Data mixing: Combining quantitative and qualitative data

An inherent element of any mixed method study, is the necessity for the researcher to ‘mix’, or integrate the data (Bryman 2007; Creswell 2015), indeed Bazeley (2009 p. 204) notes how “mixed methods research involves, as a minimum, integrating conclusions that are drawn from various strands in the research”. Essentially, this process denotes the unification of different data types, commensurate with the research design employed (Creswell 2015). Although mixed methods studies are becoming increasingly popular (Bryman 2006), illumination of the processes and procedures required for successful mixing of quantitative and qualitative data “has been marginalised in much writing on mixed methods research” (Bryman 2007 p. 8). Consequently, the issue of integrating quantitative and qualitative elements of mixed methods studies continues to be a source of contention, and challenge for mixed methods researchers (Bryman 2007). Despite

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readers being able to infer the data integration process through the preceding sections, the process will now be clarified and communicated explicitly in order to avoid some traditional criticisms of vagueness, and opaqueness in reporting of mixed methods research (Bryman 2007).

Despite the paucity of literature, Creswell (2015) suggests researchers can integrate methods at various points of the research process, for example:

• During data collection • Within the data analysis • Within discussion of results

The position of data mixing should ultimately be determined by the research design, and occur in a logical positon conducive to producing robust research (Bryman 2007). For example, the explanatory sequential design followed in this research and illustrated in Figure 8 shows data mixing should logically occur in two places; tacitly after analysis of the quantitative results in order to guide the qualitative data collection and analysis but, most predominantly, within the discussion chapter of the thesis. These were deemed the most appropriate stages at which to mix the primary quantitative, and secondary, lesser weighted qualitative results. Points of data integration are illustrated in Figure 8 below.

Figure 8: Data Integration Points

The expansion of mixed methods integration literature has been characterised by the development of multiple mixed methods research frameworks which include exploratory, explanatory and concurrent designs (see section:4.5.4), and lend some degree of gravitas and rigour to the research approach (Creswell 2015; Teddlie and Tashakkori 2009). However, Bryman (2007 p. 99) contests that “the formalisation of approaches to multi-

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strategy research through typologies has moved too far ahead of a systematic appreciation of how quantitative and qualitative research are combined in practice”, highlighting the limitations in our understanding of the practicalities of the data mixing process. To help guide further mixed methods research reporting, Bryman (2007) identifies nine barriers to mixed methods integration; the barriers are described along with their relevance to this research, as well as approaches employed to surmount them where necessary in Table 26 below.

141 Table 26: Barriers to Mixed Method Integration Adapted from Bryman 2007

Barrier Description Relevance and researcher strategy Audiences The integration process can be

influenced by the ultimate audience for the research. For example, a quantitatively oriented audience can encourage the researcher to only integrate, or report the integration of qualitative elements of the study to a limited extent.

Audience preference is not a concern in this research, given that it takes the form of a PhD thesis and not principally developed for explicit dissemination amongst managers, or for publication purposes. The primary audience will be its examiners and future readers. Method Bias Researcher’s own methodological

bias can influence the mixing of quantitative and qualitative data.

Method bias was not a concern as time and resources ensured the researcher could become competent in both methods. The researcher possesses no innate method bias. Structural

Constraints

The overall structure of the research can impede mixing of data where external factors influence the project.

Given the nature of this research project, the topic and structure is not set externally.

Time Constraints

The different speeds at which quantitative and qualitative research can be conducted and analysed can affect the quality, and volume of analysis.

Time constraints particularly affect teams who have additional time demands (e.g., lectureships, other research projects). This is not a concern as this project represented the principal occupation.

Researcher Skillset

A lack of particular specialism in particular types of analysis and research can limit data integration. Researchers tend to focus on the elements they are most skilled in.

The researcher was supervised throughout the analysis by active academic researchers with specialisms in a broad range of research and analysis techniques. Furthermore, the researcher possesses experience in publishing both qualitative and quantitative oriented journal articles.

Data

Characteristics

The findings of one data set may prove more noteworthy than the other. Researchers can be tempted to then curtail consideration of the less interesting research element.

As this study adopts a sequential mixed method explanatory design, the quantitative phase guides a smaller second phase which seeks to add a further layer of richness to the study. Philosophical

Issues

Some researchers can find it challenging to integrate data commensurate with opposing ontological and epistemological standpoints.

The adoption of a pragmatist stance mitigates this issue for the researcher, as it justifies integrating quantitative and qualitative data with emphasis on the research outcomes.

Publication Demands

The paucity of mixed methods articles published in esteemed journals can encourage researchers to focus predominantly on either quantitative or qualitative studies.

Given this is a PhD thesis publication demands are not a primary concern. A leading non-profit sector journal publishes mixed method research (e.g., Holmes and Slater 2012). Limited

Examples

Limited examples of best practice data integration have stifled the development of mixed methods research.

While there are fewer examples of mixed methods integration than other research approaches, guidance was received from a supervisory team, and multiple academic sources (e.g., Bryman 2007; Creswell 2009).

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Thus, Table 26 illustrates how the common barriers to successful mixed methods research integration, identified by Bryman (2007), were of little concern to this study, nevertheless, the researcher remained aware of them, and vigilant to their potential effects throughout the research process.

Ultimately, mixed methods research could benefit from further investigation, and more detailed presentation of best practice approaches to integrating data. In this study, the quantitative and qualitative phases were analysed separately although the quantitative phase guided the qualitative phase’s collection and analysis; both elements were then mixed at the discussion stage in relation to the theoretical underpinnings of the research. Nevertheless, this research, grounded upon a pragmatist philosophy, adheres to currently prevailing research consensus, and follows a logical order that contributes to enriching the value of the resulting conclusions and managerial implications (Creswell 2009; Creswell 2015), therefore justifying the research design followed in this thesis