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

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.

Factor Score Regression With Social Relations Model Components: A Case Study Exploring Antecedents and Consequences of Perceived Support in Families.

The family social relations model (SRM) is applied to identify the sources of variance in interpersonal dispositions in families, but the antecedents or consequences of those sources are rarely investigated. Simultaneous modeling of the SRM with antecedents or consequences using structural equation modeling (SEM) allows to do so, but may become computationally prohibitive in small samples. We therefore consider two factor score regression (FSR) methods: regression and Bartlett FSR. Based on full information maximum likelihood (FIML), we derive closed-form expressions for the regression and Bartlett factor scores in the presence of missingness. A simulation study in both a complete- and incomplete-case setting compares the performance of these FSR methods with SEM and an ANOVA-based approach. In both settings, the regression FIML factor scores as explanatory variable produces unbiased estimators with precision comparable to the SEM-estimators. When SRM-effects are used as dependent variables, none of the FSR methods are a suitable alternative for SEM. The latter result deviates from previous studies on FSR in more simple settings. As an example, we explore whether gender and past victimhood of relational and physical aggression are antecedents for family dynamics of perceived support, and whether those dynamics predict physical and relational aggression.