Publikationen

Selected Publications

Jann, M. & Spiess, M. (2024). Using external information for more precise inferences in general regression models. Psychometrika, https://doi.org/10.1007/s11336-024-09953-w.

Tutz, G., & Jordan, P. (2023). Latent Trait Item Response Models for Continuous Responses. Journal of Educational and Behavioral Statistics, 0(0).

Jann, M. (2023). Testing the coherence of data and external intervals via an imprecise Sargan-Hansen test. In International Symposium on Imprecise Probability: Theories and Applications (pp. 249-258). PMLR.

Böschen, I. (2023). Changes in methodological study characteristics in psychology between 2010-2021. PLoS ONE 18(5): e0283353.

Spiess, M. and Jordan, P. (2023). In Models We Trust: Preregistration, large samples, and replication may not suffice. Front. Psychol. 14:1266447.

Jordan, P. (2023). On Reverse Shrinkage Effects And Shrinkage Overshoot. Psychometrika, 88(1), 274--301.

Böschen, I. (2023). Evaluation of the extraction of methodological study characteristics with JATSdecoder. Sci Rep 13, 139.

Böschen, I. (2022). JATSdecoder: A Metadata and Text Extraction and Manipulation Tool Set. R package version 1.1.

Fernandez, D., McMillan, L., Arnold, R., Spiess, M. & Liu, I. (2022). Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses based on the Stereotype Model. Stats, 5, 507-520.

Spiess, M. & Augustin, T. (2021). Handling Missing Data in Large Data Bases. In: U. Engel, A. Quan-Haase, S. Xun Liu & L. E. Lyberg (eds.), Handbook of Computational Social Science, Volume 2, Data Science, Statistical Modelling, and Machine Learning Methods (Chapter 6, pp. 82-94). London and New York: Routledge.

URL for Handbook of Computational Social Science, Volume 1
https://www.routledge.com/Handbook-of-Computational-Social-Science-Volume-1-Theory-Case-Studies/Engel-Quan-Haase-Liu-Lyberg/p/book/9780367456528

URL for Handbook of Computational Social Science, Volume 2
https://www.routledge.com/Handbook-of-Computational-Social-Science-Volume-2-Data-Science-Statistical/Engel-Quan-Haase-Liu-Lyberg/p/book/9781032077703

Spiess, M., Kleinke, K. & Reinecke, J. (2021). Proper multiple imputation of clustered or panel data. In: P. Lynn (ed.), Advances in Longitudinal Survey Methodology (Chapter 17, pp. 424 - 446). Hoboken, NJ: Wiley.

Böschen, I. (2021). Evaluation of JATSdecoder as an automated text extraction tool for statistical results in scientific reports. Scientific Reports 11, 19525.

Böschen, I. (2021). Software review: The JATSdecoder package?extractmetadata, abstract and sectioned text from NISO-JATS coded XMLdocuments; Insights to PubMed central?s open access database. Scientometrics.

Spiess, M., Fernández, D., Nguyen, T. & Liu, I-Ming (2020). Generalized estimating equations to estimate the ordered stereotype logit model for panel data. Statistics in Medicine, 39, 1919–1940.

Kleinke, K., Reinecke, J., Salfrán, D. & Spiess, M. (2020). Applied Multiple Imputation. Advantages, New Developments, Pitfalls and Applications in R. Statistics for Social and Behavioral Sciences. Springer: Cham, Switzerland.

Fernandez, D., Liu, I-Ming, Arnold, R., Nguyen, T. & Spiess, M. (2019). Model-based goodness-of-fit tests for the ordered stereotype model. Statistical Methods in Medical Research.

Jordan P. (2019). The counterintuitive impact of responses and response times on parameter estimates in the drift diffusion model. British Journal of Mathematical and Statistical Psychology.

Jordan P., & Spieß, M. (2019). Rethinking the interpretation of item discrimination and Factor Leadings. Educational and Psychological Measurement. 

Jordan, P. (2019). Faktorenanalyse. Sozialwissenschaftliche Forschungsmethoden, Vol 14. Rainer Hampp Verlag: Augsburg, München.

Spiess, M., Jordan, P. & Wendt, M. (2019). Simplified Estimation and Testing in Unbalanced Repeated Measures Designs. Psychometrika, 84(1), 212-235.

Salfran, D. & Spiess, M. (2018). Generalized Additive Model Multiple Imputation by Chained Equations With Package ImputeRobust. The R Journal Vol. 10/1, 61-72.

Jordan, P. & Spiess, M. (2018). On Fair Person Classification Based on Efficient Factor Score Estimates in the Multidimensional Factor Analysis Model. Psychometrika, 83(3), 563–585

Jordan, P., Steingen, U., Terschüren, C., & Harth, V (2018). The Maslach Burnout Inventory: A test dimensionality assessment via item response theory. TPM, 2018, 25, 101-120.

Jordan, P. & Spiess, M. (2017). A New Explanation and Proof of the Paradoxical Scoring Results in Multidimensional Item Response Models. Psychometrika, 83(4), 831-846.

de Jong, R., van Buuren, S. & Spiess, M. (2016). Multiple imputation of predictor variables using generalized additive models. Communications in Statistics - Simulation and Computation, 45 (3), 968-985.

Wu, L., Spiess, M. & Lehmann, M. (2016). The effect of authenticity in music on the subjective theories and aesthetical evaluation of listeners: a randomized experiment. Musicae Scientiae, 21(4), 442-464.

Spiess, M. (2016). Dealing with missing values. In: C. Wolf, D. Joye, T.W. Smith and Y. Fu (Eds.), The SAGE Handbook of Survey Methodology (Chapter 37, pp. 595-610). 

Jordan, P. (2016). Wahrscheinlichkeits-und Matrizenrechnung für Sozialwissenschaftler. Rainer Hampp-Verlag: München-Mehring.

Salfran, D. & Spiess, M. (2015). Handling of Missing Data in Statistical Analyses. In: U. Engel (Ed.), Survey Measurements - Techniques, Data Quality and Sources of Error (pp. 192 - 208).

Spiess, M. (2015). Handling Missing Data: Overview and Introduction. In: U. Engel, B. Jann, P. Lynn, A. Scherpenzeel & P. Sturgis (Eds.), Improving Survey Methods (Chapter 30, pp. 365-367). New York: Routledge.

de Jong, R. & Spiess, M. (2015). Robust Multiple Imputation. In: U. Engel, B. Jann, P. Lynn, A. Scherpenzeel & P. Sturgis (Eds.), Improving Survey Methods (Chapter 33, pp. 397-411). New York: Routledge.

Spiess, M. & Lincoln, T. (2014). Akademische Psychologie in Hamburg: Status quo und Perspektiven. In M. Spiess (Hrsg.), 100 Jahre akademische Psychologie in Hamburg. Eine Festschrift (S. 173-180). Hamburg: University Press.

Jordan, P. (2013). Paradoxien in quantitativen Modellen der Individualdiagnostik. PhD thesis, Universität Hamburg.

Spiess, M. (2012). Ein marginaler Ansatz zur Schätzung von Item-Response-Modellen mit zufälligen individuellen Effekten. In W. Baros und J. Rost (Hrsg.), Natur- und kulturwissenschaftliche Perspektiven in der Psychologie. Methodologie - Methoden - Anwendungsbeispiele (S. 108-119). Berlin: Irena Regener.

Jordan, P. & Spiess, M. (2012). Generalizations of paradoxical results in multidimensional item response theory. Psychometrika, 77(1), 127-152.

Matiaske, W., Menges, R. & Spiess, M. (2012). Modifying the Rebound: It depends! Explaining Mobility Behavior on the Basis of the German Socio-Economic Panel. Energy Policy, 41, 29-35.

Spiess, M. (2010b). Logistische Regressionsverfahren. In H. Holling und B. Schmitz (Hrsg.), Handbuch Statistik, Methoden und Evaluation (S. 496-508). Göttingen: Hogrefe.

Spiess, M. & Tutz, G. (2010). Logistische Regressionsverfahren für Mehrkategoriale Zielvariablen. In H. Holling und B. Schmitz (Hrsg.), Handbuch Statistik, Methoden und Evaluation (S. 509-517). Göttingen: Hogrefe.

Spiess, M. (2010a). Der Umgang mit fehlenden Werten. In C. Wolf und H. Best (Hrsg.), Handbuch der sozialwissenschaftlichen Datenanalyse (Kap. 6, S. 117-142). Wiesbaden: VS Verlag.

Spiess, M. & Kroh, M. (2010). A Selection Model for Panel Data: The Prospects of Green Party Support. Political Analysis, 18, 172-188.

Pannenberg, M. & Spiess, M. (2009). GEE Estimation of a Two-Equation Panel Data Model with an Application to Wage Dynamics and the Incidence of Profit-Sharing in West Germany. AStA Advances in Statistical Analysis, 93, 427-447.

Kuchler, C. & Spiess, M. (2009). The Data Quality Concept of Accuracy in the Context of Public Use Data Sets. AStA Wirtschafts- und Sozialstatistisches Archiv, 3(1), 67-80.

Spiess, M. (2008). Missing Data Techniken. Münster: LIT Verlag.

Goebel, J., Grabka, M., Krause, P., Kroh, M., Pischner, R., Sieber, I. & Spieß, M. (2008). Mikrodaten, Gewichtung und Datenstruktur der Längsschnittstudie Sozio-Ökonomisches Panel (SOEP). Vierteljahrshefte zur Wirtschaftsforschung, 77, 77-109.

Kroh, M., Pischner, R., Spiess, M. & Wagner, G.G. (2008). On the Treatment of Non-Original Sample Members in the German Household Panel Study (SOEP) - Tracing, Weighting, and Frequencies. Methoden - Daten - Analysen, 2(2), 179-198.

Spiess, M. (2007). Corrigendum: Estimation of a two-equation panel model with mixed continuous and ordered categorical outcomes and missing data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 56 (1), 117.

Spiess, M. (2006b). Estimation of a Two-Equation Panel Model With Mixed Continuous and Ordered Categorical Outcomes and Missing Data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 55(4), 525-538.

Spiess, M. (2006a). Wozu ein tieferes Verständnis von Statistik? Ein Kommentar zu Hager (2005). Psychologische Rundschau, 57(1), 43-44.

Spiess, M. & Goebel, J. (2005). On the effect of nonresponse on the estimation of a two-panel-waves wage equation. Allgemeines Statistisches Archiv, 89(1), 63-74.

Spiess, M. & Goebel, J. (2004). A comparison of different imputation rules. In Statistisches Bundesamt (Hrsg.), Harmonisation of Panel Surveys and Data Quality (S. 293-316). Reutlingen: Metzler-Poeschl.

Spiess, M. & Tutz, G. (2004). Alternative measures of the explanatory power of general multivariate regression models. Journal of Mathematical Sociology, 28(2), 125-146.

Spiess, M. & Wagner, G. (2000, 2004). Logit- und Probit-Modelle. In W. Voß (Hrsg.) Taschenbuch der Statistik (Kap. 19, S. 609-644). München: Carl Hanser.

Spiess, M. (2001). Evaluation of a pseudo-R² measure for panel probit models. British Journal of Mathematical and Statistical Psychology, 54, 325-333.

Spiess, M. & Hamerle, A. (2000). Regression models with correlated binary responses: A comparison of different methods in finite samples. Computational Statistics & Data Analysis, 33(4), 439-455.

Spiess, M. & Keller, F. (1999). A mixed approach and a distribution free multiple imputation technique for the estimation of a multivariate probit model with missing values. British Journal of Mathematical and Statistical Psychology, 52, 1-17.

Spiess, M. (1998). A mixed approach for the estimation of probit models with correlated responses: Some finite sample results. Journal of Statistical Computation and Simulation, 61, 39-59.

Keller, F., Spiess, M. & Hautzinger, M. (1996). Statische und dynamische Prädiktoren für den Verlauf depressiver Erkrankungen: eine Auswertung mittels verallgemeinerter Schätzgleichungen. Zeitschrift für Klinische Psychologie, 25 (3), 234-243.

Melsbach, G., Wohlschläger, A., Spiess, M. & Güntürkün, O. (1996). Morphological asymmetries of motoneurons innervating upper extremities: clues to the anatomical foundations of handedness? Intern. J. Neuroscience, 86, 217-224.

Spiess, M. & Hamerle, A. (1996). On the properties of GEE estimators in the presence of invariant covariates. Biometrical Journal, 38, 931-940.