Sociometric Relations Research Articles

Relações sociométricas dos pesquisadores que patentearam inventos

Sought to identify the structure of sociometric relations of researchers from the Federal University of Lavras (UFLA) who registered patents, understand the structural connections and the nodes within this network. Document analysis was conducted to examine the network of inventors and patents filed between 2003 and 2012 by researchers at the UFLA in the National Institute of Intellectual Property (INPI) through the Center for Technology Innovation (Nintec) UFLA. There is a brief literature review on sociometry, explanatory theories and important concepts to understanding social networking. The qualitative approach was adopted as the research method. For the analyzes were used as a basis the full map of the network, including patents and inventors of UFLA with their relationships studied through the analysis of social networks.

Participation in sports and sociometric status of adolescents

Participation in sports and sociometric status of adolescents Study aim: To examine the relationships between sport participation and sociometric status of adolescent youths. Material and methods: A group of 359 secondary school students from central Serbia (143 male and 216 female) aged 16 - 19 years participated in the study. The subjects were given questionnaires pertaining to their participation in sports and sociometric relations, especially "work with" and "interact with". Results: In total, about 23% of subjects were actively engaged in sports at various levels: local (11%), regional (8%), national (3%) and international (2 subjects), the others were classified as sedentary. The sport-active subjects scored significantly (p<0.001) higher sociometric acceptance and sociometric status and lower sociometric rejection (p<0.05) than the sedentary ones. However, no significant correlations were found between sport-engagement variables and the sociometric ones. Conclusions: The presented results can be regarded as preliminary only and call for conducting the study on a larger cohort and to include more psychosocial variables.

The effect of empathy on accuracy of behavior prediction in social exchange situation

Seventy-six participants first played a prisoner's dilemna (PD) game with an anonymous partner and then predicted behavior of other participants in the same PD game. In addition, they took a test of accuracy in judging sociometric relations among group members. Of the four factors of empathy, the two reflecting emotional aspects--self-directed and other-directed emotional responses--were found to have no relation with accuracy in predicting behavior of other participants and judging sociometric relations among group members. In contrast, the other two reflecting cognitive aspects--imagination and perspective taking--were found to be related to accuracy in predicting behavior of other participants in the PD game. Of the two, imagination was a better indicator of accuracy in the prediction than perspective taking. Implications of these findings are discussed in terms of domain specificities of adaptive task that different aspects of empathy are thought to fulfill.

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.

A p* primer: logit models for social networks

Abstract A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others [Holland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association. 76, pp. 33–65 (with discussion); Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt, S. (Ed.), Sociological Methodology 1981, San Francisco: Jossey-Bass, pp. 156–192; Fienberg, S.E., Meyer, M.M., Wasserman, S., 1985. Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, pp. 51–67; Wasserman, S., Weaver, S., 1985. Statistical analysis of binary relational data: Parameter estimation. Journal of Mathematical Psychology. 29, pp. 406–427; Wasserman, S., 1987. Conformity of two sociometric relations. Psychometrika. 52, pp. 3–18] is the very strong independence assumption made on interacting individuals or units within a network or group. This limiting assumption is no longer necessary given recent developments on models for random graphs made by Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association. 81, pp. 832–842] and Strauss and Ikeda [Strauss, D., Ikeda, M., 1990. Pseudolikelihood estimation for social networks. Journal of the American Statistical Association. 85, pp. 204–212]. The resulting models are extremely flexible and easy to fit to data. Although Wasserman and Pattison [Wasserman, S., Pattison, P., 1996. Logit models and logistic regressions for social networks: I. An introduction to Markov random graphs and p*. Psychometrika. 60, pp. 401–426] present a derivation and extension of these models, this paper is a primer on how to use these important breakthroughs to model the relationships between actors (individuals, units) within a single network and provides an extension of the models to multiple networks. The models for multiple networks permit researchers to study how groups are similar and/or how they are different. The models for single and multiple networks and the modeling process are illustrated using friendship data from elementary school children from a study by Parker and Asher [Parker, J.G., Asher, S.R., 1993. Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. Developmental Psychology. 29, pp. 611–621].

Conformity of two sociometric relations

The problem of comparing two sociometric relations or measurements (A andB) recorded in distinct sociomatrices was originally discussed by Katz and Powell in the early 1950's and Hubert and Baker in the late 1970's. The problem is considered again using a probabilistic model designed specifically for discrete-valued network measurements. The model allows for the presence of various structural tendencies, such as reciprocity and differential popularity. A parameter that isolates the tendency for actors to choose other actors on both relations simultaneously is introduced, and estimated conditional on the presence of other parameters that reflect additional important network properties. The parameter is presented as a symmetric index but is also generalized to the predictive (A onB orB onA) situation. This approach to the problem is illustrated with the same data used by the earlier solutions, and the unique nature of the two relations in the data set (A = received choices,B = perceived choices), as it affects the modeling, is discussed. Significance tests for the parameter and related parameters are described, as well as an extension to more than two relations.

Statistical Analysis of Multiple Sociometric Relations

Abstract Loglinear models are adapted for the analysis of multivariate social networks, a set of sociometric relations among a group of actors. Models that focus on the similarities and differences between the relations and models that concentrate on individual actors are discussed. This approach allows for the partitioning of the actors into blocks or subgroups. Some ideas for combining these models are described, and the various models and computational methods are applied to the analysis of data for a corporate interlock network of the 25 largest organizations in Minneapolis/St. Paul and for a classic network of 18 monks in a cloister. The computational techniques all involve variations on the standard iterative proportional-fitting algorithm used extensively in the analysis of multidimensional contingency tables.

Statistical Analysis of Multiple Sociometric Relations

Abstract Loglinear models are adapted for the analysis of multivariate social networks, a set of sociometric relations among a group of actors. Models that focus on the similarities and differences between the relations and models that concentrate on individual actors are discussed. This approach allows for the partitioning of the actors into blocks or subgroups. Some ideas for combining these models are described, and the various models and computational methods are applied to the analysis of data for a corporate interlock network of the 25 largest organizations in Minneapolis/St. Paul and for a classic network of 18 monks in a cloister. The computational techniques all involve variations on the standard iterative proportional-fitting algorithm used extensively in the analysis of multidimensional contingency tables.

A mathematical model of friendship choice distribution

A mathematical model of friendship choice distribution is proposed. The model differs from other models in that it takes into account certain structural factors in sociometric relations. Two theoretical distributions are derived from a set of assumptions as two approximations to satisfy those assumptions. One distribution is a well‐known negative binomial distribution, but its derivation is new. The other distribution has an unfamiliar mathematical structure but it shares certain features with the negative binomial distribution. The two parameters of these distributions are regarded as measures of the degree of expansive‐ness and the degree of local cohesiveness of friendship ties. The model is tested with several sets of empirical data and shows usefulness as a descriptive tool.

Sociometric Relations in Elementary School Classes

Sociometric Relations in Elementary School Classes

Determinants of Sociometric Relations Among Children in the Elementary School

Determinants of Sociometric Relations Among Children in the Elementary School