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Tackling Longitudinal Round-Robin Data: A Social Relations Growth Model

Published online by Cambridge University Press:  01 January 2025

Steffen Nestler ,
Katharina Geukes ,
Roos Hutteman  and
Mitja D. Back
Steffen Nestler*
Affiliation:
University of Leipzig
Katharina Geukes
Affiliation:
University of Münster
Roos Hutteman
Affiliation:
University of Utrecht
Mitja D. Back
Affiliation:
University of Münster
*
Correspondence should be made to Steffen Nestler,University of Leipzig, Neumarkt. 9-19, 04109 Leipzig,Germany. Email: steffen.nestler@uni-leipzig.de
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Abstract

The social relations model (SRM) is commonly used in the analysis of interpersonal judgments and behaviors that arise in groups. The SRM was developed only for use with cross-sectional data. Here, we introduce an extension of the SRM to longitudinal data. The social relations growth model represents a person’s repeated SRM judgments of another person as a function of time. We show how the model’s parameters can be estimated using restricted maximum likelihood, and how the effects of covariates on interindividual and interdyad variability in growth can be computed. An example is presented to illustrate the suggested approach. We also present the results of a small simulation study showing the suitability of the social relations growth model for the analysis of longitudinal SRM data.

Keywords

social relations modellinear mixed modellongitudinal datarestricted maximum likelihood

Information

Type
Original Paper
Information
Psychometrika , Volume 82 , Issue 4 , December 2017 , pp. 1162 - 1181
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

This article is dedicated to Irmgard Laufer.

References

Ackerman, R. A. Kashy, D. A. Donellan, M. B. & Conger, R. D. (2011). Positive engagement behaviors in observed family iinteractions: A social relations perspective. Journal of Family Psychology 25, 719–730CrossRefGoogle Scholar
Bates, D., Maechler, M., Bolker, B. M., & Walker, S. (2014). Lme4: Linear mixed effects models using eigen und s4. Retrieved from http://cran.r-project.org/web/packages/lme4.Google Scholar
Bond, C. F. Dorsky, S. E. & Kenny, D. A. (1992). Person memory and memorability: A round robin analysis. Basic and Applied Social Psychology 13, 285–302CrossRefGoogle Scholar
Bond, C. F. Horn, E. M. & Kenny, D. A. (1997). A model for triadic relations. Psychological Methods 2, 79–94CrossRefGoogle Scholar
Bond, C. F. & Lashley, B. R. (1996). Round-robin analysis of social interaction: Exact and estimated standard errors. Psychometrika 61, 303–311CrossRefGoogle Scholar
Bonito, J. A. & Kenny, D. A. (2010). The measurement of reliability of social relations components from round-robin designs. Personal Relationships 17, 235–251CrossRefGoogle Scholar
Branje, SJT Finkenauer, C. Meeus, WHJ Card, N. Selig, J. & Little, T. (2008). Modeling interdependent data in developmental psychology. Modeling interdependent data in developmental psychology Mahwah, NJ: Lawrence Erlbaum 287–317Google Scholar
Branje, SJT van Lieshout, CFM & van Aken, MAG (2005). Relations between agreeableness and perceived support in family relationships: Why nice people are not always supportive. International Journal of Behavioral Development 29, 120–128CrossRefGoogle Scholar
Buist, K. L. Reitz, E. & Dekovic, M. (2008). Attachment stability and change during adolescence: A longitudinal application of the social relations model. Journal of Social and Personal Relationships 25, 429–444CrossRefGoogle Scholar
Card, N. A. Little, T. D. Selig, J. P. Card, N. A. Selig, J. P. & Little, T. D. (2008). Using the bivariate social relations model to study dyadic relationships: Early adolescents’ perceptions of friends’ aggression and prosocial behavior. Modeling dyadic and interdependent data in the developmental and behavioral sciences New York: Routledge 245–276Google Scholar
Cook, W. L. Card, N. A. Selig, J. P. & Little, T. D. (2008). Application of the social relations model formulas to developmental research. Modeling dyadic and interdependent data in the developmental and behavioral sciences New York: Routledge 245–276Google Scholar
Curran, P. J. Bollen, K. A. Collins, L. M. & Sayer, A. G. (2001). The best of both worlds: Combining autoregressive and latent curve models. New methods for the analysis of change. Decade of behavior Washington, DC: APA 107–135CrossRefGoogle Scholar
Demidenko, E. (2004). Mixed models: Theory and applications with R 2Hoboken, NJ: WileyCrossRefGoogle Scholar
Dorff, C. & Ward, M. D. (2013). Networks, dyads, and the social relations model. Political Science Research Methods 1, 159–178CrossRefGoogle Scholar
Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., & Hothorn, T. (2016). mvtnorm: Multivariate Normal and t Distributions. R package version 1.0–5. http://CRAN.R-project.org/package=mvtnorm.Google Scholar
Geukes, K., Hutteman, R., Küfner, A., Nestler, S., & Back, M. D. (2016). Explaining the longitudinal interplay of personality and social relationships in the laboratory and in the field: The PILS and the CONNECT study. Manuscript in preparation.Google Scholar
Gill, P. S. & Swartz, T. B. (2001). Statistical analyses for round robin interaction data. Canadian Journal of Statistics 29, 321–331CrossRefGoogle Scholar
Gill, P. S. & Swartz, T. B. (2007). Bayesian analysis of dyadic data. American Journal of Mathematical and Managment Sciences 27, 73–92Google Scholar
Hoff, P. D. (2005). Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association 100, 286–295CrossRefGoogle Scholar
Horn, E. M. Collier, W. G. Oxford, J. A. Bond, C. F. & Dansereau, D. F. (1998). Individual differences in dyadic cooperative learning. Journal of Educational Psychology 90, 153–16110.1037/0022-0663.90.1.153CrossRefGoogle Scholar
Jiang, J. (2007). Linear and generalized linear mixed models and their applications New York: SpringerGoogle Scholar
Kenny, D. A. (1994). Interpersonal perception: A social relations analysis New York: Guilford PressGoogle ScholarPubMed
Kenny, D. A. (2004). PERSON: A general model of interpersonal perception. Personality and Social Psychology Review 8, 265–280CrossRefGoogle Scholar
Kenny, D. A. Kashy, D. A. & Cook, W. L. (2006). The analysis of dyadic data New York: Guilford PressGoogle Scholar
Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electornic Journal of Statistics 6, 1100–1128Google ScholarPubMed
Küfner, ACP Nestler, S. & Back, M. D. (2012). The two pathways to being an (un-)popular narcissist. Journal of Personality 81, 184–195CrossRefGoogle Scholar
Leckelt, M. Küfner, ACP Nestler, S. & Back, M. D. (2015). Behavioral processes underlying narcissist’s decline in popularity. Journal of Personality and Social Psychology 109, 856–871CrossRefGoogle ScholarPubMed
LeDoux, J. A. Gorman, C. A. & Woehr, D. J. (2012). The impact of interpersonal perceptions on team processes: A social relations analysis. Small Group Research 43, 356–382CrossRefGoogle Scholar
Lüdtke, O. Robitzsch, A. Kenny, D. A. & Trautwein, U. (2013). A general and flexible approach to estimating the social relations model using Bayesian methods. Psychological Methods 18, 101–119CrossRefGoogle ScholarPubMed
Lusher, D. Koskinen, J. & Robins, G. (2013). Exponential random graph models for social networks: Theories, methods and applications New York: Cambridge University PressGoogle Scholar
Marcus, D. K. & Kashy, D. A. (1995). The social relations model: A tool for group psychotherapy research. Journal of Counseling Psychology 42, 383–389CrossRefGoogle Scholar
McCulloch, C. E. Searle, S. R. & Neuhaus, J. M. (2004). Generalized, linear, and mixed models 2Hoboken, NJ: WileyGoogle Scholar
Nestler, S. (2015). Restricted maximum likelihood estimation for parameters of the social relations model. Psychometrika. doi:10.1007/s11336-015-9474-9.CrossRefGoogle Scholar
Nestler, S. (2016). Likelihood estimation of the multivariate social relations model. Psychometrika (in press).CrossRefGoogle Scholar
Nestler, S. Grimm, K. J. & Schönbrodt, F. D. (2015). The social consequences and mechanisms of personality: How to analyse longitudinal data from individual, dyadic, round-robin and network designs. European Journal of Personality 29, 272–295CrossRefGoogle Scholar
Park, B. & Flink, C. (1989). A social relations analysis of agreement in liking judgments. Journal of Personality and Social Psychology 56, 506–518CrossRefGoogle Scholar
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software 48, 1–36CrossRefGoogle Scholar
Schönbrodt, F. D., Back, M. D., & Schmukle, S. C. (2012). TripleR: An R package for social relations analyses based on round-robin designs. Behavior Research Methods, 44, 455–470. doi:10.3758/s13428-011-0150-4.CrossRefGoogle Scholar
Schönbrodt, F. D., Back, M. D., & Schmukle, S. C. (2016). TripleR: Social Relation Model (SRM) analyses for single or multiple groups (R package version 1.5.1). Retrieved from http://cran.r-project.org/package=TripleR.Google Scholar
Snijders, TAB & Kenny, D. A. (1999). The social relations model for family data: A multilevel approach. Personal Relationships 6, 471–486CrossRefGoogle Scholar
Snijders, TAB Steglich, CEG & van de Bunt, G. G. (2010). Introduction to actor-based models for network dynamics. Social Networks 32, 44–60CrossRefGoogle Scholar
van Zalk, M. H. & Denissen, J. (2015). Idiosyncratic versus social consensus approaches to personality: Self-view, perceived, and peer-review similarity. Journal of Personality and Social Psychology 109, 121–141CrossRefGoogle Scholar
Verbeke, G. Fieuws, S. Molenbergh, G. & Davidian, M. (2014). The analysis of multivariate longitudinal data: A review. Statistical Methods in Medical Research 23, 42–59CrossRefGoogle ScholarPubMed
Verbeke, G. & Molenberghs, G. (2009). Linear mixed models for longitudinal data Berlin: SpringerGoogle Scholar
Warner, R. M. Kenny, D. A. & Stoto, M. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology 37, 1742–1757CrossRefGoogle Scholar
Wasserman, S. & Faust, K. (1994). Social network analysis: Methods and applications New York: Cambridge University PressCrossRefGoogle Scholar