Barnby, J., Haslbeck, J. M. B., Rosen, C., & Harrow, M. (2022). Modelling the Longitudinal Dynamics of Paranoia in Psychosis: A Temporal Network Analysis Over 20 Years. Manuscript submitted for publication. https://www.medrxiv.org/content/10.1101/2023.01.06.23284268v1
Dalege, J., Haslbeck, J. M. B., & Marsman, M. (2022). Idealized Modeling of Psychological Dynamics. In A-M. Isvoranu, S. Epskamp, L. J. Waldrop, & D. Borsboom (Eds.), Network Psychometrics with R: A Guide for Behavioral and Social Scientists (pp. 233-245). (Research methods and statistics). Routledge. https://doi.org/10.4324/9781003111238-17[details]
Epskamp, S., Haslbeck, J. M. B., Isvoranu, A-M., & van Borkulo, C. D. (2022). Pairwise Markov Random Fields. In A-M. Isvoranu, S. Epskamp, L. J. Waldrop, & D. Borsboom (Eds.), Network Psychometrics with R: A Guide for Behavioral and Social Scientists (pp. 93-110). (Research methods and statistics). Routledge. https://doi.org/10.4324/9781003111238-8[details]
Fishbein, J., Haslbeck, J. M. B., & Arch, J. (2022). Network Intervention Analysis of Anxiety-Related Outcomes and Processes of Acceptance and Commitment Therapy (ACT). Manuscript submitted for publication. https://psyarxiv.com/yt84w/
Gossage, L., Narayananb, A., Dipnall, J., Berk, M., Sumich, A., Haslbeck, J. M. B., Iusitinij, L., Wrapson, W., Tautolo, E-S., & Siegert, R. (2022). Understanding suicidality in Pacific adolescents in New Zealand using network analysis. Manuscript submitted for publication.
Haslbeck, J. M. B., Ryan, O., & Dablander, F. (2022). The Sum of All Fears: Comparing Networks Based on Symptom Sum-Scores. Psychological Methods, 27(6), 1061-1068. https://doi.org/10.1037/met0000418[details]
Haslbeck, J. M. B., Ryan, O., van der Maas, H. L. J., & Waldorp, L. J. (2022). Modeling Change in Networks. In A-M. Isvoranu, S. Epskamp, L. J. Waldrop, & D. Borsboom (Eds.), Network Psychometrics with R: A Guide for Behavioral and Social Scientists (pp. 193-209). (Research methods and statistics). Routledge. https://doi.org/10.4324/9781003111238-14[details]
Vlaeyen, J., Haslbeck, J. M. B., Sjouwerman, R., & Peters, M. L. (Accepted/In press). Time versus obstacles to recovery in pain management. A reply to Manhapra. Pain.
Waldorp, L. J., & Haslbeck, J. M. B. (2022). Network Inference with the Lasso. Manuscript submitted for publication. https://doi.org/10.31234/osf.io/v5yzu
Wallner, T. S., Haslbeck, J. M. B., Magnier, L., & Mugge, R. (2022). A network analysis of factors influencing the purchase intentions for refurbished electronics. Manuscript submitted for publication.
Wulff, D., Kieslich, P., Haslbeck, J. M. B., Henninger, F., & Schulte-Mecklenbeck, M. (2022). Movement tracking of cognitive processes: A tutorial using mousetrap. Manuscript submitted for publication. https://psyarxiv.com/v685r
van Dongen, N. N. N., van Bork, R., Haslbeck, J. M. B., van der Maas, H. L. J., Robinaugh, D., de Ron, J., & Borsboom, D. (2022). Improving Psychological Explanations.. Manuscript submitted for publication. https://psyarxiv.com/qd69g/
van der Wal, J., van Borkulo, C. D., Haslbeck, J. M. B., Slofstra, C., Klein, N. S., Blanken, T. F., Deserno, M. K., Lok, A., Nauta, M. H., & Bockting, C. L. H. (2022). Effects of various relapse prevention strategies on affect dynamics and its impact on depressive relapse using network analysis: a randomized controlled trial.. Manuscript submitted for publication.
2021
Aalbers, G., Engels, T., Haslbeck, J. M. B., Borsboom, D., & Arntz, A. (2021). The network structure of schema modes. Clinical Psychology and Psychotherapy, 28(5), 1065-1078. https://doi.org/10.1002/cpp.2577[details]
Haslbeck, J. M. B., & van Bork, R. (2021). Estimating the Number of Factors in Exploratory Factor Analysis via out-of-sample Prediction Errors. Manuscript submitted for publication. https://doi.org/10.31234/osf.io/qktsd
Haslbeck, J. M. B., Bringmann, L. F., & Waldorp, L. J. (2021). A Tutorial on Estimating Time-Varying Vector Autoregressive Models. Multivariate Behavioral Research, 56(1), 120-149. https://doi.org/10.1080/00273171.2020.1743630[details]
Haslbeck, J. M. B., Epskamp, S., Marsman, M., & Waldorp, L. J. (2021). Interpreting the Ising Model: The Input Matters. Multivariate Behavioral Research, 56(2), 303-313. https://doi.org/10.1080/00273171.2020.1730150[details]
Hinze, V., Ford, T., Crane, C., Haslbeck, J. M. B., Hawton, K., Gjelsvik, B., & The MYRIAD Team (2021). Does depression moderate the relationship between pain and suicidality in adolescence? A moderated network analysis. Journal of Affective Disorders , 292, 667-677. https://doi.org/10.1016/j.jad.2021.05.100[details]
Lunansky, G., van Borkulo, C. D., Haslbeck, J. M. B., van der Linden, M. A., Garay, C. J., Etchevers, M. J., & Borsboom, D. (2021). The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors. Frontiers in Psychiatry, 12, [640658]. https://doi.org/10.3389/fpsyt.2021.640658[details]
Moriarity, D. P., Horn, S. R., Kautz, M. M., Haslbeck, J. M. B., & Alloy, L. B. (2021). How handling extreme C-reactive protein (CRP) values and regularization influences CRP and depression criteria associations in network analyses. Brain, behavior, and immunity, 91, 393-403. https://doi.org/10.1016/j.bbi.2020.10.020[details]
Robinaugh, D. J., Haslbeck, J. M. B., Ryan, O., Fried, E. I., & Waldorp, L. J. (2021). Invisible Hands and Fine Calipers: A Call to Use Formal Theory as a Toolkit for Theory Construction. Perspectives on Psychological Science, 16(4), 725-743. https://doi.org/10.1177/1745691620974697[details]
Verwimp, C., Tijms, J., Snellings, P., Haslbeck, J. M. B., & Wiers, R. W. (2021). A network approach to dyslexia: Mapping the reading network. Development and Psychopathology, 1-15. https://doi.org/10.1017/S0954579421000365
Walentek, D., Broere, J., Cinelli, M., Dekker, M. M., & Haslbeck, J. M. B. (2021). Success of economic sanctions threats: coercion, information and commitment. International Interactions, 47(3), 417-448. https://doi.org/10.1080/03050629.2021.1860034[details]
Burger, J., Isvoranu, A. M., Lunansky, G., Haslbeck, J. M. B., Epskamp, S., Hoekstra, R. H. A., Fried, E. I., Borsboom, D., & Blanken, T. F. (2020). Reporting Standards for Psychological Network Analyses in Cross-sectional Data. Manuscript submitted for publication. https://psyarxiv.com/4y9nz/
Dablander, F., Ryan, O., & Haslbeck, J. M. B. (2020). Choosing between AR(1) and VAR(1) Models in Typical Psychological Applications. PLoS ONE, 15(10), [e0240730]. https://doi.org/10.1371/journal.pone.0240730[details]
Fried, E. I., von Stockert, S., Haslbeck, J. M. B., Lamers, F., Schoevers, R. A., & Pennix, B. W. J. H. (2020). Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates. Psychological Medicine, 50(16), 2682-2690. https://doi.org/10.1017/S0033291719002770[details]
Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Structure Estimation for Time-Varying Mixed Graphical Models in high-dimensional Data. Journal of Statistical Software, 93, [8]. https://doi.org/10.18637/jss.v093.i08[details]
Haslbeck, J. M. B., & Wulff, D. U. (2020). Estimating the Number of Clusters via Normalized Cluster Instability. Computational Statistics, 35(4), 1879–1894. https://doi.org/10.1007/s00180-020-00981-5[details]
Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M. B., & Schulte-Mecklenbeck, M. (2019). Mouse-Tracking: A Practical Guide to Implementation and Analysis. In M. Schulte-Mecklenbeck, A. Kühberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (2nd ed., pp. 111-130). (The Society for Judgment and Decision Making Series). New York: Routledge. https://doi.org/10.31234/osf.io/zuvqa, https://doi.org/10.4324/9781315160559-9[details]
Robinaugh, D., Haslbeck, J. M. B., Waldorp, L. J., Kossakowski, J. J., Fried, E. I., Millner, A. J., McNally, R., van Nes, E. H., Scheffer, M., Kendler, K. S., & Borsboom, D. (2019). Advancing the Network Theory of Mental Disorders: A Computational Model of Panic Disorder. Psychological Review. https://doi.org/10.31234/osf.io/km37w
Wulff, D. U., Haslbeck, J. M. B., Kieslich, P. J., Henninger, F., & Schulte-Mecklenbeck, M. (2019). Mouse-tracking: Detecting types in movement trajectories. In M. Schulte-Mecklenbeck, A. Kühberger, & J. J. Johnson (Eds.), A handbook of process tracing methods (2nd ed., pp. 131-145). New York: Routledge. https://doi.org/10.4324/9781315160559-10[details]
Haslbeck, J. M. B., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50(2), 853-861. https://doi.org/10.3758/s13428-017-0910-x[details]
Haslbeck, J. M. B., & Fried, E. I. (2017). How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychological Medicine, 47(16), 2767-2776. https://doi.org/10.1017/S0033291717001258[details]
Kossakowski, J. J., Groot, P., Haslbeck, J. M. B., Borsboom, D., & Wichers, M. (2017). Data from ‘Critical Slowing Down as a Personalized Early Warning Signal for Depression’. Journal of Open Psychology Data, 5(1). https://doi.org/10.5334/jopd.29[details]
Borsboom, D., Haslbeck, J. M. B., & Robinaugh, D. J. (2022). Systems-based approaches to mental disorders are the only game in town. World psychiatry, 21(3), 420-422. https://doi.org/10.1002/wps.21004[details]
Haslbeck, J., & van Bork, R. (2021). Estimating the Number of Factors in Exploratory Factor Analysis via out-of-sample Prediction Errors. PsyArXiv. https://doi.org/10.31234/osf.io/qktsd
2016
Haslbeck, J. M. B., Wood, G., & Witte, M. (2016). Temporal dynamics of number-space interaction in line bisection: Comment on Cleland and Bull (2015). Quarterly Journal of Experimental Psychology, 69(6), 1239-1242. https://doi.org/10.1080/17470218.2015.1095773[details]
2020
Haslbeck, J. M. B. (2020). Modeling psychopathology: From data models to formal theories. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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