METHODOLOGY
This chapter describes the employed methodological process to investigate the research questions identified in the chapter 1. The contents of this chapter are: 1) Research design, 2) Sampling procedures and data collection, and 3) Data analysis. The ultimate goal of this research is to explore the evolution of intellectual structures of the field of sport management. In specific, the research questions are:
1) How have trends and central themes changed in the field of sport management? 2) How have the patterns and relationships of the knowledge structure evolved in the field of sport management?
Research Design
The fundamental methodological premises of this research are based on the groundwork of theory and analytical tools from the field of bibliometrics and social network analysis. Scholars identify central academic themes and trends of a certain academic field by implementing keyword and citation analysis based on the assumptions developed in the field of bibliometrics. It has been assumed that heavily cited articles and frequently occurred keywords of the articles tend to have greater influence on the field
compared to less frequently cited publications or occurred keywords (e.g., Culnan, 1986; Lee & Su, 2010; Sharplin&Marby, 1985). Along with appropriate screening and a large dataset, keyword and citation analysis are helpful to identify which journals, papers, or authors are influential in a certain subject.
Keyword Analysis / Citation Analysis
Considering this fundamental, the most popular analytical tool of keyword and citation analysis is frequency analysis examining how many times the keywords occurred and how many times the publication is cited during the period. Hence, conventional statistical analysis was implemented for the first research question. Since the keyword analysis (KA) and citation analysis (CA) cannot describe the structure of influence within a field (Leong, 1989), a different approach is needed to reveal the relationships and structural patterns among authors, journals, keywords, and citations. In order to cope with this weakness of KA and CA, this study conducted keyword co-occurrence network analysis (KCNA) and co-citation network analysis (CCNA) in order to illustrate the macro-level intellectual structure of the field of sport management (White, 1990).
Keyword Co-occurrence Network Analysis / Co-citation Network Analysis
Since the structural and relational attributes are involved in keyword co-
occurrence analysis and co-citation network analysis, these two analyses need different type of data and analytical tools. For this, this study employs exploratory social network analytical procedures in order to grasp the relational and structural attributes of
intellectual structure in the field of sport management. Based on the graph theory and matrix algebra, keyword co-occurrence network (KCN) and co-citation network (CCN) were visualized. Good visualized graph in a network provides vital characteristics and
patterns of overall network structure even at a glance. In spite of the benefits of
visualization of networks, it would be tough to describe the properties of networks if the network gets larger and becomes too complicated. Consequently, diverse types of social network measurements have been developed in order to describe the relational attributes mathematically. For the keyword co-occurrence network analysis (KCNA) and co-
citation network analysis (CCNA), this study employed both methods – visualization and calculating social network index.
The biggest difference of standard social and behavioral science data from social network data is the absence of information about relations. This difference requires unique measurements for social network data. Measurements of network data consist of unit of observation, the modeling unit and the quantification of relations. The concept of unit of observation in social network analysis refers to an actor, from whom researchers extract information about ties, or pair of actors (dyad) when researchers measure ties among pairs of actors directly. In terms of modeling unit, several levels of network data can be modeled. These levels include actor, dyad (pairs of actors), triad (triples of actors), subgroup (subsets of actors in the network), and set of actors of network (the network as a whole). When researchers apply certain network methods to models and network properties of social network studies, it is critical to identify the level of modeling unit.
For the quantification of the relations, there are two properties of relations that are vital to comprehend measurements and categorize the methods in social network analysis. The first property of relations is whether the relation is directional or nondirectional and the second property of relational tie is whether the relation is dichotomous or valued. (Wasserman & Faust, 1994). The keyword co-occurrence data and co-citation data from
this study has nondirectional and valued relations. In both networks, the linkage among nodes do not have heads and tails due to the nondirectional property and the thickness of linkage among nodes are different to explain different number of co-occurrence of relationships (characteristic of valued relations). Specific analytical methods of social network will be discussed in the section of data analysis.
Sampling Procedure and Data Collection
This study identifies the prevalent themes, concepts, and paradigms and explores the structures of those concepts of the field of sport management through bibliometric analysis and social network analysis of academic articles published by the Journal of
Sport Management (JSM) between 1997 and 2010. This journal was selected because
JSM is the first academic journal initiated in the field of sport management. Conveniently, JSM is only sport management journal that has been included in Web of Science (WoS) database over ten years. The raw data of keywords and co-citations were extracted from the WoS directly using SITKIS software (Schildt, 2002).
In the traditional statistical tool, it is assumed that researchers have a collection of measurements taken on a group of independent cases. The assumption of sampling independence of observation enables researchers to apply “machinery of statistical analysis” to various research questions. However, the purpose of this study is evidently concerned about interrelatedness of social elements. The dependencies of samples are assessed with structural variables. Theories regarding social network ideas are different due to propositions related to relations among individual units and such theories claim that these individual units interact and impact each other rather than acting alone.
data analysis, and model building (Wasserman & Faust, 1994). Then how is sampling method for social network analysis different from the one for traditional analytical tools?
Sampling Procedure of Social Network Data
Most of all, social network data is different from traditional data set in that it contains at least one structural variable measured on a set of units while traditional data set mostly consist of composition variables (also called actor attribute variables) such as gender, or ethnicity of actors. Specifically, structural variables are measured on pairs of actors and its measurement assesses ties of certain types of links between pairs of actors such as friendship or communication channel. Conversely, composition variables or actor attribute variables are the standard attributes at the level of individual units such as race, or income level. Researchers should choose appropriate analytical techniques depending on the different nature of structural variables that are determined by substantive concerns and theories related to research questions of network study.
In addition, the nature of study determines which type of network will be used in certain research. For instance, it determines whether the whole network need to be studied or a sample of the actors must be examined or whether one-mode network should be explored or two-mode network need to be investigated (Wasserman & Faust, 1994). Wasserman and Faust (1994) defines mode as “a distinct set of entities on which the structural variables are measured” (p. 43). One mode network measures structural variables on a single set of actors while two-mode network measures structural variables on a two sets of actors. Affiliation network refers to a certain type of two-mode network because even though it has only one set of actors, the second mode is a set of events to
which actors belong (Wasserman & Faust, 1994). Both the keyword co-occurrence network (KCN) and co-citation network (CCN) of this study are one-mode networks.
Before collecting network data, as in traditional statistical sampling procedure, researchers should specify the boundary of population. In social network analysis, boundary of population is determined by questions such as “which actors to include”, “who are the relevant actors”, or “which actors are in the population” (Wasserman & Faust, 1994, p. 31). The boundary of actors contributes to identify the population of the research. However, it is often tough to determine whether a certain unit belongs in a set of actors due to the fact that actors may appear and disappear as time goes by. Therefore, external definition of the boundary of the set of actors is required in order to determine which actors belong in it. In my research, the external definition is published articles in Journal of Sport Management between 1993 and 2010, specifically those accessible through WoS database.
In terms of boundary specification in social network analysis, Laumann and colleagues (1989) introduced two approaches – realist approach and nominalist approach. Realist approach set boundaries on a basis of membership perceived by the actors. For instance, actors in the network street gangs can be determined by the perceived
membership of actors. On the contrary, nominalist approach focuses on the theoretical concerns of the researcher. Wasserman and Faust (1994) provided a great example of nominalist approach. Researcher might choose the group of people who published articles on the topic in the previous five years in order to investigate the flow of computer
messages among researchers in a scientific specialty (Wasserman & Faust, 1994). Corresponding to nominalist approach, the present study extracted keyword and co-
citation data from selected articles published by Journal of Sport Management between 1997 and 2010 through WoS.
There are assumptions to be considered before collecting network data. First of all, it is assumed that scholars can acquire information on all of the important actors in the actor set. However, it is possible to miss some actors unintentionally or for specific reasons so the size and compositions of the actor set is determined by both practical and theoretical concerns. In most network studies, scholars begin social network analyses with sets of actors with a complete list of units. However, if the boundary is unknown, special sampling techniques such as snowball sampling and random nets in order to establish boundaries of population.
Snowball network sampling method can be utilized when the actors in a set of sampled units are nominated by others to have specific types of ties. These nominated actors place at the “first order” zone of the network and the researcher samples all the actors in this zone. From all actors in the “first order” zone, the researchers gather the information about actors in the “second order” zone who are not included the original respondents or those in the “first order” zone. Snowballing sampling procedure continues through several zones depending on the nature of study (Goodman, 1961). Contrast to the sampling procedure of traditional statistical analysis, sampling methods in social network analysis are based on the assumption of “dependence” due to the fact that samples are collected through the relationships of actors. Similarly, chain method is another
alternative for sampling in social network analysis that combines snowball sampling and the small world technique.
If it is not definite what the relevant sampling unit should be during the sampling procedure, creating ego-centered networks can be helpful. Sampling for ego-centered networks initiate with one sample actor (or a sample set of actors) by asking them to report their ties like snowball sampling method. Egocentric network studies normally establish boundaries during data collection. In egocentric network research, the goal of scholars would be making inferences about the whole population of networks from a sample of ego-centered networks (Wasserman & Faust, 1994). However, scholars should remember that sampling designs of ego-centered networks are not statistically simple in order to overcome potential biases and thus researchers should adjust the standard statistical summaries (Reitz & Dow, 1989). The issue of external validity will be discussed in detail shortly. Because this research has a clear boundary of the whole network and a full list of units – keywords for keyword co-occurrence network and citations for co-citation network analysis extracted from published articles by Journal of Sport Management (JSM) between 1997 and2010, unique sampling methods or creating egocentric network were unnecessary.
Data Collection Methods
Typically, data collection is conducted after the problem identification, review of the literature on the problem, and research design. In traditional quantitative method, researchers employ wide range of instruments to collect data such as questionnaires, ratings, or tests focusing on attributes and descriptions of subjects such as age, gender, academic level, ability level, and so on. Traditional qualitative researchers may use unique data collecting method such as in-depth interviewing, participant observation, or document analysis (Ary, & et al., 2006).
Similarly, in social network analysis, data collection methods include
questionnaires, interviews, observations, or archival records. But the major difference between traditional data collection method and network data collection method is the type of data and different designs of instruments for collecting data in that social network analysis focuses on the relationship among actors whereas traditional method focuses on the attributes of subjects. For example, questionnaires of social network studies normally ask questions about the subjects’ relationship with others.
When it comes to disclosing these relations, three unique formats of questionnaires are normally employed (Wasserman & Faust, 1994). First of all, researchers create a complete roster so that the respondents can choose related actors from that list. On the contrary, researchers can let respondents to choose related actors freely. Secondly, researchers may choose fixed choice format so that respondents can select a fixed maximum number of tied actors while free choice format let actors to nominate unlimited number of tied actors. Lastly, for creating a network with valued relationships, rating format or complete ranking format of questionnaire can be used. Rating format of questionnaire allows respondents to rate a value of each tie and its value indicates the intensity of strength of ties while respondents rank their ties with other actors in complete ranking format of questionnaire. Compared to fixed choice design, the ratings or full rank orders format of questionnaires are desirable for higher reliability.
Interviews can be useful when researchers collect data of ego-centered networks because certain respondents tend to be more favorable to attend to face-to-face interviews than impersonal questionnaires (e.g., CEOs). Observations are especially preferable with subjects who cannot respond to questionnaires or participate in interviews. In this context,
observation methods are popular for collecting data for affiliation network or for network among non-human subjects. Archival records can another alternative to collect network data. Records of interaction are effective for exploring longitudinal relations and reconstructing of relationships among actors in the past.
These days, bibliometric data, one major type of archival data, is prevalent in order to reveal the patterns of citations or co-authors among scholars (Burt, 1978/1979; Breiger, 1976; Carley&Hummon, 1993; Doreian&Fararo, 1985; McCann, 1978;
Michaelson, 1991; Noma, 1982a; Noma, 1982b; White and McCann, 1988). The present research also uses archival records of keyword and citation data in order to comprehend the knowledge structure of sport management field. Dramatic development of technology has contributed to build large-scale bibliometric database accessible through the Internet easily. This study extracted bibliometric data including citations, authors, years, and keywords through a software called SITKIS (Schildt, 2002). More significantly, extracted information about published year from archival data allows the authors to longitudinal study design. Longitudinal network study design can be very attractive for exploring the evolution of structures by investigating network changes over time. A total of 326 articles from JSM that were published between 1997 and 2010 were retrieved in the study. Atotal of 519 keywords and 11,154 citations were retrieved from 326 focal articles.
Issues of Validity and Reliability
The study of Rice and colleagues (1989) is noteworthy on the issue of validity, reliability, and measurement error of citation analysis. Validity issues of citation data have been focused on whether citations are proper indicators of the scientific and
it is assumed that the citation implies the quality of the cited reference or idea of the reference, co-cited articles or authors share related contents or themes, and all citation have equal weight. Additionally, it has been assumed that more cited articles are more likely to have bigger impact on the subject than less frequently cited articles (Culnan, 1986; Sharplin, & Mabry, 1985). However, citations are not just a objective measure of the information flow of the previous published literatures, but they may also include a lot of different contexts such as showing respect for pioneers, criticizing or correcting related work, identifying original sources for concepts or ideas, or following disciplinary trends for citing (Smith, 1981). In fact, these issues have been identified for a long time and scholars have been suggested several ways to cope with these concerns. Recently,
Pilkington and Meredith (2009) claimed that adequate screening and large samples could deal with these weaknesses and provide meaningful insights on influential journals, articles and authors in certain academic field. In addition, the authors referred Garfield (1977)’s concerns about the inappropriate inferences of citation analysis and provided the suggestions to improve these issues.
According to the Garfield (1977), in citation analysis studies, inferences are made focusing on the first author rather than all authors, and thus researchers may miss
significant contributions and collaborations of other authors. On this issue, the authors used “publications” themselves as an individual data rather than identification of authors (Pilkington & Meredith, 2009). Moreover, Garfield (1977) was also concerned about the fact that there may be authors who have same names in citation dataset or different journals may have different policies to represent authors’ names. For this, careful and thorough screening of names, topics, and years of citations for data can resolve this issue.
Garfield (1977) also highlighted the issue of including negative citations and self- citations as same as Smith (1981). Pilkington and Meredith (2009) argued that this issue could be ignorable due to the fact that they assume that self-citations and negative citations roughly distributed equally among authors. This research has similar potential issues as many scholars recognized historically regarding to citation analysis.
Consequently, this study utilized the thorough screening and revision process of data in order to standardize the data. Furthermore, similar to the study of Pilkington and Meredith (2009), this research considered node as a “publication” rather than an individual “author” to avoid the identified confusions.
Uniquely, another concern of this research is the source of keyword data. As Lee and Su (2010) pointed out, author keywords may represent the concepts or paradigms of publications more clearly compared to the results of text mining algorithms. However, the keyword dataset of this research was retrieved from the KeywordsPlus of WoS due to