Assessment of the Social Phobia Inventory SPIN network model and testing the structure invariance of the model between males and females
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Abstract
The aim of this study was to assess the network model for the Social Phobia Inventory SPIN and to test the structure invariance of the network model between males and females. Data were collected from 2255 students from 15 Algerian universities using SPIN. Mean age 22.35 (SD=3.243). The results showed that there was two dimensions and the most important items that have a large centrality index of strength are items 17, 9 and 6. The Edge Stability Coefficient was 0.75, and the same value for The Strength Centrality Stability Coefficient. This indicates the stability and accuracy of the network structure obtained. The structural consistency reliability coefficient of the first factor was 0.720 and for the second factor 0.982. It is acceptable and indicates that most of the items have structural consistency. The network structure is invariant between the males and the females.
Keywords: Social Phobia; Network Model, Structure Invariance.
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References
كريش، أ. (2020). إعادة التحقق من صلاحية مقياس صعوبات القراءة باستخدام النموذج الشبكي، المجلة الأمريكية للبحوث الإنسانية، عدد خاص، 587–574.
Alexander P. Christensen, Katherine N. Cotter & Paul J. Silvia, (2018). Reopening Openness to Experience: A Network Analysis of Four Openness to Experience Inventories, Journal of Personality Assessment, DOI: 10.1080/00223891.2018.1467428.
American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: Author.
Borsboom, D., & Molenaar, D. (2015). Psychometrics. In J.Wright (Ed.), International encyclopedia of the social & behavioral sciences (Second ed., Vol. 19, pp. 418–422).
Boschloo, L., van Borkulo, C. D., Borsboom, D., & Schoevers, R. A. (2016). A prospective study of how symptoms in a network predict the onset of depression. Psychotherapy and Psychosomatics, 85, 183-184.
Christensen, A. P., Golino, H., & Silvia, P. (2019, March 4). A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires.
Connor, K. M., Davidson, J. R. T., Churchill, L. E., Sherwood, A., Foa, E., and Wesler, R.H. (2000). Psychometric properties of the Social Phobia Inventory (SPIN). The British Journal of Psychiatry, 176, 379-386.
Costantini, G., Richetin, J., Preti, E., Casini, E., Epskamp, S., & Perugini, M. (2019). Stability and variability of personality networks. A tutorial on recent developments in network psychometrics. Personality and Individual Differences, 136, 68–78.
Cramer, A. O. J., Waldorp, L. J., van der Maas, H. L. J., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33, 137–150.
Jones, P.J., Mair, P., Riemann, B.C., Mugno, B.L., & McNally, R.J. (2018). A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. J Anxiety Disord, 53, 1-8
Hallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2019). Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory. Multivariate Behavioral Research, 1–25.
Levinson, C.A., Brosof, L.C., Vanzhula, I., Christian, C., Jones, P., Rodebaugh, T.L., Langer, J.K., White, E.K., Warren, C., Weeks, J.W., Menatti, A., Lim, M.H., Fernandez, K.C. (2018). Social anxiety and eating disorder comorbidity and underlying vulnerabilities: Using network analysis to conceptualize comorbidity. Int J Eat Disord, 51(7), 693-709.
Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., Maris, G. (2018). An introduction to network psychometrics: Relating Ising Network models to item response theory models. Multivariate Behavioral Research, 53, 15–35.
McNally, R.J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95-104
Opsahl, T., Agneessens, F., and Skvoretz, J. (2010). Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32, 245–251. doi: 10.1016/j.socnet.2010.03.006
Rozgonjuk, D., Sindermann, C., Elhai, J.D., Christensen, A.P., & Montag, C. (2020). Associations between symptoms of problematic smartphone, Facebook, WhatsApp, and Instagram use: An item-level exploratory graph analysis perspective. J Behav Addict, 9(3), 686-697.
Schmittmann, V. D., Cramer, A. O. J., 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, 43–53.
See, A., Klimstra, T., Cramer, A., & Denissen, J. (2020). The network structure of personality pathology in adolescence with the 100-Item Personality Inventory for DSM-5 Short-Form (PID-5-SF). Frontiers in Psychology, 11, [823].
Spearman, C, (1904). General Intelligence objectively determined and measured. The American Journal of Psychology. 15, 2: 201-292.
Wang, Y., Shi, H.S., Liu, W.H., et al. (2020). Applying network analysis to investigate the links between dimensional schizotypy and cognitive and affective empathy. Journal of Affective Disorders, 277, 313-321