time zone dif- ference, but as sunrise 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.

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As you can see from these examples, participant observation can provide access to amaz- ing and one-of-a-kind data, including insights into group members’ thoughts and feel- ings. This form of investigation also provides access to groups that might be reluctant to allow in outside observers. However, this 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 without their knowledge. In order for an IRB to approve this kind of study, there has to be an extremely compelling reason to ignore informed con- sent, as well as extremely rigorous measures to protect identities. The second problem is methodological; there is ample opportunity for the objectivity of observations to be com- promised by the close contact between researcher and participant. Because the researcher is a part of the group, he or she can change the dynamics in subtle ways, possibly leading the group to confirm his or her hypothesis. In addition, the group can shape the research- er’s interpretations in subtle ways, leading him or her to miss important details.

Steps in Observational Research One of the major strengths of observational research is that it has a high degree of ecological validity; that is, the research can be conducted in situations that closely resemble the real world. Think of our examples so far—married couples observed in a living room– like laboratory; doomsday cults observed from within; bullying behaviors on the school playground seen by hidden observers. In every case, people’s behaviors are observed in the natural environment or something very close to it. But this ecological validity comes at a price; the real world is a jumble of information—some relevant, some not so much. The challenge for the researcher, then, is to decide on a system for sorting out the signal from the noise that provides the best test of the hypothesis. In this section, we discuss a three-step process for conducting observational research. The key thing you should 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 hypothesis. We have covered hypotheses in detail in other chapters, but the main points bear repeating: Your 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 our example involving Salvation Army donations, we predicted that people might donate more money to an attractive bell ringer. This could easily be tested empirically and could just as easily be disconfirmed 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 have to be observable. Because observational studies are based on observations of behaviors, our hypotheses have to be centered on behavioral measures. That is, we can safely make predictions about the amount of money people will donate because this can be directly observed. But we are unable to make predictions in this con- text about the reasons for donations. There would be no way to observe, say, that people donate more to attractive bell ringers because they were trying to impress them. In sum, one limitation of observing behavior in the real world is that we are unable to delve into the cognitive and motivational reasons behind the behaviors, as we would in phenomeno- logical research, for example.

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Step 2—Decide What and How to Sample

Once you have 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 everything in it. Imag- ine that you wanted to observe the dinner rush at a restaurant. There is a nearly infinite list of events to observe: What time does the restaurant get crowded? How many times do people send their food back to the kitchen? What are the most popular dishes? How often do people get into arguments with the waitstaff? To simplify the pro- cess of observing behavior, you will need to take samples, which are small snippets of the environ- ment that are relevant to your hypothesis. That is, rather than observing “dinner at the restaurant,” the goal is to narrow your focus to something like “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 your hypothesis. In the context of observational research, there are three strategies for sampling behaviors and events. The first strategy, time sampling, involves comparing behaviors during different time inter- vals. For example, to test the hypothesis that foot- ball teams make more mistakes when they start to get tired, the researcher could count the number of penalties in the first 5 and the last 5 minutes of the game. This data would allow one to compare mistakes at one time inter- val with mistakes at another time interval. In the case of Festinger’s study of a doomsday cult, time sampling was used to compare how the group members behaved before and after their prophecy failed to come true.

The second strategy, individual sampling, involves collecting data by observing one per- son at a time in order to test hypotheses about individual behaviors. Many of the exam- ples we have already discussed involve individual sampling. For instance, Ainsworth and colleagues tested their hypotheses about attachment behaviors by observing individual infants, while Gottman tests his hypotheses about romantic relationships by observing one married couple at a time. These types of data allow us to examine behavior at the individual level and test hypotheses 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, you 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 res- taurant without paying the check. This strategy allows for testing hypotheses about the types of behaviors that occur in a particular environment or setting. For example, you might compare the number of fights that break out in a professional football versus a

Steve Mason/Photodisc/Thinkstock

The dinner scene at a busy restaurant offers a wide variety of behaviors to sample.

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professional hockey game. Or, the next time you host a party, you could count the number of wine bottles versus beer bottles that end up in your recycling bin. The distinguishing feature of this strategy is that you focus on the occurrence of behaviors more than on the individuals performing these behaviors.

Step 3—Record and Code Behavior

Now that you have formulated a hypothesis and decided on the best sampling strategy, there is one final and critical step to take before you begin data collection. Namely, you have to develop good operational definitions of your variables by translating the underly- ing concepts into measurable variables. Gottman’s research turns the concept of marital interactions into a range of measurable variables like the number of dismissive comments and passive-aggressive sighing—all things that can be observed and counted objectively. Rosenhan’s study involving fake schizophrenic patients turned the concept of how staff treat patients into measurable variables such as the amount of time staff members spent with each patient—again, something very straightforward to observe.

It is vital to decide up front what kinds and categories of behavior you will be observing and recording. In the previous 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 we get an accurate count? What if two people are wait- ing 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 ask for directions to another place.

In short, observing behavior in the real world can be messy. The best way to deal with this mess is to develop a clear, consistent categorization scheme and stick with it. That is, in testing your hypothesis about the most crowded time at the restaurant, you would choose one method of counting people and use it for the duration of the study. In part, this choice is a judgment call, but your judgment should be informed by three criteria. First, you should consider practical issues, such as whether your categories can be directly observed. You can observe the number of people who leave the restaurant, but you cannot observe whether they became impatient. Second, you should consider theoretical issues, such as how well your categories represent the underlying theory. Why did you decide to study the most crowded time at the restaurant? Perhaps this particular restaurant is in a new, up-and-coming neighborhood and you expect the restaurant to get crowded over the course of the evening. It would also lead you to include people sitting both at tables and at the bar—because this crowd may come to the restaurant with the sole intention of staying at the bar. Third, you should consider previous research when choosing your categories. Have other researchers studied dining patterns in restaurants? What kinds of behaviors did they observe? If these categories make sense for your project, feel free to reuse them.

Last, but not least, you should take a step back and evaluate both the validity and the reli- ability of your coding system. (See Section 2.2 for a review of these terms.) Validity in this case means making sure the categories we observe do a good job of capturing the under- lying variables in our 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

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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 indicate either genuine happiness, superfi- ciality, condescension, or even awkward embarrassment. As a general rule, the better our operational definitions, the more valid our measures will be (Chapter 2).

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

Observational Research Examples To give you a sense of how all of this comes together, let’s walk through a pair of exam- ples, from forming the research question to collecting the data.

Example 1—Theater Restroom Usage

First, imagine for the sake of this example that you are interested in whether people are more likely to use the restroom before or after watching a movie. This research question could provide valuable information for theater owners in planning employee schedules (i.e., when are bathrooms most likely to need cleaning). Thus, by studying patterns of human behavior, we could gain useful applied knowledge.

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 consuming those 64-ounce sodas during the movie. And, just for fun, let’s also compare the restroom usage of men and women. Perhaps men are more likely to wait until after the movie, whereas women are 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.

Table 3.1: Hypothesized data from observation exercise

Men Women

Before movie 20% 50%

After movie 80% 50%

Total 100% 100%

The next step is to decide on the best sampling strategy to test this hypothesis. Of the three sampling strategies we discussed—individual, event, and time—which one seems most relevant here? The best option would probably be time sampling because our hypothesis

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involves comparing 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 collect- ing data. One option would be to 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 our 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.

Once we decide on a sampling strategy, the next step is to decide on the types of behav- iors we want to record. This particular hypothesis poses a challenge because it deals with a rather private behavior. In order 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 there, as opposed to primp- ing or putting on makeup. However, this strategy comes with the potential downside that your 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 here, of course, is that we don’t tech- nically 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.

So, in sum, we started with the hypothesis 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 strategy 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 run- ning time. Finally, we decided that the best strategy for recording behavior would be to station observers outside the restrooms and count the number of people who enter. Now, let’s say we conduct these observations every evening for one week and collect the data in Table 3.2.

Table 3.2: Findings from observation exercise

Men Women

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

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

Total 300 (100%) 500 (100%)

You can see that more women (n 5 500) than men (n 5 300) attended the movie theater during our week of sampling. But the real test of our hypothesis comes from examining the percentages within gender groups. That is, of the 300 men who went into the restroom, what percentage 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.

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Example 2—Cell Phone Usage While Driving

Imagine for this example that you 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, making this an important public safety hazard. Thus, if we could under- stand the contexts in which people are most likely to use cell phones, this would provide valuable information for developing guidelines for safe and legal use of these devices. So in this study we might count the number of drivers using cell phones in two settings: in rush-hour traffic and 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 we 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 typically clogged during rush hour. These observers would keep a record of each passing car, not- ing whether the driver was on the phone.

Once we decide on a sampling strategy, our next step is to decide on the types of behaviors we want to record. One challenge in this study is in deciding how broadly to define the category of cell-phone usage. Would we include both talking and text messaging? Given our interest in distraction and public safety, we probably would want to include text mes- saging. In response to tragic accidents, several states have recently banned text messaging while driving. Because we will be observing moving vehicles, the most reliable approach might be to simply note whether each driver had a cell phone in his or her hand. As with our restroom study, we are sacrificing a little bit of precision (i.e., we don’t know what the cell phone is being used for) to capture behaviors that are easier to record.

So, in sum, 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 and that they should note whether drivers were using cell phones. Finally, we decided that the best compromise for observing cell- phone usage would be to note whether each driver was holding a cell phone. Now, let’s say we conducted these observations over a 24-hour period and collected the data shown in Table 3.3.

Table 3.3: Findings from observation exercise #2

Rush Hour Moving

Cell phone 30 (30%) 200 (67%)

No cell phone 70 (70%) 100 (33%)

Total 100 300

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You can see that more cars passed by during the non–rush-hour stretch (n 5 300) than during the rush-hour stretch (n 5 100). But the real test of our hypothesis comes from examining the percentages within each stretch. That is, of the 100 people observed during rush hour and the 300 observed not during rush hour, what percentage were using cell phones? In this dataset, 30% of those in rush hour were using cell phones, compared with 67% of those not during rush hour. In other words, our hypothesis was not confirmed by the data. Drivers in rush hour were less than half as likely to be using cell phones. The next step in our research program would be to speculate on the reasons why the data con- tradicted our hypothesis.

Qualitative Versus Quantitative Approaches The general method of observation lends itself equally well to both qualitative and quan- titative approaches, although some types of observation fit one approach better than the other. For example, structured observation tends to be focused on testing hypotheses and quantifying responses. In Mary Ainsworth’s “strange situation” research (described ear- lier), 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 observation—tends to focus on learning from events as they unfold naturally. In Leon Festinger’s “doomsday cult” study, the researchers joined the group in order to observe the ways members reacted when their prophecy failed to come true.

Research: Thinking Critically

The Irritable Heart

By K. Kris Hirst

Using open-source data from a federal project digitizing medical records of veterans of the American Civil War (1860–1865) called the Early Indicators of Later Work Levels, Disease, and Death Project, researchers identified an increased risk of postwar illness among Civil War veterans, including car- diac, gastrointestinal, and mental diseases throughout their lives. In a project partly funded by the National Institutes of Aging, military service files from a total of 15,027 servicemen from 303 compa- nies of the Union Army stored at the United States National Archives were matched to pension files and surgeon’s reports of multiple health examinations. A total of 43% of the men had mental health problems throughout their lives, some of which are today recognized as related to posttraumatic stress disorder (PTSD). Most particularly affected were men who enlisted at ages under 17. Roxane Cohen Silver and colleagues at the University of California, Irvine, published their results in the Feb- ruary 2006 issue of Archives of General Psychiatry.

Studies of PTSD to date have connected war experiences to the recurrence of mental health prob- lems and physical health problems such as cardiovascular disease, hypertension, and gastrointestinal disorders. These studies have not had access to long-term health impacts, since they have focused on veterans of recent conflicts. Researchers studying the impact of modern conflict participation report that the factors increasing risk of later health issues include age at enlistment, intimate expo- sure to violence, prisoner-of-war status, and having been wounded.

The Trauma of the American Civil War

The Civil War was a particularly traumatic conflict for American soldiers. Army soldiers commonly enlisted at quite young ages; between 15% and 20% of the Union army soldiers enlisted between the

(continued)

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Research: Thinking Critically (continued) ages of 9 and 17. Each of the Union companies was made up of 100 men assembled from regional neighborhoods and thus often included family members and friends. Large company losses—75% of companies in this sample lost between 5% and 30% of their personnel—nearly always meant the loss of family or friends. The men readily identified with the enemy, who in some cases represented family members or acquaintances. Finally, close-quarter conflict, including hand-to-hand combat without trenches or other barriers, was a common field tactic during the Civil War.

To quantify trauma experienced by Civil War soldiers, researchers used a variable derived from per- centage of company lost to represent relative exposure to trauma. Researchers found that in military companies with a larger percentage of soldiers killed, the veterans were 51% more likely to have cardiac, gastrointestinal, and nervous disease.

The Youngest Soldiers Were Hardest Hit

The study found that the youngest soldiers (aged 9 to 17 years at enlistment) were 93% more likely than the oldest (aged 31 and older) to experience both mental and physical disease. The younger soldiers were also more likely to show signs of cardiovascular disease alone and in conjunction with gastrointestinal conditions, and they were more likely to die early. Former POWs had an increased risk of combined mental and physical problems as well as early death.

One problem the researchers grappled with was comparing diseases as they were recorded during the latter half of the 19th century with today’s recognized diseases and psychiatric disorders. For one, PTSD was not recognized by doctors—although they did recognize that veterans exhibited an extreme level of “nervous disease” that they labeled “irritable heart” syndrome.

Children and Adolescents in Combat

Harvard psychologist Roger Pitman, writing in an editorial in the publication, writes that the impact on younger soldiers should be of immediate concern, since “their immature nervous systems and diminished capacity to regulate emotion give even greater reason to shudder at the thought of chil- dren and adolescents serving in combat.” Although disease identification is not one-to-one, said senior researcher Roxane Cohen Silver, “I’ve been studying how people cope with traumatic life expe- riences of all kinds for 20 years and these findings are quite consistent with an increasing body of literature on the physical and mental health consequences of traumatic experiences.”

Boston University psychologist Terence M. Keane, Director of the National Center for PTSD, com- mented that this “remarkably creative study is timely and extremely valuable to our understanding of the long-term effects of combat experiences.” Joseph Boscarino, senior investigator at Geisinger Health System, added, “There are a few detractors that say that PTSD does not exist or has been exaggerated. Studies such as these are making it difficult to ignore the long-term effects of war- related psychological trauma.”

The Irritable Heart: Increased Risk of Physical and Psychological Effects of Trauma in Civil War Vets by K. Kris Hirst. © 2011 K. Kris Hirst (http://anthropology.about.com). Used with permission of About Inc., which can be found online at www.about.com. All rights reserved.

Think about it:

1. What hypotheses were 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 observa- tion? Are there elements of more than one type? Explain.

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3.2 Qualitative Research Interviews

As pointed out in our discussion of phenomenological research and case studies, interviews are an essential component of many types of projects and can yield a great deal of information on subjects’ experiences of particular events. Interviews can be utilized at any stage during the research process to identify areas of further explo- ration, function as the main source of data collection, or provide additional information on data interpretation (Breakwell, Hammond, & Fife-Schaw, 2000). Research interviews can be used for many purposes and in a wide variety of contexts. For example, a researcher may utilize an interview to uncover the subject’s beliefs, feelings, and perspectives on an experience; investigate the motives behind certain behaviors; or obtain factual data about an event or person.

Interviews yield similar data to observational research but also provide information on what participants say and how they say it. In addition, data are collected on interviewees’ nonverbal behaviors such as eye movements, facial expressions, voice tones, and body posture. All these details can provide additional information about the interviewees’ com- fortableness about a topic, intensity of feeling, or thoughts about an experience. However, the main focus of interviews should be on the verbal content the interviewees provide.

Conducting research interviews requires researchers to take a systematic approach to data collection. This gives researchers the ability “to maximize the chances of maintaining objectivity and achieving valid and reliable results” (Breakwell et al., 2000, p. 239). Inter- views require a great deal of skill and sensitivity; they are often time consuming in order for them to collect enough data to answer the research question(s). And, as with most interpersonal situations, the outcome of the interview is influenced largely by the person- ality and communication style of the interviewer and interviewee. If an interviewer is not very personable and engaging, this could influence the quality and quantity of the infor- mation that the interviewee provides and ultimately limit the interpretations that can be made from it. All interviews should include a dynamic, two-way interchange between the interviewer and the interviewee. Whereas quantitative interviews generally utilize struc- tured interviews, which include a predetermined and fixed set of questions, qualitative interviews are generally unstructured or semistructured and take on more of an informal conversational approach, with the interviewee doing most of the speaking.

Interview Characteristics and Techniques

The following five sections lay out important characteristics of the interviewer, the inter- view setting, and the types of interviews utilized in qualitative research. As with any type of interview, the interviewer’s personality, appearance, and communication style can significantly influence how the interviewee responds. In addition, where the interview occurs, or in what setting, can have influence on the quality and quantity of data collected.

Personal Characteristics of the Interviewer Although it might seem that anyone can conduct an interview, an interviewer needs to display certain characteristics in order to ensure a successful interview. Aiken and Groth- Marnat (2006) list the following characteristics of a professional interviewer:

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• maintains a friendly but neutral tone and demeanor; • shows interest in the interview but does not pry or show intense reactions to the

interviewee; • keeps a warm and open approach; • does not show approval or disapproval toward the interviewee; • times questions appropriately so that the conversation flows smoothly from topic

to topic; • allows appropriate silences or pauses so that the interviewee can collect his or

her thoughts; • allows the interviewee to complete a discussion without interrupting; • pays close attention to nonverbal behaviors, such as facial expressions, body

posture, and voice fluctuations; • displays patience throughout the interview; • checks with the interviewee to ensure that there are no misunderstandings; and • maintains eye contact.

All these characteristics can determine whether rapport is developed, how comfortable the interviewee feels about sharing information, the type of information the interviewee discusses, and the length of the interview. In qualitative research, interviews are designed to gather in-depth information, so if the interviewee feels uncomfortable, the researcher may obtain only limited information and the interview process may be cut short. There- fore, it is imperative that interviewers maintain a friendly and welcoming tone, as well as provide an open environment to ensure that the interviewee feels comfortable.

The appearance of the interviewer is another important characteristic. Interviewers that are appropriately dressed and well-groomed will be more positively received than those who look disheveled or unclean. Likewise, an interviewer dressed in a suit with a brief- case (or a lab coat) might be more intimidating than one dressed in a business-casual man- ner. Additional characteristics such as age, gender, and ethnicity may also influence how

comfortable the interviewee feels and how the interview progresses (Breakwell et al., 2000).

The Interview Setting Although interviews can take place anywhere, it is best to conduct them in a quiet, well-lit room that is free of distractions. Conducting interviews in noisy environments (the nearest Starbucks) or in rooms that have a lot of distracting décor items (e.g., posters, paintings, bookshelves) can negatively impact the quality and quantity of the conversation. For example, conducting an interview at a coffee house would not be as productive as conducting the inter- view in a conference room or at the

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The quality of an interview is impacted by the setting of the interview and the characteristics of the interviewer.

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kitchen table. Additionally, rooms that are not well-lit or are too cold or too warm will also influence how the interview progresses. For example, if a room is dimly lit and hot, the interviewee may become fatigued and less focused on the interviewer ’s questions. Another important feature is the comfortableness of the chairs. Because qualitative inter- views are fairly lengthy, setting up comfortable chairs that face each other is imperative.

Unstructured Interviews Unlike structured interviews in quantitative research that make use of fixed questions in a particular order, qualitative interviews do not include developed questions prior to the interview. Unstructured interviews are commonly used in qualitative research and utilize a looser approach. Unstructured interviews are probing in nature and are used primarily to explore new topic areas or to investigate topics that are not well understood. Although the researcher has a number of topics in mind that he or she would like to cover, no specific questions have been developed and the process of the interview is not outlined or planned. Unstructured interviews involve only open-ended questions, which allow the interviewee to respond as much or as little as he or she may want to. Although interviewers typically have checklists of topics that should be covered and will guide the interviewee with probing questions to remain on topic, interview questions are generally guided by information the interviewee provides. The purpose of unstructured interviews is to allow the researcher to obtain an in-depth understanding of the interviewee’s experi- ences from his or her own perspective.

Unstructured interviews are similar to informal conversations with a purpose (Hennink, Hutter, & Bailey, 2011). As previously mentioned, unstructured interviews let interview- ees share in-depth information in their own words and from their own perspective. Vari- ous types of information can be collected from unstructured interviews, including life narratives, the person’s identity and background characteristics, and the context in which the interviewee lives (Hennink et al., 2011). Unstructured interviews can be used in many situations, such as examining personal life stories or exploring people’s feelings, thoughts, and perceptions on a chosen topic.

Steps in Unstructured Interviews

Although the researcher does not formulate interview questions before the interview, researchers conducting unstructured interviews may want to develop an interview guide of topics they would like to cover. Interview guides for unstructured interviews may include reminders about what to tell the interviewee at the beginning of the interview (e.g., explain the purpose of the research, discuss ethical issues), a statement about the population sample being researched, a list of key topic areas that need to be addressed, and a few probing questions. For example, if researching the potential causes or influ- ences of heroin addiction, topic areas might include family history, childhood experiences, relationships with family members, peer relationships, first exposure to heroin, experi- ences with heroin, and so forth.

When meeting an interviewee for the first time, it is important not to jump right into ques- tions or research topics but rather to spend some time establishing a rapport. Establishing a rapport is extremely important in in-depth interviews because it nurtures a sense of

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trust. Making small talk about the weather or other current events is a great way to ease the interviewee into the process. It also allows the interviewer and interviewee to feel more comfortable together. Additionally, in order to maintain the flow of the conversa- tion, note taking is discouraged but tape recording is encouraged.

Unstructured interview questions should be open-ended and nonleading. An open-ended question to the beginning of an interview might take the following form: Tell me about your experiences with heroin. This type of guided question begins the interview process and allows the interviewee to respond in any manner. Closed-ended questions (used in quantitative research) should be avoided at all costs because they are not engaging. For example, asking Has heroin abuse been hard on you? elicits a yes or no response and may not further the discussion. In addition, leading questions should be avoided so that interview- ees can provide the information on their own terms and voice their own viewpoint. For example, instead of asking, How did heroin negatively impact your dental hygiene?, a nonlead- ing question would be, How did heroin affect your overall health? The latter question allows the interviewee to approach the topic in any way desired and does not just focus on the negative aspects of dental hygiene.

Data Analysis in Unstructured Interviews

Analyzing data from unstructured interviews can be complicated and tedious. Because each interview is different (based on the direction the interviewee took), the informa- tion obtained may not be consistent across interviewees. Therefore, to analyze the data, the researcher will need to first transcribe all the recorded interviews onto paper. Then, he or she will need to read through all the information and reflect on its overall mean- ing. Content analysis is often used in analyzing interview data. As discussed previously in Archival Research, the process of systematically extracting and analyzing a collection of information is known as content analysis. Because interview data can quickly become overwhelming, it is a good idea to begin developing coding categories early in the data collection process. Coding categories are symbols or words applied to a group of words in order to categorize the information. Once the information is organized into coding cat- egories, the researcher can begin to group the categories according to their patterns or themes within the data. In addition to common patterns and themes, the researcher may also want to include quotations from the interview to support his or her conclusions.

Pros and Cons of Unstructured Interviews

Although the flexibility of unstructured interviews provides a number of advantages, there are a few challenges associated with this type of data collection. First, this approach is so in-depth, unstructured interviews can be very time consuming, especially with larger sample sizes. Establishing a rapport and covering the topic areas thoroughly can take some time. Additionally, because these interviews are unstructured, the length of the interview will vary depending on the individual and the direction the interview takes. Second, because the interviewer has little control over the interview process, unstruc- tured interviews can veer off topic easily, and it can be difficult to know how to guide the interviewee back to the main topic without the risk of losing continuity, naturalness, and comfort in the discussion. And third, because data collection will vary by interviewee, it might be difficult for the researcher to make comparisons across interviewees.

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Semistructured Interviews In contrast to unstructured interviews, semistructured interviews give the interviewer more control over the process. Instead of a checklist of topic areas, the interviewer devel- ops key questions for specific topic areas before the interview and creates a more detailed interview guide. The researcher standardizes the questions across all interviewees, so all of them will be asked the same key questions. However, the key questions do not have to be asked in the same order, and the interviewer has the option to pose probing questions to explore an area further. Thus, the semistructured interview often moves between struc- tured and unstructured questions throughout the session.

The interview guide for a semistructured interview is more comprehensive than those used for unstructured interviews. It contains a written script about why the research is being undertaken and how to conduct the interview as well as sets of standardized open- ing questions, key questions, and closing questions. Specific probing questions are also usually provided. The semistructured interview is more flexible than its unstructured counterpart, as the standardized scripts and questions allow different interviewers to administer basically the same interview.

Semistructured interviews are advantageous in situations where unstructured interviews have already been conducted on the topic or when the researcher wants to obtain a larger sample size. Standardized questions make the data easier to analyze and interpret. Even so, data analysis for semistructured interviews follows the same protocols as for unstruc- tured interviews.

Focus Groups Focus groups are generally con- ducted when the researcher wants to collect data on several individuals simultaneously, in the same room. Focus groups are critical for organi- zationally based research, especially action research approaches, and are a popular method for studying political trends. A focus group can be thought of as a group interview, where par- ticipants share their thoughts, beliefs, experiences, and perspectives on a particular issue. Focus groups usu- ally contain between 10 and 12 indi- viduals and meet together for about one to two hours to discuss a particu- lar topic. There is always a moderator present, who may or may not be the researcher. He or she introduces the topics to be discussed, ensures that the discussion remains on topic, and sees to it that no one participant dominates the discussion (Leedy & Ormrod, 2010).

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A focus group can be thought of as a group interview.

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Similar to the interview guides used in unstructured and semistructured interviews, focus groups incorporate discussion guides to guide the discussion and keep the group focused (Hennink et al., 2011). The discussion guide serves primarily as a reminder of what top- ics and questions should be covered. The structure of the discussion guide is extremely important to the success of a focus group discussion because it helps the moderator intro- duce the topic, establish a rapport among group participants, focus on key topic areas, and bring the discussion to a close (Hennink et al., 2011). Successful discussion guides contain an introduction that explains the nature of the study, any ethical issues, and how the discussion will proceed, as well as standardized introductory questions, transition questions, key questions, and closing questions. As with other qualitative interview tech- niques, questions posed in focus groups should be open-ended.

There are several ways to analyze the data collected during a focus group interview. How- ever, summarizing the transcript along with any field notes is the most common and effi- cient. Because data from some focus groups are needed fairly quickly to address a timely topic, summaries are the most convenient way of communicating the results. In addition, because the data are fairly straightforward, a summary of the conclusions can easily be conducted. Data from focus groups can also be interpreted through content analysis, as discussed previously in this chapter, and through techniques that are beyond the scope of this book.

Pros and Cons of Focus Groups

As with other qualitative methods, focus groups are used for exploratory and explana- tory research. As Hennink et al. (2011) describe, focus groups are very useful for exploring new topics, obtaining a range of views about a topic, understanding typical behaviors or norms, understanding group processes, and pairing with quantitative or other qualita- tive methods. Focus groups are especially beneficial when exploring difficult or traumatic experiences because the group environment tends to be more supportive. For example, if the research topic was about losing a spouse from a traumatic accident, participants might find solace and comfort from others who have experienced the same tragedy.

It is important to determine whether a study will benefit more from a focus group or an unstructured or semistructured interview. For example, if the researcher wants to obtain detailed information about participants’ experiences, a focus group may not be the best method to use. Since focus groups include interactions among various participants, the data collected may not fully represent the individual perspectives of each participant as a one-to-one interview would. In addition, focus groups provide the researcher with very limited control over the discussions that occur, so the information obtained may not be consistent with, or fully address, the research questions proposed. And, unlike unstruc- tured and semistructured interviews, in focus groups the researcher cannot ensure confi- dentiality and anonymity of the participants because participants may share that informa- tion outside of the group.

Quantitative Structured Interviews Unlike the flexible interview techniques used in qualitative research, quantitative research involves more structured and standardized data collection methods. Structured interviews include a fixed set of either open-ended or closed-ended questions that are

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administered in a fixed order. Thus, researchers develop questions prior to the interview and administer the same questions in exactly the same order to every participant. Because of the standardized format, structured interviews are easily replicated and can be admin- istered to large sample groups by various interviewers. In addition, the data collected from structured interviews is much easier to analyze than data from unstructured and semistructured interviews because the standardized question format enables easy coding and interpretation.

Pros and Cons of Structured Interviews

Unlike qualitative interviewing techniques, structured interviews also provide a reliable source of data collection. However, this method is not without limitations. One signifi- cant weakness involves the limited amount of information that can be obtained during the interview process. Structured interviews are not intended to explore complex issues or opinions and provide no flexibility in the questions asked. If the questions are poorly written, the interviewer cannot modify the questions or present additional probing ques- tions to obtain further information. Another limitation involves the quality of the data that are collected. Because the questions in structured interviews are standardized and more directive, they do not lend themselves to elicit in-depth responses. Thus, participants end up providing only very limited responses to the questions. A further discussion of inter- view and survey techniques for quantitative research is discussed in Chapter 4.

Reliability and Validity of Interviews

Interviews are an important data collection method but, like observational methods, they too impart problems with reliability and validity. And, because reliability requires consis- tency, it is often difficult to generate high levels of reliability using unstructured and semi- structured interviews. Because unstructured and semistructured interviews differ among interviewees in their approach and process, there might be little consistency across inter- views. For example, if unstructured interviews were administered to 10 participants, it is very unlikely that any 2 interviews will follow the same process, cover the same material, or generate the same data. The interviewer’s experiences with the topic being discussed may also influence how the data are analyzed and interpreted, and in larger studies, there are bound to be several interviewers. Additionally, as with any self-report measure, the interviewee’s responses may be distorted or inaccurate based on what he or she believes the interviewer wants to hear. It is also possible that an interviewee may feel uncom- fortable with the interviewer or the topic being discussed and may withhold important details. And sometimes, interviewees are simply unable to remember all the details of an experience, so the information they provide is not complete.

The validity of interviews is extremely variable and increases along with more structured methods. Thus, structured and semistructured interviews will generally have higher levels of validity than unstructured interviews owing to the more reliable data that the structured format can generate. Interviews that focus on specific topics and are analyzed by two or more evaluators also tend to have higher validity levels. Having more than one researcher agreeing on the findings increases the likelihood that the results are valid. Additionally, utilizing other types of measurement methods, such as observations or experiments, to supplement the interview increases the validity of interview data.

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When conducting any type of interview, the interviewer is considered the assessment or measurement tool (Aiken & Groth-Marnat, 2006). Thus, the interviewer is the key instru- ment used to collect data in the study. As a result, most reliability problems associated with interviews are the result of the characteristics and behavior of the interviewer. For example, such interviewer characteristics as appearance, age, gender, demeanor, and per- sonality can influence how engaged the interviewee becomes during the interview and the type of information he or she will disclose. The interviewer’s personal biases can also influence the direction of the interview and the type of data that is gathered. Since the interviewer is in charge of the interview, the length of interview and the data collected depend on the interviewer’s questions. Ignoring specific question areas, asking the wrong questions, or asking questions that elicit short responses will affect the type of information the interviewee provides.

Although some interviewer effects cannot be eliminated, Breakwell et al. (2000) discuss a few ways to control for them. One way to do this involves having the same person conduct all interviews so that the effects of the interviewer are constant. While this tech- nique ensures that the interviewer’s appearance and personal biases will likely be con- stants, it does not ensure that variation among the interviewees will be eliminated. Some interviewees may feel more comfortable with a male, for example, or be more willing to open up to someone who is middle-aged. Another way to control for interviewer effects is to have several interviewers randomly assigned to interviewees. Instead of holding the interviewer constant across all interviews, utilizing different interviewers can minimize any strong effects that might be experienced with one particular interviewer. Finally, some researchers might find it helpful to match interviewers to interviewees based on age or gender. This is especially useful if it is known that particular interviewees feel more com- fortable with certain types of people.

Ethical Guidelines

When conducting interviews, several important ethical issues need to be addressed. First, it is imperative that the interviewer provide an explanation of the purpose of the research, the intent of the interview, and any potential risks and benefits of participating (this is also known as informed consent). Thus, all interviewees must be notified of all the features of the study so they can make intelligent decisions about their willingness to participate. If the interview includes sensitive or emotional topics, interviewees need to be informed that they will be asked to discuss some painful or embarrassing experiences. In addition, interviewees need to be told how any identifying information will be kept confidential as well as how the researchers will protect any information shared and collected.

As with all types of research, the participant (or in this case, the interviewee) must be pro- tected from harm. Interviewers must take reasonable steps to ensure that interviewees do not experience any harm during the interview or research process. In practice, this means that the risk of harm for the interviewee is not greater than the harm that he or she would experience in everyday life and that the risk is outweighed by the benefits of the study. (See APA and Other Ethical Guidelines in Chapter 1 for further discussion.)

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3.3 Critiquing a Qualitative Study

How does one assess the overall worth and credibility of a qualitative research study or proposal? What characteristics constitute strong research studies, and what characteristics constitute weak ones? The methods and guidelines for evalu- ating research studies are fairly detailed and tedious. Not all studies are worthy or rig- orous, and it is important that you, as a consumer of psychological research, are able to identify potential problems. Just because a study claims to have valid and reliable results does not necessarily mean that it does. For example, some studies may utilize incorrect statistical or data analysis procedures, so the results generated may not be correct or com- plete. Additionally, some studies may use inappropriate sampling techniques for the type of research design being conducted. As professionals in the field, you will need to be able to identify which parts of a study are valid, which parts can be considered acceptable with caution, and which parts have significant limitations or are downright misleading.

Leedy and Ormrod (2010) reviewed standards from experienced qualitative researchers and compiled a list of general criteria to apply when evaluating a qualitative study:

1. Purposefulness: Does the research question(s) drive the research process and the methods to collect and analyze the data?

2. Explicitness of Assumptions and Biases: Does the researcher describe any assumptions, expectations, or biases that might influence how the data are collected, analyzed, and interpreted?

3. Rigor: Does the researcher use rigorous, precise, and thorough methods to collect, record, and analyze the data? Does the researcher take steps to remain objective throughout the study?

4. Open-mindedness: Is the researcher willing to modify interpretations when newly collected data do not support previously collected data?

5. Completeness: Does the researcher describe the phenomenon in all its complexity? Does the researcher spend sufficient time in the field examining the phenomenon, detail all aspects of the phenomenon (e.g., setting, behaviors, perceptions), and provide a holistic picture of the phenomenon?

6. Coherence: Do the data show consistent findings with the measurement used and across multiple measurement methods used?

7. Persuasiveness: Does the researcher provide logical arguments, and does the evidence support one interpretation of the data?

8. Consensus: Do other studies and researchers in the field agree with the interpre- tations and explanations?

9. Usefulness: Does the study provide useful implications for future research, a more thorough understanding of the phenomenon, or lead to interventions that could enhance the quality of life? (p. 187)

In addition to these criteria, there are several factors to consider when evaluating the vari- ous sections of a research study or proposal. The next seven sections will discuss how to critically evaluate the literature review, the purpose statement, the sampling methods, the procedures, the instruments, the results, and the discussion section of a qualitative study.

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Evaluating the Literature Review Section

As mentioned in Chapter 1, the purpose of the literature review is to support the need for research to be conducted. Literature reviews should be thorough and comprehensive and contain all relevant research on the specific topic being studied. Literature reviews should also be objective, showing no biases toward the selection of articles being reviewed, and include previous research that relates to the current study. The following questions, adapted from Houser (2009), will assist in the evaluation of the literature review:

• Do the researchers present an adequate rationale for conducting the study? • What is the significance of the study? What difference will it make to the field? • Is the literature review thorough and comprehensive? • Do the researchers demonstrate any potential biases in the literature review? • Are all important concepts clearly defined by the researchers? • Do the researchers clearly describe previous methods that are relevant to under-

standing the purpose for conducting this study?

Evaluating the Purpose Statement

The purpose statement provides the aim or intent of the study. It is generally found in the Introduction section, as the last paragraph before the Literature Review section. Purpose statements can be written as a declarative statement or in the form of a question or ques- tions. They should include the type of research methods and design used and describe the variables and population studied. When evaluating the purpose statement, it is also important to examine whether the purpose and research problem are in fact researchable. Purpose statements and research problems are researchable only when the variables of interest can be operationalized—that is, defined in a measurable and objective way. Con- sidering these requirements, the following questions, adapted from Houser (2009), can assist in the evaluation of the purpose statement:

• Does the article clearly present the purpose statement? • Is the purpose statement clearly based on the argument developed in the litera-

ture review? • Are the variables of interest (i.e., independent and dependent) clearly identified

in the purpose statement?

Evaluating the Methods Section—Sampling

The Sampling section includes thorough and detailed information on the sample used and the techniques or methods used to select the sample. Descriptions of the sample should include all relevant demographic characteristics (e.g., age, ethnicity, sex) as well as size. Unlike quantitative approaches, which usually require large samples, qualitative tech- niques do not have any restrictions on sample size. Thus, sample size depends on what the research wants to know, the purpose of the inquiry, what it will be useful for, how credible it will be, and what can be done with available time and resources. Qualitative

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research can be very costly and time-consuming, so choosing information-rich cases will be most valuable. As noted by Patton (2002), “The validity, meaningfulness, and insights generated from qualitative inquiry have more to do with the information-richness of the cases selected and the observational/analytical capabilities of the researcher than with sample size” (p. 245).

The sampling techniques employed should also be discussed, including detailed informa- tion about how the sample was selected and what sampling methods were used (e.g., pur- posive sampling, snowball sampling). In contrast to quantitative research, which strives for generalizable representative sampling, qualitative research typically focuses on rela- tively smaller samples, and even sometimes only single cases, that are selected purpose- fully. “Purposeful sampling refers to selecting information-rich cases for study in depth” (Patton, 2002, p. 230). When evaluating the sampling methods section, it is important to examine whether appropriate sampling techniques were used for the type of research design that was employed and the research questions proposed. The following list cov- ers a few of the most common sampling procedures used in qualitative studies (these are discussed in more detail in Chapter 4):

• Purposive sampling (or judgment sampling): The researcher selects a sample that will yield the most information to answer the research questions.

• Quota sampling (a type of purposive sampling): The researcher determines the number of participants and what characteristics will be needed, and then selects a sample based on those.

• Theoretical sampling: The researcher selects a sample that will assist him or her in developing a theory.

• Convenience sampling: The researcher selects anyone who shows up for the study, regardless of individual demographics.

• Snowball sampling (a type of purposive sampling): The researcher collects data on a few participants that he or she has access to and then asks those participants for referrals to other individuals who are within the same population.

All these approaches serve a somewhat different purpose. However, the underlying prin- ciple common to all of these techniques is selecting information-rich cases.

The following questions, adapted from Houser (2009), are provided to assist in the evalu- ation of the sampling methods section:

• What type of sampling method is used? • Are the sampling procedures consistent with the purpose and research

questions? • Are relevant demographic characteristics of the sample clearly identified? • Do the methods of sample selection provide a good representative sample, based

on the population? • Are there any apparent biases in the selection of the sample? • Is the sample size large enough for the study proposed?

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Evaluating the Methods Section—Procedures

The procedures section provides a detailed description of everything that was conducted in the study. For qualitative studies, this involves primarily the type of research design that was employed. When evaluating the procedures section, it is important to examine whether the research design is appropriate for the study, as well as whether it is consistent with the purpose and research questions. The following questions, adapted from Houser (2009), are provided to assist in the evaluation of the procedures section:

• What type of research design is used (e.g., case study, phenomenological study)? • Is the research design consistent with the purpose and research questions? • Did the researcher provide a detailed description of what was conducted? • Did the researcher introduce any bias in the procedures used?

Evaluating the Methods Section—Instruments

The instruments section provides a detailed description regarding the types of instru- ments and measures that were used to collect the data. In qualitative research, common instrumentation includes interviews, observations, and journals. When evaluating the instruments section, it is important to consider whether the instruments were appropri- ate for the study and the sample and whether there were any limitations in the types of instruments utilized. The following questions, adapted from Houser (2009), are provided to assist in the evaluation of the instruments section:

• Is there a clear and adequate description of the instruments (e.g., data collection measures) used?

• What types of measures were used in the study (observations, interviews, etc.)? • What are some potential problems or limitations of the types of measures used? • Does the instrument appear to be appropriate for the sample?

Evaluating the Results Section

The results section describes findings from the study. Unlike quantitative studies, which focus on statistical analyses and results, qualitative studies include descriptions about the findings. When evaluating the Results section, it is important to examine how the data were analyzed (using themes, patterns, codes, etc.), whether concrete examples supported the themes or concepts, and how adequate the descriptions were to the findings. The fol- lowing questions, adapted from Houser (2009), are provided to assist in the evaluation of the results section:

• What strategies were used for coding and interpreting the data? Were they clearly described?

• Are concrete examples provided that link to identified themes or concepts? Are the examples adequate?

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Evaluating the Discussion Section

The discussion section summarizes the purpose of the research and what the findings imply for future research and actual practice. Additionally, the discussion section includes alternative explanations and potential limitations of the findings. The following ques- tions, adapted from Houser (2009), are provided to assist in the evaluation of the discus- sion section:

• Do the researchers clearly restate the purpose and research questions? • Do the researchers clearly discuss the implications of the findings and how they

relate to theories, other findings, and actual practice? • Do the researchers provide alternative explanations of the findings? • Do the researchers identify potential limitations of the study and the results? • Do the researchers identify possible directions for future research?

3.4 Writing the Qualitative Research Proposal

Although the format of a qualitative research proposal looks similar to the proposal provided in Chapter 1, Section 1.6, the content of some of the sections will differ from that of a quantitative study. Like quantitative studies, the qualitative proposal includes a title page, abstract page, and an introduction that discusses the research prob- lem, statement of the problem, research questions, and importance of the study. (Please refer to Chapter 1, Section 1.3, Research Problem and Questions, for further guidance.) However, the literature review and methods sections will differ with respect to focus and content. The following sections discuss the writing requirements for the qualitative litera- ture review and methods sections.

The Literature Review Section

As discussed in Chapter 1, the primary purpose of the literature review is to cover theoreti- cal perspectives and previous research findings on the research problem you have selected (Leedy & Ormrod, 2010). The literature review should demonstrate how your study will clarify or provide further information on shortcomings found in previous research as well as how your study will add to the existing literature. The purpose of the literature review for qualitative studies is slightly different from that of quantitative studies and will vary depending on the type of research design you are using. Table 3.4 summarizes the pur- poses of the literature review with respect to research design.

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Table 3.4: Purposes of the literature review in qualitative research

Type of Qualitative Research Purpose of the Literature Review

Ethnographical research Review the literature to provide a background for conducting the study

Phenomenological research Compare and combine findings from the study with the literature to determine current knowledge of a phenomenon

Grounded theory research Use the literature to explain, support, and extend the theory generated in the study

Case study research Review the literature to provide a background for the study, as well as explain and support the study

Archival/historical research Review the literature to develop research questions and provide a source of data

Observational research Review the literature to provide a background for conducting the study.

Adapted from Burns & Grove, 2005, p. 95.

Part of your literature review should include information about the research design you have selected. So you will want to include information from books and articles, as well as other research studies that have employed the same design.

The Method Section

As discussed in Chapter 1, the Method section includes a detailed description of the method of inquiry (quantitative, qualitative, or mixed design approach), research method used, the sample, data collection procedures, and data analysis techniques. The key pur- pose of the Method section is to discuss your design and the specific steps and procedures you plan to follow in order to complete your study.

Similar to quantitative proposals, qualitative proposals include the research method that will be used. However, qualitative proposals require explanations on why other meth- ods (such as quantitative or mixed designs) would have been less effective for the study. Another key difference in qualitative proposals is that they feature a discussion of the researcher’s role during data collection procedures. Since the researcher is considered the assessment or instrument tool in most qualitative studies, the impact of researcher bias and researcher effects needs to be discussed in detail. Additionally, because qualitative study samples are generally smaller than quantitative ones, the researchers should justify the appropriateness of the sample size in relation to the research design and questions.

The Method section for qualitative proposals is generally lengthier than for quantitative proposals because more thoroughness is required when describing the procedures and data collection methods used. For example, if conducting ethnographic research, you will need to describe the site that was selected, how the site was selected, how you will enter the site, how you will gain rapport with the subjects, and the various types of data collec- tion procedures you will use. You will also want to discuss the length of the data collection period and how you plan to exit the site. Additionally, data collection can be more cum- bersome since qualitative studies tend to employ different methods for it. And, because the data collected are found in detailed narratives, you will want to describe the process you will use to analyze the narratives as well as the various data analysis procedures you will use.

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3.5 Describing Data in Descriptive Research

To cap off our discussion of descriptive research designs, this section will cover the process of presenting descriptive data in both graphical and numeric form. No mat-ter how you present your data, a good description should be accurate, concise, and easy to understand. In other words, you have to represent the data accurately and in the most efficient way possible so that your audience can understand it. Another, more elo- quent way to think of these principles is to take the advice of Edward Tufte, a statistician and expert in the display of visual information. Tufte suggests that when people view your visual displays, they should spend time on “content-reasoning” rather than “design- decoding” (Tufte, 2001). The sole purpose of designing visual presentations is to com- municate your information. So the audience should spend time thinking about what you have to say, not trying to puzzle through the display itself. The following sections cover guidelines for accomplishing this goal in both numeric and visual form.

Table 3.5 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 level of daily stress. Each row in this table represents one participant in the study, and each column represents one of the variables for which data were collected. In the following sections, we will explore different options for sum- marizing these sample data, first in numeric form and then using a series of graphs. In this chapter, the focus is on ways to describe the sample characteristics. In later chapters, we will return to these principles when discussing graphs that display the relationship between two or more variables.

Table 3.5: Raw data from a sample of 20 individuals

Subject ID Gender Ethnicity Life Satisfaction Daily Stress

1 Male European American 40 10

2 Male European American 47 9

3 Female Asian 29 8

4 Male European American 32 9

5 Female Hispanic 25 3

6 Female Hispanic 35 3

7 Female European American 28 8

8 Male Hispanic 40 9

9 Male Asian 37 10

10 Female African American 30 10

11 Male European American 43 8

12 Male Asian 40 4

13 Male European American 48 7

(continued)

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Table 3.5: Raw data from a sample of 20 individuals (continued)

Subject ID Gender Ethnicity Life Satisfaction Daily Stress

14 Female African American 30 4

15 Female European American 37 7

16 Male Hispanic 40 1

17 Female European American 36 1

18 Male African American 45 8

19 Female European American 42 8

20 Female African American 38 7

Numerical Descriptions

Raw data, as shown in Table 3.5, illustrate the actual characteristics or scores for every participant in the sample. To better understand raw datasets, researchers often use numer- ical descriptions, or descriptive statistics, to summarize a set of scores or a distribution of numbers. For example, utilizing the raw data shown previously, a researcher may want to be able to communicate the total number of females and males that were included in the sample, as well as the average Life Satisfaction score across all participants. In order to cal- culate these, you would use descriptive statistics such as frequencies (how many females and males were included in the sample); measures of central tendency (i.e., the mean, median, and mode of the Life Satisfaction scores); and measures of variability or distri- bution (i.e., the variance and standard deviation of the Life Satisfaction scores). Descrip- tive statistics are generally used first to describe the sample (e.g., how many females and males are included in the sample) and then to describe the scores. The following section will discuss common procedures used to summarize sets of data.

Frequency Tables Often, a good first step in approaching your dataset is to get a sense of the frequencies for your demographic variables—gender and ethnicity in this example. The frequency tables shown in Table 3.6 are designed to present the number and percentage of the sample that fall into each of a set of categories. As you can see in this pair of tables, our sample con- sisted of an equal number of men and women (i.e., 50% for each gender). The majority of our participants were European American (45%), with the remainder divided almost equally between African American (20%), Asian (15%), and Hispanic (20%) ethnicities.

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Table 3.6: Frequency table summarizing ethnicity and sex distribution

Gender Frequency Percentage Valid Percentage Cumulative Percentage

Female 10 50.0 50.0 50.0

Male 10 50.0 50.0 100.0

Total 20 100.0 100.0

Ethnicity Frequency Percentage Valid Percentage Cumulative Percentage

African American 4 20.0 20.0 20.0

Asian 3 15.0 15.0 35.0

Hispanic 4 20.0 20.0 55.0

European American

9 45.0 45.0 100.0

Total 20 100.0 100.0

We can gain a lot of information from numerical summaries of data. In fact, numeric descriptors form the starting point for doing inferential statistics and testing our hypoth- eses. We will cover these statistics in later chapters, but for now it is important to under- stand that two numeric descriptors can provide a wealth of information about our dataset: 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 represents the most typical case in our dataset. There are three indices for representing central tendency:

The mean is the mathematical average of our dataset, calculated using the following formula:

M 5 gX N

The capital letter M is used to indicate the mean; the X refers to individual scores, and the capital letter N refers to the total number of data points in the sample. Finally, the Greek letter sigma, or , is a common symbol used to indicate the sum of a set of values.

So, in calculating the mean, we add up all the scores in our dataset (X) and then divide this total by the number of scores in the dataset (N). Because we are adding and dividing our scores, the mean can be calculated only using interval or ratio data (see Chapter 2, Sec- tion 2.3, for a review of the four scales of measurement). In our sample dataset, we could calculate the mean for both life satisfaction and daily stress. To calculate the mean value for life satisfaction scores, we would first add the 20 individual scores (i.e., 40 1 47 1 29 1 32 1 . . . 1 38), and then divide this total by the number of people in the sample (i.e., 20).

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

M 5 gX N

5 742 20

5 37.1

In other words, the mean, or most typical satisfaction rating in this sample, is 37.1.

The median is another measure of central tendency, representing the number in the mid- dle of our dataset, with 50% of scores both above and below it. The location of the median is calculated by placing the list of values in ascending numeric order, then using the

following formula: Mdn 5 (N 1 1)

2 .

For example, if you have 9 scores, the median will be the fifth one:

Mdn 5 (N 1 1)

2 5

(9 1 1) 2

5 10 2

5 5 .

If you have an even number of scores, say 8, the median will fall between two scores:

Mdn 5 (8 1 1)

2 5

9 2

5 4.5 , or the average of the fourth and fifth one.

This measure of central tendency can be used for ordinal, interval, or ratio data because it does not require mathematical manipulation to obtain. So in our sample dataset, we could calculate the median for either life satisfaction or daily stress scores. To find the median score for life satisfaction, we would sort the data in order of increasing satisfaction scores (which in this case has already been done). Next, we find the position of the median using

the formula Mdn 5 (N 1 1)

2 . Because we have an N of 20 scores:

Mdn 5 (N 1 1)

2 5

20 1 1 2

5 21 2

5 10.5 .

In other words, the median will be the average of the 10th and 11th scores. The 10th par- ticipant scored a 37, and the 11th participant scored a 38, for a median of 37.5. The median is another way to represent the most typical score on life satisfaction, so it is no accident that it is so similar to the mean (i.e., 37.1).

The final measure of central tendency, the mode, represents the most frequent score in our dataset, obtained either by visual inspection of the values or by consulting a frequency table like Table 3.6. 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 (consisting of group labels) since the numbers assigned to these data are arbitrary.

So in our sample data we could calculate the mode for any variable in the table. To find the mode of life satisfaction scores, we would simply scan the table for the most common score, which turns out to be 40. Thus, we have one more way to represent the most typi- cal score on life satisfaction. Note that the mode is slightly higher than our mean (37.1) or our median (37.5). We will return to this issue shortly and discuss the process of choosing the most representative measure. Since we have been ignoring the nominal variables so far, let’s also find the mode for ethnicity. This is accomplished by tallying up the number of people in each category—or, better yet, by letting a computer program do the tallying

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for you. As we saw earlier, the majority of our participants were European American (45%), with the remainder divided almost equally among African American (20%), Asian (15%), and Hispanic (20%) ethnicities. So the modal (most typical) value of ethnicity in this sample was European American.

One important take-home point is that your scale of measurement largely dictates the choice between measures of central tendency—nominal scales can use only the mode, and interval or ratio scales can use only the mean. 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 with a single number, so the goal is to settle on the most rep- resentative number. This process is illustrated by the examples in Table 3.7.

Table 3.7: Comparing the mean, median, and mode

Data Mean Median Mode Analysis

1, 2, 3, 4, 5, 11, 11

5.29 4 11 • Both the mean and the median seem to represent 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 representative value to describe this dataset.

Let’s look at one more example, using the “daily stress” variable from our sample data in Table 3.5. The daily stress values of our 20 participants were as follows: 1, 1, 3, 3, 4, 4, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 10, 10, and 10.

• To calculate the mean of these values, we add up all the values and divide by our sample size of 20:

M 5 gX N

5 134 20

5 6.70

• To calculate the median of these values, we use the formula Mdn 5 (N 1 1)

2 .

to find the middle score: Mdn 5 (N 1 1)

2 5

(21) 2

5 10.5 . This tells us that our

median is the average of our 10th and 11th scores, or 8. • To obtain the mode of these values, we can inspect the data and determine that 8

is the most common number because it occurs five times.

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

In analyzing these three measures of central tendency, we see that they all appear to rep- resent the data accurately. The mean is a slightly better choice than the other two because it represents the lower values as well as the higher ones.

Measures of Dispersion The second measure used to describe our dataset is a measure of dispersion, or the spread of scores around the central tendency. Measures of dispersion tell us just how typical the typical score is. If the dispersion is low, then scores are clustered tightly around the cen- tral tendency; if dispersion is higher, then the scores stretch out farther from the central tendency. Figure 3.2 presents a conceptual illustration of dispersion. The graph on the left has a low amount of dispersion because the scores (yellow curve) cluster tightly around the average value (red dotted line). The graph on the right shows a high amount of dis- persion because the scores (yellow curve) spread out widely from the average value (red dotted line).

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

One of the most straightforward measures of dispersion is the range, which is the differ- ence between the highest and lowest scores. In the case of our daily stress data, the range would be found by simply subtracting the lowest value (1) from the highest value (10) to get a range of 9. The range is useful for getting a general idea of the spread of scores, although it does not tell us 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 (abbreviated S2) is calculated by subtracting each score from the mean to get 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 our deviation scores will be, and therefore the higher our variance will be. The deviation scores are squared because otherwise their sum would always equal zero; that is, (X 2 M) 5 0. Finally, the standard deviation, abbreviated SD, is calculated by taking the square root of our variance. This four-step process is illustrated in Table 3.8, using a hypothetical dataset of 10 participants.

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

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

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

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

Table 3.8: Steps to calculate the variance and standard deviation

Values 1. Subtract values from mean.

2. Square and sum deviation scores.

3. Calculate variance.

1 (1 2 5.4) 5 24.4 24.42 5 19.36

S2 5 g(X 2 X )

N 5

82.4 10

5 8.24

2 (2 2 5.4) 5 23.4 23.42 5 11.56

2 (2 2 5.4) 5 23.4 23.42 5 11.56

4 (4 2 5.4) 5 21.4 21.42 5 1.96

5 (5 2 5.4) 5 20.4 20.42 5 0.16

7 (7 2 5.4) 5 1.6 1.62 5 2.56 4. Calculate standard deviation.

7 (7 2 5.4) 5 1.6 1.62 5 2.56

s 5 "s2 5 "8.24 5 2.87 8 (8 2 5.4) 5 2.6 2.62 5 6.76

9 (9 2 5.4) 5 3.6 3.62 5 12.96

9 (9 2 5.4) 5 3.6 3.62 5 12.96

mean 5 5.40

 5 0.00  5 82.40

Standard Scores So far, we have been discussing ways to describe one particular sample in numeric terms. But what do we do when we want to compare results from different samples or from stud- ies using different scales? Let’s say you want to compare the anxiety levels of two people; unfortunately, in this example, the 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 standard deviation of 10.

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 an 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., SD 5 2 compared with SD 5 10).

The solution for comparing these scores is to convert both of them to standard scores (or z-scores), which represent the distance of each score from the sample mean, expressed in standard deviation units. The formula for a z-score is:

z 5 x 2 M

SD

This formula subtracts the individual score from the mean and then divides this difference by the standard deviation of the sample. In order to compare Joe’s score with Deb’s score, we simply plug in the appropriate numbers, using the mean and standard deviation from

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

the scale that each one completed. This lets us put scores from very different distributions on the same scale. So, in this case:

Joe: z 5 x 2 M

SD 5

25 2 15 2

5 10 2

5 5

Deb: z 5 x 2 M

SD 5

40 2 30 10

5 10 10

5 1

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

In order to understand just how anxious Joe is, it is helpful to know a bit about why this technique works. If you have taken a statistics class, you will have encountered the con- cept of the normal distribution (or “bell curve”), a symmetric distribution with an equal number of scores on either side of the mean, as illustrated in Figure 3.3.

Figure 3.3: Standard deviations and the normal distribution

It turns out that lots of variables in the social and behavioral sciences fit this normal dis- tribution, provided the sample sizes are large enough. The useful thing about a normal distribution is that 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 devia- tion cuts off a known percentage of the curve, as illustrated in Figure 3.3. That is, 68% of scores will fall within 6 one standard deviation of the mean; 95% of scores will fall within 6 two standard deviations; and 99.7% of scores will fall within 6 three standard deviations.

These percentages allow us to understand our individual data points in even more use- ful ways, because we can easily move back and forth between z-scores, percentages, and standard deviations. Take our example of Joe and Deb’s anxiety scores: Deb has a z-score

Low –3SD –2SD –1SD +1SD +2SD +3SDMean

Score

68%

95%

99.7%

High

F re

q u

e n

c y

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

of 1, which means her anxiety is 1 standard deviation above the mean. And, as we can see by consulting the normal distribution, her anxiety level is higher than 84% of the popula- tion. Poor 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. (See http://www.measuringusability.com/pcalcz.php for a handy online calculator that con- verts between z-scores and percentages.)

This relationship between z-scores and percentiles is also commonly used in discussions of intelligence test scores. 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 are able to 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 different scales—perhaps in different laboratories and different countries—and place them on the same metric (standard of measurement) for comparison. As we have dis- cussed in several contexts, science is all about the accumulation of knowledge one study at a time. The best support for an idea comes when it is supported by data from different researchers, using different measures to capture the same concept. The ability to convert these different measures back to the same metric is an invaluable tool for researchers who want to compare research results.

Visual Descriptions

Displaying your data in visual form is often one of the most effective ways to communi- cate your findings—hence the cliché, a picture is worth a thousand words. But what sort of visual aids should you use? Your choice of graphs should be guided by two criteria: the scale of measurement and the best fit for the results.

Displaying Frequencies One common type of graph is the bar graph, which also summarizes the frequency of data by category. Figure 3.4a presents a bar graph, showing our four categories of ethnicity along the horizontal axis and the number of people falling into each category indicated by the height of the bars. So, for example, this sample contains 9 European American par- ticipants and 4 Hispanic participants. You’ll notice that these bar graphs contain exactly the same information as the frequency table in Table 3.6. When reporting your results in a paper, you would, of course, use only one of these methods; more often than not, graphi- cal displays are the most effective way to communicate information.

Figure 3.4b shows another variation on the bar graph, the clustered bar graph, which summarizes frequency by two categories at one time. In this case, our bar graph displays information about both gender and ethnicity. As in the previous graph, our 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, you can see that our 9 European American participants are divided into 5 males and 4 females; similarly, our 4 African American participants are divided into 1 male and 3 females.

F re

q u

e n

c y

10

Asian Hispanic African American

Ethnicity

European American

9

8

7

6

5

4

3

1

2

0

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

Figure 3.4: Bar graph displaying (a) frequency by ethnicity and (b) clustered bar graph displaying frequency by ethnicity and gender

The important rule to keep in mind with bar graphs is that they are used for qualitative, or nominal, categories—that is, those that do not have a numerical value. We could just as easily have listed European American 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 ordi- nal, interval, or ratio scale—we use a different kind of graph called a histogram. As seen in Figure 3.5a, histograms are drawn with the bars touching one another to indicate that the categories are quantitative and on a continuous scale. In this figure, we have broken down the “life satisfaction” values into three categories (less than 31, 31–40, and 41–50)

F re

q u

e n

c y

6

European American Asian Hispanic African American

Gender

Male

Female

5

4

3

2

1

0

of 1, which means her anxiety is 1 standard deviation above the mean. And, as we can see by consulting the normal distribution, her anxiety level is higher than 84% of the popula- tion. Poor 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. (See http://www.measuringusability.com/pcalcz.php for a handy online calculator that con- verts between z-scores and percentages.)

This relationship between z-scores and percentiles is also commonly used in discussions of intelligence test scores. 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 are able to 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 different scales—perhaps in different laboratories and different countries—and place them on the same metric (standard of measurement) for comparison. As we have dis- cussed in several contexts, science is all about the accumulation of knowledge one study at a time. The best support for an idea comes when it is supported by data from different researchers, using different measures to capture the same concept. The ability to convert these different measures back to the same metric is an invaluable tool for researchers who want to compare research results.

Visual Descriptions

Displaying your data in visual form is often one of the most effective ways to communi- cate your findings—hence the cliché, a picture is worth a thousand words. But what sort of visual aids should you use? Your choice of graphs should be guided by two criteria: the scale of measurement and the best fit for the results.

Displaying Frequencies One common type of graph is the bar graph, which also summarizes the frequency of data by category. Figure 3.4a presents a bar graph, showing our four categories of ethnicity along the horizontal axis and the number of people falling into each category indicated by the height of the bars. So, for example, this sample contains 9 European American par- ticipants and 4 Hispanic participants. You’ll notice that these bar graphs contain exactly the same information as the frequency table in Table 3.6. When reporting your results in a paper, you would, of course, use only one of these methods; more often than not, graphi- cal displays are the most effective way to communicate information.

Figure 3.4b shows another variation on the bar graph, the clustered bar graph, which summarizes frequency by two categories at one time. In this case, our bar graph displays information about both gender and ethnicity. As in the previous graph, our 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, you can see that our 9 European American participants are divided into 5 males and 4 females; similarly, our 4 African American participants are divided into 1 male and 3 females.

F re

q u

e n

c y

10

Asian Hispanic African American

Ethnicity

European American

9

8

7

6

5

4

3

1

2

0

(a)

(b)

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

and displayed the frequencies for each category in numerical order. For example, you can see that six people had life satisfaction scores falling between 31 and 40.

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

Figure 3.5: Histograms showing (a) frequencies by life satisfaction (quanti- tative) categories and (b) life satisfaction scores on a continuous scale

F re

q u

e n

c y

10

Less than 31 31–40 41–50

Life Satisfaction

9

8

7

6

5

4

3

1

2

0

F re

q u

e n

c y

4

3

2

1

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

Life Satisfaction

(a)

(b)

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CHAPTER 3Section 3.5 Describing Data in Descriptive Research

Displaying Central Tendency Another common use of graphs is to display numeric descriptors in an easy-to-understand visual format. That is, we can apply the same principles for displaying information about our sample frequencies to displaying the typical scores in the sample. If we refer back to our sample data in Table 3.5, we have information about ethnicity and gender but also about reports of daily stress and life satisfaction. Thus, a natural question to ask is whether there are gender or ethnic differences in these two variables. Figure 3.6 displays a clustered bar graph, displaying the mean level of life satisfaction in each group of partici- pants. One thing that jumps out is that males appear to report more life satisfaction than females, as seen 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) seem to report slightly more satisfaction than European American males (42).

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

These particular data are fictional, of course; but even if our graph were displaying real data, we would want to be cautious in our interpretations. One reason for caution is that this represents 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 pat- terns of differences.

The best way to appreciate this latter point is to see what happens when we tweak the graph a little bit. Our 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. But if we were trying to win an argument about gender differences in happiness, we could always alter the scale, as shown in Figure 3.7. These bars represent the same set of means, but we have compacted the y-axis to show only a small part of the range of the scale.

50

African–American Asian Hispanic European American

45

40

35

30

25

20

15

5

10

0

Male

Female

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That is, rather than ranging from 0 to 50, this misleading graph ranges from 28 to 45, in increments of 1. To the uncritical eye, this appears to show an enormous gender difference in life satisfaction; to the trained eye, this shows an obvious attempt to make the findings seem more dramatic. Any time you encounter a bar graph that is used to support a par- ticular argument, always pay close attention to the scale of the results: Does it represent the actual range of the data, or is it compacted to exaggerate the differences? Likewise, any time you create a graph to display results, it is your responsibility as a researcher to ensure that the graph accurately represents the data.

Figure 3.7: Clustered bar graph altered to exaggerate the differences

Summary

In this chapter, we have focused on qualitative and descriptive research designs, the latter being the first of three specific designs covered in the continuum of control. As discussed, qualitative methods differ from descriptive methods, with the latter allow- ing for the utilization of either qualitative, quantitative, or mixed-method approaches. Qualitative methods have minimal researcher control and are used to thoroughly explain or understand an event, situation, or phenomenon in great detail. On the other hand, the primary goal of descriptive designs is to describe attitudes and behavior, without any pre- tense of making causal claims. One common feature of both qualitative and descriptive designs is that they are able to assess behaviors that occur in their natural environment, or at least in something very close to it. Thus, this chapter first covered three qualita- tive designs and then three types of descriptive research: ethnographic, phenomenologi- cal, and grounded theory studies, and case studies, archival research, and observational research, respectively. Because each of the descriptive methods discussed has the goal of describing attitudes, feelings, and behaviors, each one can be used from either a quantita- tive or a qualitative perspective; thus, they were separated from qualitative designs that utilize only qualitative techniques.

45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28

African–American Asian Hispanic European American

Male

Female

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As mentioned previously, all the qualitative designs discussed are used to explore, explain, and understand events or situations in great detail. Thus, the goal of qualitative inquiry is to understand and explain the personal experiences of the participants from their perspec- tive and in their own environment. In qualitative research, only qualitative methods are applied.

In descriptive designs, such as a case study, the researcher studies an individual unit (such as a single person, group, or event) 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. Unlike qualitative designs, case studies can include either qualitative, quan- titative, or mixed-method approaches to data collection. On the one hand, case studies represent one of the lowest points on our continuum of control, owing to 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 difficult to generalize from the specifics of Gage’s experience, this case helped to inspire more than a century’s worth of research into the connections among mind, brain, and behavior.

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 impossible to measure in a laboratory setting. For example, we discussed Phillips’s study of copycat suicides, which he conducted by matching newspaper coverage of sui- cides to subsequent spikes in fatality rates. There would be no practical or ethical way to study these connections other than by examining the patterns as they occurred naturally. Archival studies are still relatively low on our 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—that is, developing a cod- ing strategy to extract relevant information from a broader collection of content. Content analysis involves a three-step process: identifying 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 sampling a week’s worth of prime-time programming and recording the screen time dedicated to portraying different races.

Observational research involves directly observing behavior and recording observations in a systematic way. This approach is well suited to a wide variety of research questions, provided that the variables can be directly observed. That is, one can observe what people do but not why they do it. In exchange for giving up access to internal processes, the researcher gains access to unfiltered behavioral responses—especially when finding ways to observe people unobtrusively. We discussed three main types of observational research. Structured observation involves creating a standardized situation, often in a laboratory set- ting, and tracking people’s responses. Naturalistic observation involves observing behav- ior as it occurs naturally, often in its real-world 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 variations go through a similar three- step process as archival research: Choose a hypothesis, choose a sampling strategy, and then code and record behaviors.

Summary

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This chapter next covered principles for describing data in both visual and numeric form. To move toward conducting statistical analyses, it is also useful to summarize data in numeric form. We discussed two categories of numeric summaries: central tendency and dispersion. Measures of central tendency (i.e., mean, median, and mode) provide infor- mation about the “typical” score in a dataset, whereas 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. We then covered the process of translating scores into standard scores (aka, z-scores), which express individual scores in terms of standard deviations. This technique is useful for comparing results from different studies that used different measures.

Finally, we discussed guidelines for visual presentation. If you remember one thing from this section, it should be that the sole purpose of visual information is to communicate your findings to an audience. Thus, your 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 visual display you choose, it should represent your data accurately; it is especially important to make sure that the y-axis accurately represents the range of your data.

Key Terms

archival research A descriptive design that involves drawing conclusions by ana- lyzing existing sources of data, including both public and private records.

axial coding Used in grounded the- ory, involves finding connections or relationships between categories and subcategories.

bar graph A visual display that summa- rizes 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 dataset.

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

coding categories Symbols or words applied to a group of words in order to categorize the information prior to data analysis.

content analysis The process of system- atically extracting and sifting through 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 method that focuses on an entire cultural group or a group that shares a common culture.

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

focus groups Group interviews that are generally conducted when the researcher wants to collect data on several individu- als simultaneously and in the same room.

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

gatekeeper Used in ethnographic research, facilitates access into the site.

grounded theory A method of research that builds theories from preexisting “grounded” data that have been systemati- cally analyzed and reanalyzed.

histogram A variation of a bar graph used to display ordinal, interval, or ratio data; histograms are drawn with the bars touch- ing one another to indicate that the catego- ries 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 individ- ual behaviors.

interrater reliability For research that involves more than one observer using the same system, ensuring that their data looks roughly the same.

mean A measure of central tendency that represents the mathematical average of a dataset; 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 dataset, with 50% of scores both above and below it.

mode A measure of central tendency that represents the most frequent score in a dataset, obtained either by visually inspecting 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 intervention by the researcher.

noise Amount of unexplained variation in a sample.

nominal data Categories that have no numerical meaning, such as gender, religious affiliation, or state; values that cannot be added, subtracted, or sorted in a logical fashion.

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.

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

open coding Used in grounded theory, involves the researcher labeling and cate- gorizing the data into categories or themes and smaller subcategories that describe the phenomenon being investigated.

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participant-expectancy bias or participant- observer effect The tendency of people to behave differently when they are aware of being observed or to alter their normal behaviors to be consistent with what they think the researcher is expecting from them.

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

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

phenomenological study An investiga- tion that attempts to understand the inner experiences of an event, such as a person’s perceptions of, perspective on, and under- standing of a particular experience.

range A measure of dispersion that rep- resents the difference between the highest and lowest scores.

researcher bias Researcher influences the results in order to portray or bring about a certain outcome.

samples Small snippets of the environ- ment that are relevant to the hypothesis; also selected groups of study participants, chosen purposefully or randomly.

saturation Occurs when no additional supporting or disconfirming data are being found to develop a category.

selective coding Used in grounded theory research; involves the researcher combining categories and their interrelationships into theoretical constructs (i.e., a story line).

semistructured interview A type of researcher inquiry that utilizes both preset (structured) and spontaneous (unstruc- tured) questions.

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 and then dividing by the standard deviation.

structured interview A type of research inquiry that includes asking a fixed set of either open-ended or closed-ended ques- tions that are administered in a specific order.

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

thick description Detailed representations that provide depth, breadth, and context to a particular issue.

time sampling In observational research, a technique that involves comparing behaviors during different time intervals.

unstructured interview A type of inter- view that is commonly used in qualitative research and utilizes a more open-ended, unstructured approach, allowing the inter- viewee to lead the conversation.

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 get a deviation score, squaring and sum- ming these individual deviation scores, and dividing by the sample size.

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Apply Your Knowledge

1. Compare and contrast the sets of the following terms. Your answers should demonstrate 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 descriptive research methods we have discussed in this chapter (listed below) on the continuum of control.

archival research case study naturalistic observation

3. List one advantage and one disadvantage associated with each of the following research methods. a. archival research

advantage: disadvantage:

b. case studies advantage: disadvantage:

c. ethnography studies advantage: disadvantage:

d. grounded theory research advantage: disadvantage:

e. phenomenological studies advantage: disadvantage:

f. observation studies advantage: disadvantage:

4. For each of the following datasets, compute the mean, median, mode, and standard deviation. Once you have all three measures of central tendency, decide which one is the best representation of 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.

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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

college, 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 observational design. a. Are people who own red cars more likely to drive like maniacs?

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

them?

Critical Thinking & Discussion Questions

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

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

3. What are the similarities and differences between qualitative research designs and descriptive research designs?

4. Provide examples of a phenomenological study and an ethnographical study. What are the similarities and differences between both designs?

5. Which types of qualitative and descriptive research designs described in this chapter would be considered top-down approaches, and which would be considered bottom-up?

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