Module 10: Discussion 10: Becoming Quantitative

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Application of Mixed-Methods Approaches to Higher Education and Intersectional Analyses

Kimberly A. Griffin, Samuel D. Museus

As scholars argue that intersectionality—the examination of individuals and their positionality at the intersection of multiple social identities or groupings—in higher education is a research paradigm rather than just a topic of study (e.g., Dhamoon, 2011; Hancock, 2007), there has been a great deal of discussion over the modes of analysis best suited to address how multiple identities shape the lived experience. Hancock calls atten- tion to the need for appropriate methodology for intersectional inquiry, stating that “to move beyond testing time-worn theories, to examine the as-yet unanswered questions intersectionality generates, intersectional empiricists cannot rely on the same old data, or more precisely, data col- lected in the same old unitary way” (p. 66). Hancock and others suggest that mixed methods, which integrate qualitative and quantitative modes of analysis, may be best suited for this for intersectional inquiry. For exam- ple, Trahan (2011) notes that the basic principles of intersectionality align well with a mixed-methods analytical strategy. Intersectionality suggests that there are multiple, overlapping systems of oppression that shape our lives and experiences in complex ways. Consequently, this complexity requires truly understanding multiple forms of data and analysis.

It has been approximately a decade since Borland (2001) and his col- leagues highlighted the utility of mixed-method approaches in scholarly and institutional research. Yet mixed-methods research is still not widely adopted or used in the fi eld of higher education. This chapter is based on

In this chapter, the authors discuss the utility of mixed-methods research in conducting intersectional analyses in higher education. They also discuss challenges to conducting mixed-methods intersectionality research and offer suggestions for overcoming them.

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the premise that higher education researchers can benefi t from a better understanding of how mixed-methods approaches can be used to study not only intersectionality but various individuals, groups, processes, rela- tionships, and phenomena in postsecondary education more generally.

Mixed-methods research can be defi ned in many ways (Creswell and Plano Clark, 2007; Johnson and Onwuegbuzie, 2004; Johnson, Onwueg- buzie, and Turner, 2007; Tashakkori and Teddlie, 1998). For the purposes of this chapter, we rely on the defi nition of mixed-methods research as “the type of research in which a researcher or team of researchers combines ele- ments of quantitative and qualitative research approaches (e.g., use of quantitative and qualitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration” (Johnson, Onwuegbuzie, and Turner, 2007, p.123). This defi nition of mixed-methods research excludes multimethod designs, in which researchers mix multiple quantitative (e.g., surveys and struc- tured observations) or qualitative (e.g., data from interviews and docu- ments) forms of data (Creswell and Plano Clark, 2007).

In the remainder of this chapter, we discuss the utility of combining quantitative and qualitative methods in conducting intersectional analyses (see Chapter One for complete discussion of intersectionality research). First, we discuss some of the paradigmatic underpinnings of qualitative and quantitative research, and how these methods can be used in intersec- tional analyses. We then consider how paradigmatic pragmatism informs mixed-methods research, which can be an alternative research strategy that mitigates some of the limitations of mono-method quantitative and qualitative studies of intersectionality in higher education. Next, we pre- sent important choices that researchers must make in designing mixed- method research, followed by descriptions of four mixed-methods designs that can be used in conducting intersectional analyses in postsecondary education. Finally, we discuss challenges that researchers who conduct mixed-methods intersectionality research might face and provide some recommendations to address those challenges.

Mono-Methods and Intersectionality Research

Mixed-methods emerged from and function to integrate quantitative and qualitative methods. Quantitative and qualitative methods have much in common—researchers use both types of methods to describe data, analyze and construct explanations from data, and make inferences based on those data (Sechrest and Sidani, 1995)—but the differences between these two paradigms have, more often than not, been the focus of scholarly dis- course. Table 2.1 is based on previous literature (e.g., Creswell and Plano Clark, 2007; Johnson and Onwuegbuzie, 2004; Johnson, Onwuegbuzie, and Turner, 2007) and outlines some of these distinctions. As the table shows, quantitative and qualitative paradigms differ in their philosophical

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Table 2.1. Juxtaposition of Quantitative and Qualitative Paradigms

Quantitative Component of

Research Process Qualitative

• Positivist: singular reality independent of researcher

Ontology and epistemology

• Constructivist: multiple realities dependent upon researcher

• Test hypotheses • Test and validating

theory • Prediction • Identify and confirm

existence of relationships

• Generalize to population

• Isolate focal variables or relationships

Purposes and nature of inquiry

• Understand and explain experiences, processes, events, and other phenomena

• Provide rich description

• Generate theory • Consider

complexity and context of phenomena

• Large and random Participant samples

• Small and purposeful

• Numerical (e.g., surveys, structured observations)

Data collection techniques

• Textual or pictorial (e.g., participant observations, individual and focus group interviews, documents)

• Statistical analyses Data analysis techniques

• Textural or pictorial analyses

• Internal validity (i.e., accurate measurement), external validity (i.e., generalizablity), and reliability (i.e., consistent measurement)

Assessment of quality

• Credibility (i.e., accurate representation of participant reality) and transferability (i.e., ability to infer results can be transferred to other contexts)

• Removed from process to maximize objectivity

Role of researcher

• Engaged in subjective process

• Lacks detail and context

• May miss reality because of the focus on testing and validity preexisting

Limitations of approach

• Not generalizable • Time-consuming • More easily

influenced by researcher bias

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foundations, purposes, data collection and analysis techniques, role of researchers, and limitations.

Pure quantitative and qualitative researchers have traditionally sub- scribed to different paradigms, which include various beliefs about what constitutes reality (i.e., ontology) and how knowledge is constructed (i.e., epistemology; Johnson and Onwuegbuzie, 2004; Jones, Torres, and Arminio, 2006; Tashakkori and Teddlie, 1998). The perspectives of quan- titative researchers are often rooted in positivism, which, in its purest form, suggests that there is a single reality to be explored and identifi ed through objective research. Pure positivists argue that the knower and the known are independent; they charge that research can and should be value-free. Positivists also believe researchers can use quantitative methods to establish cause-and-effect relationships that can be generalized across time and context (Johnson and Onwuegbuzie, 2004; Tashakkori and Teddlie, 1998).

Postpositivism emerged in the 1950s and tempered many of the posi- tivist beliefs about the value-laden nature of research and social construc- tion of reality; constructivism was developed as a more radical response to positivism (Tashakkori and Teddlie, 1998). Endorsed largely by qualita- tively oriented scholars, the tenets of constructivism are quite distinctive from the beliefs often embraced by positivists. Constructivists believe that multiple, individually constructed realities exist and see the knower and information to be known as inseparable. Research and knowledge are seen as value-laden, highly infl uenced by the beliefs of researchers. The subjec- tivity of inquiry and knowledge eliminates the ability to generalize across time and context. Constructivists also believe causes and effects cannot be distinguished from one another, and that our understandings of the world should be generated through an inductive process, starting with observa- tions, which are used to develop theory (Johnson and Onwuegbuzie, 2004; Tashakkori and Teddlie, 1998).

In the context of intersectionality research, both quantitative and qualitative methods can offer tools that generate rich insights depending on the research question being asked and the purpose of the inquiry. For example, quantitative research methods can be used for several purposes in intersectional analyses.

Category Comparison. Higher education scholars and institutional researchers can use quantitative techniques to identify inequities that exist at the intersections of multiple social identities or groupings. For example, postsecondary researchers can compare the persistence and degree attain- ment rates of all gender and racial groups to understand which popula- tions suffer from inequities when considering those two types of social groupings (e.g. see Harper, 2006).

Category Deconstruction. Related to category comparison, research- ers can disaggregate quantitative data to analyze subgroups within a par- ticular category to generate a more complex picture of reality than is

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presented when the entire racial category is examined (e.g. see Museus, 2009; Museus and Kiang, 2009; Teranishi, 2010).

Generalizability Assessment. Higher education scholars and insti- tutional researchers can quantify the fi ndings of a qualitative inquiry into the experiences of a person or persons who are situated at the intersection of social identities or groupings to assess whether those fi ndings are gen- eralizable to the larger population at that intersection.

Qualitative methods also have signifi cant utility in conducting inter- sectionality research. They can, for example, serve several purposes.

Voice Excavation. Higher education researchers can use qualitative approaches to excavate the unique voices of those who are situated at the intersections of multiple social identities and groupings, which can illumi- nate their unique experiences and realities that might otherwise remain unheard (e.g., see Crenshaw, 1991).

Disparity Explanation. Researchers can also use qualitative methods to answer questions regarding why particular groups at social identity intersections suffer from disparities in areas such as psychosocial well-being, moral and civic development, or postgraduate educational and occupational success.

Paradigmatic Pragmatism, Mixed Methods, and Intersectionality

Despite their potential utility in conducting intersectional analyses, both quantitative and qualitative approaches have significant limitations, par- ticularly when engaging in intersectional analyses. Quantitative methods have perhaps received the most attention for their limitations, being cri- tiqued as inadequate to address the integrative, complex nature of identity (Bowleg, 2008; Dhamoon, 2011; Hancock, 2007; Trahan, 2011). The dis- aggregation required by quantitative analysis tends to frame identity in an additive rather than integrative way and tends to ignore differences within identity groups (Hancock, 2007; Trahan, 2011). Scholars have asserted the importance of qualitative strategies to intersectional analyses (e.g. Bowleg, 2008; Hancock, 2007; Traham 2011); however, qualitative methods are not without their limitations. In addition to being time- and cost-intensive, data can be challenging to interpret; it can also be hard to understand how the specific dimensions are coming together or are most salient in a particular experience (Bowleg, 2008).

Mixed methods can serve as a useful methodological alternative and have the potential to maximize the benefi ts and balance the limitations of both qualitative and quantitative strategies (Hancock, 2007; Johnson and Onwuegbuzie, 2004; Trahan, 2011). Mixed methodology is based on a third epistemological orientation: paradigmatic pragmatism. Tashakkori and Teddlie (1998) describe paradigmatic “purists” as espousing the fun- damental belief that qualitative and quantitative methods are incompatible

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and impossible to integrate because they are based on oppositional para- digms (i.e., positivism and constructivism). Paradigmatic pragmatism, however, advocates for an epistemological middle ground. Paradigmatic pragmatists do not posit that reality is absolute or entirely based on the perceptions of individuals. Rather, they assert that knowledge is simulta- neously constructed and based on a general reality that we all inhabit (Johnson and Onwuegbuzie, 2004). Pragmatists acknowledge that values shape how researchers determine what to study and the nature of their analyses, but they do not perceive this subjectivity as problematic because research can be conducted to meet specifi c value-based needs (Johnson and Onwuegbuzie, 2004; Morgan, 2007; Tashakkori and Teddlie, 1998). Pragmatists also believe in the existence of causal relationships but acknowledge that these are extremely diffi cult to clarify and identify because they are infl uenced by context, which is constantly changing (Cherryholmes, 1994).

Paradigmatic pragmatists believe in the utility of collecting both numerical and textual data to address complex problems. They highlight the similarities, rather than the distinctions, between constructivists and positivists (Johnson and Onwuegbuzie, 2004). Absence of commitment to one philosophical worldview allows pragmatists to integrate the assump- tions and strategies of both qualitative and quantitative researchers (Creswell, 2003) to construct a design that is best suited to address the research questions and fulfi ll the purpose of the research at hand. This aligns well with the principles undergirding intersectionality. According to Hancock (2007), intersectionality resides in a philosophical space similar to pragmatism, standing “ontologically between reductionist research that blindly seeks only the generalizable and particularized research so special- ized it cannot contribute to theory” (p. 74).

When used in intersectional analyses, mixed-methods strategies can allow researchers to gain a broader and deeper understanding of the multi- dimensional nature of identity and its role in students’, professors’, and administrators’ lives. Mixed methods are a relatively new form of research design, but they are increasingly used to gain more holistic understand- ings of complex phenomena from multiple perspectives (Creswell and Plano Clark, 2007), which may constitute a powerful methodological strategy for intersectional analyses. Researchers, for instance, can use quantitative methods to identify groups that suffer from disparities and qualitative methods to illuminate the voices of individuals within that group.

Mixed-methods design also allows researchers to balance the weak- nesses of one methodology with the strengths of another (Creswell and Plano Clark, 2007; Johnson and Onwuegbuzie, 2004; Tashakkori and Teddlie, 1998). Quantitative data offers precision, is effi cient for studying large groups, and can be an effective way to test hypotheses. However, quantitative work does not necessarily address why or how phenomena

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occur. Further, these methods often ignore how culture or common under- standings within a community, which are diffi cult to quantify, can shape the relationships between variables and outcomes (Johnson and Onwueg- buzie, 2004). Qualitative methods can alleviate many of these issues, per- mitting in-depth understandings that are rich with detail. Qualitative research is known for its consideration of contextual and situational fac- tors, ability to generate theory, and utility in facilitating our understanding of how others make meaning of various phenomena. Yet the fi ndings of qualitative research are not generalizable across contexts, are more subject to the bias of the researcher, and are time-intensive, making them ineffi - cient when aiming to understand large populations (Johnson and Onwuegbuzie, 2004)—limitations for which quantitative methods can compensate. Thus, in many ways integrating both strategies in one study maximizes opportunities to conduct studies with both depth and generalizability.

Considerations in Mixed-Methods Research Design

There are several issues to consider when designing mixed-methods research. Consistent with a pragmatist epistemology, design decisions must be made according to what would create the most informative data- set to answer the research questions that guide the inquiry. In this section, we discuss four critical decisions that must be made by those engaging in mixed-methods research: (1) the emphasis placed on qualitative and quan- titative methods, (2) sequencing of qualitative and quantitative methods, (3) how the data are integrated, and (4) the purpose of mixing.

Emphasis. In designing mixed-methods studies, researchers must consider whether the qualitative or quantitative components of the study will be given greater emphasis (Jones, Torres, and Arminio, 2006; Morgan, 1998; Morse, 1991; Tashakkori and Teddlie, 1998). Researchers may choose to give dominant status to one method if it is more fundamental to answering the research question and the other is supportive in nature. Therefore, researchers can make one of three choices: (1) qualitative data collection and analysis can be dominant, (2) quantitative data collection and analysis can be dominant, or (3) the strategies can have equivalent status (Creswell and Plano Clark, 2007; Tashakkori and Teddlie, 1998).

Sequencing. Perhaps one of the most important questions that researchers must address in designing their studies is whether data should be collected and analyzed concurrently or sequentially (Johnson and Onwuegbuzie, 2004; Johnson, Onwuegbuzie, and Turner, 2007; Leech and Onwuegbuzie, 2009). In a concurrent data collection strategy, the qualita- tive and quantitative data are collected independently and roughly at the same time (Creswell and Plano Clark, 2007; Jones, Torres, and Arminio, 2006). Alternatively, researchers can choose to collect and analyze qualita- tive data in the fi rst phase and quantitative data in the second phase, or

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vice versa (i.e., sequentially). The decision regarding the sequencing of collecting and analyzing quantitative and qualitative data is closely related to the last consideration we discuss in this section: the purpose of method mixing.

Method of Mixing. The third major consideration for researchers who design mixed-methods research is how they will mix their data. There are at least three types of mixing (Creswell and Plano Clark, 2007). Researchers can (1) merge the quantitative and qualitative datasets together, (2) connect the two datasets with one building on the other, or (3) embed one dataset within another, in which case the former is designed to support the latter.

Purpose of Mixing. Finally, researchers must consider the purpose of incorporating both quantitative and qualitative methods into their analysis (Creswell and Plano Clark, 2007; Johnson and Onwuegbuzie, 2004; John- son, Onwuegbuzie, and Turner, 2007; Leech and Onwuegbuzie, 2009; Tashakkori and Teddlie, 1998). Researchers can determine that both quan- titative and qualitative methods are necessary to (1) use each method to validate the data gathered by the other, (2) use one method to inform another (e.g., use qualitative data to construct a questionnaire), (3) use one method to expand on the fi ndings of another, (4) seek paradoxes or new perspectives, or (5) maximize the probability of generating useful fi ndings.

Applying Mixed-Methods Designs to Intersectionality Research in Higher Education

The decisions researchers make regarding these four considerations will be reflected in and shaped by the form of mixed-methods design imple- mented. Given the many types of mixed-methods designs, however, it should be kept in mind that any single typology will most likely not encompass all mixed-methods designs. Nevertheless, we present four designs outlined by Cresswell and Plano Clark (2007) to give readers a framework for understanding the diversity of mixed-methods approaches that can be applied to intersectionality research. In doing so, we offer examples of how those designs might be applied to a study of intersection- ality within the context of higher education.

Triangulation Designs. The goal of the triangulation design is to obtain complimentary qualitative and quantitative data on a topic to use the strengths of both methods (Morse, 1991). In this design, researchers collect both quantitative and qualitative data in a single phase to either compare and contrast fi ndings or expand on quantitative results with qualitative fi ndings (Creswell and Plano Clark, 2007). For example, a researcher could separately collect qualitative and quantitative data on LGBT racial minority students’ perceptions of the campus climate. The qualitative and quantitative data would then be integrated into the analy- sis, comparing and contrasting the fi ndings generated by each approach.

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Data triangulation designs are cases in which researchers merge quan- titative and qualitative fi ndings during interpretation, validate quantitative survey results with open-ended survey questions, or use methods that vary according to the level of a system being studied (Creswell and Plano Clark, 2007). Another form of triangulation design is when researchers collect quantitative and qualitative data and then transform one type of data into the other form. For example, researchers could conduct inter- views with LGBT racial minority students about their experiences with prejudice and discrimination on campus, and then quantify the extent of experienced prejudice and discrimination so that it can be correlated with those participants’ responses on a quantitative survey. Thus, by transform- ing the qualitative data into numbers, researchers can corroborate the fi ndings that emerge from each method or correlate fi ndings from the quantitative component with the qualitative aspect of the investigation.

Embedded Designs. In an embedded design, researchers use second- ary methods to support another dominant method (Creswell and Plano Clark, 2007). Embedded designs include cases in which researchers use qualitative data to support a quantitative experiment or use qualitative data to explain correlations that are examined in a correlational analysis. For example, researchers might use qualitative methods to develop a diversity workshop focused on LGBT racial minority issues that can be used in a quantitative experiment to test the effect of the workshop on stu- dents’ prejudicial attitudes. In this case, the qualitative methods are used to support and prepare for the primary component of the research design: the experiment.

Explanatory Designs. In an explanatory design, qualitative data is used to expand on the fi ndings generated by a quantitative analysis. This design is particularly useful when researchers would like to explain fi nd- ings generated in a quantitative analysis or recruit participants with cer- tain characteristics before engaging them in focus groups or interviews (Creswell and Plano Clark, 2007). For example, researchers interested in using an explanatory design could fi rst conduct a regression analysis of survey data examining the relationships between LGBT racial minority students’ experiences with prejudice and discrimination and satisfaction in college, and then gain a deeper understanding of the nature of prejudice and discrimination experienced by this group by conducting student focus groups.

Exploratory Designs. In mixed-methods studies with an exploratory design, the qualitative data collection and analysis precedes the quantita- tive analysis. This design is best for studies where the goal is to explore new phenomena, as well as to generate theories and the instruments to test them (Creswell and Plano Clark, 2007). It can also be used to confi rm whether a qualitative fi nding within one sample is more generalizable to a wider population. One example of an exploratory design is if researchers interested in LGBT racial minority students’ experiences with prejudice

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and discrimination conduct focus groups with students on how they expe- rience prejudice and discrimination on campus, and then develop a quan- titative survey that is based on the fi ndings generated from the qualitative focus group data.

Dealing with the Challenges of Mixed-Methods Intersectional Analyses

Although mixed-methods designs have much utility for both higher edu- cation scholars and institutional researchers (Borland, 2001), there are several challenges. In this section, we discuss some of the challenges asso- ciated with implementing mixed-methods designs and offer recommenda- tions to address those issues for higher education researchers who would like to integrate qualitative and quantitative methods as they engage in intersectional analyses.

Conceptual Complexity. Intersectionality research is inherently complex (McCall, 2005). For each social identity (e.g., gender, race, eth- nicity, etc.) t hat is incorporated into a specifi c analysis, there is an added level of complexity. Moreover, mixed-methods designs constitute an added layer of intricacy, because researchers have to incorporate both quantita- tive and qualitative methods. To address this complexity, researchers should consider focusing on the intersection of two or three social identi- ties. If the focus expands to the intersection of four or more social identi- ties, the analysis can become increasingly complex, rendering it diffi cult to make sense of data. Although it is likely researchers will have to deal with a signifi cant amount of complexity regardless, limiting the number of identities considered within the study can provide parameters that some- what diminish complexity with which researchers must deal when con- ducting mixed-methods intersectionality research.

Required Expertise. Conducting mixed-methods research requires suffi cient expertise in quantitative, qualitative, and mixed-methods research. Researchers not only must be able to collect and analyze both quantitative and qualitative data, but they must also possess the ability to design a mixed-methods inquiry and integrate quantitative and qualitative methods. If researchers are primarily quantitative or qualitative, or they have limited familiarity with mixed methods, they might want to consider collaboration with colleagues who have more knowledge and expertise. Alternatively, researchers can hire consultants who are knowledgeable about quantitative, qualitative, and mixed methods for support to mitigate some of the challenges associated with learning and executing new and unfamiliar methodologies.

Resource Requirements. In many—but not all—cases, mixed- methods research requires more resources (e.g., time, money, energy) than implementing solely quantitative or qualitative research designs because mixed-methods studies require collection and analysis of both

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quantitative and qualitative data. This means that mixed-methods studies can present situations in which researchers are, in essence, conducting two complimentary inquiries, both of which require substantial resources. There are several ways to address this resource challenge. Researchers can use existing datasets (e.g., see Chapters Four and Six). Or if existing data- sets are not available, researchers can integrate the two components of their mixed-methods inquiry to the extent possible by, for example, solic- iting participants for the second component of the study during the fi rst phase of the inquiry in sequential designs. This can reduce the amount of energy required for recruitment.

Conclusion

Both intersectionality and mixed-methods research designs are underused in higher education research. For progress to be made in this area, higher education researchers must consider and discuss the various ways in which mixed-methods approaches can be applied to examining groups, processes, and phenomena in colleges and universities. In this chapter, we discuss the utility of mixed-methods designs generally, but also specifi- cally within the context of conducting intersectional analyses, with the hope that this will build the foundation for inquiries of this nature in scholarly and institutional research. Mixed-methods hold great potential to illuminate the complexity associated with the multiple identities of stu- dents, faculty, and staff coming together in unique ways to shape their development, experiences, and outcomes in higher education. Though not without methodological challenges, mixed-methods techniques address the limitations of reliance on qualitative or quantitative methods alone, as demonstrated in the remaining chapters in this volume.

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KIMBERLY A. GRIFFIN is an assistant professor of education policy studies at the Pennsylvania State University and a research associate in the Center for the Study of Higher Education.

SAMUEL D. MUSEUS is an assistant professor of educational administration at the University of Hawai’i Manoa.

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