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Quality of Life Research

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14-06-2019

The importance of face-to-face contact and reciprocal relationships and their associations with depressive symptoms and life satisfaction

Auteurs: Melissa Simone, Christian Geiser, Ginger Lockhart

Gepubliceerd in: Quality of Life Research | Uitgave 11/2019

Abstract

Objective

The current study aimed to examine how patterns of interpersonal relational contexts (e.g., face-to-face or technology-based) and processes (e.g., initiated or accepted) relate to depressive symptomology and life satisfaction.

Methods

Participants were recruited through Amazon’s Mechanical Turk (n = 962 adults [52.1% female; aged 18–78; 16.4% Non-White]). Quota sampling was used to closely match the sample demographics to that of the United States Census data. Latent class analyses (LCA) identified classes of interpersonal relations using the Multidimensional Interpersonal Relations Scale. Next, participants’ responses on the Beck Depression Inventory and Satisfaction With Life Scale were examined to evaluate differences in depressive symptoms and life satisfaction across classes.

Results

LCA results supported a 4-class model, in which classes were characterized by patterns of relational contexts and processes: Class 1 (50.6%) engagement across all contexts (e.g., face-to-face) and processes (e.g., initiated); Class 2 (12.7%) engagement across all contexts and processes except Facebook; Class 3 (24.0%) engagement in all contexts and only passive processes; and Class 4 (12.7%) engagement in only technology-based contexts and passive processes. Membership in Classes 1 and 2 was associated with lower depressive symptomology and higher life satisfaction as compared to Classes 3 and 4.

Conclusion

The findings suggest that patterns of relations differentially relate to depressive symptoms and life satisfaction. The findings suggest that multicontextual (e.g., face-to-face and technology-based) and reciprocal relationships with friends (e.g., initiating and accepting connections) may play an important role in the association between interpersonal relations with life satisfaction and depressive symptoms.

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Nelson, L. J. (2013). Going it alone: Comparing subtypes of withdrawal on indices of adjustment and maladjustment in emerging adulthood. Social Development, 22, 533–538. https://doi.org/10.1111/j.1467-9507.2012.00671.x.CrossRef

Rubin, K. H., Bukowski, W., & Parker, J. G. (2006). Peer interactions, relationships, and groups. In N. Eisenberg (Ed.), Handbook of child psychology: Social, emotional, and personality Development (pp. 571–645). New York: Wiley.

Measelle, J. R., Stice, E., & Hogansen, J. M. (2006). Developmental trajectories of co-occurring depressive, eating, antisocial, and substance abuse problems in female adolescents. Journal of Abnormal Psychology, 115(3), 524–538. https://doi.org/10.1037/0021-843X.115.3.524.CrossRefPubMedPubMedCentral

Rubin, K. H., Coplan, R. J., & Bowker, J. C. (2009). Social withdrawal in childhood. Annual Review of Psychology, 60, 141–171. https://doi.org/10.1146/annurev.psych.60.110707.163642.CrossRefPubMedPubMedCentral

Coplan, R. J., Girardi, A., Findlay, L. C., & Frohlick, S. L. (2007). Understanding solitude: Young children’s attitudes and responses towards hypothetical socially-withdrawn peers. Social Development, 16, 390–409. https://doi.org/10.1111/j.1467-9507.2007.00390.x.CrossRef

Peterson, C., Park, N., & Seligman, M. E. P. (2005). Orientations to happiness and life satisfaction: The full life versus the empty life. Journal of Happiness Studies, 6(1), 25–41.CrossRef

Pew Research Center. (2016). Social Media Update 2016. Washington, DC

Pew Research Center. (2012). Cell phone activities 2012: The Internet & American Life Project. Washington, DC: Duggan, M. & Rainie, L.

Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox revisited. Journal of Social Issues, 58, 49–74. https://doi.org/10.1111/1540=4560.00248.CrossRef

10.

Przybylski, A. K., & Weinstein, N. (2017). A large-scale test of the goldilocks hypothesis: Quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychological Science, 28(2), 204–2015. https://doi.org/10.1177/0956797616678438.CrossRefPubMed

11.

Birnie, S.A., & Horvath, P. (2002). Psychological predictors of internet social communication. Journal of Computer-Mediated Communication, 7(4).

12.

Sheldon, P. (2008). The relationship between unwillingness-to-communicate and students’ Facebook use. Journal of Media Psychology, 20, 67–75. https://doi.org/10.1027/1864-1105.20.2.67.CrossRef

13.

Nelson, L. J., Coyne, S. M., Howard, E., & Clifford, B. N. (2016). Withdrawing to a virtual world: Associations between subtypes of withdrawal, media use, and maladjustment in emerging adults. Developmental Psychology, 52(6), 933–942. https://doi.org/10.1037/dev0000128.CrossRefPubMed

14.

Kandel, D. B. (1978). Homophily, selection, and socialization in adolescent friendships. American Journal of Sociology, 84, 427–436.CrossRef

15.

Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 561–571. https://doi.org/10.1001/archpsyc.1961.01710120031004.CrossRefPubMed

16.

Simone, M., Geiser, C., & Lockhart, G. (2018). Development and validation of the Multicontextual Interpersonal Relations Scale (MIRS). European Journal of Psychological Assessment. https://doi.org/10.1080/10705511.2018.1540934.CrossRef

17.

Laska, M. N., Pasch, K. E., Lust, K., Story, M., & Ehlinger, E. (2009). Latent class analysis of lifestyle characteristics and health risk behaviors among college youth. Prevention Science, 10(4), 376–386.CrossRef

18.

Patterson, B. H., Dayton, C. M., & Graubard, B. I. (2002). Latent class analysis of complex sample survey data. Journal of American Statistical Association, 97(459), 721–741.CrossRef

19.

Reese, J. B., Blackford, A., Sussman, J., Okuyama, T., Akechi, T., Bainbridge, D., et al. (2015). Cancer patients’ function, symptoms and supportive care needs: A latent class analysis across cultures. Quality of Life Research, 24(1), 135–146.CrossRef

20.

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13.CrossRefPubMed

21.

Muthén, L. K., & Muthén, B. O. (1998-2011). Mplus User’s Guide (6th ed.). Los Angeles, CA: Muthén & Muthén.

22.

Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396.CrossRef

23.

Sclove, L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343. https://doi.org/10.1007/BF02294360.CrossRef

24.

McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.CrossRef

25.

Clark, S. L., & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. Manuscript submitted for publication. http://www.statmodel.com/download/relatinglca.pdf

26.

Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195–212.CrossRef

27.

Muthén, L. K., Muthén, B. O. Re: What is a good value of entropy. 2007 [Online comment]. Retrieved from http://www.statmodel.com/discussion/messages/13/2562.html?1237580237

28.

Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Analysis, 20, 351–368.CrossRef

29.

Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29, 2156–2160.CrossRef

Titel: The importance of face-to-face contact and reciprocal relationships and their associations with depressive symptoms and life satisfaction
Auteurs: Melissa Simone
Christian Geiser
Ginger Lockhart
Publicatiedatum: 14-06-2019
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 11/2019
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-019-02232-7

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Abstract

Objective

Methods

Results

Conclusion

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