6.7: The Qualitative Approach

Learning Objectives

1. Differentiate analysis of qualitative research from quantitative research.
2. Identify approaches to analyzing qualitative research.

While this section is titled “The Qualitative Approach,” it is actually inaccurate to suggest that there is just one overall approach to qualitative research. There are some core characteristics that qualitative approaches to research have in common, such as data that relies on words or images rather than numbers and a richer, more contextual understanding of the phenomena under study, but there are also many ways in which qualitative approaches to research vary. They use different methods of data collection. They take place within different paradigms and epistemologies. They focus their attention on emphasizing different standards for research quality. And, as the following sections will show, they utilize different methods for preparing and managing data, analyzing that data, and disseminating their findings.

Introduction to the Qualitative Approach

Your statistics class probably mentioned qualitative variables (sometimes called categorical variables or nominal variables) in contrast to quantitative variables. In fact, many statistics instructors go into detail about the different scales of measurement for quantitative variables.

Exercise \(\PageIndex{1}\)

Can you describe the difference between qualitative variables and quantitative variables in terms of measuring behavior?

Answer

Qualitative variables are defined as a type of variable that has different values to represent different categories or kinds. This is the same as the nominal scale of measurement, while quantiative variables are defined as a type of variable that is measured with some sort of scale that uses numbers that measure something. This means, qualitative variables are about categorizing behavior into different kinds or categories, while quantitative variables are about measuring behavior through some sort of scale.

While there is not a one-to-one comparison, qualitative research mostly measures qualitative variables, and quantitative research mostly uses quantitative variables. However, both kinds of research can use either kind of variable. This section will orient you more to qualitative research, and then describe the extensive and time-consuming process for analyzing data gathered in qualitative research.

At the most basic level, qualitative research is research that emphasizes data that is not numerical in nature, data like words, pictures, and ideas. In contrast, quantitative research emphasizes measuring behavior with numerical scales, or at least variables that can relatively easily be translated into numerical terms. In other words, quantitative data is about quantities, while qualitative data is about qualities. Beyond this basic distinction, qualitative research can look very similar to quantitative research or it can take a very different approach; when people talk about qualitative approaches to research, however, they are often focused on those approaches that are distinct from what quantitative researchers do.

So what are some of the unique features of qualitative research? First of all, qualitative research tends to rely on the use of rich, thick description. In other words, qualitative research does not just provide summaries of data and findings, it really takes the reader or consumer of research there and lets them explore the situation and make conclusions for themselves by drawing on extended descriptions and excerpts from the data. Qualitative research also leaves room for focus on feelings and emotions, elements of the social world that can be harder to get at with quantitative data. For the qualitative researcher, data should not just depict specific actions or occurrences, but rather the contexts and backgrounds that lead up to what happened. More broadly, qualitative research tends to focus on a deep understanding of a specific place or organization or of a particular issue, rather than providing a wider but shallower understanding of an area of study.

Among the strengths of qualitative research are that it provides for the development of new theories and the exploration of issues that people do not know much about. It is very high in validity since it is so connected to real life. And it permits the collection and analysis of more detailed, contextual, and complex kinds of data and information. Of course, with strengths come limitations. The higher validity of qualitative data is matched with lower reliability due to the unique circumstances of data collection and the impact of interviewer effect. The greater ability to develop new theories is matched with a greater difficulty testing existing theories, especially causal ones, given the impossibility of eliminating alternative explanations for the phenomena under investigation. The ability to collect more detailed and complex information comes in large part due to the focus on a much smaller number of participants or cases, which in turn limits generalizability and in some cases can limit representativeness. And while there is no reason to conclude that any of these factors make qualitative research more prone to bias than quantitative research, which after all can be profoundly impacted by slight variations in survey question wording or sample design, those who are not well informed about research methodology may discount the strengths of qualitative research by suggesting that the lack of numbers or the close interaction between participants and researchers bias the results.

In their classic text on qualitative data analysis, Miles and Huberman (1994) present the following as among the key elements of qualitative data analysis:

• It involves more prolonged contact with more ordinary aspects of human life;
• It has a holistic rather than a particularistic focus, aiming to keep data and findings in context;
• Multiple interpretations and understandings of data are possible, and researchers should preserve respondents’ own understandings of their worlds and lives;
• There is a lack of standardization and measurement, with the researcher themselves becoming the primary measurement instrument; and
• Analysis is done primarily with words.

However, other scholars would argue that qualitative analysis is not limited to words—it may also involve visuals ways of engaging with and presenting data.

Types of Qualitative Data

The data that we analyze in qualitative research consist primarily of words and images drawn from observation, interaction, interviewing, or existing documents. In particular, the types of data collection that tend to result in qualitative data include talking with people (interviews and focus groups) and the unobtrusive research methods discuss (observations, physical trace, archival data) which were discussed in the chapter on measuring behavior. These different data collection strategies imply a variety of analytical strategies as well, and indeed qualitative data analysis relies on a breadth of techniques. Thus, part of the process of formulating and selecting qualitative data is selecting the right kinds of strategies to apply to the particular data being utilized.

Paradigms of Research

Researchers approach their research from different perspectives or paradigms. A paradigm is a set of assumptions, values, and practices that shapes the way that people see, understand, and engage with the world, and thus the particular paradigm that a researcher inhabits shapes the fashion in which they carry out their research. Philosophers use the term epistemology to refer to the study of the nature of knowledge, and thus we can take an epistemological perspective to understanding how paradigms of research might vary.

Two paradigms that commentators often juxtapose are the positivist and interpretivist approaches. Positivism assumes that there is a real, verifiable reality and that the purpose of research is to come as close to it as possible. Thus, a positivist would argue that we can understand the world, subject it to prediction and control, and—through the processes of research and data analysis—empirically verify our claims. Positivist research projects can utilize a variety of methods, but experimental and quantitative survey data are especially likely. This might feel familiar as this is the paradigm of experimental research that most of this textbook covers. For qualitative research, positivism is often associated with the type of observational study once common in anthropology that aimed at uncovering the “real” social practices of a group. These methods tend to involve keeping some degree of distance between the researcher and the participants and positioning the researcher as the expert on both research methods and the participants’ own lives. From a positivist perspective, standards of rigor like reliability and validity (including external validity), which we've discussed in some detail, are important and attainable markers of good research as they contribute to the likelihood that the research arrives at the right answer. As this suggests, objectivity is an essential goal of positive research. Good research, to a positivist, is that which is valid, reliable, generalizable, and has strong, significant results.

In contrast, interpretivism suggests that our knowledge of the world is created by our own individual experiences and interactions, and thus that reality cannot be understood as existing on its own in a form separate from our distinct existences. Thus, an interpretivist would argue that understandings are always based in a particular time and on a particular interpreter and are always open to reinterpretation. Interpretivist research projects utilize naturalistic research methods that are rooted in real social contexts, especially in-depth interviewing and participant-observation. These methods tend to involve a closer and more reciprocal relationship between the researcher and the participants, which aligns well with qualitative reseach. There may also be greater concern for ethical treatment and, in some cases, an emphasis on possibilities for social change. Interpretivist researchers also value participants’ expertise and their understandings of their own lives rather than assuming the researcher’s perspective is necessarily more accurate. From an interpretivist perspective, validity may not be attainable due to the fact that truth is not certain, and in any case standards of rigor are far less important than considerations like ethics, morality, the degree to which biases are made clear, and what the world can learn from the research. As this might suggest, interpretivists would tend to believe that objectivity is probably not attainable, and that even it is, the pursuit of it may not be worthwhile. To an interpretivist, good research is that which is done in a careful, respectful manner, contributes to knowledge, is reflective, and takes appropriate political and ethical considerations into account.

Lisa Pearce (2012) has outlined a paradigm she calls pragmatist. This approach is sometimes understood as a kind of middle position between positivist and interpretivist ways of thinking. Thus, its proponents neither believe that strict objectivity is possible nor abandon efforts to seek objectivity at all, instead researchers reflect on their own perspectives and bias, as well as the experience and perspectives of the particiants. This process of reflecting on how the researchers' conscious and unconscious beliefs is called reflexivity. While pragmatist approaches can be used with various methods of data collection, they tend to be employed by those using mixed-methods approaches, especially those combining quantitative and quantitative strategies.

While the discussion of paradigms here is not exhaustive—there are many other approaches to research, many other epistemologies—it does provide an overview of some of the possible ways to think about research and data analysis. One important thing to remember is that while there are criteria for good research, there are no objective or empirical standards for which paradigm is “correct.” In other words, researchers approach their research from the perspective or philosophy that makes sense to them, and while others may have reasons for disapproving, they cannot say that such a choice is right or wrong. Researchers must make these sorts of decisions for themselves.

Inductive and Deductive Approaches

Another question we might ask about the epistemology of research processes is whether our data emerges from our analysis or whether our data generates our analysis. If you argue that data emerges from analysis, you are suggesting that you begin the research process with a theory and then look to the data you have collected to see whether or not you can find support for your theory. This approach enables the testing of theories. It is typically understood as a deductive approach to research. In deductive approaches, researchers develop a theory, collect data, analyze the data, and use their analysis to test their theory. Positivist research is often deductive in its approach. Your experience with null hypothesis significance testing was deductive.

Instead, if you argue that data generates analysis, you are suggesting that you begin the research process by collecting data and you then look to see what you can find within it. This approach enables the building of theories. It is typically understood as an inductive approach. In inductive approaches, researchers begin by collecting data. Then they analyze that data and use that analysis to build new understandings. Interpretivist and feminist research are often inductive in their approach.

While qualitative research can be conducted using both deductive and inductive approaches, it is a bit more common for qualitative researchers to use inductive approaches. Such approaches are far less possible in quantitative analysis because of the need for more precisely-designed data collection techniques. Thus, one advantage of qualitative research is that it permits for an inductive approach and is thus especially useful in contexts in which very little is already know or where new explanations need to be uncovered. It is also possible to conduct research using what some call abduction, or an interplay between deductive and inductive approaches (Pearce 2012). Such an approach may also be found in mixed-methods research. The next sections will focus primarily on inductive approaches to qualitative data analysis, given that they are far more common. But deductive approaches do exist. For example, consider a researcher who is interested in what sorts of circumstances give rise to nonprofit organization boards deciding to replace the organization’s director. More typically, a qualitative researcher with this question would interview a wide variety of non-profit board members and, based on the responses, would build a theory—an inductive approach. In contrast, the researcher could choose to conduct her study deductively. Then, she would read the prior literature on management and organizational decision-making and develop one or more hypotheses about the circumstances that give rise to leadership changes. She would then interview board members looking specifically for the constellation of circumstances she hypothesized to test whether these circumstances were associated with the decision to replace the director.

The Process of Qualitative Research

To analyze qualitative data, it's important to get a basic idea about how to conduct an inductive qualitative research project. While there are a series of steps researchers follow, it is important to note that, unlike quantitatie research, qualitative research and data analysis involve a high degree of fluidity and are typically iterative, meaning that they involve repeatedly returning to prior steps in the process.

First, researchers design their data collection process, which includes developing any data collection instruments such as interview guides and locating participants. Then, they collect their data. To collect data researchers might conduct interviews, observations, or they might locate documents or other sources of textual or visual data. While deductive quantitative approaches require researchers collect all their data and only then analyze it, inductive qualitative approaches provide the opportunity for more of a cyclical process in which researchers collect data, begin to analyze it, and then use what they have found so far to reshape their further data collection. Once data is collected, researchers need to ensure that their data is usable. This may require the transcription of audio or video recordings, the scanning or photocopying of documents, typing up handwritten field notes, or other processes designed to move raw data into a more manipulable form.

Next, researchers engage in data reduction. Research projects typically entail the collection of really large quantities of data, more data that can possibly be managed or utilized in the context of one paper. This is especially likely in the case of qualitative research because of the richness and complexity of the data that is collected. Therefore, once data collection is completed, researchers use strategies and techniques to reduce the hundreds or thousands of pages of transcripts, observation notes, or documents into a manageable form. Activities involved in data reduction include coding, summarization, the development of data displays, and categorization.

Once data reduction has made data more usable, researchers develop conclusions based on their data. Remember, however, that this process is iterative, which means that it is a continuing cycle. So, when researchers make conclusions, they also go back to earlier stages to refine their approaches. For example, imagine a researcher interviewed twelve African American teenage girls about their experiences in school. At the beginning, the researcher may develop codes related to academic achievement and romantic relationships. However, as the researcher keeps reading the interview transcripts she may realize that relationships with friends and family are also mentioned and impactful. The researcher would then have to review all of the interviews that they've already analyzed to look for information on friendships and family relationships.

The process of developing conclusions also requires careful consideration to limitations of the data and analytical approaches, but finally, researchers will present their findings. During each project, researchers must determine how best to disseminate results. Factors influencing this determination include the research topic, the audience, and the intended use of the results—for instance, are these the results of basic research, designed to increase knowledge about the phenomena under study, or are they the results of applied research, conducted for a specific audience to inform the administration of a policy or program? Findings might be disseminated in a graphical form like an infographic or a series of charts, a visual form like a video or animation, an oral form like a lecture, or a written form like a scholarly article or a report. Of course, many projects incorporate multiple forms of dissemination.

In sum, the qualitative research cycle looks like:

1. Design
2. Collect data
3. Data reduction and analysis (some researchers may consider these two separate steps)
4. Conclusions and presenting (again, some researchers may consider these separate steps)

If you remember all the way back to Figure 2.3.1, this qualitative approach more or less covers all of the steps of the scientific method other than deciding what topic to research (Research Question).

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References

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage Publications, Inc.

Pearce, L. D. (2012). Mixed methods inquirty in sociology. American Behavioral Scientitist, 56(6), 829-848. https://doi.org/10.1177/0002764211433798