A call for more mixed methods in sport management research

Andy Rudd a,*, R. Burke Johnson b,1

a Florida State University, Department of Sport Management, Recreation Management, & Physical Education, Tallahassee, FL 32306-4280, United Statesb University of South Alabama, College of Education, BSET, 3700 UCOM, Mobile, AL 36688, United States

Many social and behavioral science researchers have promoted the use of mixed methods to more effectively answerresearch questions (Brewer & Hunter, 1989; Johnson & Onwuegbuzie, 2004; Tashakkori & Teddlie, 1998, 2003). Such anapproach has generally been defined as the combining of at least one quantitative method and one qualitative method (e.g.,Hanson, Creswell, Plano Clark, Petska, & Creswell, 2005; Jick, 1979; Maxwell & Loomis, 2003). Combining quantitative andqualitative data in a single study can be beneficial in a variety of ways. For example, the researcher can triangulate whichinvolves combining quantitative and qualitative methods to produce a set of data that has complementary strengths andnonoverlapping weaknesses (Brewer & Hunter, 1989; Johnson & Onwuegbuzie, 2004; Johnson & Turner, 2003; Tashakkori &Teddlie, 1998).

This concept of combining approaches for complementary strengths and non overlapping weaknesses has been called thefundamental principle of mixed research (Johnson & Turner, 2003). The idea is to strategically select a mixture ofquantitative and qualitative approaches that will effectively cover the objective or set of objectives of a research study and todo it in way that eliminates overall study design weaknesses. According to Onwuegbuzie and Johnson (2006) thisfundamental principle can be adapted to multiple purposes including initiation (discovering contradictions), expansion(attaining a deeper and broader understanding), complementarity (examining overlapping parts of a phenomenon), anddevelopment (using results from one method to inform the use of a second method) (see also, Greene, Caracelli, & Graham,1989).

The potential benefits of using mixed methods approaches has stimulated its adoption in a variety of fields includingsociology, nursing, psychology, management, health sciences, evaluation, and education (Tashakorri and Teddlie, 2003). Yet,despite the popularity and strong advocacy for combining quantitative and qualitative approaches, few mixed methodsstudies can be found amid extant sport management research. As evidence, empirical articles were reviewed by the first

Sport Management Review 13 (2010) 14–24

A R T I C L E I N F O

Article history:

Received 18 September 2008

Received in revised form 1 April 2009

Accepted 18 June 2009

Keywords:

Mixed methods

Cause and effect

Causal explanation

Causal description

A B S T R A C T

Despite the popularity and strong advocacy for combining quantitative and qualitative

methods, few mixed methods approaches are found in the sport management research. As a

result, this article examines the frequency with which mixed methods research has been

used in recent sport management research, and demonstrates ways in which mixed methods

can help improve the validity of research findings in sport management related topics.

Because research in sport management often is concerned with causal questions, this article

provides mixed methods designs for improving causal inference. Examples are provided

from three areas of sport management research, including marketing, organizational

behavior, and finance. The designs that are provided are based on the mixed methods design

dimensions of time order and priority of quantitative and qualitative data.

� 2009 Sport Management Association of Australia and New Zealand. Published by

Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +1 850 645 6883.

E-mail addresses: rudd@coe.fsu.edu (A. Rudd), bjohnson@usouthal.edu (R.B. Johnson).1 Tel.: +1 251 380 2861.

Contents lists available at ScienceDirect

Sport Management Review

journal homepage: www.e lsev ier .com/ locate /smr

1441-3523/$ – see front matter � 2009 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved.

doi:10.1016/j.smr.2009.06.004

author for the presence of mixed methods approaches in three major sport management journals: Journal of SportManagement (JSM), International Journal of Sport Management (IJSM), and Sport Management Review (SMR) during theperiod of 2000–2007.2 The analysis identified the following number of mixed methods articles: JSM (10), IJSM (15), and SMR(2) (Rudd, 2007). Comparatively, Quarterman et al. (2006) reviewed 299 articles (conceptual and empirical) published in theJournal of Sport Management from 1987 to 2004. From their analysis, only 6 of the 299 articles (2%) employed a mixedmethods approach, 27 out 299 (9%) were qualitative, 165 of the 299 (55%) were quantitative, and 101 of the 299 (34%) wereconceptual. As well, Barber, Parkhouse, and Tedrick (2001) assayed 42 empirical articles published in the Journal of Sport

Management during the period of 1991–1995 and found the majority of articles to be survey research (73%). Other types ofmethods employed included content analysis (19%) and mixed methods (9%). Overall, these analyses suggest that when itcomes to empirical studies, the majority in sport management employ quantitative monomethods (a single method).

Additionally, although there are many different types of mixed methods designs (Creswell & Plano Clark, 2007; Johnson &Onwuegbuzie, 2004; Teddlie & Tashakkori, 2006), many of the mixed methods articles uncovered in our analysis involvedlimited or weak use of mixed methods. According to Johnson and Christensen (2008), research methodology can be viewedon a continuum ranging from monomethods (far left of the continuum) to fully mixed studies (far right of the continuum).The majority of the mixed methods studies observed in sport management journals would arguably fall to the left on theresearch continuum, representing relatively weak use of combining quantitative and qualitative methods. For example, 10 ofthe 15 mixed methods articles found in IJSM simply involved a questionnaire with open and closed questions. Otherexamples of weak to moderate mixed methods included eight articles involving content analysis (within JSM and IJSM) andfive articles across JSM, IJSM, and SMR that used mixed methods for instrument development (i.e., using qualitative datafrom interviews or focus groups to develop items for a questionnaire).

In sum, the results from our analysis as well others (Barber et al., 2001; Quatermen et al., 1999) suggest that few employmixed methods approaches in sport management and when they do, they are not strongly mixed designs (i.e., a substantialamount of both quantitative and qualitative strategies and data in single studies). The purpose of this article, then, is todemonstrate additional, more fully mixed methods approaches that can be useful to the sport management researcher. Inparticular, given that much research in sport management is concerned with causal questions, our article focuses onapplying mixed methods to studies dealing with causation. Because we are focusing on the question of causation, the type ofmixed methods research we are discussing in this article generally involved complementing quantitative research withqualitative approaches. The reason is that quantitative epistemologies are explicitly interested in identifying nomological(causal) relationships in their literatures.

For example, in the area of sport marketing, researchers are often interested in the factors that affect purchasing behaviorof sport fans (e.g., Kwon & Armstrong, 2002; Kwon, Trail, & James, 2007; Trail, Fink, & Anderson, 2003). Or, concerningorganizational behavior, studies have examined variables that influence organizational commitment or turnover intention(e.g., Cunningham & Sagas, 2006; Kent & Chelladurai, 2001; Whisenant, 2005). As well, in the domain of finance andeconomics, researchers commonly ask causal questions related to economic impact. For example, a professional sport team’seffect on public goods (Johnson, Mondello, & Whitehead, 2007) or a star player’s effect on fan attendance (DeSchriver, 2007).To probe cause and effect relationships many of these studies employ correlational or causal modeling techniques ratherthan experimental or quasi-experimental designs. This is presumably because it is difficult to control or manipulatenaturally occurring causal variables such as team identity, the location of a professional sport team, or the procurement ofstar players.3

Regardless of the method one uses to ascertain cause and effect, an important consideration is the distinction betweencausal description and causal explanation. The former simply allows one to suggest that there is a causal relationship orcovariance between the independent and dependent variable. The latter involves an explanation of the causal mechanismsresponsible for a particular causal relationship as well as the conditions under which the relationship holds (Cook, 2002;Shadish, Cook, & Campbell, 2002) (see a more extensive explanation later in this article). Obtaining this explanatoryknowledge allows researchers to make more accurate conclusions (increases internal and external validity) and providesclearer applicability and utility to practitioners.

Although causal or structural equation models (SEM) take into consideration mediating and moderating variables thatmay help explain a causal relationship, these models may fail to capture additional important mediating or moderatingfactors. This problem is known as specification error which refers to misidentifying or omitting important variables that are

2 It is acknowledged that mixed method approaches have been employed by researchers since at least the latter half of the 20th century (Brewer &

Hunter, 1989; Greene et al., 1989). Therefore, it is possible that the journals we reviewed carry articles employing mixed-method approaches prior to the

21st century. However, we felt a review of recent empirical articles over the last 7 years would provide a reasonable estimation of how often mixed methods

are used in sport management research.3 Some of the studies identified employed correlational or statistical modeling techniques and use language such as ‘‘relationship’’ rather than ‘‘effect.’’

We argue that many of these studies are essentially interested in a cause and effect relationship rather than a correlation. For example, Kent and Chelladurai

(2001) studied the ‘‘correlation’’ between transformational leadership and organizational commitment. A personal communication with Kent revealed that

he and his colleague were interested in a cause and effect relationship but refrained from such language given the lack of an experimental design and the

ability to control or manipulate the independent variables (A. Kent, personal communication, November 16, 2007). We surmise that many other studies

may operate under a similar rationale, and in fact, some research methods textbooks sometimes encourage writers to do so. We recommend that authors

use language appropriate to the purpose of the study while simultaneously admitting and discussing the weaknesses of the research design.

A. Rudd, R.B. Johnson / Sport Management Review 13 (2010) 14–24 15

involved in a causal relationship (Shadish et al., 2002). Specification error also occurs when the researcher incorrectlyidentifies the direction of a causal relationship or mistakes correlation for causation (Shadish et al.).

As an example of specification error, Kwon et al. (2007) examined the mediating effect of perceived value in therelationship between team identification and intent to purchase collegiate team-licensed apparel. In their discussion theyacknowledged that they were surprised not to find a direct significant relationship between team identification andpurchase intention. They surmised that the failed relationship may have been related to including the price of the t-shirtwhereas past studies had not. The researchers theorized that perhaps price sensitivity had a moderating effect on purchaseintention. Kwon et al.’s study is a prime example of how the addition of qualitative data may strengthen causal explanation.Specifically, qualitative methods including focus groups, one-on-one interviews, and field observations could have beenused to expand their understanding of various unanticipated moderating or mediating factors. This article will thereforeprovide specific strategies for adding qualitative data to quantitative data to increase causal explanation in cause and effectrelated studies. However, it is important to note that we do not intend to suggest that the addition of qualitative data canovercome all of the weaknesses involved in making causal inferences with correlational techniques like SEM. Rather, thepoint is to show how the addition of qualitative data can expand and improve one’s understanding of causal relationships.Additionally, we advocate the use of mixed method approaches for not only correlational techniques but also experimentaldesigns (see Cook, 2002; Shadish et al., 2002).

Before describing the mixed methods approaches, it is important to familiarize the reader with several key foundationalcomponents. First, we introduce the historical debate between quantitative and qualitative researchers and how mixedmethods evolved into a legitimate research paradigm. Second, we present some of the basic characteristics of quantitativeand qualitative research and how they can be used in a complementary fashion. Third, we show the reader how our mixedmethods strategies fit within a larger typology of mixed methods designs. Doing so will hopefully stimulate more thinkingabout the use of mixed methods for studies not only dealing with causation but for other research objectives as well (e.g.,prediction, description, and exploration). Fourth, we provide a brief overview of mixed methods data analysis strategies thatwill aid in ascertaining causation. We then present our mixed methods approaches for causation along with specificillustrations that are based on real life examples of sport management research. Examples come from three major areas ofsport management which include marketing, organizational behavior, and finance. The article is concluded with a discussionand future directions.

1. The paradigm wars and the emergence of mixed methods research

Throughout the latter part of 20th century, social and behavioral scientists engaged in a fervent debate over thesupremacy of quantitative versus qualitative research (e.g., House, 1994; Johnson & Onwuegbuzie, 2004; Tashakkori &Teddlie, 1998). Each side argued from two distinct paradigmatic perspectives (i.e., a researcher’s world view of how best toobtain knowledge) (Tashakkori & Teddlie, 1998). Quantitative purists, held to a positivist paradigm which makes a variety ofassumptions including the following: there is a single objective reality, cause and effect relationships can be known(especially when a randomized experiment is conducted), time and context free generalizations are possible and desired, theobserver and the observed are sufficiently independent for objectivity to be approximated in a research study, and the focusof research should be on the empirical testing of hypotheses and theories (House, 1994; Johnson & Christensen, 2008;Tashakkori & Teddlie, 1998). Alternatively, qualitative purists, espouse a constructivist (or naturalistic/interpretivist)paradigm with the following assumptions: there are multiple realities, cause and effect relationships are difficult to discernand generally not of interest, the observer and the observed are inseparable, context free generalizations are neither possiblenor desired, research is value-laden, and when there is interest in theory the focus is on the inductive generation of theoryrather than the testing of previously specified theories (House, 1994; Johnson & Christensen, 2008; Tashakkori & Teddlie,1998).

These divergent paradigms caused some researchers to eschew the combining of quantitative and qualitative methods ina single study. To the methodological purist, combining quantitative and qualitative methods means illogically conflatingtwo wholly distinct paradigms for obtaining knowledge and truth (Guba & Lincoln, 1988; Smith & Heshusius, 1986). Thisbelief is known as the ‘‘incompatibility thesis’’ (Howe, 1988; Johnson & Onwuegbuzie, 2004; Tashakkori & Teddlie, 1998).

Despite some of the arguable differences between quantitative and qualitative research, the emergence of twophilosophical schools of thought have helped overcome the incompatibility thesis. The first is the postpositvist paradigmwhich has generally replaced the ‘‘positivist’’ thinking of most quantitative researchers (e.g., Garrison, 1986; Johnson &Onwuegbuzie, 2004; Phillips & Burbules, 2000; Tashakkori & Teddlie, 1998; Trochim, 2007).4 Postpositivism takes aquantitative approach but rejects positivist principles such as the notion that there is a single external reality that can beinfallibly known (Reichardt & Rallis, 1994; Trochim, 2007). Instead, postpositivsts strive for objectivity but believe thatobservations are fallible, that research is both theory and value-laden, and that a study’s results can be partially explained bymultiple theories (Reichardt & Rallis, 1994; Tashakkori & Teddlie, 1998; Trochim, 2007). Postpositivists also believe thatsubjective reality is, in part, individually constructed (via Piagetian schemas), which is counter to the naı̈ve realist’s belief in a

4 Virtually no philosophers of social science or any quantitative researchers currently regard themselves as ‘‘positivists.’’ We therefore use the more

accurate term ‘‘postpositivists’’ that recognizes that most quantitative researchers no longer fit the caricatures often associated with the label positivism.

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single, directly visible external reality (Reichardt & Rallis, 1994; Tashakkori & Teddlie, 1998; Trochim, 2007). These tenetshave some similarities to the constructivist views of qualitative researchers (e.g., value and theory ladenness of inquiry,multiple types of realities, fallibility of knowledge, and the use of multiple theories) which consequently makes quantitativeand qualitative research more compatible (Reichardt & Rallis, 1994; Tashakkori & Teddlie, 1998). According to mixedmethods research, quantitative and qualitative research should thus not be considered incompatible but rather,complementary and strength forming.

The second philosophy is called pragmatism which is popular among many mixed methods researchers and hassignificantly influenced the acceptance and increasing use of combining quantitative and qualitative approaches. One of themajor tenets of pragmatism is the notion of ‘‘what works,’’ which is premised on the idea that researchers should makemethodological choices based on research questions rather than world views (Howe, 1988; Tashakkori & Teddlie, 1998).Thus, adopting a pragmatic philosophy which involves dropping dogmatic adherence to a single paradigm and insteadfocusing on research questions has stimulated many researchers to use whatever methodology will best answer theirresearch questions including the combining of quantitative and qualitative data.

Additionally, Patton (1988), from the field of evaluation, and Johnson and Onwuegbuzie (2004), from education, alsopropose a pragmatic philosophy but from a slightly different perspective. Rather than completely removing the paradigmsfrom consideration, Patton suggests it is possible to shift back and forth between paradigms depending on the situation.Attempting to fuse this position with pragmatism, Teddlie and Johnson (in press) call this back-and-forth movement,dialectical pragmatism. Using what Johnson and Onwuegbuzie (2004) (and also Johnson, Onwuegbuzie, & Turner, 2007) callthe contingency theory of research, some research questions/situations may call for a qualitative, constructivist approachwhile in other cases a quantitative, postpositivist methodology may be more appropriate; and many questions call for mixedmethods research. In essence, authors such as these argue for flexible and responsive thinking among applied researchers.Doing so opens up the door for more creative research methods, particularly in the form of combining quantitative andqualitative methods in a single study (c.f. Hanson et al., 2005; Maxwell & Loomis, 2003).

In sum, many social and behavioral science researchers no longer take an either-or-approach; that is, that one cannot be aquantitative and qualitative researcher at the same time. Instead, it is now commonly accepted that combining quantitativeand qualitative research is a beneficial practice.

2. Characteristics and complementary strengths of quantitative and qualitative research

Appreciating the utility of mixed methods approaches requires the reader to understand basic methodologicaldistinctions between quantitative and qualitative research and how they can be complementary. Much, but certainly not all,quantitative research is characterized by deductive hypothesis testing, the controlling or manipulating of variables, andgeneralizing from one’s data (in the form of numbers).5 Popular research objectives include explanation (cause and effect),prediction, or description. Methodologically, quantitative researchers advocate the use of standardized instruments, largerandom samples for generalization, statistical analysis, and the use of various types of experimental or quasi-experimentaldesigns (and sometimes nonexperimental designs) (Guba & Lincoln, 1988; Johnson & Christensen, 2008; Patton, 1987).

Qualitative research is characterized by in depth naturalistic description and exploration and inductive theoryconstruction (generating theory from the data). The main objectives are exploration, discovery, description, and theorydevelopment. Methodologically, qualitative researchers typically conduct in depth interviews or do extended fieldobservations (words and pictures are collected rather than numbers). Focus groups, open-ended questionnaires, anddocument analysis are also sometimes used (Guba & Lincoln, 1988; Johnson & Christensen, 2008; Patton, 1987).

Although there are distinct methodological differences between quantitative and qualitative research, these differencescan be combined advantageously. For example, qualitative data (e.g., interviews or focus groups) could be added to anexperiment to attain a deeper understanding of the cause and effect relationship (expansion). Or, in a quantitative surveydesign, the researcher could add a qualitative component (interviews or open-ended questions on a questionnaire) tocorroborate or expand the findings from the quantitative data (triangulation). Alternatively, following a qualitativeexploration one could develop a questionnaire (sequential) to broaden understanding of emerging patterns and themes(expansion) to seek contradictions in the differing forms of data (initiation), to test a grounded theory that was qualitativelygenerated (also sequential).

3. Mixed methods typologies

Researchers from a variety of disciplines have worked to advance the application of mixed methods. One of theseadvancements has been in the development of mixed methods design typologies (Creswell & Plano Clark, 2007; Greene et al.,1989; Hanson et al., 2005; Johnson & Onwuegbuzie, 2004; Maxwell & Loomis, 2003; Morgan, 1998; Morse, 2003; Tashakkori

5 Distinctions between qualitative and quantitative research should be viewed critically because there is a great deal of intra-paradigmatic variation.

Nonetheless, our focus in this section is on inter-paradigmatic distinctions because mixed methods research attempts to mix quantitative and qualitative

data and/or approaches. We do not imply that all quantitative or all qualitative research follows any single research or philosophical style. We also do not

imply that all mixed methods researchers are alike (see Johnson, Onwuegbuzie, et al., 2007).

A. Rudd, R.B. Johnson / Sport Management Review 13 (2010) 14–24 17

& Teddlie, 1998; Teddlie & Tashakkori, 2006). A mixed methods typology provides a useful framework for thinking abouthow to design mixed methods studies for a variety of purposes. Notably, many of these proposed typologies/designs containa great deal of overlapping similarities. As such, we have chosen to adopt Johnson and Onwuegbuzie’s mixed methodstypology on the basis of its understandability and because it generally represents the underlying concepts of various mixedmethods typologies.

Johnson and Onwuegbuzie’s (2004) typology is premised on two key dimensions: time order and status of thequantitative and qualitative study components The first dimension, time order, refers to when the quantitative andqualitative phases are carried out in a study. The researcher may collect data sequentially, (i.e., quantitative and qualitativedata are collected in phases) or the quantitative and qualitative components may be conducted concurrently (i.e., atapproximately the same time). The second dimension, status or priority (i.e., equal status versus dominant status) refers tothe emphasis on the research approaches. Depending on the study, one might believe that the quantitative component ismore important and thus place more emphasis and effort on the quantitative methods, while in another study the qualitativecomponent may be viewed with more importance.

To explain further, priority or status is used in two somewhat different ways in the literature. First, priority can refer to theamount of time and effort devoted to the qualitative and quantitative approaches in answering the research questions. Inpractice, this refers to whether the researcher spent more time collecting qualitative or quantitative data. Second, priority isused more at the epistemological level to refer to whether qualitative or quantitative paradigmatic assumptions aredominant. Here, dominant status implies that either the full qualitative or the full quantitative paradigmatic approach wasused while the other was deemphasized and equal status would mean that both paradigms were fully used. Using twodifferent epistemologies can be difficult; however, one can get around this problem by extensive training in both paradigmsand applying strategies and techniques for alternating between paradigms. The dialectical method (Greene, 2007) hasspecifically been developed for mixed methods research as a way of ‘‘listening’’ to both qualitative and quantitativeperspectives. As the name suggests, it involves moving back and forth between qualitative and quantitative paradigmperspectives, carefully listening, and generating a complementary synthesis of perspectives. If a researcher is not able to shiftepistemologies in this way, we recommend the use of a research team composed of a qualitative and a quantitativeresearcher. The key for equal-status mixed methods research is that both perspectives are equated in power and emphasisand that both perspectives are represented in the final report.

Given these two dimensions, Johnson and Onwuegbuzie (2004) offer nine different designs. To depict these designs thefollowing notation is used: qual refers to qualitative research, quan refers to quantitative research, uppercase letters (QUALor QUAN) represent dominant status, lowercase letters (qual or quan) represent subordinate status, arrows representsequential staging of the approaches, and a plus sign (+) represents concurrent conduct of the qualitative and quantitativeapproaches. Using this notation the following designs are obtained:

� Equal-status concurrent designs (i.e., QUAL + QUAN)� Equal-status sequential designs (i.e., QUAL!QUAN and QUAN!QUAL)� Dominant-status concurrent designs (i.e., QUAL + quan and QUAN + qual)� Dominant-status sequential designs (i.e., QUAL! quan, qual!QUAN, QUAN! qual, and quan!QUAL)

These nine designs by no means capture all of the mixed methods possibilities because design typologies can beconstructed on several additional dimensions. A few additional dimensions that have been offered for design typologydevelopment include a research continuum dimension (monomethods to fully mixed) (Johnson and Onwuegbuzie, 2008),the transformative dimension (e.g., the use of different advocacy theories such as critical or emancipatory theory) (Hansenet al., 2005), the purpose or rationale of the research study (Creswell & Plano Clark, 2007) and the use of a theoreticalperspective in the study (Creswell & Plano Clark, 2007). For the interested reader, Teddlie and Tashhakkori (2006) haveoffered a more complex but dynamic design typology based on the dimensions of number of methodological approaches,number of strands or phases in the study, type of implementation process, and stage of integration. The main point here is forthe reader to see the variety of ways in which quantitative and qualitative approaches can be combined as one constructs thedesign that is most appropriate for his or her needs.

4. Data analysis

According to Caracelli and Greene (1993), data from mixed methods research can be analyzed and interpreted separatelyor in an integrated fashion. Specific to the latter, one might for example employ the use of data transformation whichinvolves the conversion of one type of data into the other to be used in the overall analysis (e.g., convert the qualitative datainto quantitative variables which can then be used with other quantitative data in a regression analysis). Or, one couldidentify ‘‘extreme cases’’ from quantitative data that are investigated in depth with qualitative interview data, i.e., extremecase analysis (see Caracelli & Greene, 1993 for other integrative approaches). On the other hand, a questionnaire(quantitative data) might be used to increase breadth and expand one’s understanding of the qualitative findings. Or,following the measurement of a set of predetermined variables, interviews could be conducted to attain more depth anddetail and to discuss process variables. In these cases, the quantitative and qualitative data are analyzed separately and thenintegrated at the level of interpretation.

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Tashakkori and Teddlie (1998) proposed a similar analytical framework also acknowledging that data from mixedmethods can be analyzed and interpreted independently or in a more integrated fashion. However, their framework issomewhat different in that they categorize their mixed analyses into two major groupings referred to as concurrent versussequential. Concurrent mixed analyses involve either separate (parallel) analyses on each data type or the use of datatransformation techniques. Sequential mixed analyses are executed in a particular order and are ultimately integrated at thelevel of analysis. Arguably, many of the analytical approaches identified by Tashakkori and Teddlie are identical to the onesproposed by Caracelli and Greene (1993) with different labeling. They do however, offer a couple of sequential approachesthat are different than that of Caracelli and Greene and that are germane to our interest in causal studies. One approach is asequential QUAL!QUAN analysis in which a theoretical order of causal relationships is developed first from qualitative dataand then confirmed through quantitative analyses such as SEM or path analysis. Alternatively, they suggest the same conceptcan be applied through the opposite order, i.e., beginning with QUAN SEM analysis and confirming through a QUAL analysis(see Tashakkori & Teddlie for additional analytical approaches that are slightly different from that of Caracelli & Greene).

5. Cause and effect and mixed methods research

Considering a major objective of this article is to provide mixed methods approaches for causal studies, it is important toexplain the notion of cause and effect as well as two types of causal evidence: causal description and causal explanation.Shadish et al. (2002) noted that people easily intuit a variety of cause and effect relationships in their everyday lives. Forexample, one might say that poor study habits were the ‘‘cause’’ of a student’s low test grade. Or, one might declare that theirfavorite football team’s losing season was ‘‘caused’’ by a number of injuries to marquee players. In each case, an individualhas inferred a cause and effect relationship which according to Johnson and Christensen (2008) can be thought of as thefollowing: ‘‘A cause and effect relationship between an independent and dependent variable is present when changes in theindependent variable tend to cause changes in the dependent variable’’ (p. 39). These methodologists further remind us thatto make a credible causal claim, one must demonstrate evidence of (a) relationship between the causal and outcomevariables, (b) proper time ordering of these variables (i.e., the cause must occur before the effect), and (c) elimination of allplausible alternative or rival explanations for the observed relationship (p. 362). Importantly, qualitative data cansupplement quantitative data in all three of these domains.

Although the notion of cause and effect might seem rather straightforward, Shadish et al. (2002) and others (Cook &Campbell, 1979; House, 1991; Russo & Williamson, 2007) have pointed out that cause and effect relationships in actualitycan be very complex. This is largely due to what can be considered the ‘‘cause’’ of a particular effect. Shadish et al. illustratedwith an example of a lighted match as a potential cause of a forest fire. A lighted match has the capability of starting a forestfire only if other important conditions are satisfied. The match must stay hot enough, leaves in the forest must be dry, asufficient amount of oxygen is needed for combustion to occur, and the weather conditions must be suitable. Additionally,there are many other ways to start a forest fire including lightening, an unattended campfire, a burning cigarette, and soforth. Therefore, there can be a variety of required conditions for a particular effect to occur even when the independentvariable is known generally to produce changes in the dependent variable.

Cause and effect relationships are also complex because a cause can be thought of as a molar (consisting as a whole ratherthan in parts) package consisting of various molecular components (Cook & Campbell, 1979; Shadish et al., 2002). Forexample, sport participation as a medium for character development can be thought of as a molar cause consisting of variousmolecular components in the form of coaches’ behavior, the features of the sport played, and characteristics and actions ofteammates and opponents. Concurrently, the outcomes or effects from the cause can be considered as a molar package or inindividual molecular components. Rudd (2005) for example argued that character consists of two key dimensions (moral andsocial character). At the molar level one would examine overall character; at the molecular level, one would examinedifferent dimensions of character. Cause and effect relationships, then, can be examined as molar wholes or in theirmolecular parts.

The distinction between molar and molecular causes and effects parallels the concepts of causal description and causalexplanation respectively. Causal description is concerned with cause and effect on a molar level whereas causal explanationseeks to understand the underlying molecular causes and effects relationships (Shadish et al., 2002). Cook and Campbell(1979) also argue through the concept of ‘‘activity theory’’ that the strongest evidence of cause and effect is obtained whenthe independent variable is actively manipulated by the researcher. For example, to demonstrate causal description, arandomized experiment may be conducted in which the treatment group outperforms the control group. The researcherthen has evidence of a causal relationship on a descriptive or moral level (i.e., the treatment package produced changes).Here the researcher is able to ‘‘describe’’ the consequences of purposely varying or withholding the treatment (independentvariable). However, results from a basic experiment do not clarify the underlying mechanisms or molecular parts that areresponsible for the causal relationship; that is, evidence of explanatory causation (Shadish et al., 2002) or what Russo andWilliamson (2007) refer to as mechanistic evidence (see also, Maxwell, 2004b; Salmon, 1998) is lacking.

The difference between causal description and causal explanation can be further understood by comparing twoassociated philosophies: the ‘‘standard view’’ versus scientific realism. A key aspect of the standard view entails causality as amatter of ‘‘regularities’’ in one’s data (House, 1991; Manicas & Secord, 1983; Maxwell, 2004a). In other words, when x then y,when no x then no y; this approach comes from the perspective that causal laws can be established in relationship to theobserved regularities in one’s data. However, such a view assumes that causation can be examined within a closed-system or

A. Rudd, R.B. Johnson / Sport Management Review 13 (2010) 14–24 19

laboratory like setting that is devoid of influences from extraneous variables. Additionally, as Maxwell (2004a), states, ‘‘Thisview treats the actual process of causality as unobservable, a ‘black box,’ and focuses on discovering whether there is asystematic relationship between inputs and outputs’’ (p. 4). The discovery of a relationship between inputs and outputs isvery much inline with notion of causal description.

On the other hand, a philosophy of science known as scientific realism (House, 1991) is more congruent with theconcept of causal explanation. This is because scientific realism posits that reality is more than what we merely see, ‘‘butalso of the underlying causal entities that are not always directly discernible’’ (House, 1991, p. 4). Therefore, according toa scientific realist, causation is more than just observed regularities; causation involves underlying structural causalmechanisms that produce observed effects (House, 1991; Manicas & Secord, 1983). This also means that scientific realistsdo not believe causal relationships can be fully known because we do not operate in a closed system. Causation ispresumed to take place within a complex, multilayered world (Manicas & Secord, 1983). Thus, the context may have asignificant effect on the nature of the causal process that is responsible for producing a given effect (House, 1991;Maxwell, 2004a, 2004b).

6. Examples of mixed methods approaches for studies dealing with causation in sports management

In this section we provide several examples of mixed methods approaches that might be useful for sport managementstudies concerned with causation. These approaches involve a consideration of both causal description and causalexplanation and operate under the notion that quantitative approaches are well suited for studying causal description whilequalitative approaches lend themselves to causal explanation (see Cook, 2002; Maxwell, 2004b; Shadish et al., 2002). Theproposed mixed methods designs are derived from multiple sources (Hanson et al., 2005; Johnson & Onwuegbuzie, 2004;Morse, 1991; Tashakkori & Teddlie, 1998). Considering our interest in causal explanation, the proposed methods are,perhaps, most appropriate for the purpose of expansion – attaining a deeper and broader understanding of a givenphenomenon.

To facilitate better understanding we have included examples of real life sport management research that originally usedmonomethod quantitative designs in order to illustrate the application of mixed methods approaches. Examples of researchwere selected in the areas of organizational behavior, marketing, and finance given that these are areas in which researchersseek an understanding of causal relationships. However, there could certainly be other areas in sport management thatinvolve studies concerned with causation. Further, the methods proposed here by no means exhaust the possibility for otheradditional mixed methods causal studies.

6.1. Sport marketing – dominant-status sequential design (QUAN! qual)

A study by Kwon et al. (2007) is the first example of how mixed methods can be beneficial for ascertaining causation on adeeper and broader level (i.e., both causal description and causal explanation). Kwon et al. studied the mediating effect ofperceived value in the relationship between team identification and intent to purchase team-licensed apparel. To clarify,team identification refers to the way in which an individual shares a common bond with their favorite sports team (Kwonet al.). Perceived value has been defined as ‘‘the consumer’s overall assessment of the utility of a product based onperceptions of what is received and what is given’’ (Zeithaml, 1988, p. 14). Purchase intention relates to an individual’sdecision to buy a particular product (Kwon et al.).

With a sample of 110 undergraduate and graduate students, structural equation modeling was employed to understandhow team identification and perceived value affect consumers’ intent to purchase collegiate team-licensed apparel.Quantitative attitude scales were used to measure each construct. Respondents then received a score on each measure whichwas then analyzed in the SEM analysis.

In general, results from the SEM analysis showed that perceived value had a moderate mediating effect between teamidentification and purchase intention. More significantly, Kwon et al. (2007) were particularly surprised not to find a directsignificant relationship between team identification and purchase intention. They surmised that the failed relationship mayhave been related to including the price of the t-shirt whereas past studies had not. The researchers theorized that perhapsprice sensitivity had a moderating effect on purchase intention. Additionally, Kwon et al. were puzzled by the low scores onthe perceived value and purchase intention scales.

In response, an improved research design in this case would be a dominant-status sequential design (i.e., QUAN! qual).For the improved study suggested here, the quantitative methods are considered the dominant approach while an additionalqualitative component is added to obtain a deeper understanding of the quantitative findings and to explore the nature of thecausal relationships.6 In this case, a combination of repeated case observations based on a logic model, one on-one interviewswith some of the respondents, and the use of focus groups could be added to shed light on the reason or reasons why therewas not a stronger relationship between team identification and purchase intention. Repeated observations could beconducted to help untangle the direction of cause and effect in specific instances. Yin (2009), for example, recommends logic

6 Although its not the focus of this manuscript, if possible, randomized experiments causally linking specific pairs of causes and effects is a quantitative

research approach that would add to this (sport marketing) line of research.

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models as part of case study research focused on causation. He points out that ‘‘The logic model deliberately stipulates acomplex chain of events over an extended period of time. The events are staged in repeated cause–effect–cause–effectpatterns, whereby a dependent variable (event) at an earlier stage becomes the independent variable (causal event) for thenext stage’’ (p. 149).

Additionally, interviews could be used to attain a better understanding of why respondents scored low on the perceivedvalue and purchase intention scales. In the interviews, the researchers could ask respondents some pointed questions abouttheir level of team identification and their intent to purchase as well as how their perceived value of the t-shirt (inrelationship to its price) moderated their intent to purchase. Questions about participants’ reasons for their actions could beexplored (Davis, 2005; Dretske, 1989) as well as ordering of contextual and intentional factors. A qualitative data analysiscould then be conducted on the qualitative data for emerging themes or categories as well as information about sequencingof events (Creswell, 1998; Patton, 1987). Spradley (1979) explains how to locate causal language in written/transcribed text,and Miles and Huberman (1994) demonstrate the strategy of drawing network diagrams of events occurring over time todocument local or particularistic chains of causation. These themes and network diagrams could be used to expand theresearchers’ understanding of how perceived value mediates or moderates the relationship between team identification andpurchase intention.

6.2. Organizational behavior – equal-status sequential design (QUAL!QUAN)

A study by Cunningham and Sagas (2006) in the context of organizational behavior serves as a second example of howmixed methods can improve causal inference in sport management research. Cunningham and Sagas conducted aquantitative study to examine the effects of person–organization fit (‘‘P–O fit’’) and leader–member exchange (‘‘LMX’’) onturnover intentions among a sample of N = 235 male intercollegiate basketball coaches (n = 74 African American; n = 161White). Organizational commitment was included as a mediating variable. To clarify terms, P–O fit refers to a person’scompatibility with an organization (Kristoff, 1996). LMX is concerned with a leader’s relationship with their subordinates(Graen & Uhl-Bien, 1995). Organizational commitment refers to an individual’s psychological attachment to an organization(Meyer, Allen, & Smith, 1993). Last, turnover intention refers to a person’s desire to leave their place of employment(Cunningham & Sagas, 2006). Attitude scales were used to measure each variable. Structural equation modeling was used tostatistically test the hypothesized causal model.

Results from the SEM analysis supported the researchers hypothesis that P–O fit and LMX has an effect on turnoverintention and that the relationship is mediated by organizational commitment. As well, the results provided evidence tosupport their hypothesis that the relationship between P–O fit and organizational commitment would be stronger than therelationship between LMX and the same outcome variable. Results however, did not support their hypothesis that P–O fitwould have a stronger relationship with turnover intention than would LMX and turnover intention. Rather, P–O fit and LMXhad an equal relationship with the outcome variable.

An improved research design in this case would be an equal-status sequential design (i.e., QUAL!QUAN). Here, thequalitative and quantitative data would be given equal priority. The qualitative part of the study would be conducted first todig deeply into the nature of the constructs and the appropriate model to be tested in the second, quantitative, phase of thestudy. This design is recommended given the complexity of the causes Cunningham and Sagas (2006) sought to examine. Forexample, they point out that P–O fit may entail ‘‘congruence between an individual’s values, beliefs, and norms [italics inoriginal quote] with the organization’s culture’’ (Cunningham & Sagas, 2006, p. 33). This statement suggests that P–O fit maybe a complex molar package consisting of various molecular components.

Similarly, LMX is characterized by unique relationships between leaders and their subordinates and therefore may alsoinvolve a molar package that includes various molecular components. For example, in describing the nature of LMX,Cunningham and Sagas (2006) state, ‘‘high-quality relationships are marked by mutual trust, obligation, support, andrespect. On the other hand, low-quality relationships are marked by a relatively low level of mutual influence’’ (Cunningham& Sagas, 2006, p. 34). Additionally, they posit that subordinates in high quality relationships with their superiors areconsidered ‘‘ingroup members whereas followers in low quality relationships are ‘‘outgroup members.’’ These descriptionssuggest that LMX is a complex construct.

Cunningham and Sagas (2006) argue that it is important for managers and organizations more generally to know whichvariable or variables (i.e., P–O fit and LMX) contribute the most to employee turnover. Additionally, they includeorganizational commitment as an important mediating variable and thus argue that organizational commitment may alsoplay a role in turnover intention. We contend however, that it is just as important to obtain a deeper and clearerunderstanding of the specific causal mechanisms that are involved in the relationship between P–O fit and turnoverintentions and LMX and the same variable (before statistically testing the grounded theory obtained). Therefore, it issuggested that equal priority be given; that is, the full power of both approaches should be used in this study, including thequalitative approach that would allow for an in-depth investigation and uncovering of important causal processes and aquantitative approach for testing the model obtained from the qualitative phase and from study of the previous research.

Specifically, interviews and focus groups could be conducted on multiple levels with a sample of assistant coaches andhead coaches to attain a more in depth and expansive understanding of how LMX impacts organizational commitment andturnover intention. Collection of data from different levels or sources (e.g., head coaches and assistant coaches and other keyorganization members) has been referred to as multi-level use in the context of mixed methods (Tashakkori & Teddlie, 1998).

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Questions could be asked specifically about the types of circumstances and antecedents that relate to either high or lowquality relationships between assistant and head coaches and how those relationships impact organizational commitmentand turnover intention. As well, long-term field observations could be conducted to attain a deeper understanding of headcoach-assistant coach relationships and to attempt to determine variable ordering from studying particular cases (Maxwell,2004b; Patton, 1987; Yin, 2009).

A similar methodology could be used to better understand the overall relationship between P–O fit, organizationalcommitment, and turnover intentions. Specifically, one-on-one interviews or focus groups could be conducted with a sampleof participants. Within the interviews, the researchers could ask certain questions about the specific causal mechanismsinvolved in the P–O fit, organizational commitment, and turnover intention relationship. Additionally, questions could alsobe posed that might help the researchers understand which causal variables play a stronger role, if any, toward influencingturnover intention, i.e., P–O fit, LMX, and organizational commitment.

Interviews could also be conducted with comparative or extreme cases (Maxwell, 2004b; Patton, 1987). That is, thosewith lower levels of LMX and P–O fit could be compared to cases with higher levels of the same variables. These cases couldbe identified from scores on the quantitative measures used by (Cunningham & Sagas, 2006). This would contribute to theQUAN component of the mixed methods design.

For analysis, emerging themes or categories could be gleaned from the qualitative data as well as the use of rich data orthick description (i.e., the use of data that provide a detailed account of events or processes experienced by the participants(Creswell, 1998; Maxwell, 2004a, 2004b; Patton, 1987). The quantitative phase of this study would employ Cunningham andSagas’s original quantitative methods (use of attitude scales and SEM) to statistically test the broader causal modelincorporating causal relationships between P–O fit, LMX, organizational commitment, and turnover intention. However,based on emerging themes from the qualitative study, some changes or additions (more variables) might be made to thestatistical model. Taken all together, the qualitative and quantitative data and findings could be integrated at the level ofinterpretation to obtain both a deep-particular and broad-general understanding of the causal relationships.

6.3. Finance – equal-status concurrent design (QUAN + QUAL)

A study by DeSchriver (2007) provides a final example of how mixed methods can expand and deepen one’sunderstanding of causal relationships. In the context of sport economics, DeSchriver examined the causal influence of aprofessional athlete’s star status on fan attendance. Specifically, DeSchriver chose to focus on Freddy Adu of DC United inMajor League Soccer (MLS). Adu was selected for two major reasons. First, his contract was the largest in MLS history at thetime of his signing in 2003 ($500,000� 6 years). Second, Adu was extremely young – only 14 years old with no priorprofessional playing experience. Thus, DeSchriver was interested in determining what level of impact a young inexperiencedathlete with worldwide recognition would have on fan attendance. Additionally, MLS was selected among other majorprofessional leagues because it is considered a young league that operates on a minor league level. The study’s ultimatepurpose, then, was to ascertain the effect of a young callow athlete with star status on fan attendance in a less establishedprofessional sport league.

DeShriver used multiple regression analysis to statistically examine the hypothesized causal relationship between Aduand fan attendance. Included in the analysis were a variety of other variables that might also explain changes in fanattendance including home versus away games, promotional activities, weather conditions, star players on opposing teams,home team performance, previous season visiting team performance, and market specific characteristics (population size,racial composition, income, and ticket prices). The data were collected across the 2004 MLS season. The total sample size was150 games.

Results showed that the overall model was statistically significant and explained 67% of the variance in fan attendance. Ofthe 22 explanatory variables, 9 were statistically significant. The variable involving Adu and DC United as the visiting teamwas the third largest explanatory variable suggesting a strong relationship between Adu and fan attendance after controllingfor the other variables in the equation. It was estimated that an additional 10,958 spectators attended games when Adu andDC United were the visiting team. The two stronger explanatory variables were promotional activity variables – UnitedStates of America National Team double headers (20,577 additional spectators) and Fireworks Night (14,566 additionalspectators).

An improved research design in this case would be an equal-status concurrent design (i.e., QUAN + QUAL). DeSchriver’s(2007) discussion of results points to a couple of areas in which the addition of qualitative data could have expanded hisunderstanding of the causal relationship between Adu and fan attendance. For DeSchriver’s study, we believe the additionalqualitative data should be given priority equal to that of the quantitative. This is because there are some areas withinDeSchriver’s study where qualitative data could make a significant contribution to the causal relationship between Adu andfan attendance.

First, DeSchriver noted that Adu saw little playing time during the 2004 season (his first) suggesting that many fansattended games to see Adu, the celebrity rather than Adu, the player. DeSchriver compares the phenomenon to the likes offormer professional tennis player, Anna Kournikova who has been highly marketable despite a lack of on court success. Tobetter understand fans’ attraction to Adu, interviews could have been conducted with a sample of fans during pre-gamefestivities (e.g., tailgate barbecues). Fans could be asked to explain their attraction to Adu as well their interest in other starplayers. Perhaps the reason for fans’ attraction to star players varies in relationship to the particular player. This could be

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brought to bear in the qualitative analysis in which the researcher would look for emerging patterns or themes in the data.These emerging themes would help explain the meaning and nature of the relationships suggested in the original regressionmodel. Because a basic multiple regression model only investigates direct effects and usually only checks for main effects(i.e., interaction effects typically are not examined), the qualitative data could help the researcher theoretically order thepreviously unordered set of independent variables and determine if any complex interaction terms should be included in theregression model. Additionally, the qualitative interviews might uncover some other reasons for fan attendance that werenot originally specified in the regression analysis.

DeSchriver (2007) also noted that Adu’s presence did not seem to have a lasting effect. Fan attendance across MLS gamesdropped in 2005 (the year subsequent to Adu’s first). A clearer understanding of why many fans did not continue theirinterest in Adu could be obtained by conducting interviews with a sample of fans and asking questions about Adu’s lastingappeal in the 2005. Questions could also be posed to fans concerning their sustained attraction to other star players. It couldbe that there are certain athletes that maintain the ability to draw additional fans to games whereas other so-called starathletes do not. Information as such could be obtained by analyzing the qualitative data for emerging patterns or themesrelated to the qualities of star athletes. These themes could also be used to develop a questionnaire concerning qualities ofstar athletes. Administration of the questionnaire would allow for collection of data from a larger sample and thus a broaderunderstanding. Doing so however, would change our original concurrent design to a sequential one.

7. Conclusion

It has been pointed out in this article that a popular objective in sport management research is to gain an understanding ofcause and effect. When these causal studies are conducted they are almost always done with monomethod, quantitativeapproaches. Specifically, we argued that many sport management researchers seeking causal relationships may fail torecognize an important distinction between causal description and causal explanation. Causal description identifies anoverall, molar causal relationship between an independent and dependent variable; however, such relationships tell theresearcher little about the underlying causal mechanisms responsible for the causal relationship. This latter type of causalinformation has been referred to as causal explanation (Maxwell, 2004b; Salmon, 1998; Shadish et al.) or evidence ofmechanisms (Russo & Williamson, 2007).

It was argued that both causal description and causal explanation are important for testing and unpacking causalrelationships. We agree with Cook (2002) in his acknowledgement of the importance of both causal description and causalexplanation when he states, ‘‘Experiments should be designed to explain the consequences of interventions and not just todescribe them. This means adding more details to an experiment’s bare bones measurements and sampling plan, thusabjuring black box experiments’’ (p. 189). In the same vein, Shadish et al. posit:

So causal explanation is an important route to the generalization of causal descriptions because it tells us whichfeatures of the causal relationship are essential to transfer to other situations. This benefit of causal explanation helpselucidate its priority and prestige in all sciences and helps explain why, once a novel and important causal relationshipis discovered, the bulk of basic scientific effort turns toward explaining why and how it happens (p. 10).

However, despite the importance of causal explanation, Maxwell (2004a) and others have suggested that historically thestandard view in philosophy of science has dominated scientific thinking, and this has resulted in dominance of only oneapproach to the study of causation and a lack of qualitative approaches for studying causation in local or particular contexts.

We explained what we believe the mixed methods research position to be with regard to causation, which essentiallysays to use multiple forms of causal evidence, multiple logics of cause and effect, and to study causation locally (i.e., inparticular contexts) as well as more broadly (based on larger samples). We explained that the use of both quantitative andqualitative data will allow researchers to obtain a better understanding of both causal description and causal explanation.Such use of mixed methods is wanting in the field of sport management considering the small percentage of empiricalstudies in sport management currently adopting mixed methods approaches (Barber et al., 2001; Quarterman et al., 2006;Rudd, 2007).

We hope that this article will motivate sport management researchers to examine the many different ways that mixedmethods can be used to improve their understanding of the phenomena they study. This type of open-mindedness andcreativity requires sport management researchers to bracket their current research paradigm and creatively consider howthe adoption of mixed methods approaches might help the advancement of research in sport management which issomething that will benefit us all.

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