Introduction

In previous parts of this book, we have presented a wide range of techniques for analyzing social networks. We have discovered that one structural concept can often be measured in several ways (e.g., centrality). We have not encountered the reverse, that is, a single technique that is able to detect different kinds of structures (e.g., cohesion and centrality). In this final chapter, we present such a technique, which is called blockmodeling.

Blockmodeling is a flexible method for analyzing social networks. Several network concepts are sensitive to exceptions; for instance, a single arc may turn a ranking into a rankless cluster (Chapter 10). Empirical data are seldom perfect, so we need a tool for checking the structural features of a social network that allows for exceptions or error. Blockmodeling and hierarchical clustering, which are closely related, are such tools.

Although blockmodeling is a technique capable of detecting cohesion, core-periphery structures, and ranking, it does not replace the techniques presented in previous chapters. At present, blockmodeling is feasible and effective only for small dense networks, whereas the other techniques work better on large or sparse networks. In addition, blockmodeling is grounded on different structural concepts: equivalence and positions, which are related to the theoretical concepts of social role and role sets. Blockmodels group vertices into clusters and determine the relations between these clusters (e.g., one cluster is the center and another is the periphery). In contrast, the techniques discussed in previous chapters, such as the measures of centrality, compute the structural position of each vertex individually.

Solidarity, shared norms, identity, collective behavior, and social cohesion are considered to emerge from social relations. Therefore, the first concern of social network analysis is to investigate who is related and who is not. Why are some people or organizations related, whereas others are not? The general hypothesis here states that people who match on social characteristics will interact more often, and people who interact regularly will foster a common attitude or identity.

In this part of the book, which covers Chapters 3 through 5, we discuss several measures of cohesion. You will learn to detect cohesive subgroups within several types of social networks.

Introduction

Social networks usually contain dense pockets of people who “stick together.” We call them cohesive subgroups, and we hypothesize that the people involved are joined by more than interaction. Social interaction is the basis for solidarity, shared norms, identity, and collective behavior, so people who interact intensively are likely to consider themselves a social group. Perceived similarity, for instance membership of a social group, is expected to promote interaction. We expect similar people to interact a lot, at least more often than with dissimilar people. This phenomenon is called homophily: Birds of a feather flock together.

In this chapter, we present a number of techniques to detect cohesive subgroups in social networks, all of which are based on the ways in which vertices are interconnected. These techniques are a means to an end rather than an end in themselves. The ultimate goal is to test whether structurally delineated subgroups differ with respect to other social characteristics, for instance, norms, behavior, or identity. Does the homophily principle work? May we conclude that a cohesive subgroup represents an emergent or established social group?

To some of its readers, this book is an introduction to social network analysis; to other readers, it is a manual to Pajek software (http://pajek.imfm.si/doku.php). To us, it is both. As Patrick Doreian argued in his review of our book [In: Social Networks 28 (2006) 269–274], an understanding of social network analysis is required for proper use of Pajek, and, vice versa, understanding the concepts and logic of Pajek fosters comprehension of network concepts. In this second edition, we have aimed to strengthen both aspects, updating the discussion of the Pajek interface and commands to include several capabilities that have been implemented since we submitted the text of the first edition, such as multiplex networks (Section 1.3.1), eigenvector centrality (Section 6.5), matrix multiplication (Section 11.3), and using Pajek output in R (Chapters 5 and 13). The new capabilities cover some important advances in social network analysis, including random graph models to which we have dedicated a new chapter.

Experimental studies of racial and ethnic minorities in the United States have focused on the influence of racial considerations in political reasoning, information processing, and political participation. Studies have analyzed the types of messages and frames that prime racial evaluations of issues, the effect of racial arguments on opinions, and the impact of racial cues on political choices. Underlying this research is the premise, developed in observational studies (e.g., Bobo and Gilliam 1990; Dawson 1994; Tate 1994; Lien 2001; Chong and Kim 2006; Barreto 2007), that there are racial and ethnic differences in how individuals respond to cues and information. For this reason, almost all studies give special attention to the mediating and moderating influences of racial group identification, a core concept in the study of minority politics.

There are too few studies yet to constitute a research program, but the initial forays have successfully featured the advantages of experimental design and distinct perspectives of minority groups. We review the methodology and findings of these experimental studies to highlight their contributions and limitations and to make several general observations and suggestions about future directions in this field. As we show, randomization and control strengthen the internal validity of causal inferences drawn in experiments; however, of equal importance, the interpretation and significance of results depends on additional considerations, including the measurement of variables, the external validity of the experiment, and the theoretical coherence of the research design.

Until the mid-twentieth century, the discipline of political science was primarily qualitative – philosophical, descriptive, legalistic, and typically reliant on case studies that failed to probe causation in any measurable way. The word “science” was not entirely apt.

In the 1950s, the discipline was transformed by the behavioral revolution, spearheaded by advocates of a more social scientific, empirical approach. Even though experimentation was the sine qua non of research in the hard sciences and in psychology, the method remained a mere curiosity among political scientists. For behavioralists interested in individual-level political behavior, survey research was the methodology of choice on the grounds that experimentation could not be used to investigate real-world politics (for more detailed accounts of the history of experimental methods in political science, see Bositis and Steinel 1987; Kinder and Palfrey 1993; Green and Gerber 2003). The consensus view was that laboratory settings were too artificial and that experimental subjects were too unrepresentative of any meaningful target population for experimental studies to be valid. Furthermore, many political scientists viewed experiments – which typically necessitate the deception of research subjects – as an inherently unethical methodology.

The bias against experimentation began to weaken in the 1970s when the emerging field of political psychology attracted a new constituency for interdisciplinary research. Laboratory experiments gradually acquired the aura of legitimacy for a small band of scholars working at the intersection of the two disciplines.

The work in this volume provides profound evidence of the value of randomized experimentation in political science research. Laboratory and field experiments can open up new fields for exploration, shed light on old debates, and answer questions previously believed to be intractable. Although acknowledgment of the value of experimentation in political science is becoming more commonplace, significant criticisms remain. The oft-repeated shortcomings of experimental research tend to center on the practical and ethical limitations of randomized interventions. I begin this chapter by detailing some of these criticisms and then explore one means of extending the value of randomized interventions beyond their original intent to ameliorate some of these same perceived limitations.

One of the most prominent critiques of this genre is that randomized experiments tend to be overly narrow in scope in terms of time frame and subject matter, as well as high in cost. Although short-term experiments may incur costs similar to observational research, they often focus on a single or just a few variations in an independent variable, seemingly limiting their applicability to a breadth of topics that a survey could cover. The high cost associated with long-term data collection and the necessity of maintaining contact with the subjects involved impedes the likelihood of gathering information on long-term outcomes. There are also few incentives to conduct interventions in which the impacts may only be determined years down the road.

Deliberation has become, in the words of one scholar, “the most active area of political theory in its entirety” (Dryzek 2007, 237). Our exploration of the relationship between experiments and deliberation thus begins with normative theory as its starting point. Experiments can yield unique insights into the conditions under which the expectations of deliberative theorists are likely to be approximated, as well as the conditions under which theorists' expectations fall short. Done well, experiments demand an increased level of conceptual precision from researchers of all kinds who are interested in deliberative outcomes. However, perhaps most important, experiments can shed greater scholarly light on the complex and sometimes conflicting mechanisms that may drive the outcomes of various deliberative processes. In other words, experiments allow researchers to better understand the extent to which, the ways in which, and under what circumstances it is actually deliberation that drives the outcomes deliberative theorists expect.

Our strategy for this chapter is to highlight the strengths of experiments that have already been completed and to point to some aspects of the research that need further improvement and development. We aim to discuss what experiments can do that other forms of empirical research cannot and what experiments need to do in light of the normative theory.

The experimental study of politics has grown explosively in the past two decades. Part of that explosion takes the form of a dramatic increase in the number of published articles that use experiments. Perhaps less evident, and arguably more important, experimentalists are exploring topics that would have been unimaginable only a few years ago. Laboratory researchers have studied topics ranging from the effects of media exposure (Iyengar and Kinder 1987) to the conditions under which groups solve collective action problems (Ostrom, Walker, and Gardner 1992), and, at times, have identified empirical anomalies that produced new theoretical insights (McKelvey and Palfrey 1992). Some survey experimenters have developed experimental techniques to measure prejudice (Kuklinski, Cobb, and Gilens 1997) and its effects on support for policies such as welfare or affirmative action (Sniderman and Piazza 1995); others have explored the ways in which framing, information, and decision cues influence voters' policy preferences and support for public officials (Druckman 2004; Tomz 2007). And although the initial wave of field experiments focused on the effects of campaign communications on turnout and voters' preferences (Eldersveld 1956; Gerber and Green 2000; Wantchekon 2003), researchers increasingly use field experiments and natural experiments to study phenomena as varied as election fraud (Hyde 2009), representation (Butler and Nickerson 2009), counterinsurgency (Lyall 2009), and interpersonal communication (Nickerson 2008).

Affect-driven dual process models dominate contemporary psychological theorizing about how people think, reason, and decide (Chaiken and Trope 1999; Wilson, Lindsey, and Schooler 2000; Gawronski and Bodenhausen 2006). Although most dual process models focus on accuracy-efficiency trade-offs, hundreds of more recent experiments document the pervasive effects of unconscious thoughts, feelings, and behaviors on attitude formation, attitude change, preferences, and decision making. These studies reveal important differences between the influence of conscious and unconscious processing on how people think and reason. The explicit incorporation of unconscious cognition into models of political beliefs challenges the extant understanding of mass beliefs. Much of what we political scientists claim to know about citizens' political beliefs and attitudes is based on verbal self-reports. The vast majority of the empirical evidence in political behavior research is based directly on verbal responses to explicit questions. This reliance on explicit measures of political attitudes and behaviors is problematic because these measures assume people have direct access to their “true” beliefs or attitudes and are willing and able to accurately report them (Wittenbrink 2007).

Most of our daily life is experienced unconsciously, outside awareness. Consequently, it is quixotic to focus exclusively on conscious attitudes while ignoring considerations that escape conscious awareness. Recent estimates put the total human capacity for visual sensory processing in the neighborhood of 10 million bits per second, even though we can become conscious of only about 40 bits per second (Norretranders 1998).