Implicit Theories of Marital Relationships: A Grounded Theory of Socialization Influences

A qualitative study was conducted in an Eastern European university, involving 16 participants from functional and dysfunctional families who participated in semistructured interviews about the development of their implicit theories of a happy marital relationship. The students shared their ideas of a happy marriage, their parents’ marriage, including the level of happiness, conflict and other issues, what they thought were major influences on the formation of their ideas of marriage including exploration of the role of fiction media. The following major socialization influences emerged as themes: parental relationship, individual parental characteristics, observation of other relationships, gender role ideology, education, media, and experiences of own relationships.     

The Implications of Social Neuroscience for Expectation States Theories

We review recent findings from the social neuroscience literature that links status differentiation between individuals to neurological processes, and draw out the implications of those findings for expectation states theories, including its dominant theory of status‐organizing processes – Status Characteristics Theory. Our underlying assumption and implicit argument is that social neuroscience research is useful for both bolstering and corroborating some core sociological claims. In addition, it sheds light on some of the ongoing theoretical debates within sociology. We highlight and discuss the following four social neuroscience findings: 1) Neural activity varies depending on whether an individual is interacting with someone of a lower, equal, or higher status; 2) There is an interplay between status and affect such that status processes may promote the control of emotional reactions; 3) Both men and women are neurologically attuned to high status opposite sex conspecifics; and, 4) Status differences are processed in the same region of the brain as numerical or size differences, and this region is responsible for the coding of information along continuous dimensions. The implications of these points are discussed with respect to sociological theories.     

Semiparametric smoothing of sparse contingency tables

In the paper simple resampling technique based on semiparametric smoothing is introduced. Although the method is very flexible and in principle can be applied to any sparse data and ill-posed statistical problem, its efficient or even reasonable implementation requires special investigation. In the paper a problem of fitting local dependence structure of finite-state random sequences is addressed. This problem is relevant, for example, in genetics, bioinformatics, computer linguistics, etc., and usually leads to analysis of sparse contingency tables of dependent categorical data. Thus, the classical assumptions of log-linear model, a standard technique for analysis of contingency tables, do not hold. A framework convenient for implementation of semiparametric smoothing and resampling is proposed. It is based on a special representation form of data under consideration and generalized logit model. A computer experiment is carried out to gain better insight on practical performance of the procedure.