the variety of ways triangulation is used in mixed methods research and the range of ... They take provocative positions, suggesting that qualitative, construc-.
Editorial
Triangulation and Mixed Methods Research: Provocative Positions
Journal of Mixed Methods Research 6(2) 75–79 Ó The Author(s) 2012 Reprints and permission: http://www. sagepub.com/journalsPermissions.nav DOI: 10.1177/1558689812437100 http://jmmr.sagepub.com
Donna M. Mertens1 and Sharlene Hesse-Biber2
Triangulation is a measurement technique often used by surveyors to locate an object in space by relying on two known points in order to ‘‘triangulate’’ on an unknown fixed point in that same space. Early on, social scientists borrowed the concept of triangulation to argue for its use in the validation process in assessing the veracity of social science research results. There are alternative perspectives on the use of triangulation that argue for its usefulness as a ‘‘dialectical’’ process whose goals seek a more in-depth nuanced understanding of research findings and clarifying disparate results by placing them in dialogue with one another. This special issue of the Journal of Mixed Methods Research (JMMR) analyzes and explores the variety of ways triangulation is used in mixed methods research and the range of issues and controversies surrounding triangulation praxis. To date, there are few scholarly in-depth discussions of its deployment in mixed methods research. The choice of triangulation as the topic for this first special issue of JMMR is based on the claims made by many scholars in the field that triangulation provides a justification for the use of mixed methods. The contributors to this volume raise many questions about the meaning of triangulation, its philosophical positioning in the mixed methods community, and strategies for using triangulation in the design of mixed methods studies, analysis and interpretation of data, and making visible subjugated voices. They take provocative positions, suggesting that qualitative, constructivist, and interpretive pathways provide greater potential for research to address the social good than has been possible using mixed methods approaches that are more closely aligned with the postpositivist paradigm. They revisit the ‘‘paradigm wars’’ and ask this question: Are we still stuck with the incompatibility thesis that paralyzed advances in mixed methods in past decades? They explore and critique the potential of alternative methodologies for harnessing the synergy that is said to lie in the application of mixed methods research designs by asking another set of questions: Have members of the mixed methods community done an injustice to pragmatism as a philosophical frame for mixed methods? Is qualitatively framed mixed methods the way forward? Is it possible that qualitatively framed mixed methods are better suited to the ability of mixed methods researchers to demonstrate a causal relationship between variables? How and when should triangulation be brought into mixed methods research to obtain a more nuanced
1
Gallaudet University, Washington, DC, USA Boston College, Chestnut Hill, MA, USA
2
Corresponding Author: Donna M. Mertens, Department of Educational Foundations and Research, Gallaudet University, 800 Florida Avenue NE, Washington, DC 20002, USA Email: donna.mertens@ gallaudet.edu
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understanding of the phenomenon we study, especially as that applies to the experiences of women and members of other marginalized groups? These are some of the intriguing questions raised in this special issue, which we hope will contribute to an on-going rich conversation in the mixed methods research community. Denzin takes the broadest perspective in this issue in his article in which he addresses the issue of the incompatibility of paradigms and the implication of this potential incompatibility for mixed methods. He criticizes those mixed methods scholars who make claim to the use of the pragmatic paradigm as the solution to the incompatibility problem on the basis that they are misinterpreting the pragmatic philosophy and its implications for methodology. Instead of following the pragmatic philosophy, he asserts that such scholars have adopted a ‘‘what works’’ form of pragmatism that obviates the need to address the fundamental differences in the assumptions that constitute the paradigms that have been associated with quantitative and qualitative methods, that is, the postpositivist and the constructivist. He also criticizes mixed methods approaches that he perceives as supporting a methodological hierarchy in which quantitative methods dominate qualitative methods. According to Denzin, pragmatists recognize the construction of meaning through experience and believe that researchers must focus on the consequences of their interpretive activities for moral and political purposes. He urges researchers to be aware of the historical record of pragmatists, as well as the history associated with bringing the voices of marginalized communities into the research world to consciously keep their experiences in the current discussions. He proactively suggests that qualitative researchers have an ethical responsibility to change the world to further social justice. Thus, he proposes that a moratorium be declared on discussions of mixed methods designs and typologies in favor of pursuing a discussion of how researchers can contribute to the creation of social change. This raises several questions: Is the role of all qualitative researchers and all mixed methods researchers to change the world? This would depend on the beliefs that the researcher holds. Is it only possible to create this change by starting mixed methods within a qualitative interpretive framing? Howe takes on the challenge of exploring various conceptions of mixed methods and their implications for the role of triangulation in the context of determining causation. The first is the disjunctive conception in which different roles are assigned to quantitative and qualitative methods. For example, the National Research Council (Shavelson & Towne, 2002) took the position that qualitative methods are for discovery and quantitative methods are for testing causal relationships. He challenges this conceptualization of the roles for quantitative and qualitative data by introducing the concept of Agential causation (A-causation), which rests on the assertion that people act in intentional ways and that researchers can capture the complexity of collective intentionality that leads to the construction of social facts when combined with certain knowledge, skills, and dispositions. Howe reaches the somewhat controversial conclusion that the establishment of A-causation places the role of quantitative experimental methods in the role of description and the qualitative interpretive methods in the role of providing causal explanations because they can answer the ‘‘why’’ question. He labels this position as mixed methods interpretivism. Howe’s second conception of mixed methods is conjunctive mixed methods and involves triangulation based on the integration of quantitative and qualitative data, not merely to look at agreement or disagreement between the data sets, but to put the data into a more comprehensive explanatory framework. Howe uses a second type of causation as a basis for framing the role of triangulation in mixed methods research. He describes Mechanical causation (M-causation) as that which can be asserted by controlling for extraneous variables and testing the relationship between an independent and dependent variable. For example, M-causation is supported between the ingestion of lead paint and neurological damage. This knowledge can be integrated
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into a qualitative analysis of the ethics of research that placed poor African American families with children into housing with known levels of lead poisoning. The integration of these findings yields improved understandings of discrimination and racism as causal variables in the relationship between lead paint and neurological damage in particular oppressed communities. Such knowledge can be used as a basis for legal action, as well as for a revision of the ethical codes for the conduct of research. Flick, Garms-Homolova´, Herrmann, Kuck, and Ro¨hnsch provide an example of a mixed methods study in which they illustrate triangulation as a methodological framework in a study that used a constructivist approach. The case involves the differences in perspectives of patients and health care providers about the use of sleep medications in nursing homes. They take the position that the specific research issue justifies the use of mixed methods. Given this position, the choice of mixed methods is supported because of the need to gather information about multiple perspectives on this research issue. Quantitative data were gathered to understand the prevalence of sleep disorders, how serious they are in terms of health status, and the prescribing and use of medications to address the problem. However, these quantitative data needed to be placed in the context of understanding the staff’s knowledge about and attitudes toward the treatment of sleep disorders, as well as the perceptions of the nursing home residents and their beliefs about the nature of the problem and its treatment. Thus, the concept of triangulation influenced the design of this embedded mixed methods study, including the use of multiple methods and multiple researchers. Flick et al. describe those research issues that support the use of mixed methods as those that require a triangulation of perspectives to understand a complex problem. In their study, the authors acknowledge that a constructivist paradigm precedes the framing of the research issue. How did the constructivist positioning influence the development of the research problem? Do they make the assumption that research issues emerge without a paradigmatic lens that influences the form that they take? Would researchers who start from different paradigmatic stances formulate the same kinds of research questions? This might lead to additional queries about justifying the use of mixed methods based on the type of research question. There may be antecedent conditions that need to be explored in the formulation of the research questions. Hence, a different basis for the choice of mixed methods would need to be established, more than the addressing of a complex problem. Torrance draws on the literature from sociology, program evaluation, and qualitative methods to support his argument that mixed methods research could benefit from increased use of triangulation in the form of the involvement of respondents in the interpretation of quantitative and qualitative data, especially as a means to address issues of power. If the power for interpretation rests solely with the researcher, without input from the community, then this brings up questions of accurate representation and ethics. He notes that qualitative researchers recommend the use of member checks or respondent validation to ensure the accuracy of the data collected. Although this is a limited use of this strategy, Torrance contends that mixed methods would be strengthened by privileging the qualitative portion of the study and expanding the use of member checks and respondent validation as tools for triangulating quantitative and qualitative data. Torrance argues that mixed methods researchers should give priority to the use of qualitative methods in order to engage more deeply with research participants in setting the research agenda, developing questions, and constructing reports of the inquiries. His position is commensurate with that of scholars from marginalized groups, such as Chilisa’s (2012) indigenous methodologies and Harris, Holmes, and Mertens’s (2009) terms of reference for research in the deaf community. As more calls for authentic representation and involvement come from
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communities previously excluded from the decision-making process, mixed methods researchers will need to explore with these communities how they can be more responsive to their interests. Fielding brings attention to the role of triangulation in mixed methods research at the analytic stage through the combination or conversion of quantitative and qualitative data. He argues that we mix not because there is something intrinsic or distinctive about quantitative or qualitative data. Rather we do so to integrate the two fundamental ways of thinking about social phenomena; he uses this as an argument to support the quantifying of qualitative data to test hypotheses or the qualitatizing of quantitative data to show patterns or idiosyncrasies. He sees integration at the data analysis stage as the heart of mixed methods with attention to the types of data that are integrated and the methods we use for that integration. Qualitative data can be quantified by counting the frequency of occurrence of a code or developing categories based on codes that can be matched with quantitative data sets. Fielding does suggest caution in this exercise because the epistemological assumptions that led to the two types of data may not be commensurate. Thus, the rationale for the integration of the data will benefit by being built into the design of the study itself. However, this may not be an argument that will be accepted by constructivists who do not accept the conversion of qualitative data as legitimate. Quantitative and qualitative data can be mixed for the purpose of illustrating a more complete understanding of the phenomenon being studied. However, Fielding warns about a potential weakness of mixing methods for the purpose of validity convergence, that is, to compare findings from different methods to see if they agree because interpretation of agreement or disagreement is not unproblematic. Fielding recommends that the quantitative and qualitative data be put into dialogue with each other, possibly through the use of review by groups that examine both sets of data and proffer improved interpretations and better community ownership. He then discusses how mixed methods analysis can be enhanced by combining data that are available from various technological sources, such as visual data available from geographical information systems, quantitative data bases on the incidence of disease in different parts of the world, and interview data from qualitative software. This raises the specter of a new role for researchers who can provide quantitative and qualitative data in a mixed format through technology in real time as a basis for researching in contexts, such as disaster areas, in which integration of such data can contribute to informed decision making. Hesse-Biber extends our thinking about triangulation at the data analytic stage by providing examples of the use of a feminist theoretical lens to design and conduct mixed methods research. She emphasizes the importance of being aware of relevant dimensions of diversity in the communities in which we conduct our research. For example, inclusion of women may be challenging in some cultures in which they are relegated to lower status. The challenge may even be greater for women from particular social groups who are stigmatized in a society, such as those from a particular tribe or caste. A feminist approach to mixed methods praxis provides the opportunity for the voices of those who have been marginalized to be brought into conversation with data collected by other means. In this approach, neither quantitative nor qualitative data are privileged. Both are accorded legitimacy and different perspectives are brought to bear on interpreting each source of data. In this way, subjugated knowledges can be made visible and used to interpret the data collected by other means with the goal of promoting social justice and social transformation on behalf of women and other marginalized groups. We hope that this special issue promotes dialogue around theoretical and praxis issues surrounding the deployment of triangulation. A dialogic process holds the promise of fostering a multifaceted and nuanced understanding of the conditions under which triangulation can capture the synergistic potential of mixed methods research.
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References Chilisa, B. (2012). Indigenous methodologies. Thousand Oaks, CA: SAGE. Harris, R., Holmes, H. M., & Mertens, D. M. (2009). Research ethics in sign language communities. Sign Language Studies, 9, 104-131. Shavelson, R. J., & Towne, L. (Eds.). (2002). Scientific research in education. Washington, DC: Committee on Scientific Principles for Education Research, National Research Council.
