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INTERNATIONAL JOURNAL OF MULTIPLE RESEARCH APPROACHES Volume 5, Issue 3, December 2011290

Beyond the qualitative–quantitative distinction: Some innovative

methods for business and management research

BRANKA KRIVOKAPIC-SKOKO AND GRANT O’NEILL School of Business, Charles Sturt University, Bathurst, NSW, Australia

ABSTRACT Focusing upon a number of increasingly popular approaches to mixed methods research, this paper provides a brief overview of fully integrated research methods that transcend the quantitative– qualitative divide. Introducing a range of sophisticated mixed method designs that have been successfully applied in business and management research, it provides insight into the potential benefi ts of mixed methods. In addressing integrated mixed methods, and applications in business and management research, the discussion signposts how these methods allow for qualitative analysis that is systematic, formal, rigorous and procedurally replicable. Further, it identifi es how integrated mixed methods can make it possible to achieve intensity and richness associated with qualitative research when dealing with more than a handful of cases. As such, the paper has particular relevance to qualitative researchers with an interest in exploring innovative and productive mixed methods.

Keywords: qualitative and quantitative research, quantifi cation

INTRODUCTION

Mixed methods research has the potential to provide new insights into, and enhanced understanding of, phenomena being investigated. As an intellectual and practical synthesis of quali- tative and quantitative research, mixed meth- ods research can be a powerful means of gaining highly informative, exhaustive, balanced and use- ful research results (Johnson, Onwuegbuzie, & Turner, 2007). It can provide rich data, lead to new lines of thinking, and by intentionally engaging multiple perspectives and present- ing a greater diversity of views, mixed methods research can be inclusive, pluralistic and comple- mentary (Maxwell & Loomis, 2003). Further, as

argued by many in the mixed methods research movement, it offers a means by which one can act more ethically in research by mixing meth- ods in order to represent a plurality of interests, voices and perspectives (Greene & Caracelli, 1997). As a research approach, mixed methods is most strongly underpinned by the philosophical approach known as pragmatism which advocates a practical and outcome-oriented method of inquiry and a need-based approach to research methods and concept selection (Bazeley, 2003; Denscombe, 2008; Maxcy, 2003). That noted, there is growing debate around the view that epistemological and ontological issues associated with mixed methods need to be reconsidered in light of an appreciation

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While we do not wish to revisit ‘the para- digm wars’ or write at any length on a dichotomy between quantitative and qualitative research, we do wish to note that we do not accept the incom- patibility thesis, nor do we accept the assumption that qualitative research has secondary status in mixed methods inquiry. Additionally, following Fielding and Schreier (2001), we accepted that there are different levels of integrating qualita- tive and quantitative research methods, from the data gathering and data analysis through to the entire research process. In our view Fielding and Schreier (2001) offer a useful framework for thinking about mixed methods research in their categorization of two main approaches to mixed methods: basic approaches of blending of quali- tative and quantitative data analysis; and, more sophisticated approaches that integrate quantita- tive and qualitative dimensions.

This paper does not discuss of the relative importance of quantitative and qualitative compo- nents, or how they are sequenced in the context of mixed research methods (for such a discussion, see, for example: Morse, 2003; Onwuegbuzie, Slate, Leech, & Collins, 2009), rather, it focuses on the full integration of qualitative and quantitative approaches. Such integration can occur on a num- ber of levels and can allow for both hermeneutic and statistical analyses. At this point, it should also be noted that this paper is written for the qualitative researcher seeking to explore some innovative and stimulating ways of combining qualitative and quantitative research methods. We have developed this article as a straightforward and condensed over- view of some fully integrated research methods that transcend the quantitative– qualitative divide. The aim is to introduce the reader to complex and sophisticated mixed methods research designs and their application in business and management research. We strongly believe there is a need to draw greater attention to the merits and variety of fully intergraded research methods and provide a brief account of some key methods.

The discussion commences by outlining two approaches of integrating quantitative and

of the complexity and variability of qualitative and quantitative methods and reductive philosophical thinking (Bergman, 2008).

While there are many advocates of mixed meth- ods research, others such as Denzin and Lincoln (2005), and even more so Howe (2004), are rather critical of mixed methods approaches argu- ing that qualitative and quantitative paradigms cannot and should not be mixed. Differences in grounding philosophical knowledge assumptions are held up as key reasons for incompatibility between qualitative and quantitative research methods. This incompatibility thesis is partly based on claims that mixed methods designs are direct descendants of classical experimentalism, and that there is a presumed methodological hier- archy with quantitative methods at the top. Even the practice of doing mixed research has been seen by some authors (for example, Giddings, 2006, p. 195) as a cover for the continuing hegemony of positivism, or at least postpositivism.

According to Hesse-Biber (2010, p. 457) the practice of mixed methods research ‘has leaned towards a more positivistic methodological orien- tation’. In analysing more than 200 articles using mixed methods, Bryman (2006) noted the domi- nance of the quantitative approaches within the mixed method research designs. Similarly Molina- Azorin (2011) reported that in a sample of 130 mixed method articles in management research, and in around 80% of the mixed methods designs the quantitative elements dominated. It is, perhaps, not surprising then that some authors publishing in Qualitative Inquiry and Qualitative Research, such as Mason (2006) and Bryman (2006), as well as Creswell, Shope, Plano Crark, and O’Green (2006), called for a more prominent role for quali- tative research in mixed methods research. They have argued that qualitative research can valu- ably extend the logic of quantitative explanation and give voice to different perspectives. Further, this ‘qualitatively driven’ mixed method research design is seen to hold strong potential for enhanc- ing our capacity for social explanation and gener- alisation (Mason, 2006, p. 10).

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Some developments in qualitative research methods outlined below, such as Ragin’s (1987) QCA, and Heise’s (1991) and Griffi n’s (1993) ESA, further support these ‘quantifying’ tenden- cies. Such approaches can allow for qualitative analysis that is systematic, formal, rigorous and procedurally replicable, and, very importantly, it becomes possible to achieve a richness and inten- sity commonly associated with qualitative research while dealing with more than a handful of cases.

Figure 1 shows several approaches to the full integration of research methods and data analysis used in business and management research. The fi gure points the reader to scholars/authorities in a particular method and its applications.

Case-oriented quantifi cation The case-oriented quantifi cation method, devel- oped by Udo Kuckartz and associates from Humboldt University in Berlin (Kuckartz, 1995), and recently further clarifi ed in terms of its theoreti- cal background (Colins, Broekaert, Vandevelde, & van Hove, 2008), is appropriate for qualitative research dealing with a large number of individual cases and using semi-structured interviews. The basic idea is to integrate quantitative techniques within the analysis of the qualitative data. Generally, within the social sciences the case study approach refers to an intensive analysis of an issue being investigated (Yin, 2003) where many features of a few cases are thoroughly examined. Case-oriented quantifi cation allows one to achieve that intensity of analysis while examining a much larger number of cases. It combines qualitative and quantitative approaches during the evaluation of qualitative research data. The case-oriented method includes a specifi c mathematical procedure for analysing qualitative data and this can be used to classify the cases and construct a typology. The process starts with qualitative research where the goal is to unpack the subjective meaning of textual data and identify the relevant dimensions of whole cases. The dimen- sions developed from the data are then transformed into case-oriented variable and case-specifi c vari- able values. In the next stage of the data analysis,

qualitative methods of data analysis, Kuckartz’s (1995) case-oriented quantifi cation and hermeneu- tic-classifi catory content analysis (Roller, Mathes, & Eckert, 1995). Initially, these two methods started from the integration of quantitative techniques into the analysis of qualitative data. However some authors like Bos and Tarnai (1999), Kern, Sachs, and Rühli (2007), identify these as the methods which fully combine qualitative and quantitative approaches to data gathering and analysis, and the logic of inquiry, and integration of the data. This perspective is also strongly articulated by several authors from German and Dutch speaking areas who publish in FQS: Sozialforschung (see Fielding & Schreier, 2001). The paper then moves on to pres- ent some fully integrated research methods where ‘qualitative and quantitative aspects are intermin- gled at almost every point’ (Ellingsen, Størksen, & Stephens, 2010, p. 406). These are: qualitative comparative analysis (QCA), even structure analysis (ESA); and, Q methodology. We describe the main features of these methods and their application to management and business research.

QUALITATIVELY DRIVEN MIXED METHODS RESEARCH There is an increasing tendency among research- ers to include a much larger volume of unstruc- tured data than has traditionally been used in qualitative analysis (Bazeley, 2004, p. 146). There is also a growing trend around what can be seen as the quantifi cation of qualitative research (Sale, Lohfeld, & Brazil, 2002). The ‘quantitizing’ of data (Johnson & Christensen, 2004; Tashakkori & Teddlie, 2003) frequently involves converting qualitative data into numerical codes that can be counted and analysed statistically. Others, such as Mason (2006), have noted a ‘mixing [of ] methods in a qualitatively driven way’ where hermeneutic methods that aim at understanding the meaning of texts are combined with techniques for the reduc- tion and standardisation of information contained in large amounts of textual data through qualita- tive coding being converted into quantitative vari- ables which can be further analysed statistically.

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p. 167) consider hermeneutic-classifi catory con- tent analysis to be a synthetic method combining principles of quantitative and qualitative research. Being embedded within a hermeneutic tradi- tion (Schwandt, 2001), the focus is on detailed study of the text and connections among its parts, and requires detailed reading or examination of text so that the researcher may discover meaning embedded within the text. While ‘getting inside’ the text, the researcher attempts to fi rst under- stand it as a whole and only then seeks to develop a deep understanding of how its parts relate to the whole. In using this method, a large amount of information that is embedded within texts is reduced through a process of formal coding and the creation of a conceptual network of categories. Relevant information contained in text segments is transformed into a quantitative data matrix which is then statistically analysed to determine the frequency distribution of certain codes or code patterns. As such, this approach allows for the analysis of large amount of text without over- looking their inherent complexity.

formalised methods of comparison, such as cluster analysis and correspondence analysis, are applied to generate an empirically-based typology of the phe- nomenon explored.

Hermeneutic-classifi catory content analysis Recent developments in mixed forms of content analysis that allow for quantifi cation and statistical analysis of qualitative data have been summarised by Bos and Tarnai (1999). However, given that most of the authors working in this area continue to publish only in German, most of these empiri- cal content analyses combining quantitative and qualitative approaches are unknown to the wider (non-German speaking) academic audience. Roller et al. (1995) from the Free University in Berlin are an exception and their hermeneutic- classifi catory content analysis is more widely known and has been applied in business research projects such as Kern et al.’s (2007) comparative case study of corporate responsibility in the Swiss telecommunications industry. Roller et al. (1995,

FIGURE 1: SOME APPROACHES TO FULL INTEGRATION OF DATA ANALYSIS AND RESEARCH METHODS USED IN BUSINESS AND MANAGEMENT RESEARCH

CASE-ORIENTED QUANTIFICATION Scholar: Kuckartz (1995) Applications: exploring individuals’ attitudes- non- profit sector, IT management

HERMENEUTIC- CLASSIFICATORY ANALYSIS Scholar: Roller et al. (1995) Applications: corporate responsibility

Integrating quantitative and qualitative methods of data analysis

Causal explanation Quantification of subjective perceptions

QUALITATIVE COMPARATIVE ANALYSIS (QCA)– BOOLEAN METHOD; FUZZY SET LOGIC Scholar: Ragin (1987; 2001) Applications: labour, forestry, resources public and organisational management; entrepreneurship; international business studies

EVENT-STRUCTURE ANALYSIS (ESA) Scholar: Heise (1991) Applications: organisational formation; management of non-profit sector ; entrepreneurial growth

Q-METHOD Scholar: Brown (1980) Applications: landscape and tourism research; management science; rural management

A qualitative model of quantitative research’/formal analysis of qualitative data

A qualitative model of complex systems - ‘qualiquantology’

FYZZY COGNITIVE MAPPING Scholar: Özesmi and Özesmi (2004) Applications: environmental management; farm management; organisational behaviour; marketing; business; tourism management

FOCUS

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applying QCA, each case remains contextualised as a whole – a meaningful, interpretable and spe- cifi c confi guration of causal conditions/attributes and outcome variables (Krivokapic-Skoko, 2003).

Qualitative comparative analysis appears to be of substantial utility in research sites with contex- tual and multiple causal relations. The method assumes that causal variables are effective only when operating in conjunction with each other, consequently the impact of each causal variable should be discussed only in a particular context. QCA also accepts that more than one confi gu- ration of causal variables may generate the same outcome. Accordingly, QCA locates different paths to the emergence of an outcome and there- fore enables the analyst to classify the outcomes based on different confi gurations of the causal variables. Apart from deriving the patterns of causal factors leading towards the emergence of outcomes, QCA also identifi es the causal condi- tions related to the ‘negative outcomes’, that is, to the absence of the phenomena of interest.

Being based on Boolean algebra, the algebra of logic and sets, the QCA method systematises and transforms empirical evidence into algebraic forms suitable for a data reduction process and represents the attributes of the cases into presence–absence dichotomies. These dichotomies are then included in a truth table, a raw data matrix which comprises causal conditions and outcomes across the cases, thereby providing a tool for data reduction while maintaining the integrity of each case. Each row in a truth table represents either a logically possible, or an empirically observed, confi guration of attri- butes (i.e., causal and outcome conditions). The truth table is completed when all the cases and codes on the causal and outcome conditions are displayed using binary mathematical forms. This matrix of binary data is then subjected to a pro- cedure of Boolean minimisation. The procedure involves comparing groups of cases based on the presence/absence of the outcome conditions and the presence/absence of the selected causal condi- tions. These logical combinations, as represented in Boolean primitive equations, are compared

Qualitative comparative analysis: Boolean algebra and fuzzy-sets Qualitative comparative analysis, and fuzzy-sets as developed by Ragin (1987, 2000), build on case study approaches to provide a basis for rig- orous causal analysis which identifi es the neces- sary and suffi cient conditions for an outcome to occur. Ragin (1987) proposed a relatively new method for the formalisation and extension of the comparative case-study approach and con- ceptualised it as ‘a middle road’ between qualita- tive and quantitative research (Ragin, 1987). As a ‘synthetic strategy’, this method extends quali- tative and quantitative analyses by providing a more complex approach than most quantitative research methods, and it is more ‘systematic’ than most qualitative research methods. QCA also brings a rigour and quantitative methods to qualitative ones. Additionally, if offers some of the causal complexity and in-depth analysis of quali- tative methods to quantitative research methods.

Qualitative comparative analysis is essentially case-oriented comparative research that provides a ‘systematic’ and holistic analysis of a moderate number of cases. Indeed, most applications have included between 15 and 70 cases (Krivokapic- Skoko, 2001; Marx, 2010). The method builds on the strengths of explanatory and interpretive research by primarily bringing the complexity and intensity of in-depth investigation to a moderate number of cases, while maintaining rigour, repli- cable procedures and the use of formal logic. In terms of technical procedure, QCA systematises and transforms empirical evidence into algebraic forms, and then uses Boolean algebra to gener- ate comparisons. Here the dialogue between theory and evidence is well structured. Starting from theoretical arguments that determine the minimum set of case attributes, QCA proceeds by simplifying the complexity of the evidence in a sys- tematic, stepwise manner. In using QCA, cases are transformed into unique combinations of selected causal conditions and associated outcomes, and these are then compared and interpreted holisti- cally focusing on their attributes. Therefore, in

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(2000) has recently incorporated ideas from fuzzy-set logic into QCA and the new method has become quite popular (Kvist, 2003; Pajunen, 2008). The fuzzy-sets allow for continuous cod- ing of variables according to the degree of their association with the qualitative categories of inter- est. With fuzzy-sets, the values of both indepen- dent and dependent variables are not restricted to the binary values of 0 and 1, but may instead be defi ned using membership scores ranging from ordinal up to continuous values. As such, fuzzy- sets allow the researcher to introduce greater vari- ety into the analysis.

Some of the fuzzy-set approaches to QCA have proven to be highly illuminating, such as compar- ison of international approaches to resource man- agement (Stokke, 2007) and comparative analysis of the national competitive advantages of Turkish and Greek economies using Porter’s well-known model of competitiveness (Özlem, 2004).

Event structure analysis Event structure analysis, or ‘a qualitative model of quantitative research’ as Heise (1991) referred to it, is a formal and replicable technique of qualita- tive data research that is used for analysing and interpreting events. ESA is a formal technique of narrative analysis and tracks the temporal order- ing and sequencing of actions in order to explain a singular event (Griffi n, 1993). This method is considered more rigorous than a case-study approach and focuses on the temporal order and sequencing of actions. It provides narrative expla- nation, goes inside singular events, and systemati- cally organises information about events so as to explain how something happens. The method is formal as it uses a set of logical rules to analyse cases. The formal rules produce results that can be replicated and generalised to other cases. The method is qualitative in the sense that it draws on some subjective criteria and the understanding of the researcher, and it seeks to preserve the context of circumstances in which events take place. ESA is considered appropriate for causal analysis with an emphasis on process and contingency, and it

with each other and are then logically simplifi ed (Ragin, 1987). The comparison concludes with a logically minimal Boolean expression as an output of the analysis. This provides logically minimal confi gurations, the most parsimonious descrip- tion of the combinations of causal conditions, that produce a given outcome.

Qualitative comparative analysis has become increasingly popular among social science researchers, and in the area of business and man- agement research it has been applied to topics including organisational management (Romme, 1995), labour management (Coverdill, Finlay, & Martin, 1994) public management (Kithenerm, Beynon, & Harrington, 2002), forestry man- agement (Hellström, 1998), and entrepreneur- ship (Fairweather & Krivokapic-Skoko, 1998; Krivokapic-Skoko, 2001). QCA was also used in policy analyses such as labour policy analysis (Biggert, 1997) and social policy analysis (Amenta & Poulsen, 1996). Romme (1995) developed a model of self-organising processes among top man- agement teams and then used QCA to capture the dynamic complexity of processes at this top level of management within an organisation. As QCA allows for consideration of both systematic and ideographic elements in a single analysis, Coverdill et al. (1994) applied it to the analysis of labour management in an effort to defi ne and compare different labour management strategies in a particu- lar industry. QCA was also used to analyse multiple case-study evidence of 22 land based industries in order to identify necessary and suffi cient condi- tions associated with success and failure of new land based industries in New Zealand (Fairweather & Krivokapic-Skoko, 1998). Finally, QCA has been applied to a study of ethnic entrepreneurship in New Zealand agriculture, more specifi cally the causes of the emergence of ethnic business networks in agricultural settings (Krivokapic-Skoko, 2001).

A common concern with the employment of QCA and Boolean algebra is that they require dichotomous variables, and they do not allow for fi ne-grained measures of the attributes in ques- tion. In order to overcome that limitation, Ragin

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as described by respondents recounting how their organisations closed. Through ESA mapping, Krivokapic-Skoko (2007) identifi ed key patterns and processes of co-operative development and their generative causal mechanisms. In this latter research, ESA helped pinpoint critical actions and steps in organisational developmental processes.

Q methodology Q methodology has been used by a number of qualitative researchers for eliciting, evaluating, and comparing human subjectivity. It has been conceptualised as a hybrid approach, an approach that Stenner and Stainton Rogers (2004) have labelled ‘qualiquantology’ to refl ect its qualitative and quantitative features. Originally developing within a positivist tradition, Q methodology is increasingly proving to be an innovative approach to qualitative analysis. Interestingly, articles on Q methodology have been published in both quantitative and qualitative oriented scholarly journals, and is described as being ‘neither entirely quantitative nor qualitative in nature, but a suc- cessful combination of the two differing styles of research’ (Ray and Montgomery, 2006, p. 3).

The Q method strengthens conceptual catego- rization through the quantifi cation of patterned subjectivities using Q-sorts. These Q-sorts are statements that are broadly representative of the discourse on the topic being researched and they enable participants to respond to issues based on their individual experience (Previte, Pini, & Mc-Kenzie, 2007). Individual responses captured by Q-sorts are then factor-analysed to identify pat- terns of subjective perspectives across individuals. Application of Q methodology can be found in psychology (Shemmings, 2006), landscape and tourism research (Fairweather & Swaffi eld, 2002), management science (Steelman & Maguire, 1999) and political science (Brown, 1980). Q method- ology was used very successfully in identifying farm management styles (Fairweather & Keating, 1994), and was recently advocated as having con- siderable potential and benefi t within rural research (Previte et al., 2007). The use of Q methodology

can be used to interpret cases or events holistically (Griffi n & Ragin, 1994). Therefore, ESA is con- sidered highly appropriate for developing a frame- work for the analysis of the formation processes and organisational changes in general.

Even structure analysis offers deterministic rather that probabilistic explanations and gener- ally expresses causal relations as complex conjec- tures of factors and conditions (Griffi n & Ragin, 1994). It focuses on a single culturally or histori- cally specifi c event and, more specifi cally, a nar- rative of the event. Here, narrative is understood as an analytic construct that is used to identify a group of events and incorporate them into a single story (Stevenson & Greenberg, 1998). Narratives have a specifi c beginning, a series of intervening actions, and an end point, which can be based upon a number of paths and intercon- nection between the actors. In effect, ESA is a for- mal technique of narrative analysis, and it tracks the temporal ordering and sequencing of actions in order to explain a singular event (Brown, 2000; Griffi n, 1993; Griffi n & Ragin, 1994).

While ESA was originally developed to study cultural routines (Corsaro & Heise, 1990), it has, for example, since been applied to a study of racial confl icts in the USA (Griffi n, 1993) and to labour strikes and the consequences of labour union campaigns (Brown, 2000). ESA is deemed to be very appropriate for analysing com- plex social processes and collaborative actions (Stevenson, Zinzow, & Sridharan, 2003) as well as examining the processes of organisational for- mation (Hager & Galaskiewicz, 2002a, 2002b). Morse (1998) used ESA to explain the temporal dynamics of an entrepreneurial fi rm. Hager and Galaskiewicz (2002a, 2002b) used ESA to anal- yse the closure of non-profi t organisations based on narratives provided by former board mem- bers and administrators. Their approach was to use the narratives to study closure as a process or sequences of events and identify how precipitat- ing events eventually lead to closure of non-profi t organisations. They analysed empirical evidence on key events within the broader web of events

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The research question, and the appropriateness of particular fully integrated mixed methods to the research question and objectives, are of primary importance when contemplating the use of mixed methods research. A researcher may consider using QCA for providing causal explanations about the emergence of an outcome, and this method appears to be of a substantial utility in research sites with contextual and multiple causal relations. QCA is also suitable for evaluating theoretical propositions, par- ticularly those derived from the composite models. Generally speaking, QCA is considered appropriate for studies where a great deal of information is accu- mulated relating to a moderate number of cases. In terms of comparative method, QCA identifi es simi- larities among the cases with the same outcome, and differences between cases conforming to different outcomes. If, however, the focus of research is a sin- gle, culturally or historically specifi c event, or more specifi cally a narrative of the event, ESA can provide very powerful casual explanations focusing on the temporal ordering and sequencing of actions. In contrast to QCA, ESA is more historical than com- parative in its approach to causal explanations, and it tends to equate a temporal order of actions with a causal explanation. Alternatively, Q method can be very helpful in exploring the subjective experience of participants, particularly if there is a range of percep- tions and experiences across the target population. Researchers interested in inherently such mixed methods can use a range of software applications for data analysis such as the winMAX software program designed to facilitate case- oriented quantifi cation, ETHNO for ESA, FS/QCA for QCA or PCQ soft- ware for Q methodology.

Critical assessment of the methods outlined in this paper has not been provided as the aim has been to provide a brief overview of methods that are sophisticated and complex. Such an assessment was beyond the scope of the discussion presented. Indeed, we actually believe that such a discus- sion of the advantages and limitations of research methods is best undertaken in the context of a specifi c empirically-based research design. Finally, we acknowledged that the list of hybrid methods

to investigate the customer relationship in the context of Dutch banking (de Graaf, 2001) is a rather interesting and innovative application as it focuses on discourse analysis. Another inspiring application has seen the use of photographs in Q methodology to study perceptions of the envi- ronment and tourists’ experiences in New Zealand (Fairweather & Swaffi eld, 2002).

Q methodology has been also combined with fuzzy cognitive mapping (Özesmi & Özesmi, 2004) in environmental and farm management studies (Fairweather, 2010). The use of fuzzy cog- nitive mapping allows for the development of a qualitative model of a system, something that is especially useful for modelling complex relation- ships between variables.

CONCLUDING COMMENTS Mixed methods research can be a highly useful and appropriate means of accessing and interpreting the social world and the problems and issues that con- front researchers. Mixed methods can allow for the generation of knowledge that it is rich and nuanced, as researchers can variously apply methods that may offer opportunities to achieve greater insight and understanding than would be the case pursu- ing solely qualitative or quantitative methods. The focus of this paper was on outlining key qualities of several mixed methods approaches and identify- ing their applications to business and management research. To date, students and researchers have gained their knowledge of these methods from mul- tiple text books or journal articles. This paper has sought to provide a straightforward, and we trust useful, introduction into a range of these methods for researchers contemplating business and man- agement research. As we have noted, the popular- ity of these integrated methods can be explained by the fact that they are designed to achieve both generalisation and in-depth analysis. They tend to blend the interpretive and the formal analytical approaches, and allow a researcher to gain some- thing ‘additional’ from the use of these methods that is over and above that which would result from using either qualitative or quantitative approaches.

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In C. Clogg, (Ed.), Sociological methodology (pp. 1–57). Cambridge, MA: Basil Blackwell.

Coverdill, J. E., Finlay, W., & Martin, J. K. (1994). Labour management in the southern textile industry: Comparing qualitative, quantitative, and qualitative comparative analyses. Sociological Methods & Research, 23(1), 54–85.

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outlined above is far from exhaustive, but as noted above, our goal was to speak to a number of methods which have been successfully utilized by business and management scholars.

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