2025
Fokkema, M., Henninger, M. & Strobl, C. (2025). One model may not fit all: Subgroup detection using model-based recursive partitioning. Journal of School Psychology, 103, 101394. https://doi.org/10.1016/j.jsp.2024.101394
Schwerter, J., Lauermann, F., Doebler, P. & Fokkema, M. (in press). Putting the Pieces of the Puzzle Together in Modeling Gendered Educational Choices . International Journal of STEM Education. https://osf.io/preprints/psyarxiv/4fgmn
Mudarris, M. A., Ruitenberg, M., Fokkema, M., & Schaefer, R. S. (2025). Cognitive and motor abilities predict auditory cued finger-tapping in a dual task. Frontiers in Neuroscience 19. https://doi.org/10.3389/fnins.2025.1553548
Driessen, E., Efthimiou, O., Wienicke, F. J., Breunese, J., Cuijpers, P., Debray, T. P., Fisher, D.J., Fokkema, M. Furukawa, T.A., Hollon, S.D., Mehta, A.H.P., Riley, R.D., Schmidt, M.R., Twisk, J.W.R. & Cohen, Z. D. (2025). Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. PLoS One, 20(4), e0322124. https://doi.org/10.1371/journal.pone.0322124
2024
Fokkema, M., & Zeileis, A. (2024). Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees. Behavior Research Methods. https://doi.org/10.3758/s13428-024-02389-1
van Loon, W., Fokkema, M., Szabo, B., & de Rooij, M. (2024). View selection in multi-view stacking: choosing the meta-learner. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-024-00587-5
van Loon, W., Fokkema, M., de Vos, F., Koini, M., Schmidt, R., & de Rooij, M. (2024). Imputation of missing values in multi-view data. Information Fusion, 111, 102524. https://doi.org/10.1016/j.inffus.2024.102524
Schroeder, V., Schwerter, J., Fokkema, M., & Doebler, P. (2024). Interpretable Prediction Rule Ensembles in the Presence of Missing Data. arXiv preprint arXiv:2410.16187.
Aria, M., Gnasso, A., Iorio, C., & Fokkema, M. (2024). Extending Explainable Ensemble Trees (E2Tree) to regression contexts. arXiv preprint arXiv:2409.06439.
2023
Wang, M., Rücklin, M., Poelmann, R. E., de Mooij, C. L., Fokkema, M., Lamers, G. E., ... & Richardson, M. K. (2023). Nanoplastics causes extensive congenital malformations during embryonic development by passively targeting neural crest cells. Environment International, 173, 107865. https://doi.org/10.1016/j.envint.2023.107865
De Rooij, M. Karch, J.D., Fokkema, M., Bakk, Z., Pratiwi, B.C., Kelderman, H. (2023). SEM-based out-of-sample predictions. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2022.2061494
Poot, C. C., Meijer, E., Fokkema, M., Chavannes, N. H., Osborne, R. H., & Kayser, L. (2023). Translation, cultural adaptation and validity assessment of the Dutch version of the eHealth Literacy Questionnaire: a mixed-method approach. BMC Public Health, 23(1), 1-17. https://doi.org/10.1186/s12889-023-15869-4
Driessen, E., Fokkema, M., Dekker, J. J., Peen, J., Van, H. L., Maina, G., ... & Cuijpers, P. (2023). Which patients benefit from adding short-term psychodynamic psychotherapy to antidepressants in the treatment of depression? A systematic review and meta-analysis of individual participant data. Psychological Medicine, 1-12. https://doi.org/10.1017/S0033291722003270
Guineau, M. G., Ikani, N., Tiemens, B., Voshaar, R. O., Fokkema, M., & Hendriks, G. J. (2023). Age related differences in symptom networks of overall psychological functioning in a sample of patients diagnosed with anxiety, obsessive compulsive disorder, or posttraumatic stress disorder. Journal of Anxiety Disorders, 100, 102793. https://doi.org/10.1016/j.janxdis.2023.102793
Rohrbach, P. J., Fokkema, M., Spinhoven, P., Van Furth, E. F., & Dingemans, A. E. (2023). Predictors and moderators of three online interventions for eating disorder symptoms in a randomized controlled trial. International Journal of Eating Disorders, 56(10), 1909-1918. https://doi.org/10.1002/eat.24021
2022
Fokkema, M., Iliescu, D., Greiff, S., & Ziegler, M. (2022). Machine learning and prediction in psychological assessment: Some promises and pittfalls. European Journal of Psychological Assessment 38(3), 165-175. https://doi.org/10.1027/1015-5759/a000714
van Loon, W., de Vos, F., Fokkema, M., Szabo, B., Koini, M., Schmidt, R., & de Rooij, M. (2022). Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification. Frontiers in Neuroscience 16, 830630. https://doi.org/10.3389/fnins.2022.830630
de Wijn, A.N., Fokkema, M., Van der Doef, M. (2022). The prevalence of stress-related outcomes and occupational well-being among emergency nurses in the Netherlands and the role of job factors: A regression tree analysis. Journal of Nursing Management 30(1), 187-197. https://doi.org/10.1111/jonm.13457
Rohrbach, P. J., Dingemans, A. E., Spinhoven, P., Van Ginkel, J. R., Fokkema, M., Wilderjans, T. F., ... & Van Furth, E. F. (in press). Effectiveness of an online self‐help program, expert‐patient support, and their combination for eating disorders: Results from a randomized controlled trial. International Journal of Eating Disorders. https://doi.org/10.1002/eat.23785
Iliescu, D., Greiff, S., Ziegler, M., & Fokkema, M. (in press). Artificial intelligence, machine learning, and other demons. European Journal of Psychological Assessment 38(3), 163-164. https://doi.org/10.1027/1015-5759/a000713
Iliescu, D., Rusu, A., Greiff, S., Fokkema, M. & Scherer, R. (2022). Why we need systematic reviews and meta-analyses in the testing and assessment literature. European Journal of Psychological Assessment, 38(2). https://doi.org/10.1027/1015-5759/a000705
2021
Markovitch, B., & Fokkema, M. (2021). Improved prediction rule ensembling through model-based data generation. arXiv preprint arXiv:2109.13672. https://arxiv.org/abs/2109.13672
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer, D., Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154-170. https://doi.org/10.1002/wps.20882
Fokkema, M., Edbrooke-Childs, J. & Wolpert, M. (2021). Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data. Psychotherapy Research, 31(3), 313-325. https://doi.org/10.1080/10503307.2020.1785037
Iliescu, D., Greiff, S., Proyer, R., Ziegler, M., Allen, M., Claes, L., Fokkema, M., Hasking, P., Hiemstra, A., Maes, M., Mund, M., Nye, C., Scherer, R., Wetzel, E. & Zeinoun, P. (2021). Supporting Academic Freedom and Living Societal Responsibility. European Journal of Psychological Assessment, 37(2), 81-85.
2020
Fokkema, M. (2020). Fitting prediction rule ensembles with R package pre. Journal of Statistical Software, 92(12), 1-30. http://doi.org/10.18637/jss.v092.i12
Fokkema, M. & Strobl, C. (2020). Fitting prediction rule ensembles to psychological research data: An introduction and tutorial. Psychological Methods 25(5), 636–652. http://doi.org/10.1037/met0000256 https://arxiv.org/abs/1907.05302
van Loon, W., Fokkema, M., Szabo, B., de Rooij, M. (2020). Stacked penalized logistic regression for selecting views in multi-view learning. Information Fusion, 61, 113-123. https://doi.org/10.1016/j.inffus.2020.03.007
Wolpert, M., Zamperoni, V., Napoleone, E., Patalay, P., Jacob, J., Fokkema, M., Promberger, M., Costa da Silva, L., Patel, M., & Edbrooke-Childs, J. (2020). Predicting mental health improvement and deterioration in a large community sample of 11- to 13-year-olds. European Child & Adolescent Psychiatry. http://doi.org/10.1007/s00787-019-01334-4
2019
van Ballegooijen, W., Eikelenboom, M., Fokkema, M., Riper, H., van Hemert, A. M., Kerkhof, A. J., ... & Smit, J. H. (2019). Comparing factor structures of depressed patients with and without suicidal ideation, a measurement invariance analysis. Journal of Affective Disorders, 245, 180-187. http://doi.org/10.1016/j.jad.2018.10.108
Rohrbach, P., Dingemans, A.E., Spinhove, P., Van Ginkel, J., Fokkema, M., Van den Akker-Van Marle, E., Van Furth, E., Moessner, M. & Bauer, S. (2019). A randomized controlled trial of an internet-based intervention for eating disorders and the added value of expert-patient support: study protocol. Trials 20, 509. http://doi.org/10.1186/s13063-019-3574-2
de Rooij, M., Pratiwi, B.C., Fokkema, M., Dusseldorp, E. & Kelderman, H. (2019). The Early Roots of Statistical Learning in the Psychometric Literature: A review and two new results. Preprint: arxiv:1911.11463
2018
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T. & Kelderman, H. (2018). Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Behavior Research Methods, 50(5), 2016-2034. http://doi.org/10.3758/s13428-017-0971-x
Fokkema, M. & Greiff, S. (2018). Would you prefer your coefficients with a little bias, or rather with a lot of variance? European Journal of Psychological Assessment, 34(6), 363-366. http://doi.org/10.1027/1015-5759/a000514
Driessen, E., Abbass, A. A., Barber, J. P., Gibbons, M. B. C., Dekker, J. J., Fokkema, M., ... & Town, J. M. (2018). Which patients benefit specifically from short-term psychodynamic psychotherapy (STPP) for depression? Study protocol of a systematic review and meta-analysis of individual participant data. BMJ open, 8(2), e018900. http://doi.org/10.1136/bmjopen-2017-018900
2017
Fokkema, M., & Greiff, S. (2017). How performing PCA and CFA on the same data equals trouble. European Journal of Psychological Assessment, 33(6), 399–402. http://doi.org/10.1027/1015-5759/a000460
Aardoom, J.J., Dingemans, A.E., Fokkema, M., Spinhoven, P., & Van Furth, E.F. (2017). Moderators of change in an Internet-based intervention for eating disorders with different levels of therapist support: What works for whom? Behaviour Research and Therapy, 89, 66-74. http://doi.org/10.1016/j.brat.2016.11.012
Kraan, T. C., Ising, H. K., Fokkema, M., Velthorst, E., van den Berg, D. P., Kerkhoven, M., ... & Wunderink, L. (2017). The effect of childhood adversity on 4-year outcome in individuals at ultra high risk for psychosis in the Dutch Early Detection Intervention Evaluation (EDIE-NL) Trial. Psychiatry Research, 247, 55-62. http://doi.org/10.1016/j.psychres.2016.11.014
Meijer, E., Van Laar, C., Gebhardt, W.A., Fokkema, M., Van den Putte, B., Dijkstra, A., Fong, G. T., & Willemsen, M.C. (2017). Identity change among smokers and ex-smokers: Findings from the ITC Netherlands survey. Psychology of Addictive Behaviors, 31(4), 465-478.http://doi.org/10.1037/adb0000281
2016
De Beurs, D. Fokkema, M., O'Connor, R. (2016). Optimizing the assessment of suicidal behavior: The application of curtailment techniques. Journal of Affective Disorders, 196, 218-224. http://doi.org/10.1016/j.jad.2016.02.033
2015
De Beurs, D.P., Fokkema, M., De Groot, M.H., De Keijser, J. & Kerkhof, A.J.F.M. (2015). Longitudinal measurement invariance of the Beck Scale for Suicide Ideation. Psychiatry Research, 225(3), 368–373. http://doi.org/10.1016/j.psychres.2014.11.075
Fokkema, M., Smits, N., Kelderman, & Penninx, B.W.J.H. (2015). Connecting clinical and actuarial prediction with rule-based methods. Psychological Assessment, 27(2), 636-644. http://doi.org/10.1037/pas0000072
Zhang, B., Gao, Q., Fokkema, M., Alterman, V., Liu, & Q. (2015). Adolescent Interpersonal Relationships, Social Support and Loneliness in High Schools: Mediation Effect and Gender Differences. Social Science Research, 53, 104–117. http://doi.org/10.1016/j.ssresearch.2015.05.003
2014
Fokkema, M., Smits, N., Finkelman, M. D., Kelderman, H., & Cuijpers, P. (2014). Curtailment: A method to reduce the length of self-report questionnaires while maintaining diagnostic accuracy. Psychiatry Research, 215(2), 477-482. http://doi.org/10.1016/j.psychres.2013.11.003
Fokkema, M., Smits, N., Kelderman, H., Carlier, I. V., & Van Hemert, A. M. (2014). Combining decision trees and stochastic curtailment for assessment length reduction of test batteries used for classification. Applied Psychological Measurement, 38(1), 3-17. http://doi.org/10.1177/0146621613494466
Geraedts, A.S., Fokkema, M., Kleiboer, A.M., Smit, F., Wiezer, N.W., Majo, M.C., Van Mechelen, W., Cuijpers, P., & Penninx, B.W.J.H. (2014). The longitudinal prediction of costs due to health care uptake and productivity losses in a cohort of employees with and without depression or anxiety. Journal of Occupational and Environmental Medicine, 56(8), 794-801. http://doi.org/10.1097/JOM.0000000000000234
2013
Fokkema, M., Smits, N., Kelderman, H., & Cuijpers, P. (2013). Response shifts in mental health interventions: An illustration of longitudinal measurement invariance. Psychological Assessment, 25(2), 520-531. http://doi.org/10.1037/a0031669
Rietdijk, J., Fokkema, M., Stahl, D., Valmaggia, L., Ising, H. K., Dragt, S., ... , van der Gaag, M. (2013). The distribution of self-reported psychotic-like experiences in non-psychotic help-seeking mental health patients in the general population; a factor mixture analysis. Social Psychiatry and Psychiatric Epidemiology, 49(3), 349-358. http://doi.org/10.1007/s00127-013-0772-1
2011 and before
Zhang, B., Fokkema, M., Cuijpers, P., Li, J., Smits, N., & Beekman, A. (2011). Measurement invariance of the center for epidemiological studies depression scale (CES-D) among Chinese and Dutch elderly. BMC Medical Research Methodology, 11(1), 74. http://doi.org/10.1186/1471-2288-11-74
De Wit, L. M., Fokkema, M., van Straten, A., Lamers, F., Cuijpers, P., & Penninx, B. W. (2010). Depressive and anxiety disorders and the association with obesity, physical, and social activities. Depression and Anxiety, 27(11), 1057-1065. http://doi.org/10.1002/da.20738