scholarly journals A Practical Tool for Family Assessment Based on the Social Relations Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Tom Loeys ◽  
Marieke Fonteyn ◽  
Justine Loncke
Keyword(s):  
Social Relations ◽  
Specific Effect ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Family Assessment ◽  
Family Therapists ◽  
Social Relations Model ◽  
The Social ◽  
The Family ◽  
Norm Sample

An empirically based family assessment can help family therapists understand how a family functions. In systemic therapy a family is seen as a dynamic system in which the family members form interdependent subsystems. The Social Relations Model (SRM) is a useful tool to study such interdependence within a family. According to the SRM, each dyadic score is viewed as the sum of an unobserved family effect, an individual actor and partner effect, and a relation-specific effect. If dyadic data are obtained for a specific family using a round robin design, these different SRM effects can be calculated using an ANOVA-approach. To gain insight into the functioning of a particular family, the family-specific SRM effects can be compared to those from a norm sample and it can be deduced whether that family has deviating scores on a particular SRM effect. Currently, such a family assessment relies on the mean and variance of the SRM ANOVA scores in the norm sample. However, family therapists may not always have access to these data, making the current approach of SRM family assessment not as useful in practice. In this article, we introduce a user-friendly web application that uses an alternative method for SRM family assessment. This alternative strategy requires as input the population parameter estimates of SRM means and variances more commonly described in SRM family literature.

scholarly journals A family assessment based on the Social Relations Model

2010 ◽  
Vol 32 (3) ◽  
pp. 259-279 ◽  
Author(s):  
Jan De Mol ◽  
Ann Buysse ◽  
William L. Cook
Keyword(s):  
Social Relations ◽  
Family Assessment ◽  
Social Relations Model ◽  
The Social

Likelihood Estimation of the Multivariate Social Relations Model

2017 ◽  
Vol 43 (4) ◽  
pp. 387-406 ◽  
Author(s):  
Steffen Nestler
Keyword(s):  
Maximum Likelihood ◽  
Social Relations ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Social Relations Model ◽  
Maximum Likelihood Approach ◽  
The Social ◽  
Likelihood Approach ◽  
Research Questions ◽  
And Behavior

The social relations model (SRM) is a mathematical model that can be used to analyze interpersonal judgment and behavior data. Typically, the SRM is applied to one (i.e., univariate SRM) or two variables (i.e., bivariate SRM), and parameter estimates are obtained by employing an analysis of variance method. Here, we present an extension of the SRM to an arbitrary number of variables and show how the parameters of this multivariate model can be estimated using a maximum likelihood or a restricted maximum likelihood approach. Overall, the two likelihood approaches provide consistent and efficient parameter estimates and can be used to investigate a multitude of interesting research questions.

The SRM Approach to Family Assessment

2005 ◽  
Vol 21 (4) ◽  
pp. 216-225 ◽  
Author(s):  
William L. Cook
Keyword(s):  
High Risk ◽  
Risk Behaviors ◽  
Social Relations ◽  
Family Relationships ◽  
Family Dynamics ◽  
Family Assessment ◽  
High Risk Behaviors ◽  
Analysis Group ◽  
The Social ◽  
Direction Of Effects

Abstract. In family systems, it is possible for one to put oneself at risk by eliciting aversive, high-risk behaviors from others ( Cook, Kenny, & Goldstein, 1991 ). Consequently, it is desirable that family assessments should clarify the direction of effects when evaluating family dynamics. In this paper a new method of family assessment will be presented that identifies bidirectional influence processes in family relationships. Based on the Social Relations Model (SRM: Kenny & La Voie, 1984 ), the SRM Family Assessment provides information about the give and take of family dynamics at three levels of analysis: group, individual, and dyad. The method will be briefly illustrated by the assessment of a family from the PIER Program, a randomized clinical trial of an intervention to prevent the onset of psychosis in high-risk young people.

A description and illustration of the Triadic Relations Model: Who perceives whom as bullying whom?

2010 ◽  
Vol 34 (4) ◽  
pp. 374-383 ◽  
Author(s):  
Noel A. Card ◽  
Philip C. Rodkin ◽  
Claire F. Garandeau
Keyword(s):  
Peer Relations ◽  
Social Relations ◽  
Fourth Grade ◽  
Social Relations Model ◽  
Interpersonal Behaviors ◽  
The Social ◽  
Peer Nominations ◽  
Dyadic Variables

Analyses of children’s peer relations have recently begun considering interpersonal behaviors and perceptions from the perspective of the Social Relations Model. An extension of this model, the Triadic Relations Model (TRM), allows for consideration and analysis of more complex three-person data to understand triadic processes; separate individual, dyadic, and triadic variance; and model co-occurrences among dyadic phenomena. The goal of this article is to provide a didactic introduction to the TRM and its potential for studying peer relations. The TRM is applied to data from nine classes (N = 162) of third and fourth grade boys and girls involving perceptions (peer nominations) of actors’ (aggressors’) behavior toward partners (victims). We report and illustrate interpretation of 7 variance and 16 covariance estimates from this TRM analysis of who perceives whom as bullying whom. In particular, triadic analyses revealed a tendency for children to perceive others as sharing the same aggressors and the same targets for aggression as themselves. We discuss implications of findings for studying aggression, as well as extensions of this model, such as incorporating multiple constructs or connecting the TRM estimates with individual and dyadic variables, and challenges of using the TRM.

scholarly journals FAMILY EXPECTATIONS ABOUT A PSYCHIATRIC INPATIENT UNIT

2015 ◽  
Vol 24 (4) ◽  
pp. 984-992 ◽  
Author(s):  
Gustavo Costa de Oliveira ◽  
Jacó Fernando Schneider ◽  
Cíntia Nasi ◽  
Marcio Wagner Camatta ◽  
Agnes Olschowsky
Keyword(s):  
Social Relations ◽  
Psychiatric Inpatient ◽  
Family Care ◽  
Inpatient Unit ◽  
University Hospital ◽  
Family Expectations ◽  
Phenomenological Sociology ◽  
Psychiatric Inpatient Unit ◽  
The Social ◽  
The Family

ABSTRACT We aimed to understand the expectations of families about a Psychiatric Inpatient Unit in the perspective of Alfred Schutz's phenomenological sociology. This is a qualitative and phenomenological research, with families of patients at a psychiatric inpatient unit of a university hospital in the state of Rio Grande do Sul, Brazil. Data were collected through phenomenological interviews, and the analysis was constructed in the light of phenomenological sociology. The results show that the expectations of the family in the Psychiatric Inpatient Unit are related to the interpretation and experiences they have in the world of everyday life; that these expectations should be valued in patient and family care; and that they may integrate the family in care for the patient. We hope to contribute so that professionals and managers reflect about the importance of understanding the expectations of families on a Unit, aiming to implement more effective health actions, based on the social relations among the subjects.

Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model

2021 ◽  
pp. 107699862110565
Author(s):  
Steffen Nestler ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Social Relations ◽  
Structural Equation ◽  
Applied Research ◽  
Structural Equation Models ◽  
Social Groups ◽  
Equation Model ◽  
Model Data ◽  
Cross Sectional ◽  
Social Relations Model ◽  
The Social

The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.

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