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Qual Quant (2011) 45:10671089DOI 10.1007/s11135-011-9492-3

Tackling connections, structure, and meaning

in networks: quantitative and qualitative methodsin sociological network research

Jan Fuhse Sophie Mtzel

Published online: 6 April 2011 Springer Science+Business Media B.V. 2011

Abstract The paper systematizes the role of qualitative methods, statistical analyses, andformal network analysis in sociological network research, and argues for their systematiccombination. Formal network analysis mainly aims at a description of network structuresas well as at an explanation of the behavior of the network at the systemic level. Formalnetwork analysis can also be used in order to explain individual behavior or the existence ofindividual connections from network structure. Statistical analyses of ego-centered networks

are used to correlate individual attributes with the structure and composition of the individualembeddedness, thus providing a statistical explanation of network effects and determinants.Qualitative methods are important for exploring network structures, and for understandingthe meaning connected to them. A historical overview shows that these three strands havelong co-existed in sociological network research without engaging in combined researchefforts. Combinations of these methods prove useful when considering the various aspectsof networks (individual connections, structural patterns, and meaning).

Keywords Social network analysis Culture Meaning Relational sociology Qualitative methods

1 Introduction

In the development of network analysis, social networks have often been reduced to ana-lytical constructs of quantitative, structural network methods. However, historically, socialnetworks have been and are studied with multiple methods, combining both qualitative andquantitative approaches. Moreover, in the past 20years, a rich and multi-faceted theory of

J. Fuhse (B)University of Bielefeld, P.O. Box 10 01 31, 33501 Bielefeld, Germany


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1068 J. Fuhse, S. Mtzel

social networks has emerged, which blends the traditional structural intuition with a strongemphasis on culture and meaning in networks (Breiger 2004, p. 518; Fuhse 2009; Mische2003; Mtzel 2002, 2009; Pachucki and Breiger 2010; White 1992, 2008). Starting fromthis approach, we argue that quantitative and qualitative methods of network research tackle

different dimensions of social structures. While it is useful to model the structure of rela-tionships between actors with formal network analysis, and the embeddedness of actors andconnections in statistical analyses of personal networks, qualitative research is indispens-able for an understanding of the meaning inextricably intertwined with any structure ofsocial networks.1 Networks are thus not seen as mere analytical constructs, but as real socialstructures with three dimensions: the structure of social relationships; the individual actorsand their connections; and the meaning associated with networks and their connections. Weshow that these three dimensions are tackled by the three types of methods (formal networkanalysis, statistical analyses of personal networks, and qualitative methods). We argue thatthe combination of more than one of these methods and thus the analysis of more than one

of the dimensions of networks proves particularly fruitful. In addition to the methodologicalissues raised, we also contribute to the ongoing discussions on a mixed methods approach innetwork research.2

We start from a historical overview of several strands of network research with particularemphasis on their treatment of connections, structure, and meaning, and on their usage ofdifferent quantitative and qualitative methods (2). The third section provides a systematiccomparison of the three types of methods in sociological network research with regard to theirresearch ambitions and the dimensions of social networks tackled by these methods. The lastsection discusses a few research examples that combine at least two of these methodological

approaches. The conclusion calls for a systematic incorporation of meaning into the studyof social networks, which can be achieved with a combination of qualitative and quantitativemethods.

2 Historical overview

Today, network studies most often use methods of formal network analysis and quantitativesurvey research. In the early days of British social anthropology, US American communitystudies, and Norbert Eliass figurational sociology however, qualitative approaches to net-

works used to be much more prominent. The aim of this section is, firstly, to show that thevarious strands of network research have always deployed both quantitative and qualitativemethods, and, secondly, to distil theoretical concepts for a systematization of their usage inthe following section.

The early works ofGerman formal sociology show little sophistication in their methods.In both Georg Simmels and Leopold von Wieses works, formal sociology remains a theo-retical tool without methodological rigor. But we do find discussions on the role of structure

1 The concept meaning here refers to the body of symbols, attitudes, values, schemata, logics, and scripts

as theorized by Weber (1972 [1922], p. 1ff), Schtz (1967 [1932]), and Luhmann (1990). Meaning can belocated at the subjective level, in actors heads, or at the social level in communication process or attached tosocial structures (like relationships, groups or organizations), as will be argued in more detail in Sect. 3. The
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Tackling connections, structure, and meaning in networks 1069

and meaning in webs of social relations. Simmel famously argued that social regularities aremainly due to the forms of social life, and not to their content (Simmel 1992 [1908],p. 17ff). Examples of this are the effects of group size (p. 89f) and the advantages of a brokerposition (tertium gaudens; Burt 1992, p. 30; Simmel 1992, p. 128ff). These social forms

thus stand for structural characteristics of inter-personal formations. The individual motiva-tions, feelings, or meaningsthe contentare less important than the forms in whichthey are embedded (1992, p. 17ff). Simmel thus argues for the primacy of form over con-tent, of structure over meaningand much of network research has followed this structuralintuition (Freeman 2004, p. 2ff).

According to Max Weber, in contrast, all action is rooted in subjective meaning (1972[1922], p. 1ff): social relationships, be it friendship, love, or the feeling of national com-munity (p. 13f), are characterized by the subjective meaning that the actors attribute to them.For Weber, the meaning in and of a relationship determines the chance (probability) thatthe individuals involved act in certain ways. While action (on the basis of subjective mean-

ing) is his most fundamental concept, his notion of social relationships encompasses a widearray of social structures. In line with his typological approach, Weber argues that socialrelationships can be grounded in the four ideal types of action motivationtraditional,affectual, value-rational, and instrumentally rational (p. 21ff). This motivation byideal types should be responsible for some of the dynamics of social structures, thus lead-ing to an explanation of social phenomena by way of the typological understanding ofthe subjective meaning of actors. Following Webers formulations, social networks shouldbe seen as structures which embody specific, relatively stable bundles of individual motiva-tions. A sect, for example, would result from, as well as determine value-rational motivations

on the part of its members, while economic market structures would induce instrumentallyrational action. Thus networks can explain individual action, but only with a comple-mentary understanding of the subjective meaning tied to networks. Commonly, quantitativemethods in sociology focus on the task of explaining by identifying causal connections atthe statistical level; and qualitative methods are primarily used in order to discern meaning,leading to an interpretive understanding of the social phenomena at hand.

These early foundational arguments in German formal sociology have been picked upby various strands of network research in diverse ways. In particular, we find that Simmelsstructural intuition is put into diverse research practices as will be discussed in the fol-lowing. One approach that is often underrated in network research can be found in Norbert

Eliass figurational sociology. For Elias, social phenomena cannot be explained with refer-ence to individual and isolated actors. Moreover, these are always in interdependence witheach other in so called configurations. These configurations resemble what we now termnetworks in many aspects, and indeed Elias sometimes referred to them as networks afterthe term became widely used in the social sciences (1978). While Eliass configurations areprimarily structural, they also show a strong cultural component. For example, structurallydisconnected groups will reinforce this separation with the drawing of boundaries, and theformation of stereotypes and of separate cultures (Elias and Scotson 2000). Here, Elias andScotson use qualitative interviews and ethnographic observations to document the split into

anestablishedandanoutsidergroupinbothstructureandculture.Apartfromthat,Eliasreliedmainly on historiographic analysis, documenting changes in manner books or using detaileddescriptions from biographies or even novels to account for his theoretical statements.
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his collaboratorsthe Yankee City studiesrelied on a large scale, standardized survey of17,000 individuals instead of personal observations. Although complemented by qualitativematerial, the research method was mainly quantitative.

As in most of the community studies that followed, surveys were designed to analyze the

properties of personal or ego-centered networks and their correlations with individual attri-butes such as class, age, status, or ethnic descent (e.g. Lazarsfeld et al. 1968 [1944]; Laumann1973; Pappi 1973; Verbrugge 1977). While network studies usually remain confined to thestructure of relations among a limited set of individuals, the survey methods were used toarrive at a bigger picture of social structure in larger communities. The main purpose of thesesurveys was to identify the tendency (or statistical probability) for friendships or intimaterelations to arise between people from particular categories. Thus, it is not the actual patternof relations between individuals which is observed. Instead, the focus of these studies lieson relations between categories like gender, professional groups, status, age, religion, andethnic descent.

Barry Wellman also studies personal networks in communities, yet not in order to dis-cover categorical differences (1979) but to identify levels of social integration (communityvs. society in Tnnies sense). The statistical approach of deriving relational patterns fromsurvey data on personal networks was later adopted in larger surveys no longer confinedto singular communities, for example in Claude Fischers comparison of urban and ruralfriendship networks (1983), in Wellmans work on online communities (Wellman and Gulia1999; Wellman et al. 1996) or in the US General Social Survey.

Most of the social capital literature that picks up on Mark Granovetters strength ofweak ties argument (1973) applies a similar focus on personal networks, often on the basis

of survey data.4

In the diverse usages of the social capital concept, an actors embeddednessinto more or less dense networks with more or less homogeneous ties is seen as enabling orconstraining individual action (Lin 2001; Portes 1998). In that sense, networks are reduced toindividual resources, rather than treated as meso-structures, which explain social phenomenafrom their supra-personal structuring. Generally, weak ties and heterogeneous networks areseen as leading to better access to information and thus to superior opportunities for action(Burt 1992). Homogeneous networks with high degrees of transitivity, in contrast, allow forhigh degrees of trust and cooperation (Coleman 1988), but may also feature the dark sideof social capital when people are stuck in groups with little opportunities and motivationsfor upward mobility (Portes 1998). Aspects of meaning are by and large disregarded in the

social capital concept in order to factor networks into statistical analyses of inequality in astraightforward way.

In sum, survey research on personal networks has yielded important results and insights.Yet it remains tied to categories already known by the researcher and assumes that theseare the categories that actually structure the relationships analyzed. Individual motivations(subjective meaning) and the negotiation of cultural forms (intersubjective meaning) arebracketed in these analyses. Moreover, they focus on individuals rather than network struc-tures as the units of analysis, thus assuming that networks can be reduced to individualproperties (such as access to structural holes, heterogeneous networks, or categorical hom-

ophily).What is known as social network analysis (SNA) fully develops the structural intui-tion. SNA constitutes both a series of formal procedures for the analysis of observable
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relations between at least two actors and their patterns as well as a theoretical perspectiveon these relations. The methodological tools for analyzing social networks have developedhistorically at the intersection of disciplines, such as sociology, anthropology, mathematics,psychology, and physics (e.g. Freeman 2004; Knox et al. 2006; Scott 2000; Watts 2004).

Relations which connect or separate actors are the units of analysis in SNAas opposed tostatistical correlates between properties of discrete individual actors. Network analysis thusdiffers fundamentally from typical models of variable centered sociology (Abbott 1988) byassuming interdependence of its units of analysis. This opposition to mainstream empiricalsocial research is captured in the structuralist credo (Wellman 1988, p. 31):

Structured social relationships are a more powerful source of sociological explanationthan personal attributes of system members.

Socialexplanationsarethustostartfromsocialnetworkswithdiverseexplananda(individ-ual behavior, systems behavior) resulting from structural patterns. Meaning, the motivations

of actors and the norms within the system are bracketed in this approach. Thus, it aimsat a structuralist explanation without an understanding of the social phenomena. Typically,SNA researchers establish ways of systematically quantifying gathered data, before they areanalyzed by a set of algebraic measures, which examine their structural characteristics (e.g.Wasserman and Faust 1994). The graphic representation of network relationsusually a mapof lines and nodesthen offers a cognitively easy access to the underlying social structure(Freeman 2000).

As Ronald Burt has argued, network analysts follow two analytical strategies in order toexplain the effects of networks on social action (1980). The relational approach focuses

on the direct and indirect connections of actors with othersi.e. how they connect. Socialrelations in this regard are understood as channels through which particular resources, suchas information, friendship, goods, or money flow, depending on the type of embeddedness(Granovetter 1985). Different measures can then be used to establish, for instance, the levelof density, centrality, or strength of relations, for a focal actor (ego-centered analysis), forcliques and overlapping subgroups, or for entire systems. The social capital studies men-tioned above constitute examples of this relational approach, as do the studies on (varioustypes of) centrality in a network.

The positional approach focuses on the pattern of relations actors form on the basisof having similar ties with others. This strategy is based on the idea of structural equiva-

lence of actors (Lorraine and White 1971). Structurally equivalent actors do not need to havedirect ties between themselves or even belong to the same clique. What is of relevance is thepattern of relations which defines the position and the role structure of one actor relative toall others in the network. Thus, this approach does not explain social action on the basis ofdirectly established social relations through which resources flow, but looks at the structureof all relations and similarities in structural position. The algorithmic implementation, calledblockmodeling, partitions the network into blocks of structurally equivalent actors (Breigeret al. 1975; White et al. 1976). As the result of blockmodeling, an entire network is reducedin its complexity: the network is represented as a few blocks of network members according

to maximal similarities in their ties to others, also indicating how the partioned groups relateto each other. Blockmodels are thus first and foremost models of multirelational networks;they highlight relations of positions in the network, rather than information on individual
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(e.g. Carley 1986; Krackhardt 1987), historical networks (e.g. Gould 1991; Padgett andAnsell 1993) and networks of cultural actors (e.g. DiMaggio 1987), which had begun to pushthe structuralist network tradition to include cultural aspects such as meanings, discourse,and identities in order to remedy its integrated shortcomings. Emirbayer and Goodwin show

that both perspectives just outlined, i.e. the relational approach focusing on connectivity aswell as the positional approach focusing on structural equivalences, prove to be structurallydeterministic: they reify social relations and leave no room for cultural content and process. Intheir attempt to contest individualistic and functionalist sociologies, seminal works in socialnetwork analysis self-consciously bypassed the issue of cultural content and meanings ofties to focus on structural arguments only (e.g. White et al. 1976, p. 734). Network analysis,Emirbayer and Goodwin argue, shouldovercomesuch bracketing of culture since any relationentails several meanings (which are unfolding and changing over time) in particular culturaland intersubjective contexts and cannot be reified to one particular, atemporal dimension.

Harrison Whites Identity and Control (1992, 2008) presents a concentrated effort to shift

the understanding of networks as reified, non-cultural structures to networks being dynamic,sociocultural formations.Identity and Control pushes sociological theory beyond individual-centered and purely structuralist approaches towards a more dynamic and contextual modelby considering how meaning arises in a relational context and, dually, how relations createmeaning. For White, social networks are cultural structures of relations, in which storiesconnect identities defining them on the way. Thus, White argues that networks are inextrica-bly intertwined with domains of forms of meaning, including narratives, expectations, andlanguage (White 1995, p. 1038f; Mische and White 1998, p. 702ff). Subsequently, Identityand Control has become a central reference in the movement for a relational sociology

(Emirbayer 1997), which regards relations between terms or units as preeminently dynamicin nature, as unfolding, ongoing processes rather than as static ties among inert substances(p. 289).

Whites network theory constitutes a turn towards the incorporation of both meaning andbasic interactional processes leading to the formation of networks and cultural forms intonetwork research. White departs radically from the static orderliness of structuralist networkthinking. He also refrains from turning relational properties into individual attributesasdo social capital approaches or research on personal networks. In sum, relational sociologyunderstands networks as composed of culturally constituted processes of communicativeinteractions (Mische 2003, p. 262), providing for an inseparable commingling of network

relations and discursive processes. Culture and structure, language and relational ties arefusedwithin a sociocultural setting.

Another approach that takes meaning into account when studying social relations is that ofactor-network-theory (ANT) (e.g. Latour 2005; Law and Hassard 1999). In this perspective,which developed in anthropological science and technology studies in France and the UK,networks are a heterogeneous chain of associations made up of multidimensional and evolv-ing entanglements of human, non-human or collective actors (all are actants). Analyticalfocus is first on the multifaceted interconnections of a local, ego-centric network of an actor,before moving to the next connected local bundle of entanglements in which meaning gets

collectively established. Eventually these shifts and redefinitions betweenonemicro-networkof associations to the next over space and time add up to a larger narrative on transformationsof ideas and practices. The meaning of a network is realized collectively and distributed in
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work with large datasets shows some convergences (Mtzel 2009). In ANT, the term net-work is used as an evocative image, a conceptual heuristic, and a descriptive tool. The ANTapproach offers descriptions as explanations (Latour 2005): ANT accounts present stories inwhich actants are traced as to how they accomplish the processes of establishing associations,

i.e. constructing persons, groups, and the social by the attribution of meaning. Not surpris-ingly, with this analytical perspective ANT studies are particularly equipped to account forthe emergence of new social actors (e.g. Latour 1988) or categories (e.g. Bowker and Star1999) especially in the field of technological and scientific innovation.

To summarize, different strands of network research have applied three kinds of meth-ods: the early works of Norbert Elias, symbolic interactionism, and social anthropology, andthe ANT approach all rely almost exclusively on qualitative research techniques in order toexplore social network formations, but also to tackle the level of meaning connected to socialnetworks. The community studies tradition has mainly developed the statistical analysis ofpersonal networks in survey studies, which was later picked up by large-scale surveys such as

the GSS. Only classical network analysis has developed a strong focus on theformal analysisof full networks that we now see as representative for all of network research. The relationalsociology of Harrison White and others has picked up on this, while also complementingit with qualitative research techniques at times. The aim of relational sociology is to simul-taneously address structure and meaning in networks. We pick up on this by arguing thatthe combination of different methods that tackles the various dimensions of social networks(structure, connections, and meaning) offers a particularly fruitful route of research.

3 Systematizing quantitative and qualitative network research

As we have seen, sociological network research incorporates three main methodologicalapproaches: formal network analysis, statistical analysis of personal networks, and qualita-tiveresearch techniques. By andlarge these three approaches haveremained separate, withouta systematic integration either on the theoretical or on the methodological level. This sectionaims at a first systematic overview of these three approaches, and of their relations to eachother.

First, we briefly discuss the various aspects or dimensions of social networks that thesethree approaches are supposed to tackle. We follow relational sociology in claiming that

network research ideally simultaneously addresses the structure of networks, the formationor dissolution of particular connections, and the meaning attached to these structures andconnections. Then we show that the three approaches are methodologically related to thesethree dimensions, with formal network analysis focusing on structure, statistical analysis ofpersonal networks dealing with connections, and qualitative approaches tackling the meaningof networks.

3.1 Describing and explaining structure in formal network analysis

What are the units of analysis of the various strands of network research? The mainstreamof current network research is concerned with the analysis ofwhole networks in relativelybounded social contexts for example in firms or class rooms The aim is to detect regulari
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reduced to structures of relations, and that these mathematical procedures yield meaningfuldescriptions of complex social reality; and (2) that the important causes for the behavior ofthe units (usually: individual or corporate actors) and of the system as a whole are to befound within the pattern of relations in the system (and not somewhere in its environment).

In general, network analysis regards the structure of relations as the main cause for particulareffects, e.g. the rise of the Medici in Renaissance Florence (Padgett and Ansell 1993), theexodus of particular monks from a monastery (White et al. 1976, p. 749ff), the professionalsuccess of managers in a large company (Burt 1992), or the distribution of prestige and otherrewards in the literary market (Anheier et al. 1995).

In the sense of the structuralist credo, network structure is seen as explaining events in thesystem.5 We pick up here on the famous distinction of Max Weber between explanation andunderstanding as the main tasks of sociology. Understanding refers to the reconstructionof the meaning incorporated in or connected to social structures. Explanation, in contrast,establishes a causal connection between two phenomena (cause and effect). Other impor-

tant research ambitions include the probing exploration of specific properties of socialphenomena, and their mere description without establishing any causal connections.

In formal network analyses, both cause and effect are first of all located at the meso-levelof the social context. The cause is the structure of the network; its effects are manifold butconcern the relations of the parts in the system. However, these effects can then be traceddown to the level of the individual actor: the Medici family, for instance, benefits from itsfocal position in the credit and marriage network of Florentine families (Padgett and Ansell1993). Similarly, writers who occupy specific positions in the literary market can expectcertain turnouts (Anheier et al. 1995).

Rather than explaining the behavior of or rewards for individual actors, network analysissometimes explains the formation or dissolution of particular ties at the micro-level, thusshifts the unit of analysis from one actor to the dyad. This is typically done by relating var-ious measures of centrality, positions in blocks (from blockmodel analysis) or in cliques toindividual and dyadic attributes. For example, the friendship between members of rival fac-tions will probably undergo considerable strain at times of conflict and potentially dissolve; aperipheral actor is expected to defer to a member of the core elite in a network. The networkstructure (factional separation and core-periphery) are seen as explaining that a particulartie (friendship or deferral) exists at a particular point of time. Many network algorithms thusyield R2 values, indicating how much variance in the distribution of ties can be captured

(explained) by the particular network model (for example a blockmodel). While this focuson the explanation of connections is an integral part of network analysis, the explanation ofthe behavior of or rewards for individual actors requires an additional step, for instance anaction theory that accounts for why network position leads to individual behavior.

One advantage of focusing on the micro level (of actors behavior or of the propertiesof ties) is that a proper statistical explanation is possible, with rigorous hypothesis testingwith margins of error (significance). Here, the recent development of p*-models constitutesa major progress (Robins et al. 2007a, b; Wasserman and Robins 2005). P*-models compareactual network structures with random graphs. This allows the assessment to what extent the

actual networks display certain structural tendencies compared to what is expected by chance(e.g. Lazega and Pattison 1999). These results, of course, are context specific. This attention
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Table 1 Overview of aims of different methods in sociological network research

Qualitative methods Statistical analysis ofpersonal networks

Formal network analysis

Ambitions andresearch techniquesused

Understanding ofmeaning in

networks

Subjective meaning:qualitativeinterviews;intersubjective

meaning: conversa-tion/documentanalysis,participatoryobservation

Exploration anddescription

Situational analysis,ethnography

Pre-testing/critique

of data collection

methods

Qualitative interviews

Explanation at theindividual/ dyadic

level

Statistical analysis ofcorrelationsbetween networkcomposition andindividual attributesin combination withSNA: description ofnetwork populationsby attributes

explanation ofindividualdeterminants andeffects of networkpositions

Explanation ofindividual behavior

and connections

from network

position

Centrality measures,positional analysisfrom cliques orblocks

Exploration and

description of

network structure,

explanation ofsystems behavior

Blockmodel analysis,core/periphery andfaction analyses

to context is a major advantage of network research (Abbott 1997, p. 1166f). At the sametime, it poses a major problem if we want to generalize findings across contexts. One wayof combining context sensitivity with generalization is the use of multi-level analysis withparticular social contexts (e.g. school classes) as the meso-level influencing the individuallevel. Here it is possible to assess, for example, how much the individual tendency of highschool students to form inter-ethnic friendships is influenced by the ethnic composition ofthe student body of their schools (Baerveldt et al. 2007).

Analyses at the systemic level of whole networks, in contrast, usually remain confined tothe particular cases at hand. Although using quantitative research techniques, they are casestudies and not generalizable (Bellotti 2010). Ideally, then, network analytical results should

compare different networks, paying close attention to the comparability of measures andalgorithms. For example, if blockmodel analyses leads to different results in two contrastingnetworks, this does not tell us much about how different the two networks really are. Giventhe fact that changing a small number of ties could lead to entirely different blockmodel parti-tioning, the significance of such results remains small. Often, then, formal network analyseslead to exploration and description of social contexts, rather than statistical explanations.Typical methods applying to this systemic level include blockmodels, analyses of factions,and the identification of core/periphery structures.

A brief overview of the various approaches, their ambitions, and typical methods is given

in Table 1. For reasons of simplicity, the table lumps together methods of data collection anddata analysis under the heading of research techniques, rather than properly distinguishingbetween the two. To be sure, it is possible to collect data qualitatively, thus reconstructing the
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3.2 Explaining connections and individual attributes in personal networks

The statistical analysis of ego-centrically gathered personal networks, in contrast, is di-rectly concerned with the individual or with particular connections. As it focuses on the

embeddedness of individual actors, it cannot pay attention to the structure of a larger net-work. However, it can analyze structural elements such as triadic closure or density and canidentify weak ties. Statistical analyses of ego-centric networks are unable to consider theeffects of contextsone of the strengths of formal network analysisunless they can bereduced to individual attributes (e.g. place of residence or categories like age, class, andgender). This is precisely the strength of personal networks analyses: they allow relatingindividual attributes to the embeddedness of individuals in networks. This requires, how-ever, that embeddedness is reduced to an individual attribute: rudimentary measures of thestructure (density, weak ties etc.) and of the composition (by types of tie or by catego-ries like gender, age, class) of personal networks are computed as variables signifying this

embeddedness. These can be analyzed with regard to their correlations to other individualattributes. For example, Gwen Moore shows that personal discussion networks of women inthe US tend to comprise more kin and more neighbors, but less friends and coworkers thanthose of men (1990, p. 729f). And Omar Lizardo demonstrates that the density of personalnetworks correlates with class background and with the diversity of cultural consumption(2006).

Alternatively, individual ties are used as the cases of statistical analysis. The analysisthen deals with the question of why particular ties show specific properties (like transitivityor homophily, e.g. Hallinan and Williams 1989; Louch 2000). If this is done, it is advis-

able to control for the impact of individual respondents on their connections, for exampleby entering them as the meso-level in a multi-level analysis (Lubbers et al. 2010). Thisallows for the simultaneous analysis of effects on connections and on the personal networksof individuals. Statistical analyses of personal networks lead to explanations in the statis-tical sense of the properties of connections. They can also be used to explain the effectsof connections on individual attributes like income or subjective happiness. They allow forthe testing of hypotheses concerning the interrelations of these variables. This is the mainresearch ambition of the community studies and of the subsequent research on personalnetworks and social capital as briefly discussed in the second section. These explanationsat the individual level, however, have to bracket context. Statistical analyses of personal

networks thus amount to considerable advantages in the research process (mainly in therigorous testing of hypothesized network effects when controlling for effects of individ-ual attributes). But they do so by reducing networks to attributes of actors, thereby losingmuch of the structural intuition guiding sociological network research (Freeman 2004,p. 3).

Instead of using variables about personal networks, it is also possible to enter measuresof network position derived from formal analysis of whole networks (see Sect. 3.1) intostatistical analyses. For example, various measures of centrality, memberships in cliquesor in blocks (in blockmodels) can then be related to the individual attributes of the actors,treating the network position either as the cause or as the effect of these attributes (e.g.Anheier et al. 1995). In these cases, network position is explainedby individual attributes,or explains these causally. Less demanding, other formal network analyses often feature a
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3.3 Understanding of meaning in qualitative network research

Both statistical analyses of personal networks and formal network analysis follow the struc-tural intuition by treating networks as the aggregate of connections (personal networks) or

as patterns of connections (network structure). The strategy of structural analysis developedas an interdisciplinary endeavor. Largely due to new computational models, it has gainednew prominence and widespread acceptance in the social sciences. As a research strategyfocusing on proximate causes, it may be useful to reduce networks to the mere structure ofrelations (Granovetter 2007), without asking for the underlying meaning, thereby reducingthe complexity of social reality considerably (Holland and Leinhardt 1977, p. 387). However,as research in the past has already indicated, and as it has been criticized repeatedly (e.g.Brint 1992; Emirbayer and Goodwin 1994; Mitchell 1974), an analysis of structures andconnections has to be complemented with a concern for meaning, for the linguistic forms,narratives, cultural practices, and expectations embodied in networks. Networks are not mere

structures but actually cultural in nature, as the discussion of relational sociology in the sec-ond section has pointed out (see also Pachucki and Breiger 2010). Yet the problem is thatcultural aspects are qualitative and particular, pushing researchers toward taxonomic spec-ificity, whereas concrete social relations lend themselves to analysis by formal and highlyabstract methods (DiMaggio 1992, p. 120). Another problem yet to be addressed (whichsymbolic interactionism, relational sociology, and actor-network theory have pointed to) isto conceptualize networks not as stable structures, but as continuously created, reproduced,and modified in social processes. These can be coined as action, interaction, transaction, orcommunication, but they have to be seen as both effecting and resulting from networks.

Indeed, how the phenomenological and interacted reality of expectations, stories, andidentities in networks is translated into measurement constructs is a big question in networkresearch. We argue that qualitative research techniques including different types of qualitativeinterviews (problem-centered, narrative, biographical etc.), conversation analysis, documentanalysis, and participant observation provide a major route to these processual and culturalaspects, thus aiming primarily for an understanding (in the Weberian sense) of the meaningembodied in networks, and the processes of creating, sustaining, and modifying this meaning.

Meaning can be theorized at the subjective level of individual motivations and perceptions(following Weber), or at the inter-subjective level where meaning is diffused and reproducedin communication (following Luhmann 1990, 1996,p.59ff).The subjective level of meaning

relates to a focus on individual actors and their agency and assumes that peoples thoughtsare consequential for what they dohence it would be important to understand peoplesmotivations when pursuing particular relationships and dropping others and thereby activelyconstructing their networks. According to Fine and Kleinman, social relationships are verymuch shaped by the subjective meaning of the people involvedwhich qualities do theyascribe to the relation, how do they view the other, and what expectations do they haveregarding the others actions (1983, p. 101ff). The level of subjective meaning is usuallylinked to a focus on individual action and aims at identifying individual motivations. This isusually done in qualitative or quantitative interviews.

At the same time, meaning is not only to be found in actors heads, but also inter-subjectively realized and negotiated in the process of communication (following Luhmannsconcept of meaning). Thus we find that dyadic relationships are constructed and consti-
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of meaning can be analyzed by looking at actual communication processes, for example,exchanges of Florentine patronageletters (McLean 1998, 2007), editorial exchanges betweennewspapers (Mtzel 2002), turn-taking among managers in business meetings (Gibson 2003,2005), or interaction in teenage baseball teams (Fine 1979). While interviews reactively aim

at reconstructing the subjective level of meaning, the inter-subjective level of meaning canbest be tapped with non-reactive methods: the analysis of documents (as in the work ofMcLean and Mtzel) or participatory observation (as in the work of Gibson and Fine). Asthese examples show, the analysis of the communicative processes creating, sustaining, andmodifying networks (and the meaning embodied in them) can proceed qualitatively (Fineand McLean), but it can also include the codification and subsequent quantitative analysis ofturn-takings (Gibson) or the negotiation of identities and positions (Mtzel). As always, qual-itative methods are more geared towards detecting meaning, whereas quantitative methodsaim at network structure (and its construction in communicative processes).

The inter-subjective level of meaning is part of a perspective which views transactional

processes and social relations as the basic components of social life, as in Harrison Whitesnetwork theory (White 1992, 2008; White et al. 2007).6 In order to point to its shared andsocially reproduced nature, this perspective refers to meaning as stories (Tilly 2002,pp.8ff,26ff) and culture (Mische 2003; Yeung 2005). Stories construct relational configurationsthey relate actors to each other in narratives about their interaction. All communicative pro-cesses involve this kind of story-telling which leads to a joint construction of social reality.Apart from stories, the culture of a network comprises the identities of the actors involved,individual as well as corporative and collective (McCall and Simmons 1978). Other aspectsof meaning in networks include attitudes, values, categories, and cultural blueprints or mod-

els for social relationships (e.g. Erickson 1988; McLean 1998; Rytina and Morgan 1982;White 1993; Yeung 2005). All of these forms of meaning are to be found at both levels: thesubjective and the inter-subjective. They form part of the meaning structure of social net-works (Fuhse 2009), and its understanding is the first important goal of qualitative networkresearch.

To be sure, qualitative research does not aim at an explanation of processes in or resultingfrom networks based on hypothesis testing in the statistical sense. Qualitative research mayenrich and sharpen quantitative analyses in mixed method designs, and qualitative data canbe used to follow initial hypotheses about a social phenomenon. But qualitative methods innetwork research are primarily used to gain an understanding of the meaning networks have

for the actors, as well as of the inter-subjective meaning diffused, shared, and modified innetworks.7 Apart from understanding as its first goal, qualitative network research can alsoaim at two other important tasks:

6 In this regard, Whites (and our) approach resembles symbolic interactionism which starts from interactionas the basic process creating meaning (and social structures). However, while authors like Mead and Blumerresort to the subjective level of meaning as somehow being involved and creating social life, Tilly (2002,2005) and White (White et al. 2007) bracket the subjective level, pointing to the supra-personal nature ofsocial processes.7 To base the testing of hypotheses on qualitative data is tricky because network researchers cannot trust the

descriptions the actors involved provide of the social structure they are embedded in: For example, Barnesfamously introduced the network concept to show that the Norwegian fishermen in Bremnes may perceiveand describe their social structure in terms of three classes even though they are actually enmeshed in a
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The second goal consists of the exploration and description of social networks with regardto their structure, composition, and the meaning associated with them. These are of particu-lar importance for a first approximation of networks in situations that have not been studiedas networks before, and where network data are not easily collected. For example, social

anthropology relies heavily on the qualitative description of networks in so-called situa-tional analysis, through lengthy ethnographic immersion of the researcher into the socialcontext studied (that sometimes eludes the use of more formal research techniques like inter-views etc.). Historical sociology often derives from documents and exemplary data (e.g.composition of small groups seen as representative for a wider movement) a fairly accuratepicture of the overall structure of networks in a particular context without being able to studythem quantitatively (e.g. Ikegami 2005; Mann 1986; Tilly 1995). Historical sociologys net-works are removed in time and therefore do not lend themselves to the collection of networkdata (unless available in documents). A parallel problem emerges in marginal networks, par-ticularly in criminal networks like gangs, terrorist groups, or the mafia. Here, researchers may

be able to interview a few participants (like prosecuted members) or external experts, or theycan draw on interrogation and court protocolsbut they are hardly able to interview the fullnetwork of members or even a representative sample. In all these cases, the formal analysisof full networks or the statistical analysis of personal networks is not possible. Therefore, theresearch accounts have to focus on producing a convincing narrative, combining a thoroughtheoretical argument with forceful exemplary findings from the qualitative (and quantitative)material.

The third goal of qualitative research techniques consists of pre-testing or criticizingquantitative methods of data collection. As all other quantitative social scientists, we need to

develop thorough techniques for collecting network data (Marsden 2005). Data for statisticalanalysis of personal networks are usually collected by way of network generator questions,like the famous Burt name generator that asks for the people ego talked with about importantmatters over the last 6months (Burt 1984). For formal network analyses, data are typicallygathered asking respondents about their relationships to fixed lists of alters (like all the chil-dren in their school class). In both cases an understanding of what the respondents meanwith their answers is needed. In particular, we have to ask ourselves whether respondentsattribute the same meaning to the same question, or whether there is systematic variation(by age, gender, network context etc.) in the way these questions are answered. For example,respondents may have different things in mind when responding to the Burt name generator

question (Bearman and Parigi 2004), when saying that a relationship is a loving one (Yeung2005), or when referring to people as friends (Fischer 1982). The important task here isto elicit categories that will prompt to answer to these questions in a similar way. Of coursethey should not give the same answers; but at least they should have the same question inmind.

4 Combining methods in network research

As the discussion so far has shown, the three main methodological approaches pursue dif-ferent aims and enter the research process at different phases. It is quite natural, then, thatthe three approaches should be combined in order to arrive at a fuller picture of social net-
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networks (ideally comparing various networks). But it can also lead to propositions aboutthe meaning attached to these networks, to be studied with qualitative research techniquessuch as problem-centered or narrative-biographical interviews. Since networks feature struc-ture and meaning as two dimensions that are inextricably intertwined, approaches com-

bining understanding with explanation are particularly promising. Here, we only want torefer to a small number of exemplary studies pointing in this direction that has emergedas one of the most fruitful and innovative strands in the social sciences over the pastyears.

4.1 Combining personal networks and formal network analysis

We already briefly pointed out two important ways in which statistical analysis of personalnetworks can be combined with formal network analysis (Sect. 3.2). First, the samples of

actors in full networks are describedby looking at their composition in terms of individualattributes like sex, age, class, and race. This procedure is as common as it is theoreticallysimple: we want to know who the people in the study are (of course we will not really findout by looking at a small set of attributes), if only to point out to what kinds of populationsour findings might apply. Things get more complicated when we identify different positionsor subgroups by way of formal network algorithms, and then describe these with regard toindividual attributes. Then some kind of causal connection between attributes and networkposition is implied or suggested.

For example, Anheier et al. (1995) identify seven blocks of structurally equivalent actors(from cultural elite to light culture and periphery) in a sample of 139 writers, and

then compare these blocks in their distribution of economic capital (income), social capital(membership in professional associations), cultural capital (education), and symbolic capital(types of texts produced). Anheier et al. do not state whether network position explains theseindividual attributes, or whether these attributes explain the network position. Following aBourdieuan theoretical framework, they establish a correspondence between network loca-tion and, for example, membership in professional organizations without claiming that oneleads to the other. In any case, the combination of formal network analysis with statisti-cal analysis of individual attributes allows for the detection of systematic correlations andpossibly even to an explanation of causes or effects of network position on the individuallevel.

In their famous study of the credit and marriage networks among Florentine patricians,Padgett and Ansell (1993) similarly relate the position in network blocks to the gross wealthand the neighborhood residence of the families. Here the implication is that network positionis more important than economic wealth in explaining the rise of the Medicithus networkposition and individual attributes are compared with regard to their impact on individualsuccess. The rise of the Medici is more than individual attainment, though: it concerns thereconfiguration of the whole network and should be regarded as an emergent property of theentire network.

An algorithm that systematically aims at identifying causal connections between attributes

and network formation is SIENA (Simulation Investigation for Empirical Network Analysis).SIENA is a tool to test for causal effects between various individual and dyadic characteristicsin longit dinal net ork data In this a it becomes possible for e ample to assess hether
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2006). Here the ambition is clearly to explain social phenomena, but these are only locatedat the individual level (of attributes and connections) rather than at the systemic level.

Other important examples for the combination of statistical analyses with formal networkalgorithms include Burts work on the effects of social capital varying by gender (1998),

research by Baerveldt et al. on ethnic homophily in high schools (2007),andMartinsworkonthe correlation between status positions and the attribution of sexiness in commune networks(2005). The latter is particularly interesting because it not only relates individual attributes(like gender and age), but also relational attribution (of sexiness) by others to positions innetworks, thus aiming at a phenomenology of the inter-subjective (Martin 2003, p. 38) ofself- and other-ascriptions. All of these studies, however, have to remain at the individuallevel, and can only aim at explanations of properties of individuals or their connections. Anexplanation at the systems level, in contrast, has to emerge from a comparison of multiplesocial contexts (which is partly possible by way of multi-level analyses).

4.2 Personal networks and qualitative methods

Research that combines statistical analyses of personal networks with qualitative methodsfocuses on the level of the individual actors. It looks both at the structural embeddedness ofactors and at the meaning the actors derive from and attach to their connections to others.Thus it comes closest to Webers original intention, combining a structural explanation ofindividual actions with an understanding of the subjective meaning leading to these actions.The main ambitions are to look for systematic connections between individual cognition andembeddedness, but also to criticize and develop interview techniques for the collection of

quantitative network data.Exemplary research tackling the first of these ambitions comes from the research group

around Laura Bernardi on networks and the decision to have children and from Elisa Bellottion friendship structures and strategies of young single people in Northern Italy. Bernardiand her coauthors focus on the impact of personal relationships to friends, family, and oth-ers on the decision of young couples in two German cities to have or not to have children(Keim et al. 2009). They thus look specifically for the subjective meaning that comes frominfluence processes in networks, and relate it to the structure and composition of personalnetworks. Similarly, Bellotti links quantitative data on personal networks of young Italiansingles to a qualitative study of the subjective meaning attached to these networks (Bellotti2008, p. 323ff). Very much in line with Webers approach, Bellotti constructs four idealtypes of structures of personal networks and links these with typical individual strategiesand motives. Both Bernardi and Bellotti view personal networks and subjective meaning asinter-dependent: while networks produce specific individual orientations, these in turn havean impact on how actors construct their intimate relationships. Other research pointing in thisdirection includes Ann Misches research on the organizational affiliations and leadershipstyles of Brazilian youth activists (2008, pp. 38ff, 186ff).

Brian Uzzis work on structural embeddedness as a logic of exchange that shapes motivesand expectations and promotes coordinated adaptation (1996) also combines an inquiry into

personal networks,qualitativemethods and subjective cognition. First, Uzzi conductedethno-graphic fieldwork and problem-oriented interviews at New York City apparel firms, inquiringinto the s bjecti emeanings and f nction of interfirm ties On the basis of these inter ie s he
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The second intention of combinig personal networks statistics and qualitative methods canbe exemplified by the study ofBearman and Parigi (2004). Their work aims at finding outwhat people think of when they answer the common network generator question: Lookingback over the last 6months, who are the people with whom you discussed matters impor-

tant to you? After answering, the respondents were asked (in an open question) what kindof topics they had thought about when answering the questionwhat counts as importantmatters in the minds of the respondents. The goal was to find out what subjective meaningrespondents associated with the rather vague name generator question, and whether therewas systematic variation in the way respondents answered the question. For this, the answersto the second, open question were coded into nine domains of topics from general news andpolitical issues to relationships and religion, thus quantifying the qualitative results. Mostimportantly, Bearman and Parigis analysis shows a number of gender effects:

married women (try to) talk about relationships with their husbands while their hus-

bands talk about relationships with their friends. Similarly, married men report (mono-logue?) conversations about ideological issues with their wives, the latter whom reporttalking about ideological issues with their acquaintances. (2004, p. 544)

Given the fact that husbands and wives should statistically report the same topics ofconversations in their conversations, Bearman and Parigi conclude that gender differencesobserved in studies of personal networks may be an artefact of the different understandingsof the network generator question, and of different respondent behavior (2004, pp. 547, 553).Like the more quantitative studies ofFischer (1982)and Yeung (2005), Bearman and Parigiswork forms part of a research tradition that looks for systematic distortions in the way peoplerespond to personal network questions in surveys. Qualitative methods are particularly usefulfor this because they can discern diverse interpretations from different groups of respondents.

4.3 Formal network analysis and qualitative methods

Research that combines formal network analysis with qualitative methods studies the struc-ture of relations in whole networks. It provides descriptions of complex social situations,points to particular positions individual or groups of actors hold and offers structural causesfor the actions of individuals and the network as a whole. While some studies focus on the

exploration of social contexts and the subjective meaning these contexts have for participat-ing actors, others explain emergent properties of the network as a whole based on sharedmeaning and distributed cognition of its actors. Such combinations thus provide a structuralexplanation of individual or systemic actions with an understanding of the network meaningleading to these actions. The main ambition of this combination is to look for systematicconnections between the pattern of relations and the shared cognition of the actors.

From its inception, formal social network analysis has included theuseof qualitative meth-ods in the process of data gathering. Indeed, network analytic measures are well-equipped forthe (algebraic) quantification of qualitative data (Breiger 2004). Seminal examples include

Sampsons ethnographic study of the relations in a New England monastery (1968), or Faulk-ners study of the patterning of business relations between movie directors and composers inHollywod using formal algorithms on data collected in ethnographic field work (1983).
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distinctive crowds within one stock exchange by using qualitative data from interviews andparticipant observation, Baker formally analyzes the network structure of actual exchangesbetween the traders. In a third step, he interviews members of the two groups about theircognition and interpretation for such structure. His study thus combines a formal network

analysiswiththesubjective,cognitiveinterpretationofthestudiedactors.Heisabletoexplainaction of the network overall and the subjective meaning of its individual actors.8

Works that have been pushing explanations of the entire network using formal networkanalysis, qualitative research techniques and a focus on inter-subjective meaning can in par-ticular be found in the field of economic sociology. Joel Podolnys work shows how meaningand structure conflate when actors signal status positions and receive certain status positionsbased on the ascription of others (Podolny 2005). For example, the system of wine appella-tion creates meaning for its constituents (i.e. wineries) by forming a network of associationson the basis of similarity and difference. The wineries then search for cultural cues, throughobservation, critics evaluation or media reports, as to how to position themselves in the

market of competitors (Benjamin and Podolny 1999). This is in line with Whites model forproduction markets (1981, 2002) in which market actors relate to each other through signalsand through the stories they tell about themselves and each other. It is through the telling ofstories, as discussed above, that meaning emerges. Empirical studies of this mechanism, usingqualitative research techniques (content analyses of written documents and interview data)and formal network analyses, include Kennedys work on the early computer workstationmarket (2005) and Mtzels work on a newspaper market (2002).9

5 Conclusion

This paper has outlined how different quantitative and qualitative techniques have been fruit-fully combined to analyze different aspects of networks, in particular network structure andthe meaning connected to it. We have shown that historically, both quantitative and qual-itative analyses have been important in sociological network research, and that the role ofqualitative research primarily relates to aspects of meaning in networksactors orientationsand motivations, the symbols, schemes, and scripts circulated in communicative processes.We follow relational sociology of Harrison White and others in claiming that (1) networksare real social structures and not just measurement constructs (White 2008, p. 36). (2)

As real social structures, they are inextricably intertwined with meaning, that is: symbols,narratives, identities, expectations, and categories (Fuhse 2009; Mtzel 2009). Networks area constructed reality in the sense of the Thomas theorem: If men define situations as realthey are real in their consequences (Merton 1948, p. 193). Thus networks do not consist ofmere structural patterns, but of the subjective and interactive definitions of these structuralpatterns. (3) While it may be useful to reduce networks to formal algebraic representationsof patterns of relationships, the simultaneous attention to meaning can lead to additionalinsights, complement or even correct some of the important research findings of sociologicalnetwork research.

8 The challenge for these types of studies is to collect interview data on all members of the network. This iseasier done in small, bounded settings, and gets much more difficult in larger settings. See Kirke (2010) for a
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Consequently, we call for network research (4) to recognize the importance of lookingat networks from different angles, applying different research techniques in order to arriveat a fuller and more accurate picture of complex and multi-faceted social structure, andspecifically (5) to embrace qualitative research techniques as an important complement to

its quantitative and structuralist stance. Since the three different research methods tackledifferent dimensions of networks, and pursue different aims (description, understanding,explanation) at different levels (individual, systemic), (6) research designs that combinetwo (or even three) types of method proves particularly fruitful. Examples of such researchhave been briefly discussed in Sect. 4. While meaning can be found and studied both onthe subjective level (in peoples heads) and on the inter-subjective level of communicationbetween actors, we claim that (7) too much research focuses solely on the subjective level, asmainly tapped in qualitative and quantitative interviews. Instead, sociology should pay moreattention to the inter-subjective level and look at the cultural forms (symbols, categories etc.)diffused and reproduced in processes of communication. In recent years, a few studies have

paid closer attention to the level of communicated meaning (e.g. McLean 1998; Mtzel 2002;Gibson 2005). We speculate that research, using both quantitative and qualitative techniques,dealing with questions of how networks get enacted and constructed, how identities of actorsare defined and negotiated, and of how network formations change in the course of com-munication might be one of the most challenging and fruitful areas of sociological networkresearch in the future.

Acknowledgements Wewouldliketothanktheparticipantsinaworkshoponmixedmethodsinsociologicalnetwork research in Berlin in January 2009 and two anonymous reviewers for their helpful comments.

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