Marjolein Fokkema
Associate professor
- Name
- Dr. M. Fokkema
- Telephone
- +31 71 527 7996
- m.fokkema@fsw.leidenuniv.nl
- ORCID iD
- 0000-0002-9252-8325
Marjolein Fokkema works on statistical modelling (or as we nowadays call it: machine learning or artificial intelligence) and psychological assessment. Marjolein is an associate editor at The European Journal of Psychological Assessment and a member of the Dutch Committee on Testing Matters (COTAN) of the Dutch Institute for Psychologists (NIP).
Short CV
Marjolein Fokkema works on statistical modelling (or as we nowadays call it: machine learning or artificial intelligence) and psychological assessment. Marjolein is an associate editor at The European Journal of Psychological Assessment and a member of the Dutch Committee on Testing Matters (COTAN) of the Dutch Institute for Psychologists (NIP).
Research
Marjolein develops methods that provide results that are easy to understand and apply in decision making. She has a fond interest in decision-tree methods. Such decision trees can provide results that are as accurate as more complex statistical models, but they are easier to understand and require less information for making decisions.
Teaching
Marjolein teaches master-level courses on Statistical Learning and Prediction, both at the Institute of Psychology and Mathematical Institute. She teaches a bachelor-level course on Psychometrics at the Institute of Psychology. In previous years, she taught a master-level course on Latent Variable Modelling.
Grants
- 2021: German Academic Exchange Service (DAAD) - 3-month stay at Max Planck Institute, Berlin (main applicant)
- 2017: Swiss National Science Foundation (SNF) - International Visit Grant for 6-month stay at University of Zurich (main applicant)
- 2017: ZonMW - Onderzoeksprogramma GGZ Middellange termijn (co-applicant)
- 2014: Psychometric Society - Student Travel Award (main applicant)
Relevant links
Associate professor
- Faculteit der Sociale Wetenschappen
- Instituut Psychologie
- Methodologie & Statistiek
- Loon W.S. van, Fokkema M., Rooij M.J. de & Szabo B.T. (2024), View selection in multi-view stacking choosing the meta-learner: choosing the meta-learner, Advances in Data Analysis and Classification (2024): .
- Loon W.S. van, Fokkema M., Szabo B. & Rooij M.J. de (2024), View selection in multi-view stacking: choosing the meta-learner, Advances in Data Analysis and Classification : .
- 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): 1006.
- Wijn A.N. de, Fokkema M. & Doef M.P. van der (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.
- De Rooij M.J., Karch J.D., Fokkema M., Bakk Z., Pratiwi B.C. & Kelderman H. (2022), SEM-based out-of-sample predictions, Structural Equation Modeling: A Multidisciplinary Journal : 1-17.
- Loon W.S. van, Vos F. de, Fokkema M., Szabo B.T., Koini M., Schmidt R. & Rooij M.J. de (2022), Analyzing hierarchical multi-view MRI Data With StaPLR An Application to Alzheimer's disease classification: an application to Alzheimer's disease classification, Frontiers in Neuroscience 16: 1-36 (830630).
- Fokkema M., Iliescu D., Greiff S., Ziegler M. & (2022), Machine Learning and Prediction in Psychological Assessment: Some Promises and Pitfalls, European Journal of Psychological Assessment 38(3): 165-175.
- Driessen E., Fokkema M., Dekker J.J. M., Peen J., Van Henricus L. Maina Gi., Rosso G., Rigardetto S., Cuniberti F., Vitriol V.G., Andreoli A., Burnand Y., López R. J., Villamil S. V., Twisk J. W. R. & Wienicke F.J. Cuijpers P. (2022), 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.
- 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): 73-77.
- Iliescu D., Greiff S., Ziegler M. & Fokkema M. (2022), Artificial intelligence, machine learning, and other demons, European Journal of Psychological Assessment 38(3): 163-164.
- Rohrbach P.J., Dingemans A. E., Spinhoven P., Van Ginkel J.R., Fokkema M., Wildermans T.F., Bauer S. & Van Furth E.F. (2022), 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 55(10): 1361-1373.
- 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): 329-341.
- Fokkema M. & Christoffersen B. (2021), pre: Prediction Rule Ensembles [software package and manual].
- 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.
- Wijn A.N. de, Fokkema M. & Doef M. P. van der (2021), 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 : 1-11.
- Markovitch B. & Fokkema M. (2021), Improved prediction rule ensembling through model-based data generation arXiv. [Working paper].
- Loon W. van, Vos F. de, Fokkema M., Szabo B., Koini M., Schmidt R. & Rooij M. de (2021), Analyzing hierarchical multi-view MRI data with StaPLR: an application to Alzheimer's disease classification . [working paper].
- 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 U. & Zeinoun P. (2021), Supporting academic freedom and living societal responsibility, European Journal of Psychological Assessment 37(2): .
- Fokkema M. & Strobl C. (2020), Fitting prediction rule ensembles to psychological research data: an introduction and tutorial, Psychological Methods 25(5): 636-652.
- Fokkema M., Edbrooke-Childs J. & Wolpert M. (2020), Generalized linear mixed-model (GLMM) trees: a flexible decision-tree method for multilevel and longitudinal data, Psychotherapy Research 31(3): 329-341.
- 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 and Adolescent Psychiatry 29: 167-178.
- Loon W.S. van, Fokkema M. & Szabo B.T. Rooij M.J. de (2020), Stacked penalized logistic regression for selecting views in multi-view learning, Information Fusion 61: 113-123.
- Fokkema M. & Strobl C. (2020), Fitting prediction rule ensembles to psychological research data: an introduction and tutorial, Psychological Methods 25(5): 636-652.
- Fokkema M. (2020), Fitting prediction rule ensembles with R package pre, Journal of Statistical Software 92(12): 1-30.
- Rohrbach P.J., Dingemans A.E., Spinhoven P., Van den Akker-Van Marle E., Van Ginkel J.R., Fokkema M., Moessner M., Bauer S. & Van Furth E.F. (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: e509.
- Fokkema M. & Zeileis A. (2019), glmertree: Generalized Linear Mixed Model Trees [software package and manual]: Fitting Generalized Linear Mixed-Effects Model Trees.
- 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.
- Ballegooijen W. van, Eikelenboom M., Fokkema M., Riper H., Hemert A.M. van, Kerkhof A.J.F.M., Penninx B.W.J.H. & Smit J.H. (2018), Comparing factor structures of depressed patients with and without suicidal ideation, A measurement invariance analysis, Journal of Affective Disorders 245: 180-187.
- Driessen E., Abbass A.A., Barber J.P., Gibbons M.B.C., Dekker J.J.M., Fokkema M., Fonagy P., Hollon S.D., Jansma E.P., Maat S.C.M. de, Town J.M., Twisk J.W.R., Van Henricus L., Weitz E. & Cuijpers P. (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: e018900.
- 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.
- 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.
- Kraan T.C., Ising H.K., Fokkema M., Velthorst E., Berg D.P.G. van den, Kerkhoven M., Veling W., Smit F., Linszen D.H., Nieman D.H., Wunderink L., Boonstra N., Klaassen R.M.C., Dragt S., Rietdijk J., Haan L. de & Gaag M. van der (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.
- Meijer E., Van Laar C., Gebhardt W.A., Fokkema M., Van den Putte S.J.H.M., Dijkstra A., Fong G. & Willemsen M. (2017), Identity change among smokers and ex-smokers: Findings from the ITC Netherlands Survey, Psychology of Addictive Behaviors 31(4): 465-478.
- 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.
- De Beurs D.P., Fokkema M. & O’Connor R.C. (2016), Optimizing the assessment of suicidal behavior: The application of curtailment techniques, Journal of Affective Disorders 196: 218-224.
- Kraan T., Ising H., Fokkema M., Velthorst E., Berg van den D.P.G., Kerkhoven M., Veling W. Smit F., Linszen D.H., Nieman D.H., Wunderink L., Boonstra N., Klaassen R.M.C., Dragt S., Rietdijk J., Haan L. de & Gaag M. van der (2016), 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.
- Fokkema M. (20 September 2016), Waiting time. Leiden Psychology Blog. Leiden: the Institute of Psychology and the Faculty of Social and Behavioural Sciences, Leiden University. [blog entry].
- Fokkema M. (4 April 2016), London 2012: Curse or Blessing?!. Leiden Psychology Blog. Leiden: Leiden University. [blog entry].
- Fokkema M., Smits N., Kelderman H. & Penninx B.W.J.H. (2015), Connecting clinical and actuarial prediction with rule-based methods, Psychological Assessment 27(2): 636-644.
- 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.
- 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.
- Fokkema M., Smits N., Zeileis A., Hothorn T. & Kelderman H. (2015), Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Working paper. Working Papers in Economics and Statistics University of Innsbruck.
- Fokkema M. (10 November 2015), Connecting clinical decision- making and psychological research with rule-based methods. Leiden Psychology Blog. Leiden: Leiden University. [blog entry].