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| An introduction of network psychometric analysis |
| HE Jingyi, DENG Jiaxin, WANG Mengcheng |
| Department of Psychology, Guangzhou University, Guangzhou 510006 |
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Abstract Network psychometric analysis is a method that conceptualizes psychological behavior/phenomenon as the process and result of the interaction between psychological characteristics. The constructed network is composed of nodes representing observed variables and connected by edges representing the relationship between nodes, so as to reveal the relationship between variables in a visual way. With analyzing individual nodes' characteristics and global network structure, network psychometric analysis can provide a new perspective to understand various psychological characteristics. It can make up the limitations of traditional modeling methods, and has been widely applied in the fields of psychopathology, clinical psychology and psychometrics, etc. Future studies should further examine the replicability of the findings, and learn from the advantages of latent variable modeling to improve network model for more spacious applications.
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[1] 王孟成, 张新彤, 吕昌嵩, 黎志华. (2021). 男性服刑人员抑郁焦虑症状网络结构及其与父母养育方式的关系. 中国临床心理学, 29, 496-500.
[2] 王孟成, 邓嘉欣. (2023). 网络分析. 王孟成, 刘拓(主编).心理与行为定量研究手册. 重庆: 重庆大学出版社.
[3] Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology.Annual Review of Clinical Psychology, 9, 91-121.
[4] Bringmann L. F., Vissers N., Wichers M., Geschwind N., Kuppens P., Peeters F., … Tuerlinckx F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE, 8, e60188.
[5] Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces.Biometrika, 95, 759-771.
[6] Costantini, G., & Epskamp, S. (2017). Estimate Group Network: Perform the joint graphical lasso and selects tuning parameters. R package (Version 0.1.2) [Computer software]. Retrieved from https://cran.rproject.org/web/packages/EstimateGroupNetwork/index.html
[7] Costantini, G., & Perugini, M. (2012). The defnition of components and the use of formal indexes are key steps for a successful application of network analysis in personality psychology.European Journal of Personality, 26, 434-435.
[8] Costantini, G., & Perugini, M. (2016). The network of conscientiousness. Journal of Research in Personality, 65, 68-88.
[9] Cox, D. R., & Wermuth, N. (1994). A note on the quadratic exponential binary distribution.Biometrika, 81, 403-408.
[10] Danaher P., Wang P., & Witten D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes.Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76, 373-397.
[11] De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with pajek (Vol. 27). Cambridge, UK: Cambridge University Press.
[12] Deng J., Wang M. C., Shou Y., & Gao Y. (2021). Core features of callous-unemotional traits: Network analysis of the Inventory of Callous-Unemotional Traits in community and offender samples.Journal of Clinical Psychology, 77, 1487-1498.
[13] Efron, B. (1979). Bootstrap methods: Another look at the jackknife.The Annals of Statistics, 7, 1-26.
[14] Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617-634.
[15] Epskamp S., Borsboom D., & Fried E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper.Behavior Research Methods, 50, 195-212.
[16] Epskamp S., Cramer A. O. J., Waldorp L. J., Schmittmann V. D., & Borsboom D. (2012). qgraph: Network visualizations of relationships in psychometric data.Journal of Statistical Software, 48, 1-18.
[17] Epskamp S., Rhemtulla M., & Borsboom D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82, 904-927.
[18] Forbes M., Wright A., Markon K., & Krueger R. (2017a). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126, 1011-1016.
[19] Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models.Advances in Neural Information Processing Systems, 23, 2020-2028.
[20] Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology.Perspectives on Psychological Science, 12, 999-1020.
[21] Fried E. I., Eidhof M. B., Palic S., Costantini G., Huisman-van Dijk H. M., Bockting C., Engelhard I., Armour C., Nielsen A., & 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, 335-351.
[22] Fried, E. I., & Nesse, R. M. (2015). Depression sum-scores don't add up: Why analyzing specific depression symptoms is essential.BMC Medicine, 13, 1-11.
[23] Friedman J. H., Hastie T., & Tibshirani R. (2008). Sparse inverse covariance estimation with the graphical lasso.Biostatistics, 9, 432-441.
[24] Friedman J. H., Hastie T., & Tibshirani R. (2014). glasso: Graphical lassoestimation of gaussian graphical models [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=glasso(R package version 1.8)
[25] Guo J., Levina E., Michailidis G., & Zhu J. (2011). Joint estimation of multiple graphical models.Biometrika, 98, 1-15.
[26] Holzinger, K.,Swineford, F. (1939). A study in factor analysis: The stability of a bifactor solution. Supplementary Educational Monograph, no. 48. Chicago: University of Chicago Press.
[27] Kindermann, R., & Snell, J. L. (1980) Markov Random Fields and their Applications. American Mathematical Society Providence, RI.
[28] Koller D.,& Friedman, N. (2009). Probabilistic graphical models: Principles and techniques Cambridge, MA: MIT Press Principles and techniques. Cambridge, MA: MIT Press.
[29] Lauritzen S. L.(1996). Graphical models. Oxford, UK: Clarendon Press.
[30] R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
[31] Santos H. P., Jr., Kossakowski J. J., Schwartz T. A., Beeber L., & Fried E. I. (2018). Longitudinal network structure of depression symptoms and self-efficacy in low-income mothers. PLoS ONE, 13, e0191675.
[32] 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.
[33] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society Series B, 58, 267-288.
[34] van Borkulo C. D., Borsboom D., Epskamp S., Blanken T. F., Boschloo L., Schoevers R. A., & Waldorp L. J. (2014). A new method for constructing networks from binary data.Scientific Reports, 4, 1-10.
[35] van Borkulo C. D., van Bork R., Boschloo L., Kossakowski J. J., Tio P., Schoevers R. A., ... & Waldorp, L. J. (2022). Comparing network structures on three aspects: A permutation test.Psychological Methods, 2022, 1-13.
[36] Van Borkulo C., Boschloo L., Borsboom D., Penninx B. W., Waldorp L. J., & Schoevers R. A. (2015). Association of symptom network structure with the course of longitudinal depression.JAMA Psychiatry, 72, 1219-1226.
[37] Wang M. C., Deng J., Shou Y., & Sellbom M. (2022). Cross-cultural examination of psychopathy network in Chinese and US prisoners.Journal of Psychopathology and Behavioral Assessment, 44, 620-635.
[38] Zhang X., Deng J., Shou Y., & Wang M-C. (2022). Longitudinal network structure of child psychopathy across development in Chinese community children.Current Psychology, 2022, 1-11.
[39] Zhang X., Wang M. C., Gong J., Gao Y., & Yang W. (2023). Network analysis of psychopathic traits among Chinese male offenders based on three self-report psychopathy measures.Current Psychology, 42(24), 20967-20982. |
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