Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models

Fuchs P, Nussbeck FW, Meuwly N, Bodenmann G (2017)
Frontiers in Psychology 8: 429.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA 1.87 MB
Autor*in
Fuchs, PeterUniBi; Nussbeck, Fridtjof W.UniBi ; Meuwly, Nathalie; Bodenmann, Guy
Abstract / Bemerkung
The analysis of observational data is often seen as a key approach to understanding dynamics in romantic relationships but also in dyadic systems in general. Statistical models for the analysis of dyadic observational data are not commonly known or applied. In this contribution, selected approaches to dyadic sequence data will be presented with a focus on models that can be applied when sample sizes are of medium size (N = 100 couples or less). Each of the statistical models is motivated by an underlying potential research question, the most important model results are presented and linked to the research question. The following research questions and models are compared with respect to their applicability using a hands on approach: (I) Is there an association between a particular behavior by one and the reaction by the other partner? (Pearson Correlation); (II) Does the behavior of one member trigger an immediate reaction by the other? (aggregated logit models; multi-level approach; basic Markov model); (III) Is there an underlying dyadic process, which might account for the observed behavior? (hidden Markov model); and (IV) Are there latent groups of dyads, which might account for observing different reaction patterns? (mixture Markov; optimal matching). Finally, recommendations for researchers to choose among the different models, issues of data handling, and advises to apply the statistical models in empirical research properly are given (e.g., in a new r-package “DySeq”).
Erscheinungsjahr
2017
Zeitschriftentitel
Frontiers in Psychology
Band
8
Art.-Nr.
429
ISSN
1664-1078
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2910257

Zitieren

Fuchs P, Nussbeck FW, Meuwly N, Bodenmann G. Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models. Frontiers in Psychology. 2017;8: 429.
Fuchs, P., Nussbeck, F. W., Meuwly, N., & Bodenmann, G. (2017). Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models. Frontiers in Psychology, 8, 429. doi:10.3389/fpsyg.2017.00429
Fuchs, Peter, Nussbeck, Fridtjof W., Meuwly, Nathalie, and Bodenmann, Guy. 2017. “Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models”. Frontiers in Psychology 8: 429.
Fuchs, P., Nussbeck, F. W., Meuwly, N., and Bodenmann, G. (2017). Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models. Frontiers in Psychology 8:429.
Fuchs, P., et al., 2017. Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models. Frontiers in Psychology, 8: 429.
P. Fuchs, et al., “Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models”, Frontiers in Psychology, vol. 8, 2017, : 429.
Fuchs, P., Nussbeck, F.W., Meuwly, N., Bodenmann, G.: Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models. Frontiers in Psychology. 8, : 429 (2017).
Fuchs, Peter, Nussbeck, Fridtjof W., Meuwly, Nathalie, and Bodenmann, Guy. “Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models”. Frontiers in Psychology 8 (2017): 429.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
Dieses Objekt ist durch das Urheberrecht und/oder verwandte Schutzrechte geschützt. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-06T09:18:47Z
MD5 Prüfsumme
db709e1d37d3efcbaf20a637b03a777c


51 References

Daten bereitgestellt von Europe PubMed Central.

Strings of adulthood: a sequence analysis of young british women's work-family trajectories
Aassve A., Billari F., Piccarreta R.., 2007
Sequence analysis: new methods for old ideas
Abbott A.., 1995
Sequence analysis and optimal matching methods in sociology review and prospect
Abbott A., Tsay A.., 2000
Multilevel mixture models
Asparouhov T., Muthen B.., 2008

Bakeman R., Gottman J.., 1997
LMest: an R package for latent Markov models for categorical longitudinal data
Bartolucci F., Farcomeni A., Pandolfi S., Pennoni F.., 2015
Fitting linear mixed-effects models using lme4
Bates D., Mächler M., Bolker B., Walker S.., 2015
Career patterns of executive women in finance: an optimal matching analysis 1
Blair-Loy M.., 1999

Bodenmann G.., 1995
Effects of Stress on the Social Support Provided by Men and Women in Intimate Relationships.
Bodenmann G, Meuwly N, Germann J, Nussbeck FW, Heinrichs M, Bradbury TN., Psychol Sci 26(10), 2015
PMID: 26341561
An introduction to Markov modelling for economic evaluation.
Briggs A, Sculpher M., Pharmacoeconomics 13(4), 1998
PMID: 10178664
Latent class models for stage-sequential dynamic latent variables
Collins L., Wugalter S.., 1992
The actor–partner interdependence model: a model of bidirectional effects in developmental studies
Cook W., Kenny D.., 2005
Introduction: the need for multimethod measurement in psychology
Eid M., Diener E.., 2006

Everitt B.., 1992
G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences.
Faul F, Erdfelder E, Lang AG, Buchner A., Behav Res Methods 39(2), 2007
PMID: 17695343

Field A.., 2013

Gabadinho A., Ritschard G., Studer M., Müller N.., 2009
How much does it cost? Optimization of costs in sequence analysis of social science data
Gauthier J., Widmer E., Bucher P., Notredame C.., 2009

AUTHOR UNKNOWN, 2002

Helske S., Helske J.., 2016

Henning C.., 2015
A simplified Monte Carlo significance test procedure
Hope A.., 1968

Hox J., Moerbeek M., van R.., 2010

Kaufman L., Rousseeuw P.., 1990

Kaufman L., Rousseeuw P.., 2009
Models of non-independence in dyadic research
Kenny D.., 1996

Kenny D., Kashy D., Cook W.., 2006
The 'Trier Social Stress Test'--a tool for investigating psychobiological stress responses in a laboratory setting.
Kirschbaum C, Pirke KM, Hellhammer DH., Neuropsychobiology 28(1-2), 1993
PMID: 8255414
Analyzing change at the dyadic level: the common fate growth model.
Ledermann T, Macho S., J Fam Psychol 28(2), 2014
PMID: 24611693

Levenshtein V.., 1966
Global self-assessment
Lucas R., Baird B.., 2006
Sufficient sample sizes for multilevel modeling
Maas C., Hox J.., 2005

Maechler M., Rousseeuw P., Struyf A., Hubert M., Hornik K.., 2015
A multivariate hierarchical model for studying psychological change within married couples
Raudenbush S., Brennan R., Barnett R.., 1995

Ross S.., 2014
Missing data: our view of the state of the art.
Schafer JL, Graham JW., Psychol Methods 7(2), 2002
PMID: 12090408
Estimating the dimension of a model
Schwarz G.., 1978
A mathematical theory of communication
Shannon C.., 2001
Clustering in an object-oriented environment
Struyf A., Hubert M., Rousseeuw P.., 1997
Mixed Markov latent class models
Van F., Langeheine R.., 1990

Venables W., Ripley B.., 2002

Vermunt J.., 1993
depmixS4: an R-package for hidden Markov models
Visser I., Speekenbrink M.., 2010
Hierarchical grouping to optimize an objective function
Ward J.., 1963

Warnes G., Bolker B., Lumley T., Johnson R.., 2015
Recent trends in hierarchic document clustering: a critical review
Willett P.., 1988
Hidden semi-Markov models
Yu S.., 2010

Zucchini W., MacDonald I.., 2009
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 28443037
PubMed | Europe PMC

Suchen in

Google Scholar