A brief overview of qualitative research
This chapter provides general descriptions and definitions for a variety of qualitative research approaches. The chapter is framed by discussion of the anthropological foundations of ethnographic and other qualitative methods.
This chapter will provide a cursory overview of qualitative research approaches. Keep in mind that this is not meant to be a research manual, or a how-to book, so there are many features that are crucial to the research process that are not discussed. These include constructing a theoretical framework, creating a research design, conducting a literature review, and planning the actual research study. There are a number of good books that cover the research process in its entirety in great detail, including Designing Qualitative Research (Marshall and Rossman, 2006); The Practice of Qualitative Research (Hesse-Biber and Leavy, 2006); Handbook of Qualitative Research (Denzin and Lincoln, 2000); The Practice of Social Research (Babbie, 2006); and Qualitative Research Methods for the Social Sciences (Berg, 2009).
Qualitative and quantitative approaches to conducting research are often put into two different camps—one that uses numeric data and statistics, and one that uses mostly non-numeric data such as narrative text. Along with this division, there is an unwritten hierarchy in research circles, and quantitative research is considered to be more rigorous, more reliable, and more precise (Berg, 2009, p. 2). In the social sciences, qualitative methods often take a back seat to quantitative approaches. Berg suggests a number of reasons for this, in addition to the ones named above. Qualitative research can often be far more involved, intense work, and produce data that require hours of analysis that cannot be done solely by a software program (Berg, 2009, p. 2). These days, many in the role of teaching research methods emphasize the importance of selecting the correct approach for any given study, as opposed to being loyal to a particular category or design. Research practices that are integrative and combine both qualitative and quantitative activities oftentimes meet the needs of the researcher. Qualitative research can involve statistics and numbers, and quantitative approaches may include narrative descriptions and storytelling. So, at the end of the day, the main goal is to be open-minded in deciding what is most appropriate. The concept of triangulation (Nachimas and Worth-Nachimas, 2008; Wildemuth, 2009; Lee, 1991; Gable, 1994; Mingers, 2001; Ragin, 1987) highlights the importance of using a variety of approaches, depending on the nature of the research.
So how is qualitative research defined? “Qualitative researchers attempt to understand behavior and institutions by getting to know the persons involved and their values, rituals, symbols, beliefs, and emotions” (Nachimas and Worth-Nachimas, 2008, p. 257). Berg writes that “qualitative research focuses on innovative ways of collecting and analyzing qualitative data collected in natural settings” (2009, p. 2). “Qualitative research refers to the meanings, concepts, definitions, characteristics, metaphors, symbols, and descriptions of things. In contrast, quantitative research refers to counts and measures of things” (Berg, 2009, p. 3). Myers emphasizes understanding as a motivation for conducting qualitative research: “The motivation for doing qualitative research, as opposed to quantitative research, comes from the observation that, if there is one thing which distinguishes humans from the natural world, it is our ability to talk! Qualitative research methods are designed to help researchers understand people and the social and cultural contexts within which they live” (Myers, 2009). Elliot, Fischer and Rennie (1999) offer this definition:
The aim of qualitative research is to understand and represent the experiences and actions of people as they encounter, engage, and live through situations. In qualitative research, the researcher attempts to develop understandings of the phenomena under study, based as much as possible on the perspective of those being studied. Qualitative researchers accept that it is impossible to set aside one’s own perspective totally (and do not claim to). Nevertheless, they believe that their self-reactive attempts to “bracket” existing theory and their own values allow them to understand and represent their informants’ experiences and actions more adequately than would be otherwise possible. (p. 215)
In some texts, participant observation is emphasized as one of the key strategies for collecting qualitative data. In other texts, the practice of ethnographic research and field research are central. There are many variations on the most important elements within the qualitative spectrum, but there is one very central theme that they all share, and that is the importance of meaning. Statistics without context can really only convey so much. Berg (2009) refers to this additional layer of meaning as being related to the “quality of things,” by way of words, images, and descriptions (p. 3). Any method that allows the researcher to capture the worlds of others can be a valid qualitative technique.
There are several methods that can be used to collect data when using a qualitative approach. There are many different strategies, ranging from the popular to the more obscure. Powell (1999) highlights a few, including “phenomenological methods, hermeneutics, ethnomethodology, reflexivity, discourse analysis, and semiotics” (p. 91). One important point to remember about the type of data one chooses to work with is that the data collection technique will often dictate the type of analysis that can be used later to help interpret the data. As well, collecting more than one type of data will allow for a more diverse analysis. These aspects of data collection and the type of research can often be confusing to some. Surveys can yield quantitative data (Likert-scaled responses) that can be statistically analyzed, and they can also produce qualitative data (open-ended textual responses), which may require content analysis. Focus group data, which is normally discussed within the context of qualitative approaches, can be coded and counted, and thus analyzed using statistical methods as well. So, the most important thing to remember when deciding on data collection approaches is matching the research goals and the nature of the research environment to the method.
To illustrate the array of data collection methods that librarians and others may draw from, a few are briefly described below. There are many, many others that researchers should be aware of, which are not covered here. For instance, Carter and Mankoff (2005) explore the use of media in diary studies, since media of all types are an ever-increasing part of everyday life. These approaches are described in the research monographs mentioned at the beginning of this chapter, and discussed in the literature.
Participant observation is a meta-category for a type of data collection strategy that can actually include other techniques such as questionnaires, interviews, and focus groups. Participant observation has a special relevance when it comes to examining library and information service phenomena, as it can be an extension of the normal day-to-day work practices of librarians. Librarians typically spend at least some portion of their day actively engaged with users, participating to some extent in the users’ research activities by assisting them with instruction and reference. In this way, they are already participants, and observers, in the users’ environment.
Anthropologists have been long familiar with participant observation; noted anthropologist Malinowski (1922, p. 7) talked about his research in the Omarkana Trobriand Islands in the South Pacific:
As I went on my morning walk through the village, I could see intimate details of family life, of toilet, cooking, taking of meals; I could see the arrangements for the day’s work, people starting on their errands, or groups of men and women busy at some manufacturing tasks. Quarrels, jokes, family scenes, events usually trivial, sometimes dramatic but always significant, form the atmosphere of my daily life, as well as theirs.
Jorgensen (1989) describes participant observation this way:
The methodology of participant observation is appropriate for studies of almost every aspect of human existence. Through participant observation, it is possible to describe what goes on, who or what is involved, when and where things happen, how they occur, and why—at least from the standpoints of participants. The methodology of participant observation is exceptional for studying processes, relationships among people and events, the organization of people and events, continuities over time, and patterns, as well as the immediate sociocultural contexts in which human existence unfolds. (p. 12)
.DeWalt and DeWalt (2002) suggest that participation observation is most useful when researchers want to understand both explicit (behaviors, thoughts, actions, and practices that are easy to articulate) and tacit (thoughts, norms, and practices that may not be a part of our conscious awareness) phenomena within a culture. Participant observation is seen by many as one of the hallmarks of cultural exploration within the field of cultural anthropology (DeWalt and DeWalt, 2002), and one of the foundations for ethnographic approaches to research (Schensul, 1999). Specifically, participant observation goes beyond just watching what people do and say—it also includes the rigorous recording of the researcher’s experience, and the analysis and interpretation of this documentation. While participant observation is standard in many anthropological field research settings, its application is rare within information and library settings (Cooper, Lewis and Urquhart, 2004). Why might this be the case, especially when library and information environments seem the perfect place to observe user behavior? The challenges are some of the same faced by the application of qualitative approaches in general—the amount of time, human resources, and attention to detail required is extremely high, and far greater than most librarians or information professionals have the time to devote to.
Focus groups are very familiar to anyone who has dabbled in research, and are frequently used within library and information service settings to gather data on a specific topic. Focus groups provide a way to collect data, and include the use of carefully selected groups of people that come together to discuss specific questions or issues related to some research question or phenomenon. Interaction is key, and one of the more distinctive characteristics of the focus group is the ability of group members to share their thoughts and ideas in a group setting, as opposed to in a one-on-one interview. According to Wildemuth (2009), this interaction can lead to “a more nuanced perspective on a topic than could have been discovered through individual interviews” (p. 242). The benefits of this approach include the value of actually hearing and experiencing the thoughts and feelings of group members expressed in person as opposed to unilateral, non-interactive collection methods such as surveys. There are certainly limitations to using focus groups, including those related to the transcription of the sessions and to confidentiality. The focus group moderator must be carefully chosen, and may not always be the principal investigator. Sample size, recruiting of participants, and videorecording of the groups are also issues that have to be carefully addressed before the group convenes. As well, making sure that the group stays on task in terms of discussing the issue is important. Focus groups can be paired with other data collection techniques such as questionnaires, usability tests, think-alouds, and interviews.
Interviews are widely used as a way to gather information from study participants. Berg (2009) cites three different types of interviews: standardized, semistructured, and unstructured (p. 105). Berg further defines interviewing as “a conversation with a purpose” (p. 101). There are many different elements to the interview, and although there is no one right way to conduct interviews, most researchers would agree that it is a cross between an art and a skill, and it takes time to learn to do it well. Interviews can be done over the phone, in person, and, increasingly, over the web using such applications as Skype, instant messaging, and chat (Opdenakker, 2006). Regardless of the type of interview, this approach can provide first-person accounts and feedback that can be difficult to gather any other way. Hutchinson, Wilson and Wilson (1994, p. 161) suggest that “catharsis, self-acknowledgement, sense of purpose, self-awareness, empowerment, healing, and providing a voice for the disenfranchised” can all be seen as “unanticipated benefits” of interviews. Interviews can also be combined with other data collection methods for more diverse data coverage.
The case study is an approach that features the intense examination of a particular “unit of analysis” (Trochim, 2006). Organizations, individuals in certain settings, or events can be explored via case study (Wildemuth, 2009, p. 53). The case study can yield qualitative or quantitative data, and like focus groups, can have other data collection strategies embedded within such as interviews, questionnaires, participant observation, and think-alouds. A case study of an organization may, for instance, include a survey with data that is subject to further statistical analysis. Wildemuth (2009, p. 52) suggests that there are four questions that should be asked to determine whether the case study is a good research approach:
Within library and information settings, case studies might explore attitudes and work practices of staff over a certain period of time, or the research habits of a cohort of doctoral students within a department.
Narratives and storytelling provide yet another way for researchers to gather rich, personal, qualitative data. Stories and narratives have many functions within any given culture and the greater society, and these practices may play out differently in oral and literate cultures. Oftentimes, in literate cultures, stories are read from or at least associated with a text. In cultures that are primarily oral, there is no written text to draw from, and memory serves as the place where these stories are kept for retelling: “Of all verbal genres, narrative has the most evident and straightforward relationship to memory” (Ong, 1982, p. 12). Ong reminds the reader that stories in oral cultures may not necessarily proceed in the same order that we are used to, although there is always some kind of storyline (1982, p. 19); “Oral narrative is not much concerned with exact sequential parallelism, which becomes an objective of the mind possessed by literacy” (p. 19). According to Koch (1998), “stories can be therapeutic”; “stories can inform social policy”; “stories can facilitate change in organizations”; “stories can allow marginalized groups to have a voice” (p. 1183). Bamberg (1997) suggests that stories help to create a moral order, and Bruner (1991) explicates the value of the narrative in sharing morals and values. Stories play a role in religion, history, development of cultures and the self, and are frequently shared between adult and child. “The practice of giving our children moral lessons in the form of stories is common not only in western traditions, but in many, perhaps most, other cultures” (Walton and Brewer, 2001, p. 308).
Researchers illustrate the various distinctions between the story and the narrative, although it can sometimes be hard to differentiate the two. Labov (1972) defines the narrative as a discourse of related events told in an organized manner. These events can be real or not. Labov and Waletsky 1997) articulate in painstaking detail the components of the personal narrative by identifying its structural components. The authors were particularly concerned with differences in class and race (p. 5) and examined sequence, grammatical structure, and social context of the narrators during the course of their study. Mischler (1995) discusses a typology for narrative analysis with three possible perspectives from which to evaluate the narrative: reference and temporal order, textual coherence and structure, and narrative functions (p. 90). Mischler also used Halliday’s (1973) well-known model of language function to classify narrative approaches (Mischler, 1995, p. 89). Richardson (1997) suggests that narratives are both “a means of knowing and a method of telling” (p. 58).
Heath (1986) defines the narrative as “verbalized memories of past or ongoing experiences” (p. 84). Heath suggests that narratives exist in all societies, that these narratives are produced in predictable ways, and that they can be shared in oral or written form (p. 85). Heath clarifies that, while the narrative and the story are sometimes synonymous, there are cultures where fictional narratives are rare (p. 85). Heath’s (1986) research in this area focuses on the social context for language learning, and connecting language and the study of the narrative to the role of adults in children’s daily lives; the goals adults have for their children’s futures; and the connections of adults and children to schools and other community organizations (p. 85). According to Heath (1986), there are four universal types of narratives: recounts, eventcasts, accounts, and stories. Recounts feature “experiences of the past in which the speaker had one of several possible roles” (Heath, 1986, p. 88). The eventcast is a “verbal replay or explanation of activity scenes that are either in the current attention of those participating in the eventcast or are being planned for the future” (p. 88). The account involves the teller sharing what they have experienced. The last genre, the story, may also be a retelling of events, but with far more structure. Stories may also contain more fictional elements, and language use beyond what would be expected in everyday use (p. 89).
Researchers frequently develop typologies or frameworks to better understand and interpret stories or narratives. Lieblich, Tuval-Mashiach and Zibler (1998) defined four models for interpretation: holistic-content, holistic-form, categorical-content, and categorical-form. Mischler (1986) also discusses ways to analyze the narrative. Keats (2009) discusses the ability of the researcher to deepen their understanding of the participant’s narratives by examining multiple texts, including the visual, the written and the spoken (p. 188).
Any given library may contain thousands of stories. Every user and every employee is a potential storyteller. Moreover, library artifacts such as books, computers, and desks also tell us stories about their own use, in some way. Deciding whether to solicit narratives for a particular research endeavor depends on the research question. A staff member who has been employed by the library in a variety of positions and over a long period of time may provide a very interesting narrative that converges with modern-day changes in service and user populations. Such a narrative, or a group of similar narratives, may help to focus in on the elements within the library that have not changed much, and that need further exploration. The narrative is a great complement to other data that may not convey the same level of personal detail, but it can be time-consuming and complicated in terms of coding and analysis.
So far, all of the methods discussed above require the active participation of the subject, whether that means participating in a focus group or an interview, or taking a survey. These methods all intrude upon the participants in some way, and necessitate their being engaged in the research process. There are a number of methods for collecting data that do not require the active participation of subjects, known as unobtrusive data collection techniques. Berg (2009) suggests that unobtrusive methods examine the “traces people either intentionally or inadvertently leave behind” (p. 269). Unobtrusive techniques include, but are not limited to, the examination of archival and historical data and artifacts, census records, vital records such as birth, marriage, and death certificates, and written accounts such as diaries, biographies, and autobiographies. These days, data collected from internet use—for instance, which sites people visit, which sites get the most referrals from web searches, and which advertisements generate the most traffic—all generate data that can be explored to learn more about Internet users without the solicited participation of the user. GIS data are also being used increasingly to map location and personal activity on many levels, sometimes without the awareness of the individual. Furthermore, personal artifacts and those things that are left behind can tell a story about the behavior or experience of people or groups, and can be explored using behavior trace or physical trace observation (Berg, 2009). The best known study of this type is the Tucson Garbage Study, which was initiated in 1973 by Dr William Rathje at the University of Arizona (Berg, 2009). Over the past 30 years, Dr Rathje and his colleagues and students have sifted through and classified the contents of “more than 14 tons of excavated material” (Rybczynski, 1992) from local garbage dumps. The idea is that garbage can tell us a lot about people’s lifestyles and behaviors. In a physical library setting, water bottles in garbage cans, reorganized furniture, and circulation records can tell us a lot about users’ habits and behaviors, and lead to further exploration. There is no limit to the type of information that may actually yield useful data about different phenomena, groups, or individuals.
I am devoting a separate section to this topic because it is a potentially valuable approach to consider within the framework of library-related research. In 1967, Glaser and Strauss published The Discovery of Grounded Theory. Some tend to associate qualitative research in general with the generation of hypotheses (rather than hypothesis testing), but this is a false distinction. Grounded theory simply represents one way to generate theory from data, and make sense of qualitative data that are textual in nature. All qualitative research does not necessarily use a grounded theory approach.
Grounded theory allows researchers to generate hypotheses after data collection, and after careful examination of the data (Auerbach and Silverstein, 2003). The method has two main principles: questioning rather than measuring, and hypotheses generation using theoretical coding (Auerbach and Silverstein, 2003, p. 7). The “grounding” of theory thus first takes place in relation to the actual data. Glaser and Strauss (1967) state that grounded theory is the discovery of theory from data systematically obtained from social research (p. 3), and that this method is the best way to generate theory that is “suited to its supposed uses” (p. 3). The discovery of categories from the data is one of the strengths of the approach because the origins are clear (Glaser and Strauss, 1967).
One of the most critical steps in this process is the coding of the data, which in the case of this study will be inductive in nature, and facilitated by a qualitative content analysis. Auerbach and Silverstein (2003, p. 35) discuss the seven elements in the grounded theory coding process:
These steps progress from the most elementary to the most sophisticated, with the first step being the initial examination of the raw text. The development of the theoretical constructs are key to surfacing the research concerns or questions. It should be noted that the process of coding the data is iterative, not linear, in nature (Auerbach and Silverstein, 2003, p. 43).
Grounded theory approaches can be extremely time-consuming and involved. Moreover, it can be difficult to determine reliability and validity. To this end, Silverstein et al. (2006) suggest that transferability, and not generalizability, should be one of the guidelines for evaluating qualitative research. Transferability is facilitated in part by the researchers providing great detail about the researchers themselves, the participants, the context, and “the dynamic interaction between researcher and participants” (Silverstein et al., 2006, p. 352). Given the limitations, there may still be instances within library settings where grounded theory approaches are warranted, especially when trying to understand more about why a particular phenomenon may be occurring.
If the data collection method involves the collection of textual content, it has to be analyzed and made sense of in some way in order to be useful. This content—whether transcripts from focus groups, interviews, oral histories, or videotaped storytelling sessions—has to be “decoded” so it can be summarized and understood by others. Content analysis provides a way to do this. Although this is not the only technique for textual analysis, it is a major one. It is in this type of analysis that the most striking differences between quantitative and qualitative approaches can be seen.
According to Berg (2009), content analysis is a “careful, detailed, systematic examination and interpretation of a particular body of material in an effort to identify patterns, themes, biases, and meanings” (p. 338). Berg goes on to suggest that content analysis has been used in many different disciplines, including psychology, education, business, sociology, political science, art, and others, and that it is “chiefly a coding operation and data interpretation process” (p. 339). Wildemuth (2009) clarifies the difference between content analysis and qualitative content analysis, suggesting that the latter “goes beyond merely counting words or extracting objective content from text to examine meanings, themes, and patterns that may be manifest or latent in a particular text” (p. 309).
The content itself can be represented by interview transcripts, survey responses, focus groups, print media such as books and newspapers, or observations, and be verbal, print, or electronic in its format (Kondracki and Wellman, 2002). A number of researchers have discussed the application of this qualitative approach, including Wildemuth (2006, Cavanagh (1997), Babbie (2006), Miles and Huberman (1994), Glaser and Strauss (1967), Budd et al. (1967), Downe-Wamboldt (1992), Lincoln and Guba (1985), and many others.Downe-Wamboldt (1992, p. 314) states that content analysis is meant to provide “knowledge and understanding” of the situation being studied. Content analysis was first used as early as the eighteenth century for data analysis (Barcus, 1959), and, although the approach is mostly seen as a qualitative one, it has also been applied as a quantitative approach (Morgan, 1993).
Hsieh and Shannon (2005) define three clear and succinct categories for the application of content analysis: conventional content analysis, directed content analysis, and summative content analysis (p. 1277). Conventional content analysis is a way to describe a phenomenon when the existing information or theory on its occurrence is limited (Hsieh and Shannon, 2005, p. 1279). Researchers examine the text, and allow names for categories to emerge from the text, as opposed to assigning pre-determined categories (p. 1279). Directed content analysis is less flexible in terms of identifying key themes and categories. This approach begins with the assumption that existing theory is helpful, and can be used to explain a phenomenon, but it is incomplete. Researchers use this approach to “validate or extend” current theory (p. 1281). Thus, categories are predetermined from the existing literature, as opposed to surfacing from the textual examination. For instance, Maslow’s (1943) hierarchy of needs may serve as pre-set categories from which to code open-ended questions about college students’ adjustment experiences in college.
The last category that Hsieh and Shannon (2005) discuss is the summative content analysis approach. This approach has two distinct stages. First, certain content or words in the text are identified and quantified, in an effort to better understand their context and use within the text (2005, p. 1283). The second step involves what Holsti (1969) refers to as latent content analysis, that is, the interpretation of the words in the text with the purpose of discovering their underlying meaning (Hsieh and Shannon, 2005, p. 1284). Summative analysis connects the frequency of a given word with its contextual meaning, and also aims to deepen the understanding of the phenomenon by the researcher. Meanings associated with different words, related symbolism, and euphemistic versus explicit meaning are all areas where the researcher may discover rich connections and meaning associated with the text (Hsieh and Shannon, 2005).
There are several limitations to the use of qualitative data discussed in the literature (Berg, 2009; Marshall and Rossman, 2006; Barbour, 2001; Hesse-Biber and Leavy, 2006). As a result, researchers from various disciplines have long struggled to better define what makes a good qualitative study (Stiles, 1993; Guba and Lincoln, 1989; Miles and Huberman, 1984; Mischler, 1986; Rennie, 1999; Barbour, 2001; Seale and Silverman, 1997). A must-read for any librarian interested in qualitative research is Sandstrom and Sandstrom (1995), which highlights the (sometimes misguided) application of anthropologic methods within Library and Information Studies research, and offers some suggestions for improving these practices (see Chapter 3 for more on Sandstrom and Sandstrom).
Elliot et al. (1999) propose a set of “evolving guidelines” for the review of qualitative research in order “to contribute to the process of legitimizing qualitative research; to ensure more appropriate and valid scientific reviews of qualitative manuscripts, theses, and dissertations; to encourage better quality control in qualitative research through better self- and other-monitoring; and to encourage further developments in approach and method” (Elliot et al., 1999, p. 215).
Sandelowski (1986) addresses the problem of rigor in qualitative approaches, another reason why researchers may be hesitant to invest time and effort in its application. The author suggests that “four factors complicate the debate about the scientific merits of qualitative research: the varieties of qualitative methods, the lack of clear boundaries between quantitative and qualitative research, the tendency to evaluate qualitative research against conventional scientific criteria of rigor, and the artistic features of qualitative inquiry” (1986, p. 27).
Barbour (2001, p. 1116) provides a checklist of five “technical fixes” that researchers can employ to improve the quality control aspects of qualitative work. The author suggests that purposive sampling can address bias; the use of grounded theory can address the creation of original theories; multiple coders can address inter-rater reliability; triangulation can address internal validity; and respondent validation can address the interpretations being made by the researchers.
Particular data collection methods have drawbacks, as well. For instance, Potter and Hepburn (2005) explain the types of problems that can be encountered during the interview and the subsequent reporting of the interview: “(1) the deletion of the interviewer; (2) the conventions for representing interaction; (3) the specificity of analytic observations; (4) the unavailability of the interview set-up; (5) the failure to consider interviews as interaction” (p. 281).
The most important thing for librarians is to be aware of the limitations of whatever method they choose, and how those limitations might impact the research process and the outcomes. Librarians can also employ multiple methods to collect data—such as the aforementioned triangulation (Ragin, 1987; Kaplan and Duchon, 1988; Mingers, 2001; Wildemuth, 2009), thus eliminating the reliance on just one type of collection technique.
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