What Can Network Analysis Tell Us About the Intolerance of Uncertainty?
DOI:
https://doi.org/10.19090/pp.v17i3.2519Keywords:
intolerance of uncertainty, intolerance of uncertainty scale, network analysis, vulnerability, anxietyAbstract
In this study, we explored the network structure of intolerance of uncertainty (IU) using a community sample. We tested the interplay of emotions, behaviors, and beliefs about uncertainty (as measured by the Serbian Intolerance of Uncertainty-11 Scale) and evaluated whether our results would align with those obtained by the Italian researchers, considering the use of somewhat different versions of the scale in somewhat different cultural settings. The walktrap community detection algorithm yielded two communities referring to 1) Inhibitory anxiety and 2) Prospective anxiety. Thus, our findings suggest that IU can be decomposed into these two aspects regardless of which approach is used – network approach or factor analysis. The three most central nodes referred to perceiving uncertainty as upsetting and intolerable and believing one must avoid all the uncertainty. Two central nodes belonged to the Prospective anxiety community, and the third one belonged to the Inhibitory anxiety community and indicated reduced overall quality of life due to uncertainty. The roles of these three constituents in understanding the nature of IU are discussed further in the paper.
Metrics
References
Berenbaum, H., Bredemeier, K., & Thompson, R. J. (2008). Intolerance of uncertainty: Exploring its dimensionality and associations with need for cognitive closure, psychopathology, and personality. Journal of Anxiety Disorders, 22(1), 117–125. https://doi.org/10.1016/j.janxdis.2007.01.004 DOI: https://doi.org/10.1016/j.janxdis.2007.01.004
Birrell, J., Meares, K., Wilkinson, A., & Freeston, M. (2011). Toward a definition of intolerance of uncertainty: A review of factor analytical studies of the Intolerance of Uncertainty Scale. Clinical Psychology Review, 31(7), 1198-1208. https://doi.org/10.1016/j.cpr.2011.07.009 DOI: https://doi.org/10.1016/j.cpr.2011.07.009
Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121. https://doi.org/10.1146/annurev-clinpsy-050212-185608 DOI: https://doi.org/10.1146/annurev-clinpsy-050212-185608
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 1-18. https://doi.org/10.1038/s43586-021-00055-w DOI: https://doi.org/10.1038/s43586-021-00055-w
Bottesi, G., Ghisi, M., Novara, C., Bertocchi, J., Boido, M., De Dominicis, I., & Freeston, M. H. (2015). Intolerance of Uncertainty Scale (IUS-27 e IUS-12): Due studi preliminari [Intolerance of Uncertainty Scale (IUS-27 and IUS-12): Two preliminary studies]. Psicoterapia Cognitiva e Comportamentale, 21(3), 345–365.
Bottesi, G., Iannattone, S., Carraro, E., & Lauriola, M. (2023). The assessment of Intolerance of uncertainty in youth: An examination of the Intolerance of Uncertainty Scale-Revised in Italian nonclinical boys and girls. Research on Child and Adolescent Psychopathology, 51(2), 209–222. https://doi.org/10.1007/s10802-022-00944-y DOI: https://doi.org/10.1007/s10802-022-00944-y
Bottesi, G., Marchetti, I., Sica, C., & Ghisi, M. (2020). What is the internal structure of intolerance of uncertainty? A network analysis approach. Journal of Anxiety Disorders, 75, 102293. https://doi.org/10.1016/j.janxdis.2020.102293 DOI: https://doi.org/10.1016/j.janxdis.2020.102293
Bottesi, G., Noventa, S., Freeston, M. H., & Ghisi, M. (2019). Seeking certainty about Intolerance of Uncertainty: Addressing old and new issues through the Intolerance of Uncertainty Scale-Revised. PloS One, 14(2), e0211929. https://doi.org/10.1371/journal.pone.0211929 DOI: https://doi.org/10.1371/journal.pone.0211929
Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128(8), 892-903. https://doi.org/10.1037/abn0000446 DOI: https://doi.org/10.1037/abn0000446
Buhr, K., & Dugas, M. J. (2002). The intolerance of uncertainty scale: psychometric properties of the English version. Behaviour Research and Therapy, 40(8), 931–945. https://doi.org/10.1016/s0005-7967(01)00092-4 DOI: https://doi.org/10.1016/S0005-7967(01)00092-4
Carleton, R. N., Collimore, K. C., & Asmundson, G. J. G. (2010). "It's not just the judgements—It's that I don't know": Intolerance of uncertainty as a predictor of social anxiety. Journal of Anxiety Disorders, 24(2), 189–195. https://doi.org/10.1016/j.janxdis.2009.10.007 DOI: https://doi.org/10.1016/j.janxdis.2009.10.007
Carleton, R. N., Norton, M. P. J., & Asmundson, G. J. (2007). Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. Journal of Anxiety Disorders, 21(1), 105-117. https://doi.org/10.1016/j.janxdis.2006.03.014 DOI: https://doi.org/10.1016/j.janxdis.2006.03.014
Carleton, R. N. (2016). Into the unknown: A review and synthesis of contemporary models involving uncertainty. Journal of Anxiety Disorders, 39, 30-43. https://doi.org/10.1016/j.janxdis.2016.02.007 DOI: https://doi.org/10.1016/j.janxdis.2016.02.007
Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771. https://doi.org/10.1093/biomet/asn034 DOI: https://doi.org/10.1093/biomet/asn034
Cramer, A. O., Van Borkulo, C. D., Giltay, E. J., Van Der Maas, H. L., Kendler, K. S., Scheffer, M., & Borsboom, D. (2016). Major depression as a complex dynamic system. PloS One, 11(12), e0167490. https://doi.org/10.1371/journal.pone.0167490 DOI: https://doi.org/10.1371/journal.pone.0167490
Cs´ardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9. https://igraph.org.
Dablandor, F., & Hinne, M. (2019). Node centrality measures are poor substitute for causal inference. Scientific Reports, 9, 6846. https://doi.org/10.1038/s41598-019-43033-9 DOI: https://doi.org/10.1038/s41598-019-43033-9
Dugas, M. J., Gagnon, F., Ladouceur, R., & Freeston, M. H. (1998). Generalized anxiety disorder: A preliminary test of a conceptual model. Behaviour Research and Therapy, 36(2), 215–226. https://doi.org/10.1016/s0005-7967(97)00070-3 DOI: https://doi.org/10.1016/S0005-7967(97)00070-3
Eid, M., Krumm, S., Koch, T., & Schulze, J. (2018). Bifactor Models for Predicting Criteria by General and Specific Factors: Problems of Nonidentifiability and Alternative Solutions. Journal of Intelligence, 6(3), 42. https://doi.org/10.3390/jintelligence6030042 DOI: https://doi.org/10.3390/jintelligence6030042
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212. https://doi.org/10.3758/s13428-017-0862-1 DOI: https://doi.org/10.3758/s13428-017-0862-1
Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(1), 1-18. http://dx.doi.org/10.18637/jss.v048.i04 DOI: https://doi.org/10.18637/jss.v048.i04
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617- 634. https://psycnet.apa.org/doi/10.1037/met0000167 DOI: https://doi.org/10.1037/met0000167
Freeston, M. H., Rhéaume, J., Letarte, H., Dugas, M. J., & Ladouceur, R. (1994). Why do people worry?. Personality and Individual Differences, 17(6), 791-802. https://doi.org/10.1016/0191-8869(94)90048-5 DOI: https://doi.org/10.1016/0191-8869(94)90048-5
Hale, W., Richmond, M., Bennett, J., Berzins, T., Fields, A., Weber, D., Beck, M., & Osman, A. (2016). Resolving uncertainty about the Intolerance of Uncertainty Scale–12: Application of modern psychometric strategies. Journal of Personality Assessment, 98(2), 200-208. https://doi.org/10.1080/00223891.2015.1070355 DOI: https://doi.org/10.1080/00223891.2015.1070355
Fried, E. (2016, October 19). R tutorial: how to identify communities of items in networks. Psych Networks. Retrieved December 20, 2021, from https://psych-networks.com/r-tutorial-identify-communities-items-networks/
Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016). What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314-320. https://doi.org/10.1016/j.jad.2015.09.005 DOI: https://doi.org/10.1016/j.jad.2015.09.005
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441. https://doi.org/10.1093/biostatistics/kxm045 DOI: https://doi.org/10.1093/biostatistics/kxm045
Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force‐directed placement. Software: Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102 DOI: https://doi.org/10.1002/spe.4380211102
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75-174. https://doi.org/10.1016/j.physrep.2009.11.002 DOI: https://doi.org/10.1016/j.physrep.2009.11.002
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. arXiv preprint arXiv:1011.6640.
Golino, H., & Christensen, A. P. (2024). EGAnet: Exploratory Graph Analysis – A framework for estimating the number of dimensions in multivariate data using network psychometrics. R package version 2.0.5, https://r-ega.net.
Golino, H., Christensen, A. P., & Garrido, L. E. (2022). Invited commentary: Exploratory graph analysis in context. Psicologia: Teoria e Prática, 24(3), 1-10. https://doi.org/10.5935/1980-6906/ePTPIC15531.en DOI: https://doi.org/10.5935/1980-6906/ePTPIC15531.en
Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12(6), Article e0174035. https://doi.org/10.1371/journal.pone.0174035 DOI: https://doi.org/10.1371/journal.pone.0174035
Golino, H., Moulder, R., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2021). Entropy fit indices: new fit measures for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research, 56(6), 874–902. https://doi.org/10.1080/00273171.2020.1779642 DOI: https://doi.org/10.1080/00273171.2020.1779642
Haslbeck, J. M., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50, 853-861. https://doi.org/10.3758/s13428-017-0910-x DOI: https://doi.org/10.3758/s13428-017-0910-x
Hayes, A. M., Yasinski, C., Barnes, J. B., & Bockting, C. L. (2015). Network destabilization and transition in depression: New methods for studying the dynamics of therapeutic change. Clinical Psychology Review, 41, 27-39. https://doi.org/10.1016%2Fj.cpr.2015.06.007 DOI: https://doi.org/10.1016/j.cpr.2015.06.007
Helsen, K., Van den Bussche, E., Vlaeyen, J. W., & Goubert, L. (2013). Confirmatory factor analysis of the Dutch Intolerance of Uncertainty Scale: Comparison of the full and short version. Journal of Behavior Therapy and Experimental Psychiatry, 44(1), 21-29. https://doi.org/10.1016/j.jbtep.2012.07.004 DOI: https://doi.org/10.1016/j.jbtep.2012.07.004
Hernández-Posadas, A., De la Rosa-Gómez, A., Lommen, M., Bouman, T., Mancilla-Díaz, J., & Valdés, D. (2023). Psychometric properties of the Mexican version of the Intolerance of Uncertainty Scale: The IUS-12M. Interacciones, 9, e358. https://doi.org/10.24016/2023.v9.358 DOI: https://doi.org/10.24016/2023.v9.358
Isvoranu, A. M., & Epskamp, S. (2021). Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000439 DOI: https://doi.org/10.31234/osf.io/mbycn
Kretzmann, R. P., & Gauer, G. (2020). Psychometric properties of the Brazilian Intolerance of Uncertainty Scale - Short Version (IUS-12). Trends in Psychiatry and Psychotherapy, 42(2), 129–137. https://doi.org/10.1590/2237-6089-2018-0087 DOI: https://doi.org/10.1590/2237-6089-2018-0087
Lauriola, M., Mosca, O., Trentini, C., Foschi, R., Tambelli, R., & Carleton, R. N. (2018). The intolerance of uncertainty inventory: validity and comparison of scoring methods to assess individuals screening positive for anxiety and depression. Frontiers in Psychology, 9, 388. https://doi.org/10.3389/fpsyg.2018.00388 DOI: https://doi.org/10.3389/fpsyg.2018.00388
Lauritzen, S. L. (1996). Graphical models. Clarendon Press. DOI: https://doi.org/10.1093/oso/9780198522195.001.0001
McEvoy, P. M., & Mahoney, A. E. (2011). Achieving certainty about the structure of intolerance of uncertainty in a treatment-seeking sample with anxiety and depression. Journal of Anxiety Disorders, 25(1), 112-122. https://doi.org/10.1016/j.janxdis.2010.08.010 DOI: https://doi.org/10.1016/j.janxdis.2010.08.010
Mihić, L., Sokić, J., Samac, N., & Ignjatović, I. (2014). Srpska adaptacija i validacija upitnika netolerancije na neizvesnost. [Serbian adaptation and validation of the intolerance of uncertainty scale]. Primenjena Psihologija, 7(3-1), 347-370. https://doi.org/10.19090/pp.2014.3-1.347-370 DOI: https://doi.org/10.19090/pp.2014.3-1.347-370
Norton P. J. (2005). A psychometric analysis of the Intolerance of Uncertainty Scale among four racial groups. Journal of Anxiety Disorders, 19(6), 699–707. https://doi.org/10.1016/j.janxdis.2004.08.002 DOI: https://doi.org/10.1016/j.janxdis.2004.08.002
Sahib, A., Chen, J., Cárdenas, D., & Calear, A. L. (2023). Intolerance of uncertainty and emotion regulation: A meta-analytic and systematic review. Clinical Psychology Review, 101, 102270. https://doi.org/10.1016/j.cpr.2023.102270 DOI: https://doi.org/10.1016/j.cpr.2023.102270
Sankar, R., Robinson, L., Honey, E., & Freeston, M. (2017). ‘We know intolerance of uncertainty is a transdiagnostic factor but we don’t know what it looks like in everyday life’: A systematic review of intolerance of uncertainty behaviours. Clinical Psychology Forum, 296, 10-15. DOI: https://doi.org/10.53841/bpscpf.2017.1.296.10
Saulnier, K. G., Allan, N. P., Raines, A. M., & Schmidt, N. B. (2019). Depression and intolerance of uncertainty: Relations between uncertainty subfactors and depression dimensions. Psychiatry, 82(1), 72-79. https://doi.org/10.1080/00332747.2018.1560583 DOI: https://doi.org/10.1080/00332747.2018.1560583
Sexton, K. A., & Dugas, M. J. (2009). Defining distinct negative beliefs about uncertainty: validating the factor structure of the Intolerance of Uncertainty Scale. Psychological Assessment, 21(2), 176–186. https://doi.org/10.1037/a0015827 DOI: https://doi.org/10.1037/a0015827
Shihata, S., McEvoy, P. M., & Mullan, B. A. (2018). A bifactor model of intolerance of uncertainty in undergraduate and clinical samples: Do we need to reconsider the two-factor model? Psychological Assessment, 30(7), 893–903. https://doi.org/10.1037/pas0000540 DOI: https://doi.org/10.1037/pas0000540
van Bork, R., Rhemtulla, M., Waldorp, L. J., Kruis, J., Rezvanifar, S., & Borsboom, D. (2021). Latent Variable Models and Networks: Statistical Equivalence and Testability. Multivariate Behavioral Research, 56(2), 175–198. https://doi.org/10.1080/00273171.2019.1672515 DOI: https://doi.org/10.1080/00273171.2019.1672515
van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. J. (2022). Comparing network structures on three aspects: A permutation test. Psychological Methods, 10.1037/met0000476. Advance online publication. https://doi.org/10.1037/met0000476 DOI: https://doi.org/10.1037/met0000476
Volarov, M., Saulnier, K. G., Allan, N. P., Shapiro, M. O., & Mihić, L. (2021). Are we still uncertain about the latent structure of intolerance of uncertainty: Results from factor mixture modeling in a Serbian sample. Journal of Affective Disorders, 294, 505-512. https://doi.org/10.1016/j.jad.2021.07.081 DOI: https://doi.org/10.1016/j.jad.2021.07.081
Watts, A. L., Poore, H. E., & Waldman, I. D. (2019). Riskier Tests of the Validity of the Bifactor Model of Psychopathology. Clinical Psychological Science, 7(6), 1285–1303. https://doi.org/10.1177/2167702619855035 DOI: https://doi.org/10.1177/2167702619855035
Williams, D. R., & Rast, P. (2020). Back to the basics: Rethinking partial correlation network methodology. The British Journal of Mathematical and Statistical Psychology, 73(2), 187–212. https://doi.org/10.1111/bmsp.12173 DOI: https://doi.org/10.1111/bmsp.12173
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Marija Volarov, Mina Velimirović, Bojan Janičić, Ljiljana Mihić
This work is licensed under a Creative Commons Attribution 4.0 International License.