|Subfields and other major theories|
Qualitative research is a scientific method of observation to gather non-numerical data. This type of research "refers to the meanings, concepts, definitions, characteristics, metaphors, symbols, and description of things" and not to their "counts or measures". This research answers why and how a certain phenomenon may occur rather than how often. Qualitative research approaches are employed across many academic disciplines, focusing particularly on the human elements of the social and natural sciences; In less academic contexts, areas of application include qualitative market research, business, service demonstrations by non-profits, and journalism.
As a field of study, qualitative approaches include research concepts and methods from multiple established academic fields. The aim of a qualitative research project may vary with the disciplinary background, such as a psychologist seeking in-depth understanding of human behavior and the reasons that govern such behavior for example. Qualitative methods are best for researching many of the why and how questions of human experience, in making a decision for example (not just what, where, when, or "who"); and have a strong basis in the field of sociology to understand government and social programs. Qualitative research is widely used by political science, social work, and education researchers.
In the conventional view of statisticians, qualitative methods produce explanations only of the particular cases studied (e.g., as part of an ethnography of a newly implemented government program). Any general conclusions beyond the study context are considered tentative propositions (informed assertions), since the general propositions are not usually arrived at on the basis of statistical theory. Quantitative methods are, therefore, needed to seek mathematical evidence and justification for such hypotheses for further research.
In contrast, a qualitative researcher might argue that understanding of a phenomenon or situation or event, comes from exploring the totality of the situation (e.g., phenomenology, symbolic interactionism), often with access to large amounts of "hard data" of a nonnumerical form. It may begin as a grounded theory approach with the researcher having no previous understanding of the phenomenon; or the study may commence with propositions and proceed in a 'scientific and empirical way' throughout the research process (e.g., Bogdan & Taylor, 1990).
We can distinguish between those which follow the logic of quantitative methods in their rules and criteria and make generalizations in a numerical sense (i. e. from numerous cases to more numerous cases), and those clearly qualitative methods where interpretations and generalizations are not based on the frequency of occurrence of certain social phenomena but on a logic of generalizing from an individual case, whether this case is a personal biography, an organization or a particular milieu or social setting; this includes making microscopic and thick descriptions (see Geertz 1973) of the phenomena in which we are interested, likewise with the aim of generalizing from an individual case.— Gabriele Rosenthal, (2018: 13): Interpretive Social Research. An Introduction. Göttingen: Universitätsverlag Göttingen.
A popular method of qualitative research is the case study (Stake 1995, Yin 1989), which examines in depth 'purposive samples' to better understand a phenomenon (e.g., support to families; Racino, 1999); the case study method exemplifies the qualitative researchers' preference for depth, detail, and context, often working with smaller and more focused samples, compared with the large samples of primary interest to statistical researchers seeking general laws.
Qualitative methods are an integral component of the five angles of analysis fostered by the data percolation methodology. These methods may be used alongside quantitative methods, scholarly or lay reviews of the literature, interviews with experts, and computer simulation, as part of multimethod attitude to data collection and analysis (called Triangulation).
To help navigate the heterogeneous landscape of qualitative research, one can further think of qualitative inquiry in terms of 'means' and 'orientation'. In particular, one could argue that qualitative researchers often reject natural science models of truth, prefer inductive, hypothesis-generating research processes and procedures (over hypothesis-testing models), are oriented towards investigations of meaning(s) rather than behaviour, and prefer data in the form of words and images, that are ideally naturally derived (e.g. in-depth observation as opposed to experimentation).
Sociologist Earl Babbie notes that qualitative research is "at once very old and very new." He documents that qualitative methods have been used for several centuries, but anthropologists brought qualitative field research methods to the forefront through their 19th century observations of preliterate societies.
Robert Bogdan in his advanced courses on qualitative research traces the history of the development of the fields, and their particular relevance to disability and including the work of his colleague Robert Edgerton and a founder of participant observation, Howard S. Becker. As Robert Bogdan and Sari Biklen describe in their education text, "historians of qualitative research have never, for instance, included Freud or Piaget as developers of the qualitative approach, yet both relied on case studies, observations and indepth interviewing".
In the early 1900s, some researchers rejected positivism, the theoretical idea that there is an objective world which we can gather data from and "verify" this data through empiricism. These researchers embraced a qualitative research paradigm, attempting to make qualitative research as "rigorous" as quantitative research and creating myriad methods for qualitative research. Such developments were necessary as qualitative researchers won national center awards, in collaboration with their research colleagues at other universities and departments; and university administrations funded Ph.D.s in both arenas through the ensuing decades. Most theoretical constructs involve a process of qualitative analysis and understanding, and construction of these concepts (e.g., Wolfensberger's social role valorization theories).
In the 1970s and 1980s, the increasing ubiquity of computers aided in qualitative analyses, several journals with a qualitative focus emerged, and postpositivism gained recognition in the academy. In the late 1980s, questions of identity emerged, including issues of race, class, gender, and discourse communities, leading to research and writing becoming more reflexive. Throughout the 1990s, the concept of a passive observer/researcher was rejected, and qualitative research became more participatory and activist-oriented with support from the federal branches, such as the National Institute on Disability Research and Rehabilitation (NIDRR) of the US Department of Education (e.g., Rehabilitation Research and Training Centers for Family and Community Living, 1990). Also, during this time, researchers began to use mixed-method approaches, indicating a shift in thinking of qualitative and quantitative methods as intrinsically incompatible. However, this history is not apolitical, as this has ushered in a politics of "evidence" (e.g., evidence-based practices in health and human services) and what can count as "scientific" research in scholarship, a current, ongoing debate in the academy.
Qualitative researchers face many choices for techniques to generate data ranging from grounded theory development and practice, narratology, storytelling, transcript poetry, biographical narrative interviews, classical ethnography, state or governmental studies, research and service demonstrations, focus groups, case studies, participant observation, qualitative review of statistics in order to predict future happenings, or shadowing, among many others. Qualitative methods are used in various methodological approaches, such as action research which has sociological basis, or actor-network theory.
The interview (structured, semi-structured or unstructured) is a common source of data on the qualities/categories of interest. Other sources include focus groups, observation (without a predefined theory like statistical theory in mind for example), reflective field notes, texts, pictures, photographs and other images, interactions and practice captured on audio or video recordings, public (e.g. official) personal documents, historical items, and websites and social media.
To analyse qualitative data, the researcher seeks meaning from all of the data that is available. The data may be categorized and sorted into patterns (i.e., pattern or thematic analyses) as the primary basis for organizing and reporting the study findings (e.g., activities in the home; interactions with government). Qualitative researchers, often associated with the education field, typically rely on the following methods for gathering information: Participant Observation, Non-participant Observation, Field Notes, Reflexive Journals, Biographical Narrative Interviews, Structured Interview, Semi-structured Interview, Unstructured Interview, and Analysis of documents and materials.
The ways of participating and observing can vary widely from setting to setting as exemplified by Helen Schwartzman's primer on Ethnography in Organizations (1993). or Anne Copeland and Kathleen White's "Studying Families" (1991). Participant observation is a strategy of reflexive learning, not a single method of observing. and has been described as a continuum of between participation and observation. In participant observation researchers typically become members of a culture, group, or setting, and adopt roles to conform to that setting. In doing so, the aim is for the researcher to gain a closer insight into the culture's practices, motivations, and emotions. It is argued that the researchers' ability to understand the experiences of the culture may be inhibited if they observe without participating.
The data that is obtained is streamlined (texts of thousands of pages in length) to a definite theme or pattern, or representation of a theory or systemic issue or approach. This step in a theoretical analysis or data analytic technique is further worked on (e.g., gender analysis may be conducted; comparative policy analysis may be developed). An alternative research hypothesis is generated which finally provides the basis of the research statement for continuing work in the fields.
Some distinctive qualitative methods are the use of focus groups and key informant interviews, the latter often identified through sophisticated and sometimes, elitist, snowballing techniques. The focus group technique (e.g., Morgan, 1988) involves a moderator facilitating a small group discussion between selected individuals on a particular topic, with video and handscribed data recorded, and is useful in a coordinated research approach studying phenomenon in diverse ways in different environments with distinct stakeholders often excluded from traditional processes. This method is a particularly popular in market research and testing new initiatives with users/workers.
The research then must be "written up" into a report, book chapter, journal paper, thesis or dissertation, using descriptions, quotes from participants, charts and tables to demonstrate the trustworthiness of the study findings.
In qualitative research, the idea of recursivity is expressed in terms of the nature of its research procedures, which may be contrasted with experimental forms of research design. From the experimental perspective, its major stages of research (data collection, data analysis, discussion of the data in context of the literature, and drawing conclusions) should be each undertaken once (or at most a small number of times) in a research study. In qualitative research however, all of the four stages above may be undertaken repeatedly until one or more specific stopping conditions are met, reflecting a nonstatic attitude to the planning and design of research activities. An example of this dynamicism might be when the qualitative researcher unexpectedly changes their research focus or design midway through a research study, based on their 1st interim data analysis, and then makes further unplanned changes again based on a 2nd interim data analysis; this would be a terrible thing to do from the perspective of an (predefined) experimental study of the same thing. Qualitative researchers would argue that their recursivity in developing the relevant evidence and reasoning, enables the researcher to be more open to unexpected results, more open to the potential of building new constructs, and the possibility of integrating them with the explanations developed continuously throughout a study.
Qualitative methods are often part of survey methodology, including telephone surveys and consumer satisfaction surveys.
One traditional and specialized form of qualitative research is called cognitive testing or pilot testing which is used in the development of quantitative survey items. Survey items are piloted on study participants to test the reliability and validity of the items. This approach is similar to psychological testing using an intelligence test like the WAIS (Wechsler Adult Intelligence Survey) in which the interviewer records "qualitative" (i.e., clinical observations)throughout the testing process. Qualitative research is often useful in a sociological lens. Although often ignored, qualitative research is of great value to sociological studies that can shed light on the intricacies in the functionality of society and human interaction.
There are several different research approaches, or research designs, that qualitative researchers use. In the academic social sciences, the most frequently used qualitative research approaches include the following points:
As a form of qualitative inquiry, students of interpretive inquiry (interpretivists) often disagree with the idea of theory-free observation or knowledge. Whilst this crucial philosophical realization is also held by researchers in other fields, interpretivists are often the most aggressive in taking this philosophical realization to its logical conclusions. For example, an interpretivist researcher might believe in the existence of an objective reality 'out there', but argue that the social and educational reality we act on the basis of never allows a single human subject to directly access the reality 'out there' in reality (this is a view shared by constructivist philosophies).
To researchers outside the qualitative research field, the most common analysis of qualitative data is often perceived to be observer impression. That is, expert or bystander observers examine the data, interpret it via forming an impression and report their impression in a structured and sometimes quantitative form.
In general, coding refers to the act of associating meaningful ideas with the data of interest. In the context of qualitative research, interpretative aspects of the coding process are often explicitly recognized, articulated, and celebrated; producing specific words or short phrases believed to be useful abstractions over the data.
As an act of sense making, most coding requires the qualitative analyst to read the data and demarcate segments within it, which may be done at multiple and different times throughout the data analysis process. Each segment is labeled with a 'code' – usually a word or short phrase suggesting how the associated data segments inform the research objectives. In contrast with more quantitative forms of coding, mathematical ideas and forms are usually under-developed in a 'pure' qualitative data analysis. When coding is complete, the analyst may prepare reports via a mix of: summarizing the prevalence of codes, discussing similarities and differences in related codes across distinct original sources/contexts, or comparing the relationship between one or more codes.
Some qualitative data that is highly structured (e.g., open-ended responses from surveys or tightly defined interview questions) is typically coded with minimal additional segmentation of the data. Quantitative analysis based on codes from statistical theory is typically the capstone analytical step for this type of qualitative data. A common form of coding is open-ended coding, while other more structured techniques such as axial coding or integration have also been described and articulated (Strauss & Corbin, 1990). Because qualitative analyses are often more inductive than the hypothesis testing nature of most quantitative research, the existing 'theoretical sensitivity' (i.e., familiarity with established theories in the field) of the analyst becomes a more pressing concern in producing an acceptable analysis.
Contemporary qualitative data analyses are often supported by computer programs (termed computer-assisted qualitative data analysis software) used with or without the detailed hand coding and labeling of the past decades. These programs do not supplant the interpretive nature of coding, but rather are aimed at enhancing analysts' efficiency at applying, retrieving, and storing the codes generated from reading the data. Many programs enhance efficiency in editing and revision of codes, which allow for more effective work sharing, peer review, recursive examination of data, and analysis of large datasets.
Common qualitative data analysis software includes:
A frequent criticism of quantitative coding approaches is against the transformation of qualitative data into predefined (nomothetic) data structures, underpinned by 'objective properties'; the variety, richness, and individual characteristics of the qualitative data is argued to be largely omitted from such data coding processes, rendering the original collection of qualitative data somewhat pointless.
To defend against the criticism of too much subjective variability in the categories and relationships identified from data, qualitative analysts respond by thoroughly articulating their definitions of codes and linking those codes soundly to the underlying data, thereby preserving some of the richness that might be absent from a mere list of codes, whilst satisfying the need for repeatable procedure held by experimentally oriented researchers.
As defined by Leshan 2012, this is a method of qualitative data analysis where qualitative datasets are analyzed without coding. A common method here is recursive abstraction, where datasets are summarized; those summaries are therefore furthered into summary and so on. The end result is a more compact summary that would have been difficult to accurately discern without the preceding steps of distillation.
A frequent criticism of recursive abstraction is that the final conclusions are several times removed from the underlying data. While it is true that poor initial summaries will certainly yield an inaccurate final report, qualitative analysts can respond to this criticism. They do so, like those using coding method, by documenting the reasoning behind each summary step, citing examples from the data where statements were included and where statements were excluded from the intermediate summary.
Some data analysis techniques rely on using computers to scan and reduce large sets of qualitative data. At their most basic level, numerical coding relies on counting words, phrases, or coincidences of tokens within the data; other similar techniques are the analyses of phrases and exchanges in conversational analyses. Often referred to as content analysis, a basic structural building block to conceptual analysis, the technique utilizes mixed methodology to unpack both small and large corpuses. Content analysis is frequently used in sociology to explore relationships, such as the change in perceptions of race over time (Morning 2008), or the lifestyles of temporal contractors (Evans, et al. 2004). Content analysis techniques thus help to provide broader output for a larger, more accurate conceptual analysis.
Mechanical techniques are particularly well-suited for a few scenarios. One such scenario is for datasets that are simply too large for a human to effectively analyze, or where analysis of them would be cost prohibitive relative to the value of information they contain. Another scenario is when the chief value of a dataset is the extent to which it contains "red flags" (e.g., searching for reports of certain adverse events within a lengthy journal dataset from patients in a clinical trial) or "green flags" (e.g., searching for mentions of your brand in positive reviews of marketplace products). Many researchers would consider these procedures on their data sets to be misuse of their data collection and purposes.
A frequent criticism of mechanical techniques is the absence of a human interpreter; computer analysis is relatively new having arrived in the late 1980s to the university sectors. And while masters of these methods are able to write sophisticated software to mimic some human decisions, the bulk of the "analysis" is still nonhuman. Analysts respond by proving the value of their methods relative to either a) hiring and training a human team to analyze the data or b) by letting the data go untouched, leaving any actionable nuggets undiscovered; almost all coding schemes indicate probably studies for further research.
Data sets and their analyses must also be written up, reviewed by other researchers, circulated for comments, and finalized for public review. Numerical coding must be available in the published articles, if the methodology and findings are to be compared across research studies in traditional literature review and recommendation formats.
Contemporary qualitative research has been conducted using a large number of paradigms that influence conceptual and metatheoretical concerns of legitimacy, control, data analysis, ontology, and epistemology, among others. Qualitative research conducted in the twenty-first century has been characterized by a distinct turn toward more interpretive, postmodern, and critical practices. Guba and Lincoln (2005) identify five main paradigms of contemporary qualitative research: positivism, postpositivism, critical theories, constructivism, and participatory/cooperative paradigms. Each of the paradigms listed by Guba and Lincoln are characterized by axiomatic differences in axiology, intended action/impact of research, control of research process/outcomes, relationship to foundations of truth and knowledge, validity and trust (see below), textual representation and voice of the researcher and research participants, and commensurability with other paradigms. In particular, commensurability involves the extent to which concerns from 2 paradigms e.g., "can be retrofitted to each other in ways that make the simultaneous practice of both possible". Positivist and post positivist paradigms share commensurable assumptions, but are largely incommensurable with critical, constructivist, and participatory paradigms of research and knowledge. Likewise, critical, constructivist, and participatory paradigms are commensurable on certain issues (e.g., the intended action and textual representation of research).
Qualitative research in the 2000s has also been characterized by concern with everyday categorization and ordinary storytelling. This "narrative turn" is producing an enormous literature as researchers present sensitizing concepts and perspectives that bear especially on narrative practice, which centers on the circumstances and communicative actions of storytelling. Catherine Riessman (1993) and Gubrium and Holstein (2009) provide analytic strategies, and Holstein and Gubrium (2012) present the variety of approaches in recent comprehensive texts. More recent developments in narrative practice has increasingly taken up the issue of institutional conditioning of such practices (see Gubrium and Holstein 2000).
However, not all scholars agree on the usefulness of paradigms. A critical view of understanding qualitative inquiry vis-à-vis paradigms has been recently put forth by Pernecky (2016), who has argued that problems arise when paradigms are "interpreted in a rigid fashion and compartmentalized into static schemata" (p. 18). It is therefore more fruitful to think in terms of flows and continuums, and even embrace a post-paradigmatic qualitative research. In his words:
"The problem with laying down prescriptive rules about what qualitative research is and how it ought to proceed lies in the narrowing of the possibilities of an abundant and constantly devolving body of philosophical thought. When we accept paradigms uncritically as the ‘givens’, qualitative knowledge becomes habituated, and paradigms grow into hegemonic systems of organization (Pernecky, 2016, p. 194)".
A central issue in qualitative research is trustworthiness (also known as credibility, or in quantitative studies, validity). There are many different ways of establishing trustworthiness, including: member check, interviewer corroboration, peer debriefing, prolonged engagement, negative case analysis, auditability, confirmability, bracketing, and balance. Most of these methods are described in Lincoln and Guba (1985). As exemplified by researchers Preston Teeter and Jorgen Sandberg, data triangulation and eliciting examples of interviewee accounts are two of the most commonly used methods of establishing trustworthiness in qualitative studies. Dependability is equivalent to the notion of reliability in quantitative methods and is the extent to which two or more people are likely to come to the same conclusions by examining the same evidence. Again, Lincoln and Guba (1985) is the salient reference.
By the end of the 1970s many leading journals began to publish qualitative research articles and several new journals emerged which published only qualitative research studies and articles about qualitative research methods. In the 1980s and 1990s, the new qualitative research journals became more multidisciplinary in focus moving beyond qualitative research's traditional disciplinary roots of anthropology, sociology, and philosophy. In the late 1980s to 1990s, early academic articles emerged beginning the transformation from institutional studies (e.g., Taylor's "Let them eat programs") to studies of community, community services and community life reviewed and cited in professional journals. These studies ranged from extremely controversial concerns involving the death penalty and disability (Bogdan, 1995) to the efforts of families with service providers (O'Connor, 1995) to the government divisions which regulate families by "coming to take" the children away (Taylor, 1995).
Wilhelm Wundt, the founder of scientific psychology, was one of the first psychologists to conduct qualitative research. Early examples of his qualitative research were published in 1900 through 1920, in his 10-volume study, Völkerpsychologie (translated to: Social Psychology). Wundt advocated the strong relation between psychology and philosophy. He believed that there was a gap between psychology and quantitative research that could only be filled by conducting qualitative research. Qualitative research dove into aspects of human life that could not adequately be covered by quantitative research; aspects such as culture, expression, beliefs, morality and imagination.
There are records of qualitative research being used in psychology before World War II, but prior to the 1950s, these methods were viewed as invalid. Owing to this, many of the psychologists who practiced qualitative research denied the usage of such methods or apologized for doing so. It was not until the late 20th century when qualitative research was accepted in elements of psychology though it remains controversial. The excitement about the groundbreaking form of research was short-lived as few novel findings emerged which gained attention. Community psychologists felt they didn't get the recognition they deserved. A selection of autobiographical narratives of community psychologists can be found in "Six Community Psychologists Tell Their Stories: History, Contexts and Narratives" (Kelly & Song, 2004), including the well known Julian Rappaport.
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