Measurement quality issues in dyadic models of relationships

Interpersonal relationships are an important and integral part of numerous social science research agendas. Analytical tools have been created in the last 10 years that model dyadic interactions. In particular, this article focuses on the dyadic models of Fienberg and Wasserman [Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt, S. (Ed.), Sociological Methodology. Jossey-Bass, San Francisco.], Holland and Leinhardt [Holland, P.W., Leinhardt, S., 1981. An exponential family of probability densities for directed graphs. Journal of the American Statistical Association 76 (1981) 33–51.], Iacobucci and Wasserman [Iacobucci, D., Wasserman, S., 1988. A general framework for the statistical analysis of sequential dyadic interaction data. Psychological Bulletin 103 (1988) 379–390.] and Wasserman and Iacobucci [Wasserman, S., Iacobucci, D., 1986. Statistical analysis of discrete relational data. British Journal of Mathematical and Statistical Psychology 39 (1986) 41–64.]. However, measurement issues like reliability and validity, as discussed by Allen and Yen [Allen, M.J., Yen, W.M., 1979. Introduction to Measurement Theory. Brooks/Cole, Monterey, CA, 1979.], Nunnally [Nunnally, J., 1978. Psychometric Theory, 2nd edn. McGraw-Hill, New York, NY, 1978.] and Uebersax [Uebersax, J.S., 1988. Validity inferences from interobserver agreement. Psychological Bulletin 104 (1988) 405–416.], have not been considered in conjunction with these models, and little is known about the empirical performance of the dyadic models under sub-optimal measurement quality conditions. We offer two essential approaches to ascertaining the level of measurement error in the observed indicators of social ties and relationships. The first approach combines latent class and social network models in one integrated framework and allows for the simultaneous study of measurement and dyadic structural issues. The second approach is an alternative that may be more useful to social science researchers, both because the method is more accessible and because researchers could apply the techniques to data they have already partially analyzed. This approach is a two-staged procedure whereby in the first stage, a probability model based on latent class analysis is estimated which provides an indication of the measurement quality in the data. In the second stage, traditional social network models are estimated. To investigate the implications of different levels of measurement error for interpreting the nature of the network ties and the dyadic parametric performance, we also designed a Monte Carlo experiment. Measurement error is simulated as the likelihood of a binary relational choice (for simplicity) being inaccurately classified, where incorrect diagnoses can result from poor interitem agreement (i.e., unreliability) or poor interrater agreement. The simulation can be used by researchers in combination with the two-stage approach. The results of the simulation provide guidelines for situations when social network models can withstand a reasonable degree of sub-optimal measurement quality and highlight adverse conditions which can significantly affect the performance of the modeling approach. Further, the simulation shows that sample size assists in reducing the chances of making Type II errors, but it does not compensate for biases in parameter estimates in the presence of increasing error. Finally, the measurement and dyadic analytical methods are applied to a real dataset describing interorganizational relational activity using multiple raters. Recommendations are offered to guide the researcher in making decisions about research design when using dyadic models.     

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.     

Group based trajectories of network formation and dynamics

In this paper, we propose the application of a semi-parametric statistical methodology called Group-Based Developmental Trajectory Analysis to studying the dynamics of social networks. We begin with a discussion of theoretical problems in network analysis that may benefit from this approach. Next, we describe the methodology and how it can be applied to dyadic network data as well as aggregated node level data. We then demonstrate the methodology by analyzing the Newcomb Fraternity and the van de Bunt student data sets. Finally, we conclude with a discussion of potential directions for further research.     

Egocentric,sociocentric, or dyadic?: Identifying the appropriate level of analysis in the study of organizational networks

This paper examines the use of individual, dyadic and system-level analyses in the study of relational data in organizational networks. We argue that dyadic analyses are particularly appropriate when the dependent variable is quantitative and/or involves multiple behaviors. We show that system-level analyses, by aggregating potentially significant information, provide a less grounded account of the relations across networks than do dyadic analyses. Using examples from a study of corporate political behavior, we contrast dyadic analyses with those at both the individual and system-levels. Variables measured in raw dyadic form consistently perform better in accounting for similarity of corporate political behavior than do variables measured by taking system-level properties into account. Our findings suggest that although individual and system-level analyses are useful in a number of situations, dyadic analyses are a flexible means to examine the effects of multiple networks at multiple levels.     

The ties that bind: The effect of clustering on dyadic relationships

This study explores the extent to which one can explain the dynamics of a given relationship or set of relationships by mapping the social network in which they exist. Specifically, it focuses on the effect of cluster membership on the evolution and intensity of dyadic relationships. The results suggest that relationships are as much a product of the social networks of the two individuals involved as of their personal needs, histories or desires.     

A Liang–Zeger Method for Modeling Dyadic Interdependence in the Analysis of Social Networks

This article introduces a modified Liang–Zeger method for the estimation of the variance–covariance matrix of parameter estimates for models of social network data that include variables to characterize dyadic nonindependence. While the pseudolikelihood method has been used recently to estimate parameters for such models, the issue of estimating their standard errors, or the variance–covariance matrix more generally, has been neglected. This article addresses the issue by proposing a method for such estimation and also presents an illustrative application of the method to empirical social network data.     

Prestige in social dilemmas: A network analytic approach to cooperation among Bogotá’s art organizations

What brings about cooperation in social dilemmas? The literature has proposed motivational and strategic solutions to social dilemmas. In motivational solutions, actors cooperate because they care about fairness. In strategic solutions actors cooperate because their chances to exploit others are reduced. This paper argues that actors also cooperate in order to gain prestige. The effect of prestige on cooperation is examined relying on a unique set of network and covariate data on art organizations (museums, independent art spaces, galleries and art foundations) in Bogotá, the capital of Colombia. Bogotá is a not a very likely environment for cooperation, since distrust is still widespread after a long civil war. The temporal exponential random graph models predict dyadic cooperation (i.e., between two actors) based on past and present cooperation in the network as well as of the organizations’ characteristics. The analysis thus controls for the effect of actors’ past and present interaction on the present cooperation dilemma. The paper shows that prestige affects cooperation in social dilemmas independent of past and present interactions.     

Sexual Portrait of Couples Seeking Relationship Therapy

This study sought to provide information about the sexual well‐being of 298 mixed‐sex couples seeking relationship therapy and determined the extent to which problems with sexual functioning and dyadic adjustment of both partners are associated with sexual satisfaction. Partners completed measures of dyadic adjustment, sexual satisfaction, and sexual functioning. Thirty percent of couples reported a clinically significant sexual problem. Compared to their male partners, the women were more likely to report a sexual problem as well as lower dyadic adjustment, sexual satisfaction, and overall sexual functioning. Path analysis indicated that relationship adjustment uniquely predicted individuals' own sexual satisfaction; problems in sexual functioning uniquely predicted own and partner sexual satisfaction. Findings underscore the need to address sexual problems in relationship therapy.     

Konfliktmoderation mit Gruppen

One professional form of conflict resolution known as ‘mediation’ has itself established globally as an accepted alternative to litigation within the past four decades—albeit most notably for dyadic conflict between two individuals. Conflicts involving a higher number of parties both within and between social groups in organizations or public communities are far more complex and much tougher to be defined than in dyadic settings. They require an expanded repertoire of intervention techniques and more advanced competencies of neutrals as—in addition to the resolution efforts—the conflicting parties’ group dynamics demands attention and participative facilitation. Moreover, this type of conflict is difficult to study. An action strategy for professional group-based conflict resolution is exemplified and contrasted to dyadic mediation of conflict. Based on the analysis, problems for future research are outlined.     

Actors and networks as objects

This paper explores the application of two contemporary computational methods to the development of sociological theory. Specifically, we combine the methods of object-orientation with discrete event simulation. This approach has several advantages for constructing and evaluating dynamic social theories.In object-oriented program design, objects combine and integrate the traditional concepts of data structures and algorithms, the building blocks of structured programming. Algorithms associated with objects are called methods or member functions. Constructing social actors as objects involves defining both their data attributes and the methods associated with these attributes. We also treat a social network as a computational object. It has data types of nodes and ties. As an object, the network must also have methods that add and delete nodes and ties. Once a network exists, we can create other data types and methods that describe and analyze the network. For example, networks have in-degree and out-degree vectors, and measures of hierarchy. In principle, we can create attributes of networks for all of the structural measures we use to describe networks.We use actor and network objects in a discrete event simulation of a process of formation of dominance structures, exploring several dynamic variations of the underlying theoretical model.     

Spatial Statistics for Dyadic Data: Analyzing the Relationship Landscape

Spatial statistics has a rich tradition in earth, economic, and epidemiological sciences and has potential to affect the study of couples as well. When applied to couple data, spatial statistics can model within‐ and between‐couple differences with results that are readily accessible for researchers and clinicians. This article offers a primer in using spatial statistics as a methodological tool for analyzing dyadic data. The article will introduce spatial approaches, review data structure required for spatial analysis, available software, and examples of data output.     

Reliability and validity of egocentered network data collected via web: A meta-analysis of multilevel multitrait multimethod studies

Our goal in this article is to assess reliability and validity of egocentered network data collected through web surveys using multilevel confirmatory factor analysis under the multitrait multimethod approach.In this study, we analyze a questionnaire of social support of Ph.D. students in three European countries. The traits used are the frequency of social contact questions. The methods used are web survey design variants.We consider egocentered network data as hierarchical; therefore, a multilevel analysis is required. Within and between-ego reliabilities and validities are defined and interpreted.Afterwards, we proceed to a meta-analysis of the results of the three countries where within and between-ego validities and reliabilities are predicted from survey design variables which have to do with question order (by questions or by alters), response category labels (end labels or all labels) and lay-out of the questionnaire (graphical display or plain text). Results show that question order by questions, all-labeled response categories and a graphical display lay-out with images lead to a better data quality. Our basic approach consisting on multilevel and meta-analysis can be applied to evaluate the quality of any type of egocentered network questionnaire, regardless of the data collection mode.     

Statistical power of the social network autocorrelation model

The network autocorrelation model has become an increasingly popular tool for conducting social network analysis. More and more researchers, however, have documented evidence of a systematic negative bias in the estimation of the network effect (ρ). In this paper, we take a different approach to the problem by investigating conditions under which, despite the underestimation bias, a network effect can still be detected by the network autocorrelation model. Using simulations, we find that moderately-sized network effects (e.g., ρ = .3) are still often detectable in modest-sized networks (i.e., 40 or more nodes). Analyses reveal that statistical power is primarily a nonlinear function of network effect size (ρ) and network size (N), although both of these factors can interact with network density and network structure to impair power under certain rare conditions. We conclude by discussing implications of these findings and guidelines for users of the autocorrelation model.     

Social context,spatial structure and social network structure

Frequently, social networks are studied in their own right with analyses devoid of contextual details. Yet contextual features – both social and spatial – can have impacts on the networks formed within them. This idea is explored with five empirical networks representing different contexts and the use of distinct modeling strategies. These strategies include network visualizations, QAP regression, exponential random graph models, blockmodeling and a combination of blockmodels with exponential random graph models within a single framework. We start with two empirical examples of networks inside organizations. The familiar Bank Wiring Room data show that the social organization (social context) and spatial arrangement of the room help account for the social relations formed there. The second example comes from a police academy where two designed arrangements, one social and one spatial, powerfully determine the relational social structures formed by recruits. The next example is an inter-organizational network that emerged as part of a response to a natural disaster where features of the improvised context helped account for the relations that formed between organizations participating in the search and rescue mission. We then consider an anthropological example of signed relations among sub-tribes in the New Guinea highlands where the physical geography is fixed. This is followed by a trading network off the Dalmatian coast where geography and physical conditions matter. Through these examples, we show that context matters by shaping the structure of networks that form and that a variety of network analytic tools can be mobilized to reveal how networks are shaped, in part, by social and spatial contexts. Implications for studying social networks are suggested.     

A Basic Accounting of Variation in Municipal Solid‐Waste Generation at the County Level in Texas, 2006: Groundwork for Applying Metabolic‐Rift Theory to Waste Generation

Abstract Environmental social scientists debate whether or not modern development reduces society's impact on the biosphere. The empirical research informing the discussion has not yet adequately examined the social determinants of municipal solid‐waste (MSW) generation, an increasingly relevant issue, both ecologically and sociologically. A primary problem for this research concerns the increasing exchange of MSW across state and county borders. A convenient way to avoid this problem is to examine variation between counties with landfills that service only the county in which the landfills are located. I applied this restriction to a cross‐sectional analysis of total MSW generated in Texas at the county level for 2006. Using robust regression, this study found that two basic measures of modern development, population per square mile and per capita income, are both positively related to total MSW generated at the county level and together explain about 73 percent of the variation in the dependent variable. The evidence corroborates the metabolic‐rift theory. The concentration of people onto smaller land areas, or urbanization, and increasing wealth in a capitalist economy contribute to the metabolic rupture between human society and the environment.     

Do transnational brokers always win? A multilevel analysis of social support

Migrants often maintain relationships with significant others located in their countries of origin, which results in having transnational interpersonal ties in addition to local ones. The majority of previous studies indicate that financial and social remittances flow from countries of immigration to the countries of emigration through migrants and their networks. However, less is known about who is involved in those exchanges, what kind of supportive resources flow within and across nation-state borders, and what level of individual cross-border engagement of migrants is related to those flows. We ask whether and how transnationality as an individual attribute, together with other personal, dyadic, and supradyadic characteristics, explain received social support. Drawing on data from 100 ego-centric networks collected from Turkish migrants in Germany, the results indicate that not only the dyadic level but also network structure, the position occupied by individuals in the network and their level of transnationality explain supportive resource flows within and across borders.     

Anatomie von Kommunikationsrollen

The identification of generalizable roles in social systems has been one of the most central issues in social science ever since. Particularly in the field of interpersonal communication, the notion of communication roles has been used to describe and better understand the complex processes in social groups and society. This contribution identifies the theoretical and methodological concepts that have to be considered when roles in directed networks are operationalized. Based on a network analytic approach and critically evaluating the shortcomings of some widely used models, this study aims to propose new concepts for the operationalization of communication roles. These concepts can be divided into two distinctive approaches. One possibility is to use microstructures like dyadic and triadic communication roles as basic units. For this approach, different aggregation rules are discussed which are necessary for their application in more complex networks. The second approach takes the overall structure of a network into account and identifies different roles by applying centrality measures, blockmodelling or hierarchical structure analysis tools. For illustrative reasons, this study limits its focus on the operationalization of communication roles as a prominent object in social science research. The concepts presented, however, are applicable for directed graphs in general.     

Are those streetpeople part of the new poor,too? Toward an applied sociology of social problems

This paper proposes an applied sociology of social problems that encompasses concern with the definition of problems and the conditions defined as problematic. The literature on applied sociology focuses on social problems, but exhibits no concern with the social definition of problems, an issue that should be of primary concern according to the constructionist perspective on social problems. I propose an applied research orientation that is capable of assessing the definition of a problem as well as describing the condition defined as problematic. I illustrate the proposed orientation by discussing an applied study of the people who inhabit the streets in Santa Cruz, California. The study succeeded in redefining those people as streetpeople instead of as transients, but not as part of the new poor. The paper considers the implications of assessing the definition of a problem for policy development, the factors that constrain the redefinition of a problem, and other issues of general relevance. The paper concludes by underscoring the importance of definitional processes and politics, and indicating the prospects of an applied sociology of social problems.     

Effects of missing data in social networks

We perform sensitivity analyses to assess the impact of missing data on the structural properties of social networks. The social network is conceived of as being generated by a bipartite graph, in which actors are linked together via multiple interaction contexts or affiliations. We discuss three principal missing data mechanisms: network boundary specification (non-inclusion of actors or affiliations), survey non-response, and censoring by vertex degree (fixed choice design), examining their impact on the scientific collaboration network from the Los Alamos E-print Archive as well as random bipartite graphs. The simulation results show that network boundary specification and fixed choice designs can dramatically alter estimates of network-level statistics. The observed clustering and assortativity coefficients are overestimated via omission of affiliations or fixed choice thereof, and underestimated via actor non-response, which results in inflated measurement error. We also find that social networks with multiple interaction contexts may have certain interesting properties due to the presence of overlapping cliques. In particular, assortativity by degree does not necessarily improve network robustness to random omission of nodes as predicted by current theory.