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Multiple imputation of item scores when test data are factorially complex

van Ginkel, J. R., van der Ark, L. A. & Sijtsma, K., 2007, In : British Journal of Mathematical and Statistical Psychology. 60, 2, p. 315-337

Research output: Contribution to journalArticleScientificpeer-review

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Multiple imputation of item scores in test and questionnaire data, and influence on psychometric results

van Ginkel, J. R., van der Ark, L. A. & Sijtsma, K., 2007, In : Multivariate Behavioral Research. 42, 2, p. 387-414

Research output: Contribution to journalArticleScientificpeer-review

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Multiple imputation of incomplete categorical data using latent class analysis

Vermunt, J. K., van Ginkel, J. R., van der Ark, L. A. & Sijtsma, K., 2008, In : Sociological Methodology. 38, 1, p. 369-397

Research output: Contribution to journalArticleScientificpeer-review

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Multiple imputation in data that grow over time: A comparison of three strategies

Kavelaars, X. M., Buuren, S. V. & Ginkel, J. R. V., 2019, arXiv.org.

Research output: Working paperOther research output

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Multiple imputation for incomplete test, questionnaire, and survey data

van Ginkel, J. R., 2007, Ridderkerk: Ridderprint. 155 p.

Research output: ThesisDoctoral Thesis

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287 Downloads (Pure)

Multi-niveau latent klasse analyse: Met een toepassing bij het simultaan clusteren van landen en consumenten

Vermunt, J. K., Bijmolt, T. H. A. & Paas, L. J., 2006, Ontwikkelingen in marktonderzoek. Jaarboek 2006. Bronner, A. E., Dekker, P., de Leeuw, E., Paas, L. J., de Ruyter, K., Smidts, A. & Wieringa, J. W. (eds.). Haarlem: Spaar en Hout, p. 161-173

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Multi-method analysis of the internal structure of the Type D Scale-14 (DS14)

Straat, J. H., van der Ark, L. A. & Sijtsma, K., 2012, In : Journal of Psychosomatic Research. 72, 4, p. 258-265

Research output: Contribution to journalArticleScientificpeer-review

Multilevel modeling for data streams with dependent observations

Ippel, L., 2017, Vianen: [s.n.]. 132 p.

Research output: ThesisDoctoral Thesis

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Multilevel mixture models for the analysis of the university effectiveness

Varriale, R., 2008, Florence: [n.n.].

Research output: ThesisDoctoral Thesis

Multilevel mixture item response theory models: An application in education testing

Vermunt, J. K., 2007, International Statistics Institue (ISI) 56th Session. Lisboa: ISI, (Bulletin of the International Statistical Institute).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Multilevel mixture factor models

Varriale, R. & Vermunt, J. K., 2012, In : Multivariate Behavioral Research. 47, 2, p. 247-275

Research output: Contribution to journalArticleScientificpeer-review

Multilevel mixed-measurement IRT analysis: An explication and application to self-reported emotions across the world

Tay, L., Diener, E., Drasgow, F. & Vermunt, J. K., 2011, In : Organizational Research Methods. 14, 1, p. 177-207

Research output: Contribution to journalArticleScientificpeer-review

Multilevel latent variable modeling: An application in educational testing

Vermunt, J. K., 2008, In : Austrian Journal of Statistics. 37, 3-4, p. 285-299

Research output: Contribution to journalArticleScientificpeer-review

Multilevel Latent Class Models

Vermunt, J. K., 2003, In : Sociological Methodology. 33, 1, p. 213-239

Research output: Contribution to journalArticleScientificpeer-review

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Multilevel growth mixture models for classifying groups

Palardy, G. & Vermunt, J. K., 2010, In : Journal of Educational and Behavioral Statistics. 35, 5, p. 532-565

Research output: Contribution to journalArticleScientificpeer-review

Multidimensional assessment of fatigue in patients with brain metastases before and after Gamma Knife radiosurgery

Verhaak, E., Schimmel, W. C. M., Sitskoorn, M. M., Bakker, M., Hanssens, P. E. J. & Gehring, K., 2019, In : Journal of Neuro-Oncology. 144, 2, p. 377-384

Research output: Contribution to journalArticleScientificpeer-review

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MPS5 for Windows. A program for Mokken scale analysis for polytomous items

Molenaar, I. W. & Sijtsma, K., 2000, Groningen: iec ProGAMMA. 105 p.

Research output: Book/ReportBookScientific

Moving forward: Challenges and directions for psychopathological network theory and methodology

Fried, E. I. & Cramer, A. O. J., 2017, In : Perspectives on Psychological Science. p. 999-1020

Research output: Contribution to journalArticleScientificpeer-review

Mover-stayer model

Vermunt, J. K., 2004, The Sage encyclopedia of social sciences research methods. Lewis-Beck, M. S., Bryman, A. & Liao, T. F. (eds.). Thousand Oakes: Sage, p. 665-666 1524 p.

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

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Motivation: Individual differences in students' educational choices and study success

Meens, E., 2018, s.l.: Ipskamp. 247 p.

Research output: ThesisDoctoral Thesis

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Mortality differences related to socioeconomic status and the progressivity old-age pensions and health insurance: The Netherlands

Nelissen, J. H. M., 1999, In : European Journal of Population. 15, p. 77-97

Research output: Contribution to journalArticleScientificpeer-review

Mokken scale analysis using hierarchical clustering procedures

van Abswoude, A. A. H., Vermunt, J. K., Hemker, B. T. & van der Ark, L. A., 2004, In : Applied Psychological Measurement. 28, 5, p. 332-354

Research output: Contribution to journalArticleScientificpeer-review

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Mokken scale analysis using hierarchical clustering procedures

van Abswoude, A. A. H., Vermunt, J. K., Hemker, B. T. & van der Ark, L. A., 2002, Arnhem: Cito. 21 p.

Research output: Book/ReportReportProfessional

Mokken scale analysis in R

van der Ark, L. A., 2007, In : Journal of Statistical Software. 20, 11, p. 1-19

Research output: Contribution to journalArticleScientificpeer-review

Mokken scale analysis for dichotomous items using marginal models

van der Ark, L. A., Croon, M. A. & Sijtsma, K., 2008, In : Psychometrika. 73, 2, p. 183-208

Research output: Contribution to journalArticleScientificpeer-review

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Mokken scale analysis as time goes by: An update for scaling practitioners

Sijtsma, K., Meijer, R. R. & van der Ark, L. A., 2011, In : Personality and Individual Differences. 50, 1, p. 31-37

Research output: Contribution to journalArticleScientificpeer-review

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Mokken models

Sijtsma, K. & Molenaar, I. W., 2016, Handbook of item response theory: Vol.1. Models. van der Linden, W. J. & Hambleton, R. K. (eds.). Boca Raton, FL: Chapman & Hall/CRC, p. 303-321

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Mokken (version 2.0): A R package for Mokken scale analysis

van der Ark, L. A., 2009

Research output: Non-textual formSoftwareOther research output

Modern statistical methods for HCI

Robertson, J. (ed.) & Kaptein, M. C. (ed.), 2016, Springer. 348 p. (Human–Computer Interaction Series)

Research output: Book/ReportBook editingScientificpeer-review

Models of the welfare state

Arts, W. A. & Gelissen, J. P. T. M., 2010, The Oxford Handbook of the Welfare State. Castles, F. G., Leibfried, S., Lewis, J., Obinger, H. & Pierson, C. (eds.). Oxford: Oxford University Press, p. 569-583 912 p. (Oxford Handbooks in Politics & International Relations).

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Model selection in principal covariates regression

Vervloet, M., Van Deun, K., van den Noortgate, W. & Ceulemans, E., 2016, In : Chemometrics & Intelligent Laboratory Systems. 151, p. 26-33

Research output: Contribution to journalArticleScientificpeer-review

Modello multilevel a classi latenti: Estensione al modello multidimensionale

Del Giovane, C., 2008, Bologna: [n.n.]. 122 p.

Research output: ThesisDoctoral Thesis

Modeling the effect of differential motivation on linking educational tests

Keizer-Mittelhaëuser, M-A., 2014, S.l.: [s.n.]. 105 p.

Research output: ThesisDoctoral Thesis

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Modeling psychological attributes: Merits and drawbacks of taxometrics and latent variable mixture models

Hillen, R., 2017, S.l.: [s.n.]. 133 p.

Research output: ThesisDoctoral Thesis

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Modeling predictors of latent classes in regression mixture models

Kim, M., Vermunt, J. K., Bakk, Z., Jaki, T. & Van Horn, M. L., 2016, In : Structural Equation Modeling. 23, 4, p. 601-614

Research output: Contribution to journalArticleScientificpeer-review

Modeling joint and marginal distributions in the analysis of categorical panel data

Vermunt, J. K., Rodrigo, M. F. & Ato-Garcia, M., 2001, In : Sociological Methods and Research. 30, 2, p. 170-196

Research output: Contribution to journalArticleScientificpeer-review

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Modeling interactions between latent variables in research on Type D personality: A Monte Carlo simulation and clinical study of depression and anxiety

Lodder, P., Denollet, J., Emons, W. H. M., Nefs, G., Pouwer, F., Speight, J. & Wicherts, J. M., 2019, In : Multivariate Behavioral Research. 54, 5, p. 637-665

Research output: Contribution to journalArticleScientificpeer-review

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Modeling differences in test-taking motivation: Exploring the usefulness of the mixture Rasch model and person-fit statistics

Mittelhaëuser, M., Beguin, A. A. & Sijtsma, K., 2013, New developments in quantitative psychology. Presentations from the 77th Annual Psychometric Society Meeting. Millsap, R. E., Bolt, D. M., van der Ark, L. A. & Woods, C. M. (eds.). New York: Springer, p. 357-370

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Modeling differences between response times of correct and incorrect responses

Bolsinova, M. & Tijmstra, J., 2019, In : Psychometrika. 84, 4, p. 1018-1046

Research output: Contribution to journalArticleScientificpeer-review

Modeling conditional dependence between response time and accuracy

Bolsinova, M., de Boeck, P. & Tijmstra, J., 2017, In : Psychometrika. 82, 4, p. 1126-1148

Research output: Contribution to journalArticleScientificpeer-review

Model-based approaches to synthesize microarray data: A unifying review using mixture of SEMs

Martella, F. & Vermunt, J. K., 2013, In : Statistical Methods in Medical Research. 22, 6, p. 567-582

Research output: Contribution to journalArticleScientificpeer-review

Mixture simultaneous factor analysis for capturing differences in latent variables between higher level units of multilevel data

De Roover, K., Vermunt, J. K., Timmerman, M. E. & Ceulemans, E., 6 Mar 2017, In : Structural Equation Modeling. 24, 4, p. 506-523

Research output: Contribution to journalArticleScientificpeer-review

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Mixture models for multilevel data sets

Vermunt, J. K., 2010, Handbook of advanced multilevel analysis. Hox, J. & Roberts, J. K. (eds.). New York: Routledge, p. 59-81

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Mixture models

Vermunt, J. K. & Paas, L. J., 2017, Advanced methods for modeling markets. Leeflang, P. S. H., Wieringa, J. E., Bijmolt, T. H. A. & Pauwels, K. H. (eds.). Springer, p. 383-403 (International series in quantitative marketing).

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Mixture models: Latent profile and latent class analysis

Oberski, D. L., 2016, Modern statistical methods for HCI. Robertson, J. & Kaptein, M. (eds.). Springer, p. 275-287 ( Human–Computer Interaction Series).

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Mixture model clustering with covariates using adjusted three-step approaches

Gudicha, D. & Vermunt, J. K., 2013, Algorithms from and for nature and life: Classification and data analysis. Lausen, B., van den Poel, D. & Ultsch, A. (eds.). Heidelberg: Springer, p. 87-94

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Mixture model

Vermunt, J. K., 2004, The Sage encyclopedia of social sciences research methods. Lewis-Beck, M. S., Bryman, A. & Liao, T. F. (eds.). Thousand Oakes: Sage, p. 653 1524 p.

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Mixture hidden Markov models in finance research

Dias, J. G., Vermunt, J. K. & Ramos, S., 2010, Advances in data analysis, data handling and business intelligence. Fink, A., Lausen, B., Seidel, W. & Ultsch, A. (eds.). Berlin-Heidelberg: Springer, p. 451-459

Research output: Chapter in Book/Report/Conference proceedingChapterScientific