Jean Paul Fox is a well-established researcher in the area of Bayesian response modelling. He developed a multilevel IRT model for analysing item response data and accounting for the nesting of respondents in clusters. This Bayesian hierarchical latent variable framework was the first to incorporate such a complex survey design in the psychometric model. The fully-integrated model correctly specifies dependencies at different hierarchical levels and can handle disaggregated and aggregated explanatory data. He received the 2001 Psychometric Association Dissertation award for his work on multilevel IRT modelling. Currently, for secondary analysis, large-scale surveys such as PISA, TIMMS, and PIAAC have followed this strategy by adopting the multilevel sampling design in the psychometric model. As a direct result, new statistical models and methods have been developed (by Fox and co-workers), amongst others:
1.Bayesian estimation of a multilevel IRT model. Psychometrika, 2001, 315 citations;
2.Bayesian Item Response Modeling, Monograph, 2010, 184 citations.
3.Bayesian modeling of measurement error in predictor variables using item response theory, Psychometrika, 2003, 85 citations.
The early contributions in the area of complex psychometric models of the PI have been recognized worldwide. In 2004, he received a personal grant (VENI, an innovational research incentives scheme) from the Netherlands Organisation for Scientific Research (NWO) for his research on multilevel IRT modelling. He is well-known for his work on multilevel IRT modelling and is world-wide frequently asked to give (keynote) lectures or courses. In later years, he has developed methods, in different directions, for large-scale survey research. A new approach has been developed for dealing with measurement invariance in cross-national comparative survey research, which avoids the complex specification of anchor items. Parts of the results were published in top journals in the field of marketing research. This research was supported by a personal grant in 2007 (VIDI, an innovational research incentives scheme) from the Netherlands Organisation for Scientific Research. Other new approaches focused on model extensions to deal with background questionnaire data and retrieving sensitive survey information. He has made novel contributions integrating the randomized response technique in survey methods to obtain accurately sensitive respondent information, which are currently implemented in large-scale survey studies on excessive alcohol consumption (Netherlands) and illegal downloading behaviour (Germany). Besides these survey studies, he is also involved in the Programme for International Student Achievement (PISA, cycle 2009).
His monograph entitled “Bayesian Item Response Modeling” was published in 2010 by Springer Science and has been positively reviewed in three excellent journals, covering research fields in statistics and psychometrics, and generally marked as an important contribution of a high technical level.
In 2005 he was offered a permanent position in the Research Institute for Social Sciences and Technology at the University of Twente. As a result of the successful research activities on Bayesian psychometric response modelling he was appointed a position on September 1st 2007. Recently, he also obtained a research position in the Institute for Innovation and Governance Studies with a focus on survey methodology in health assessment research using patient reported outcomes.
Some recent publications
1.Keuning, T., van Geel, M., Visscher, A., Fox, J.-P., and Molenaar, N. (in press). The transformation of schools’ social networks during a Data-Based Decision-Making Reform. Teachers College Record.
2.Verhagen, A.J., Levy, R., Millsap, R.E., and Fox, J.-P. (2015). Evaluating evidence for invariant items: A Bayes factor applied to testing measurement invariance in IRT models. Journal of Mathematical Psychology.
3.Azevedo, C.L.N., Fox, J.-P. and Andrade, D.F. (2015). International Journal of Quantitative Research in Education, 2, 213-243.
4.Camilli, G., and Fox, J.-P. (2015). An aggregate IRT procedure for exploratory factor analysis. Journal of Educational and Behavioral Statistics, 40 (4). DOI:10.3102/1076998615589185.
5.Gorter, R., Fox, J.-P., and Twisk, J.W.R. (2015). Why item response theory should be used for longitudinal questionnaire data analysis in medical research. BMC Medical Research Methodology, 15(1):55. DOI:10.1186/s12874-015-0050-x.
6.Van den Hout, A., Fox, J.-P., and Muniz-Terrera, G. (2015). Longitudinal mixed-effects models for latent cognitive function. Statistical Modelling, 15(4), 366-387. DOI:10.1177/1471082X14555607.
7.De Jong, M.G., J.-P. Fox, and Steenkamp, J.E.B.M. (2015). Quantifying under- and over-reporting in surveys through a dual questioning-technique design. Journal of Marketing Research.
8.Trompetter, H.R., Bohlmeijer, E.T., Fox, J.-P., Schreurs, K.M.G. (2015). Psychological flexibility and catastrophizing as associated change mechanisms during online Acceptance & Commitment Therapy for chronic pain. Behaviour Research and Therapy, 74:50-59. DOI:10.1016/j.brat.2015.09.001.
9.Gosselt, J., van Hoof, J., Gent, B., and Fox, J.-P. (2015). Violent frames. Analyzing Internet Movie Database reviewers’ text descriptions of media violence and gender differences from 39 years of U.S. action, thriller, crime, and adventure movies. International Journal of Communication, 9, 547-567. (doi: 1932–8036/20150005).
10.Azevedo, C.L.N., Fox, J.-P., and Andrade, D.F. (2015). Bayesian longitudinal item response modeling with restricted covariance pattern structures. Statistics and Computing. (doi 10.1007/s11222-014-9518-5).
11.Fox, J.-P., M. Marsman, J. Mulder, and J.A. Verhagen (2015). Complex latent variable modeling in educational assessment. Communications in Statistics – Simulation and Computation. (doi:10.1080/03610918.2014.939518)
12.Marianti, S., Fox, J.-P., Avetisyan, M., Veldkamp, B.P., and Tijmstra, J. (2014). Journal of Educational and Behavioural Statistics, 39, 426-451.
13.Fox, J.-P., Klein Entink, R.H., Timmers, C. (2014). The joint multivariate modeling of multiple mixed response sources: Relating student performances with feedback behavior. Multivariate Behavioral Research, 49, 54-66, doi:10.1080/00273171.2013.843441.
14.Fox, J.-P., Klein Entink, R.H., Avetisyan, M. (2014). Compensatory and noncompensatory multidimensional randomized item response models. British Journal of Mathematical and Statistical Psychology, 67, 133-152.