Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science

Multivariate Behavioral Research

Volumn 51, Issue 2-3, 2016, Pages 240-256

  Ferrer, Emilio ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

dyadic interactions; Dynamical systems; exploratory data analysis; longitudinal data analysis; networks

Indexed keywords

CASE REPORT; CONSENSUS DEVELOPMENT; CONTROLLED STUDY; EXPLORATORY RESEARCH; HUMAN; PRACTICE GUIDELINE; SCIENTIST; SOCIAL INTERACTION; TIME SERIES ANALYSIS; AFFECT; ALGORITHM; FAMILY SIZE; HUMAN RELATION; MARKOV CHAIN; MULTIVARIATE ANALYSIS; PROCEDURES; PSYCHOLOGY; STATISTICAL ANALYSIS; TIME FACTOR;

AFFECT; ALGORITHMS; DATA INTERPRETATION, STATISTICAL; FAMILY CHARACTERISTICS; HUMANS; INTERPERSONAL RELATIONS; MULTIVARIATE ANALYSIS; PSYCHOLOGY; STOCHASTIC PROCESSES; TIME FACTORS;

EID: 84964053105     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1080/00273171.2016.1140629     Document Type: Article

References (60)

1. R.Albert,, H.Jeong,, & A.-L.Barabási, (1999). Diameter of the World-Wide Web. Nature, 401, 130–131. doi:10.1038/43601.
2. L.A.N.Amaral,, A.Diaz–Guilera,, A.A.Moreira,, A.L.Goldberger,, & L.A.Lipsitz, (2004). Emergence of complex dynamics in a simple model of signaling network. Proceedings of the National Academy of Sciences, 101, 15551–15555. doi:10.1073pnas.0404843101.
3. C.M.Bishop, (2006). Pattern recognition and machine learning. Cambridge, UK: Springer.
4. L.Bringmann,, L.Lemmens,, M.Huibers,, D.Borsboom,, F.Tuerlinckx, (2015). Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychological Medicine, 45(4), 747–57. doi:10.1017/S0033291714001809.
5. L.Bringmann,, N.Vissers,, M.Wichers,, N.Geschwind,, P.Kuppens,, F.Peeters,, D.Borsboom,, F.Tuerlinckx, (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS One, 8(4), art.nr. e60188, 1–13. doi:10.1371/journal.pone.0060188.
6. D.Borsboom,, A.O.J.Cramer,, V.D.Schmittmann,, S.Epskamp,, & L.J.Waldorp, (2011). The small world of psychopathology. PLoS One, 11, e27407. doi:10.1371/journal.pone.0027407.
7. L.Castro-Schilo,, & E.Ferrer, (2013). Comparison of nomothetic versus idiographic-oriented methods for making predictions about distal outcomes from time series data. Multivariate Behavioral Research, 48, 175–207. doi:10.1080/00273171.2012.736042.
8. N.A.Christakis, & J.H.Fowler, (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4), 370–379. doi:10.1056/NEJMsa066082.
9. N.A.Christakis,, & J.H.Fowler, (2010). Connected: The surprising power of our social networks and how they shape our lives. New York, NY: Little, Brown and Company.
10. P.Diaconis, (1985). Theories of data analysis: From magical thinking through classical statistics. In D.Hoaglin, F.Mosteller, & J.Tukey (Eds.), Exploring data tables, trends and shapes (pp. 1–36). New York, NY: Wiley.
11. T.J.Dishion,, & J.Snyder, (2004). An introduction to the Special Issue on advances in process and dynamic system analysis of social interaction and the development of antisocial behavior. Journal of Abnormal Child Psychology, 32, 575–578. doi:10.1023/B:JACP.0000047317.96104.ca.
12. Z.Drezner,, & G.A.Marcoulides, (2006). Mapping the convergence of genetic algorithms. Journal of Applied Mathematics and Decision Sciences, 15, 1–16. doi:10.1155/JAMDS/2006/70240.
13. C.Dolan,, V.Schmittmann,, G.Lubke,, & M.Neale, (2005). Regime switching in the latent growth curve mixture model. Structural Equation Modeling, 12(1), 94–119. doi:10.1207/s15328007sem1201_5.
14. J.-P.Eckmann,, S.Kamphorst,, & D.Ruell, (1987). Recurrence plots of dynamical systems. Europhysics Letters, 5, 973–977.
15. P.Ekman, (1992). An argument for basic emotions. Cognition and Emotion, 6, 169–200. doi:10.1080/02699939208411068.
16. E.Ferrer,, S.Chen,, S.M.Chow,, & F.Hsieh, (2010). Exploring intra-individual, inter-individual and inter-variable dynamics in dyadic interactions. In S.M.Chow, E.Ferrer, & F.Hsieh (Eds.), Statistical methods for modeling human dynamics: An interdisciplinary dialogue (pp. 381–411). New York, NY: Taylor and Francis.
17. E.Ferrer, & J.R.Nesselroade, (2003). Modeling affective processes in dyadic relations via dynamic factor analysis. Emotion, 3, 344–360. doi:10.1037/1528-3542.3.4.344.
18. E.Ferrer,, & J.Steele, (2012). Dynamic systems analysis of affective processes in dyadic interactions using differential equations. In G.R.Hancock & J.R.Harring (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 111–134). Charlotte, NC: Information Age Publishing.
19. E.Ferrer,, J.Steele,, & F.Hsieh, (2012). Analyzing dynamics of affective dyadic interactions using patterns of intra- and inter-individual variability. Multivariate Behavioral Research, 47, 136–171. doi:10.1080/00273171.2012.640605.
20. Ferrer, E., & Widaman, K. F. (2008). Dynamic factor analysis of dyadic affective processes with inter-group differences. In N.A. Card, J. P. Selig., & Little, T. D. (Eds.), Modeling dyadic and interdependent data in the developmental and behavioral sciences (pp. 107--137). Hillsdale, NJ: Psychology Press.
21. D.W.Fiske,, & L.Rice, (1955). Intra-individual response variability. Psychological Bulletin, 57(3), 217–250. doi:10.1037/h0045276.
22. S.Frühwirth-Schnatter, (2006). Finite mixture and Markov switching models. New York, NY: Springer.
23. J.E.Gonzales,, & E.Ferrer, (2014). Individual pooling for group-based modeling under the assumption of ergodicity. Multivariate Behavioral Research, 42, 245–260. doi:10.1080/00273171.2014.902298.
24. I.Granic,, T.J.Dishion,, T.Hollenstein,, & G.R.Patterson, (2003). Longitudinal analysis of flexibility and reorganization in early adolescence: A dynamic systems study of family interactions. Developmental Psychology, 39, 606–617. doi:10.1037/0012-1649.39.3.606.
25. F.Hsieh,, E.Ferrer,, S.Chen,, & S.-M.Chow, (2010). Exploring nonstationary dynamics in dyadic interactions via hierarchical segmentation. Psychometrika, 75, 351–372. doi:10.1080/00273171.2012.640605.
26. F.Hsieh,, E.Ferrer,, S.Chen,, I.B.Mauss,, J.Oliver,, & J.L.Gross, (2011). A network approach for evaluating coherence in multivariate systems: An application to psycho-physiological emotion data. Psychometrika, 76, 124–152. doi:10.1007/S11336-010-9194-0.
27. F.Hsieh,, C.-R.Hwang,, H.-C.Lee,, Y.-C.Lan,, & S.-B.Horng, (2006). Testing and mapping non-stationarity in animal behavioral processes: A case study on an individual female bean weevil. Journal of Theoretical Biology, 238(4), 805–816. doi:10.1016/j.jtbi.2005.06.031.
28. T.Hollenstein, (2007). State space grids: Analyzing dynamics across development. International Journal of Behavioral Development, 31, 384–396. doi:10.1177/0165025407077765.
29. L.V.Jones, (Ed.). (1986). The collected works of John W. Tukey: Philosophy and principles of data analysis 1949–1964 (Vols. III & IV). London, UK: Chapman & Hall.
30. C.-J.Kim,, & C.R.Nelson, (1999). State-space models with regime switching: Classical and Gibbs-sampling approaches with applications. Cambridge, MA: MIT Press.
31. D.E.Knuth, (2000). Selected papers on analysis of algorithms. Stanford, CA: Center for the Study of Language and Information.
32. D.T.Larose, (2005). Discovering knowledge in data: An introduction to data mining. Hoboken, NJ: Wiley.
33. R.S.Lazarus, (1991). Emotion and adaptation. London, UK: Oxford University Press.
34. G.A.Marcoulides, (2005). Review of Discovering knowledge in data: An introduction to data mining. Journal of the American Statistical Association, 100, 1465–1465. doi:10.1198/jasa.2005.s61.
35. N.Marwan,, & J.Kurths, (2002). Nonlinear analysis of bivariate data with cross recurrence plots. Physics Letters A, 302, 299–307. doi:10.1016/S0375-9601(02)01170-2.
36. D.Matsumoto,, J.B.Nezlek,, & B.Koopmann, (2007). Evidence for universality in phenomenological emotion response system coherence. Emotion, 7, 57–67. doi:10.1037/1528-3542.7.1.57.
37. J.J.McArdle,, & G.Ritschard, (2014). Contemporary issues in exploratory data mining. New York, NY: Routledge Press.
38. M.McAssey,, J.Helm,, F.Hsieh,, D.A.Sbarra,, & E.Ferrer, (2012). Methodological advances for detecting physiological synchrony during dyadic interactions. Methodology. doi:10.1027/1614-2241/a000053.
39. G.J.McLachlan,, & D.Peel (2000). Finite mixture models. New York, NY: Wiley.
40. S.Milgram, (1967). The small world problem. Psychology Today, 2, 60–67.
41. P.C.M.Molenaar, (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology—this time forever. Measurement, 2, 201–218. doi:10.1207/s15366359mea0204_1.
42. P.C.M.Molenaar,, D.I.Boomsma,, & C.V.Dolan, (1993). A third source of developmental differences. Behavior Genetics, 23, 519–524. doi:10.1007/BF01068142.
43. P.C.M.Molenaar,, & J.R.Nesselroade, (2012). Merging the idiographic filter with dynamic factor analysis to model processes. Applied Developmental Science, 16, 210–219. doi:10.1080/10888691.2012.722884
44. P.C.M.Molenaar,, & M.E.J.Raijmakers, (1999). Additional aspects of third source variation for the genetic analysis of human development and behavior. Twin Research, 2, 49–52. doi:10.1375/136905299320566112.
45. J.R.Nesselroade, (1991). The warp and woof of the developmental fabric. In R.Downs, L.Liben, & D.Palermo (Eds.), Visions of development, the environment, and aesthetics: The legacy of Joachim F. Wohlwill (pp. 213–240). Hillsdale, NJ: Erlbaum.
46. M.Newman,, A.-L.Barabási,, & D.J.Watts, (2006). The structure and dynamics of networks. Princeton, NJ: Princeton University Press.
47. V.D.Schmittmann,, A.O.J.Cramer,, L.J.Waldorp,, S.Epskamp,, R.A.Kievit,, & D.Borsboom, (2011). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31, 43–53. doi:10.1016/j.newideapsych.2011.02.007.
48. D.A.Sbarra,, & C.Hazan, (2008). Coregulation, dysregulation, self-regulation: An integrative analysis and empirical agenda for understanding adult attachment, separation, loss, and recovery. Personality and Social Psychology Review, 12, 141–167. doi:10.1177/1088868308315702.
49. L.B.Smith,, & E.Thelen, (2003). Development as a dynamic system. Trends in Cognitive Sciences, 7, 343–348. doi:10.1016/S1364-6613(03)00156-6.
50. J.Steele,, E.Ferrer,, & J.R.Nesselroade, (2014). An idiographic approach to estimating models of dyadic interactions with differential equations. Psychometrika, 79, 675–700. doi:10.1007/S11336-013-9366-9.
51. E.Thelen,, & L.B.Smith, (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.
52. J.W.Tukey, (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
53. H.L.J.van der Maas,, C.V.Dolan,, R.P.P.Grasman,, J.M.Wicherts,, H.M.Huizenga,, & M.E.J.Raijmakers, (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113, 842–861. doi:10.1037/0033-295X.113.4.842.
54. P.Van Geert, (2000). The dynamics of general developmental mechanisms: From Piaget and Vygotsky to dynamic systems models. Current Directions in Psychological Science, 9, 64–68. doi:10.1111/1467-8721.00062.
55. V.N.Vapnik, (2000). The nature of statistical learning theory. New York, NY: Springer-Verlag.
56. D.Watson,, A.C.Lee,, & A.Tellegen, (1988). Development and validation of brief measures of positive and negative affect: The PANAS scale. Journal of Personality and Social Psychology, 54(6), 1063–1070. doi:10.1037/0022-3514.54.6.1063.
57. D.J.Watts,, & S.H.Strogatz, (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. doi:10.1038/30918.
58. D.J.Watts, (1999). Small worlds: The dynamics of networks between order and randomness. Princeton, NJ: Princeton University Press.
59. I.H.Witten,, & E.Frank, (2005). Data mining: Practical machine learning tools and techniques. San Francisco, CA: Elsevier.
60. R.Xu,, & D.C.Wunsch, (2009). Clustering. Hoboken, NJ: John Wiley & Sons.