Getting the most out of family data with the R package fSRM.

Stas, Lara; Schönbrodt, Felix D.; Loeys, Tom

doi:10.1037/fam0000058

Cited by 14 publications

(20 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As Eichelsheim et al ( 2011 ) also emphasized he relevance of SRM means, more recent SRM analyses now also report these means. Thanks to specific software tools that facilitate SRM analyses (Stas et al, 2015 ) and automatically deliver SRM means and (co)variances, we hope to see even more papers reporting both estimated means and variance from a CFA-analysis in the future.…”

Section: Discussionmentioning

confidence: 99%

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

2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the example graph, there are 5 ties and 4 times 3 possible ties, resulting in a density of 5/12 2 When the members of one or more round-robin groups have clear roles, such as 'mother', 'father' or 'child' in the case of families, the SRM with roles has to be employed (Cook, 1994;Cook, 2005). There is also an R package to handle this variant of the SRM (fSRM; Schönbrodt, Stas, & Loeys, 2014;Stas, Schönbrodt, & Loeys, 2015). Figure 8.…”

Section: Descriptive Social Network Analysismentioning

confidence: 99%

The Social Consequences and Mechanisms of Personality: How to Analyse Longitudinal Data from Individual, Dyadic, Round–Robin and Network Designs

2015

View full text Add to dashboard Cite

There is a growing interest among personality psychologists in the processes underlying the social consequences of personality. To adequately tackle this issue, complex designs and sophisticated mathematical models must be employed. In this article we describe established and novel statistical approaches to examine social consequences of personality for individual, dyadic, and group (round-robin and network) data. Our overview includes response surface analysis (RSA), autoregressive path models, and latent growth curve models for individual data; actor-partner interdependence models and dyadic RSAs for dyadic data; and social relations and social network analysis for round robin and network data. Altogether, our goal is to provide an overview of various analytical approaches, the situations in which each can be employed, and a first impression about how to interpret their results. Three demo data sets and scripts show how to implement the approaches in R.Keywords: social relations model, social network analysis, dyadic data, response surface analysis, longitudinal data analysis, mediation ANALYZING THE CONSEQUENCES OF PERSONALITY 3The social consequences and mechanisms of personality: How to analyze longitudinal data from individual, dyadic, round-robin, and network designs Psychologists assume that personality has a number of interesting and wide-ranging consequences for the development and maintenance of social relationships. It is posited, for instance, that agreeable individuals become more popular the longer one knows them (see Selfhout, Burk, et al., 2010) or that narcissists are liked less the more one gets familiar with them (see Back, Schmukle, & Egloff, 2010; Küfner, Nestler, & Back, 2013). Similarly, the agreeableness of a member of a couple is believed to influence the relationship satisfaction of the other couple member (see Dyrenforth, Kashy, Donnellan, & Lucas, 2010). Importantly, however, current theorizing does not only focus on such personality-consequence effects, but also on the processes or mechanisms underlying them. Do agreeable individuals, for example, become more popular because others have more valuable communications with them or do narcissists get less popular due to their aggressive communication style?To answer such sophisticated questions adequately, researchers do not only need longitudinal designs in which multiple measures of personality, process indicators, and indicators of the consequence variable are assessed (see Wrzus & Mehl, 2015), but they also have to employ complex mathematical models to statistically evaluate their research questions. Partially due to this need for more complex statistical models, there has been an explosion of research in the methodological field providing researchers with an ever-growing set of analytic tools. Our goal is to provide a comprehensive overview of the novel statistical approaches that can be used to examine social consequences of personality. We highlight several commonly utilized approaches, briefly discuss their mathematical ...

show abstract

“…Their study focuses on the application of the SRM in groups without roles (for e.g., students within classes) while we are considering the family setting here (with a fixed group size equal to 4, and 4 distinct roles within each group). While most applications of the SRM without roles rely on an ANOVA-based approach (Schönbrodt et al, 2012 ), the SRM with roles is typically analyzed using Confirmatory Factor Analysis in the SEM-framework (Stas et al, 2015 ). As argued by several scholars (Gill and Swartz, 2001 ; Nestler, 2016 ) the SRM could be implemented in a multilevel modeling framework too, hereby relying on (Restricted) Maximum Likelihood (ML) Estimation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Getting the most out of family data with the R package fSRM.

Cited by 14 publications

References 28 publications

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

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

The Social Consequences and Mechanisms of Personality: How to Analyse Longitudinal Data from Individual, Dyadic, Round–Robin and Network Designs

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

Contact Info

Product

Resources

About