Prof. Dr. Heinz Holling
Prof. Dr. Heinz Holling
Leiter der Arbeitseinheit
Raum 244
Fliednerstr. 21
48149 Münster
Tel: 0251 83 38485
holling@uni-muenster.de
Sprechstunde: mittwochs 12:00 Uhr - 13:00 Uhr

Studium:

  • Studium der Mathematik, Psychologie and Soziologie (1969 – 1976)
  • Dr. phil. (1980)
  • Dr. rer. nat. (1987)

Positionen:

  • wissenschaftlicher Mitarbeier an der Universität Berlin und der Universität Osnabrück (1974-1987)
  • Professor (temporär) an den Universitäten  Oldenburg, Münster und Mannheim (1987-1993)
  • Professor an der Universität Münster (since 1993)

Forschungsinteressen:

  • optimal design
  • conjoint analysis
  • adaptive testing
  • intelligence

Aktuelle Projekte:

  • Optimal design for online generated adaptive intelligence tests (DFG, 2007 – 2018)
  • Meta-Analysis of the validity of binary diagnoses based on dichotomous criteria (DFG, 2008 – 2016)
  • Messung divergenter Denkprozesse von Jugendlichen unter besonder Berücksichtigung kultureller Einflüsse (DFG, 2014-2017)
  • Dyscalculia in elementary school - Development and evaluation of a working memory-based assessment battery and an integrative training program ( BMBF, 2010 - 2016)

Editorial Board:

  • 2013 - now: Associate Editor of Journal of Probability and Statistics
  • 2012 - 2014: Associate Editor of ISRN Probability and Statistics
  • 2009 - now: Associate Editor of Zeitschrift für Psychologie
  • 2008 - now: Associate Editor of Thailand Statistician: Journal of the Thai Statistical Association
  • 1995 - 2003: Associate Editor of Diagnostica

Reviewer (ausgewählt):

  • Science
  • Biometrics
  • Psychometrika
  • Journal of Mathematical Psychology

Sonstige Akademische Tätigkeiten:

  • 2008 - now: General Secretary of the European Association of Methodology
  • 2000 - now: Member of Advisory Council for Giftedness of the Federal Ministry of Education and   Research
  • 2000 - 2005: Member of the Scientific Advisory Council of German Telekom


Ausgewählte Publikationen seit 2011:

  • Hodzic, S., Scharfen, J., Ripoll, P., Holling, H., Zenasni, F. (in press). How Efficient are Emotional Intelligence Trainings: A Meta-Analysis. Emotion Review.
  • Sangnawakij, P., Böhning, D. Niwitpong, S., Adams, S., Stanton, M. & Holling, H. (in press). Meta-Analysis without study-specific variance information: Heterogeneity case. Statistical Modelling.

  • Benda N., Bürkner P.-C., Freise F., Holling, H., & Schwabe R. (2017). Adaptive Designs for Quantal Dose-Response Experiments with False Answers. Journal of Statistical Theory and Practice, 11, 361-374.
  • Bürkner, P., Bittner, N., Holling, H., & Buhlmann, U. (2017). D-Cycloserine Augmentation of Behavior Therapy for Anxiety and Obsessive-Compulsive Disorders: A Meta-Analysis. Plos one, 12, doi: 10.137.
  • Masoudi, E., Holling, H. & Wong, W.K. (2017). Application of imperialist competitive algorithm to fond minimax and standardized maximin optimal designs. Computational Statistics & Data Analysis, Vol. 113, 330-345.
  • Ngamjarus, C., Chongsuvivatwong, V., McNeil, E. & Holling, H. (2017). Enhancement of learning on sample size calculation with a smartphone application: a cluster-randomized controlled trial. The Southeast Asian Journal of Tropical Medicine and Public Health, 48, 240-252.
  • Raddatz, J., Kuhn, J.-T., Holling, H., Doebel, C. (2017). Comorbidity of Arithmetic and Reading Disorder: Basic Number Processing and Calculation in Children with Learning Impairments. Journal of Learning Disabilities, 50, 298-308.
  • Sangnawakaij, P., Böhning, D. Adams, S., Stanton, M. & Holling, H. (2017). Statistical methodology for estimating the mean difference in a meta.analysis without study-specific variance information. Statistics in Medicine, 36, 1395-1413.
  • Schwenk, C., Sasanguie, D., Kuhn, J.-T., Kempe, S., Doebler, P., Holling, H. (2017). (Non-)symbolic magnitude processing in children with mathematical difficulties: a meta-analysis. Research in Development Disabilities, 165, 152-167.

  • Blum, D., Holling, H., Galibert, M.S., & Forthmann, B. (2016). Task difficulty prediction of figural analogies. Intelligence, 56, 72-81.
  • Böhning, D., Rocchetti,I., Alfó, M., & Holling, H. (2016). A flexible ratio regression approach for zero-truncated capture-recapture counts. Biometrics, 72, 697-706.
  • Bürkner P. C., Doebler P., & Holling, H. (2016). Optimal design of the Wilcoxon-Mann-Whitney-test. Biometrical Journal, 59, 25-40.
  • Graßhoff, U., Holling, H., & Schwabe,R. (2016). Optimal Desingn for the Rasch Poisson-Gamma Model. In Kunert, J., Müller,C., Atkinson, A. (Eds.). Advances in Model-Oriented Desing and Analysis. 133-141.
  • Forthmann, B., Gerwig, A., Holling, H., Celik, P., Storme, M., & Lubart, T. (2016). The Be-Creative Effect in Divergent Thinking: The Interplay of Instructions and Objekt Frequency. Intelligence, 57, 25-32.
  • Forthmann, B., Wilken, A., Doebler, P., & Holling, H. (2016) Strategy induction enhances creativity in figural divergent thinking. Journal of Creative Behavior, doi:10.1002/jocb.159.
  • Forthmann, B., Holling, H., Zandi, N., Gerwig, A., Celik, P., Storme, M., & Lubart, T. (2016). Missing creativity: The effect of cognitive workload on rater (dis-)agreement in subjective divergent-thinking scores. Thinking Skills and Creativity, Vol. 23, 129-139.
  • Holling, H., & Schwabe, R. (2016). Statistical Optimal Design Theory. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of Item Response Theory Vol. 2, 313-339.
  • Masoudi, E., Holling, H., & Wong, W. K.  (2016). Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs. Computational Statistics & Data Analysis, Vol. 113, 330-345.
  • Raddatz, J., Kuhn, J.-T., Holling, H., Moll, K., & Dobel,C. (2016). Comorbidity of arithmetic and reading disorder: Basic number processing and calculation in children with learning inpairments. Journal of Learning Disabilities, Vol. 50, 298-308.
  • Stein, A., Wunderlich, R., Lau, P., Engell, A., Wollbrink, A., Shaykevich, A., Kuhn, J.-T., Holling, H., Rudack, C., & Pantev, C. (2016). Clinical trial on tonal tinnitus with tailor-made notched music training. BMC Neurology, 16, 1-17.

  • Alavash, M., Doebler, P., Holling, H., Thiel, C. M., & Gießing, C. (2015). Is functional integration of resting state brain networks an unspecific biomarker for working memory performance? NeuroImage, 108, 182-193.
  • Biehler M., Doebler P., & Holling, H. (2015). Saddlepoint approximations of the distribution of the person parameter in the two parameter logistic model. Psychometrika, 665-688.
  • Chodura, S., Kuhn, J.-T., & Holling, H. (2015). Interventions for children with mathematical difficulties: A meta-analysis. Zeitschrift für Psychologie, 223, 2, 129-144.
  • Doebler, P., & Holling, H. (2015). Meta-analysis of diagnostic accuracy and ROC curves with covariate adjusted semiparametric mixtures. Psychometrika, 1084-1104.
  • Doebler, P., & Holling, H. (2015). A processing soeed test basded on rule-based item generation: An analysis with the Rasch Poisson Counts model. Learning and Individual Differences, 2015, 1-8.
  • Graßhoff, U., Holling, H., & Schwabe, R. (2015). Poisson model with three binary predictors: When are saturated designs optimal? (Stocastic Models, Statistics and Their Applications). In A. Steland, E. Rafajlowicz & K. Szajkowski (Eds.), Stochastic Models, Statistics and Their Applications. (pp.75-82).Cham: Springer.
  • Holling. H., Böhning, W., & Böhning, D. (2015). The covariate-adjusted frequency plot for the Rasch Poisson counts model. Thailand Statistician, 67-78.

  • Charoensawat, S., Böhning, W., Böhning, D., & Holling, H. (2014). Meta-analysis and meta-modelling for diagnostic problems. BMC Medical Research Methodolody, 14-56.
  • Doebler, P., Doebler, A., & Holling, H. (2014). A latent ability model for count data and an application to processing speed. Applied Psychological Measurement, 38, 587-598.
  • Kuhn, J.-T., & Holling, H. (2014). Number Sense or working memory? The effect of two computer-based trainings on mathematical skills in elementary school. Advances in Cognitive Psychology, 10, 59-67.
  • Sangnawakij, P., Böhning, D., Adams, S., Stanton, M., & Holling, H. (2014). Statistical methodology for estimating the mean difference in a meta-analysis without study-specific variance information. Statistics in Medicine., 36, 1395-1413.

  • Doebler, A., Doebler, P., & Holling, H. (2013). Optimal and Most Exact Condence Intervals for Person Parameters in Item Response Theory Models. Psychometrika, 78(1), 98-115.
  • Graßhoff U., Holling, H., & Schwabe, R. (2013). Optimal design for count data with binary predictors in item response theory. Advances in Model-Oriented Designs and Analysis, 117-124.
  • Graßhoff, U., Holling, H., & Schwabe, R. (2013). Optimal design for count data with binary predictors in item response theory. In D. Ucinski, A. C. Atkinson & M. Patan (Eds.) 10-Adcances in Model-Oriented Design and Analysis. (pp. 117-124). Heidelberg: Physica.
  • Holling, H., Böhning, W., Böhning, D., & Formann, A. K. (2013). The Covariate-Adjusted Frequency Plot. Statistical Methods in Medical Research,  Vol. 25, issue 2, 902-916.
  • Holling, H. ,& Schwabe, R. (2013).  An introduction to optimal design. Zeitschrift für Psychologie, 221, 3, S. 124-144.
  • Kuhn, J.-T., Raddatz, J., Holling, H., & Dobel, C. (2013). Dyskalkulie vs. Rechenschwäche: Basisnumerische Verarbeitung in der Grundschule. Lernen und Lernstörungen, 2, 229-247.
  • Skodzik, T., Holling, H., & Pedersen, A. (2013). Long-term memory performance in adult ADHD - a Meta-Analysis. Journal of Attention Disorders. Online publication.

  • Doebler, P., Holling, H., & Böhning, D. (2012). A Mixed Model Approach to Meta-Analysis of Dignostic Studies with Binary Test Outcome. Psychological Methods, 17, 418-436.
  • Graßhoff, U., Doebler, A., Holling, H., & Schwabe, R. (2012). Optimal design for linear regression models in the presence of heteroscedasticity caused by rendom coefficients. Journal of Statistical Planning and Inference 142, 1108-1113.
  • Graßhoff, U., Großmann, H., Holling, H., & Schwabe, R. (2012). Optimal Design for Descrete Choice Experiments. Journal of Statistical Planning and Inference 143, 167-175.
  • Graßhoff, U., Holling, H., & Schwabe, R. (2012). Optimal design for the Rasch model. Psychometrika 77, 710-723.
  • Holling, H., & Böhning, W., & Böhning, D. (2012). Likelihood Based Clustering of Meta-Analytic SROC Curves. Psychometrika, 77, 106-126.
  • Holling, H., Böhning, W., & Böhning, D. (2012). Meta-analysis of diagnostic studies based upon SROC-curves: A mixed model approach using the Lehmann family. Statistical Modelling - An International Journal 12, issue 4, 347.
  • Holling, H., & Schwabe, R. (2012). Discussion of the paper Optimum design of experiments for statistical inference by Gilmour, S. G. and Trinca, L. A. Journal of the Royal Statistical Society, Series C (Applied Statistics), 61, 385.
  • Niwitpong, S., Böhning, D., van der Heijden, P. G. H., & Holling, H. (2012). Capture-Recapture Estimation Based Upon the Geometric Distribution allowing for Heterogenity. Metrika, 76, 495-519.

  • Böhning, D., Holling, H., & Pantilea, V. (2011). A limitation of the diagnostic-odds ratio in determining an optimal cut-off value for a continuous diagnostic test. Statistical Methods in Medical Research, 20, 541-550.
  • Freund, P. A. & Holling, H. (2011). Who wants to take an intelligence test? Personality and achievement motivation in the context of ability testing. Personality and Individual Differences, 50, 723-728.
  • Freund, P. A., & Holling, H. (2011). How to get really smart: Modeling retest and training effects in ability testing using computer-generated figural matrix items. Intelligence, 39, 233-243.
  • Freund, P. A., & Holling, H. (2011). Retest effects in matrix test performance: Differential impact of predictors at different hierarchy levels in an educational setting. Learning and Individual Differences, 21, 597-601.
  • Freund, P. A., Kuhn, J.-T., & Holling, H. (2011). Measuring current achievement motivation with the QCM: Short form development and investigation of measurement invariance. Personality and Individual Differences, 51, 629-634.
  • Holling, H., & Schwabe, R. (2011). The usefulness of Bayesian optimal designs for descrete choice experimentes by Roselinde Kessels, Bradley Jones, Peter Goos and Martina Vandebroek. Applied Stochastic Models in Business and Industry, 27, 189-192.
  • Vock, M., Preckel, F., & Holling, H. (2011). Mental abilities and school achievement: A Test of a mediation hypothesis. Intelligence, 39, 357-369.
  • Zeuch, N., Holling, H., & Kuhn, J.-T. (2011). Analysis of the Latin Square Task with linear logistic test models. Learning and Individual Differences, 21, 629-632.

Bücher:

  • Holling, H., & Gediga, G. (2015) Statistik - Testverfahren [Statistics-Tests]. Göttingen: Hogerefe.
  • Holling, H., & Gediga, G. (2013). Statistik - Wahrscheinlichkeitstheorie und Schätzverfahren. [Statistics - Probability theory and estimation methods]. Göttingen: Hogrefe.
  • Holling, H., & Gediga, G. (2011). Statistik - Deskriptive Verfahren. [Statistics - Descriptive methods]. Göttingen: Hogrefe.