Abstract
Network analysis is becoming more widely used as a method of understanding the structure and potential causal and influential relationships between symptoms within and across psychiatric syndromes such as posttraumatic stress disorder. Because large samples are often needed to yield more stable findings in network analysis, researchers often struggle to overcome limitations associated with small sample size. So, researchers often combine samples to ensure appropriate statistical power for analyses. Little research has been done, however, to determine whether such strategies are appropriate. The present study evaluates the network structure and indices of two college student samples (N = 668 and 456) from mid-sized cities to examine similarities and differences that might inform whether similar samples can be combined for network analysis. The findings suggest that the overall network structures are not different based on a network comparison analysis; however, centrality stability coefficients for centrality indices across both networks were below recommended cut-offs, indicating that the networks were generally unstable. We discuss the implications for these findings in the paper, highlighting that network comparison alone is likely insufficient in determining whether or not to combine samples for network analysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.
Armour, C., Tsai, J., Durham, T. A., Charak, R., Biehn, T. L., Elhai, J. D., & Pietrzak, R. H. (2015). Dimensional structure of DSM–5 posttraumatic stress symptoms: Support for a hybrid Anhedonia and externalizing behaviors model. Journal of Psychiatric Research, 61, 106–113. https://doi.org/10.1016/j.jpsychires.2014.10.012.
Armour, C., Fried, E. I., Deserno, M. K., Tsai, J., & Pietrzak, R. H. (2017). A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in US military veterans. Journal of Anxiety Disorders, 45, 49–59. https://doi.org/10.1016/j.janxdis.2016.11.008.
Banducci, A. N., Connolly, K. M., Vujanovic, A. A., Alvarez, J., & Bonn-Miller, M. O. (2017). The impact of changes in distress tolerance on PTSD symptom severity post-treatment among veterans in residential trauma treatment. Journal of Anxiety Disorders, 47, 99–105. https://doi.org/10.1016/j.janxdis.2017.01.004.
Beard, C., Millner, A. J., Forgeard, M. J., Fried, E. I., Hsu, K. J., Treadway, M. T., Leonard, C. V., Kertz, S. J., & Björgvinsson, T. (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine, 46(16), 3359–3369. https://doi.org/10.1017/S0033291716002300.
Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., & Domino, J. L. (2015). The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. Journal of Traumatic Stress, 28, 489–498. https://doi.org/10.1002/jts.22059.
Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121.
Bovin, M. J., Marx, B. P., Weathers, F. W., Gallagher, M. W., Rodriguez, P., Schnurr, P. P., & Keane, T. M. (2016). Psychometric properties of the PTSD checklist for diagnostic and statistical manual of mental disorders–fifth edition (PCL-5) in veterans. Psychological Assessment, 28(11), 1379–1391. https://doi.org/10.1037/pas0000254.
Cloitre, M., Garvert, D. W., Brewin, C. R., Bryant, R. A., & Maercker, A. (2013). Evidence for proposed ICD-11 PTSD and complex PTSD: A latent profile analysis. European Journal of Psychotraumatology, 4. https://doi.org/10.3402/ejpt.v4i0.20706.
Elhai, J. D., Contractor, A. A., Palmieri, P. A., Forbes, D., & Richardson, J. D. (2011). Exploring the relationship between underlying dimensions of posttraumatic stress disorder and depression in a national, trauma-exposed military sample. Journal of Affective Disorders, 133(3), 477–480. https://doi.org/10.1016/j.jad.2011.04.035.
Elliott, M. R., Raghunathan, T. E., & Schenker, N. (2018). Combining estimates from multiple surveys. Wiley StatsRef: Statistics Reference Online, 1–10. https://doi.org/10.1002/9781118445112.stat08079.
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(4), 1–18. https://doi.org/10.18637/jss.v048.i04.
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 1–18. https://doi.org/10.3758/s13428-017-0862-1.
Forbush, K. T., Siew, C. S. Q., & Vitevitch, M. S. (2016). Application of network analysis to identify interactive systems of eating disorder psychopathology. Psychological Medicine, 46(12), 2667–2677. https://doi.org/10.1017/S003329171600012X.
Franklin, C. L., Walton, J. L., Cuccurullo, L. A., Raines, A., Ball, J., Vaught, A., Chambliss, J. L., & Maieritsch, K. P. (2016). Examining potential overlap of DSM-5 PTSD criteria D and E. Psychiatry Research, 246, 250–254. https://doi.org/10.1016/j.psychres.2016.10.002.
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.
Fried, E. I., Eidhof, M. B., Palic, S., Costantini, G., Huisman-van Dijk, H. M., Bockting, C. L., & Karstoft, K. I. (2018). Replicability and generalizability of posttraumatic stress disorder (PTSD) networks: A cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clinical Psychological Science, 6(3), 335–351. https://doi.org/10.1177/2167702617745092.
Gray, M. J., Litz, B. T., Hsu, J. L., & Lombardo, T. W. (2004). Psychometric properties of the life events checklist. Assessment, 11, 330–341. https://doi.org/10.1177/1073191104269954.
Greene, T., Gelkopf, M., Epskamp, S., & Fried, E. (2018). Dynamic networks of PTSD symptoms during conflict. Psychological Medicine, 48(14), 2409–2417. https://doi.org/10.1017/S0033291718000351.
Hansen, M., Armour, C., Wang, L., Elklit, A., & Bryant, R. A. (2015). Assessing possible DSM- 5 ASD subtypes in a sample of victims meeting caseness for DSM-5 ASD based on self-report following multiple forms of traumatic exposure. Journal of Anxiety Disorders, 31, 84–89. https://doi.org/10.3402/ejpt.v6.28766.
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381.
Kilpatrick, D. G., Resnick, H. S., Milanak, M. E., Miller, M. W., Keyes, K. M., & Friedman, M. J. (2013). National estimates of exposure to traumatic events and PTSD prevalence using DSM-IV and DSM-5 criteria. Journal of Traumatic Stress, 26(5), 537–547. https://doi.org/10.1002/jts.21848.
King, L. A., King, D. W., Fairbank, J. A., Keane, T. M., & Adams, G. A. (1998). Resilience–recovery factors in post-traumatic stress disorder among female and male Vietnam veterans: Hardiness, postwar social support, and additional stressful life events. Journal of Personality and Social Psychology, 74(2), 420–434. https://doi.org/10.1037/0022-3514.74.2.420.
Maercker, A., Brewin, C. R., Bryant, R. A., Cloitre, M., van Ommeren, M., Jones, L. M., et al. (2013). Diagnosis and classification of disorders specifically associated with stress: Proposals for ICD-11. World Psychiatry, 12(3), 198–206. https://doi.org/10.1002/wps.20057.
McNally, R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95–104.
McNally, R. J., Robinaugh, D. J., Wu, G. W., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: a network approach to posttraumatic stress disorder. Clinical Psychological Science, 3(6), 836–849. https://doi.org/10.1080/20008198.2017.1341276.
Mitchell, K. S., Wolf, E. J., Bovin, M. J., Lee, L. O., Green, J. D., Rosen, R. C., et al. (2017). Network models of DSM–5 posttraumatic stress disorder: Implications for ICD–11. Journal of Abnormal Psychology, 126(3), 355. https://doi.org/10.1037/abn0000252.
R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria.
Resick, P. A., Wachen, J. S., Mintz, J., Young-McCaughan, S., Roache, J. D., Borah, A. M., et al. (2015). A randomized clinical trial of group cognitive processing therapy compared with group present-centered therapy for PTSD among active duty military personnel. Journal of Consulting and Clinical Psychology, 83(6), 1058–1068. https://doi.org/10.1037/ccp0000016.
Rhemtulla, M., Fried, E. I., Aggen, S. H., Tuerlinckx, F., Kendler, K. S., & Borsboom, D. (2016). Network analysis of substance abuse and dependence symptoms. Drug and Alcohol Dependence, 161, 230–237. https://doi.org/10.1016/j.drugalcdep.2016.02.005.
Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125(6), 747–757. https://doi.org/10.1037/abn0000181.
Ross, J., Murphy, D., & Armour, C. (2018). A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatment-seeking veterans. Journal of Anxiety Disorders, 57, 7–15. https://doi.org/10.1016/j.janxdis.2018.05.007.
Schenker, N., & Raghunathan, T. E. (2007). Combining information from multiple surveys to enhance estimation of measures of health. Statistics in Medicine, 26(8), 1802–1811.
Schmittmann, V. D., Cramer, A. O., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43–53. https://doi.org/10.1016/j.newideapsych.2011.02.007.
Simms, L. J., Watson, D., & Doebbelling, B. N. (2002). Confirmatory factor analyses of posttraumatic stress symptoms in deployed and nondeployed veterans of the Gulf war. Journal of Abnormal Psychology, 111(4), 637–647. https://doi.org/10.1037//0021-843X.111.4.637.
Tsai, J., Harpaz-Rotem, I., Armour, C., Southwick, S. M., Krystal, J. H., & Pietrzak, R. H. (2015). Dimensional structure of DSM–5 posttraumatic stress disorder symptoms: Results from the National Health and Resilience in Veterans Study. Journal of Clinical Psychiatry, 76, 546–553. https://doi.org/10.4088/JCP.14m09091.
van Borkulo, C. D., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D., & Boschloo, L. (2017). Comparing network structures on three aspects: A permutation test. Unpublished manuscript. https://doi.org/10.13140/RG.2.2.29455.38569.
Weathers, F.W., Litz, B.T., Keane, T.M., Palmieri, P.A., Marx, B.P., & Schnurr, P.P. (2013). The PTSD Checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at http://www.ptsd.va.gov.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Jasmine R. Eddinger, Meghan E. McDevitt-Murphy, Jinxiang Hu, Lisa Jobe-Shields, Joah L. Williams declare that there is no conflict of interest.
Ethical Participants
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
ESM 1
(DOCX 234 kb)
Rights and permissions
About this article
Cite this article
Eddinger, J.R., McDevitt-Murphy, M.E., Hu, J. et al. Comparison of PTSD Symptom Centrality in Two College Student Samples. J Psychopathol Behav Assess 42, 354–363 (2020). https://doi.org/10.1007/s10862-020-09792-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10862-020-09792-w