Han Oud

Johan H.L. Oud


Email j.oud@pwo.ru.nl
Website http://www.socsci.ru.nl/~hano/
Voice +31-24-3230930

Johan H.L. (Han) Oud is associate professor in longitudinal research methods and statistics at the Behavioural Science Institute of the Radboud University Nijmegen

Selected Publications

METHODOLOGICAL AND STATISTICAL

Voelkle, M. C., & Oud, J. H. L. (in press). Relating latent change score and continuous time models. Structural Equation Modeling.

Oud, J. H. L., & Voelkle, M. C. (2014). Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling. Quality & Quantity. DOI: 10.1007/s11135-013-9955-9
(downloads: Oud&Voelkle2014.zip)

Oud, J. H. L. (2014). System dynamics modeling. In Michalos, A. C. (Ed.), Encyclopedia of Quality of Life Research (pp. 6555-6558). Dordrecht: Springer.

Oud, J. H. L. (2014). Event-history analysis. In Michalos, A. C. (Ed.), Encyclopedia of Quality of Life Research (pp. 2053-2055). Dordrecht: Springer.

Oud, J. H. L., & Voelkle, M. C. (2014). Continuous time analysis. In Michalos, A. C. (Ed.), Encyclopedia of Quality of Life Research (pp. 1270-1273). Dordrecht; Springer.

Angraini, Y., Toharudin, T., Folmer, H., & Oud, J.H.L. (2014). The relationships between individualism, nationalism, ethnocentrism, and authoritarianism in Flanders: A continuous time - structural equation modeling approach. Multivariate Behavioral Research, 49, 41-53. DOI: 10.1080/00273171.2013.836621

Voelkle, M. C., & Oud, J. H. L. (2013). Continuous time modelling with individually varying time intervals for oscillating and nonoscillating processes. British Journal of Mathematical and Statistical Psychology, 66, 103-126. DOI:10.1111/j.2044-8317.2012.02043.x

Voelkle, M. C., Oud, J. H. L., von Oertzen, T., & Lindenberger, U. (2012). Maximum likelihood dynamic factor modeling for arbitrary N and T using SEM. Structural Equation Modeling, 19, 329-350. DOI:10.1080/10705511.2012.687656

Voelkle, M. C., Oud, J. H. L., Davidov, E., & Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia. Psychological Methods,17, 176-192. DOI: 10.1037/a0027543

Oud, J. H. L., & Folmer. H., Patuelli, R., & Nijkamp, P. (2012). Continuous-time modeling with spatial dependence. Geographical Analysis, 44, 29-46. (downloads: OudFolmerPatuelli&Nijkamp2012.zip)

Oud, J. H. L. & Folmer. H. (2011). Reply to Steele & Ferrer: Modeling oscillation, approximately or exactly? Multivariate Behavioral Research, 46, 985-993. (downloads: Voelkle&Oud2011.zip)

Liu, A., Folmer, H., & Oud, J. H. L. (2011). Estimating spillovers by classical W-based or latent spatial autoregressive models: evidence from Monte Carlo simulations. Letters in Spatial and Resource Sciences, 4, 71-80.

Oud, J. H. L. (2010). Second-order stochastic differential equation model as an alternative for the ALT and CALT models. Advances in Statistical Analysis, 94, 202-215.

Oud, J. H. L., & Delsing, M. J. M. H. (2010). Continuous time modeling of panel data by means of SEM. In K. van Montfort, J. Oud & A. Satorra (Eds.), Longitudinal research with latent variables (pp. 201-244). New York: Springer.

Folmer, H., & Oud, J. H. L. (2008). How to get rid of W: a latent variables appoach to modelling spatially lagged variables. Environment and Planning A, 40, 2526-2538. (downloads: Folmer&Oud2008.zip)

Oud, J. H. L., & Folmer, H. (2008). A structural equation approach to models with spatial dependence. Geographical Analysis, 40, 152-166. (downloads: Oud&Folmer2008.zip)

Oud, J. H. L., & Singer, H. (2008). Continuous time modeling of panel data: SEM versus filter techniques. Statistica Neerlandica, 62, 4-28.

Delsing, M. J. M. H., & Oud, J. H. L. (2008). Analyzing reciprocal relationships by means of the continuous-time autoregressive latent trajectory model. Statistica Neerlandica, 62, 58-82. (downloads: Delsing&Oud2008.zip)

Toharudin, T., Oud, J. H. L., & Billiet, J. B. (2008). Assessing the relationships between Nationalism, Ethnocentrism, and Individualism in Flanders using Bergstrom's approximate discrete model. Statistica Neerlandica, 62, 83-109.

Oud, J. H. L. (2007). Continuous time modeling of reciprocal relationships in the cross-lagged panel design. In S.M. Boker & M.J. Wenger (Eds.), Data analytic techniques for dynamical systems in the social and behavioral sciences (pp. 87-129). Mahwah, NJ: Lawrence Erlbaum Associates.  (downloads: Oud2007b.zip)

Oud, J. H. L. (2007). Comparison of four procedures to estimate the damped linear differential oscillator for panel data. In K. van Montfort, J. Oud & A. Satorra (Eds.), Longitudinal models in the behavioral and related sciences (pp. 19-39). Mahwah, NJ: Lawrence Erlbaum Associates. (downloads: Oud2007a.zip)

Delsing, M. J. M. H., Oud, J. H. L., & De Bruyn, E.E.J. (2005). Assessment of bidirectional influences between family relationships and adolescent problem behavior: Discrete vs. continuous time analysis. European Journal of Psychological Assessment, 21, 226-231.

Oud, J. H. L. (2004). SEM state space modeling of panel data in discrete and continuous time and its relationship to traditional state space modeling. In K. van Montfort, J. Oud & A. Satorra (Eds.), Recent developments on structural equations models: Theory and applications (pp. 13-40). Dordrecht: Kluwer Academic Publishers.

Oud, J. H. L. (2002). Continuous time modeling of the cross-lagged panel design. Kwantitatieve Methoden, 23 (69), 1-26.(downloads: Oud2002.pdf)

Oud, J. H. L. (2001). Quasi-longitudinal designs in SEM state space modeling. Statistica Neerlandica, 55, 200-220.

Oud, J. H. L., & Jansen, R. A. R. G. (2000). Continuous time state space modeling of panel data by means of SEM. Psychometrika, 65, 199-215. (downloads: Oud&Jansen2000.zip)

Oud, J. H. L., Jansen, R. A. R. G., van Leeuwe, J.F.J., & Aarnoutse, C. A. J., & Voeten, M. J. M. (1999). Monitoring pupil development by means of the Kalman filter and smoother based upon SEM state space modeling. Learning and Individual Differences, 11, 121-136.

Oud, J. H. L., Jansen, R. A. R. G., & Haughton, D. M. A. (1999). Small samples in structural equation state space modeling. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 288-305). Thousand Oakes: Sage.

Oud, J. H. L., & Jansen, R. A. R. G. (1996). Nonstationary longitudinal LISREL model estimation from incomplete panel data using EM and the Kalman smoother. In U. Engel & J. Reinecke (Eds.), Analysis of change: advanced techniques in panel data analysis (pp. 135-159). Berlin: Walter de Gruyter.

Oud, J. H. L., & Jansen, R. A. R. G. (1995). An ARMA extension of the longitudinal LISREL model for LISKAL. In I. Partchev (Ed.), Multivariate analysis in the behavioral sciences: philosophic to technical (pp. 49-69). Sofia: "Prof. Marin Drinov" Academic Publishing House.

Jansen, R. A. R. G., & Oud, J. H. L. (1995). Longitudinal LISREL model estimation from incomplete panel data using the EM algorithm and the Kalman smoother. Statistica Neerlandica, 49, 362-377.

Oud, J. H. L., van Leeuwe, J. F. J., & Jansen, R. A. R. G. (1993). Kalman filtering in discrete and continuous time based on longitudinal LISREL models. In J. H. L. Oud & A. W. van Blokland-Vogelesang (Eds.), Advances in longitudinal and multivariate analysis in the behavioral sciences (pp. 3-26). Nijmegen: ITS.

Molenaar, J., & Oud, J. H. L. (1991). Optimality and initialisation of the Kalman filter. Kwantitatieve Methoden, 12 (38), 45-52.

Oud, J. H., van den Bercken, J. H., & Essers, R. J. (1990). Longitudinal factor score estimation using the Kalman filter. Applied Psychological Measurement, 14, 395-418.

Oud, J. H. L., & Molenaar, J. (1988). The Kalman filter as a factor score estimator compared with the regression and Bartlett estimators. In M. G. H. Jansen & W. H. van Schuur (Eds.), The many faces of multivariate analysis: Proceedings of the SMABS-88 conference (pp. 210-223). Groningen: RION.

 

OTHER SUBJECTS

Suparman, Y., Folmer, H., & Oud, J. H. L. (2013) Hedonic price models with omitted variables and measurement errors: A constrained autoregression-structural equation modeling approach with application to urban Indonesia. Journal of Geographical Systems. DOI 10.1007/s10109-013-0186-3

Hirve, S., Oud, J. H. L.,Sambhudas, S., Juvekar, S., Blomstedt, Y., Tollman, S., Wall, S., & Ng, N. (2013). Unpacking self-rated health and quality of life in older adults in India: A structural equation modelling approach. Social Indicators Research. DOI:10.1007/s11205-013-0334-7

Delsing, M. J. M. H., & Oud, J. H. L. (2012). Causal directions between adolescents' externalizing and internalizing problems: A continuous time analysis. Netherlands Journal of Psychology, 67, 68-80.

Gräser, H., Lux, C., & Oud, J.H.L. (2010). Dynamische Prozesse in stabilen Partnerschaften: Multivariate Analysen mit Daten aus einem 25-Jahres-Längsschnitt. Trierer Psychologische Berichte, 37, Heft 1. (downloads: http://www.uni-trier.de/fileadmin/fb1/PSY/tripsyberichte/2010_37_1.pdf)

Gerris, J., Delsing, M. J. M. H. & Oud, J. H. L. (2010). Big-Five personality factors and interpersonal trust in established marriages. Family Science, 1, 48-62.

Van Oort, F. G., Oud, J. H. L., & Raspe, O. (2009). The urban knowledge economy and employment growth: A spatial structural equation modeling approach. The Annals of Regional Science, 43, 859-877.

Landsheer, J. A., Oud, J. H. L., & van Dijkum C. (2008). Male and female development of delinquency during adolescence and early adulthood: A differential autoregressive model of delinquency using an overlapping cohort design. Adolescence, 43, 89-98.

Manders, W. A., Cook, W. L., Oud, J. H. L., Scholte, R. H. J., Janssens, J. M. A. M., & De Bruyn, E. E. J. (2007). Level validity of self-report whole-family measures. Journal of Family Psychology, 21, 605-613.

Delsing, M. J. M. H., Oud, J. H. L., van Aken, M. A. G., De Bruyn, E.E.J., & Scholte, R.H.J. (2005). Family loyalty and adolescent problem behavior: The validity of the family group effect. Journal of Research on Adolescence, 15, 127-150.

Delsing, M. J. M. H., Oud, J. H. L., De Bruyn, E.E.J., & van Aken, M. A. G. (2003). Current and recollected perceptions of family relationships: The social relations model approach applied to members of three generations. Journal of Family Psychology, 17, 445-459.

Oud, H. & Stemerdink, G. (2002). Een leraar moet een combinatie zijn van een zendeling en een toneelspeler: Prof. Dr. W. Molenaar benoemd tot erelid van de VVS. [A teacher should be a combination of a missionary and an actor: Prof. Dr. W. Molenaar nominated honorary member of the Netherlands Society for Statistics and Operations Research] . STAtOR, 3(1), 4-9.

Aarnoutse, C., van Leeuwe, J., Oud, H., Voeten, R., Manders, D., Hoffs, J., & van Kan, N. (2000). Leer in zicht: het leerlingvolgsysteem dat voorspelt [Learning in sight: the pupil monitoring system that predicts]. Lisse: Swets & Zeitlinger.

Oud, H. (2000). Statistiek leeft niet in het bestuurlijke circuit: Prof. Dr. W.R. van Zwet benoemd tot erelid van de VVS [Statistics is not alive in government circles: Professor van Zwet nominated honorary member of the Netherlands Society for Statistics and Operations Research]. STAtOR, 1, 4-8.

Hendriks, A. H. C., de Moor, J. M. H., Oud, J. H. L., Franken, W. M. (2000). Service needs of parents with motor or multiply disabled children in Dutch therapeutic toddler classes. Clinical Rehabilitation, 14, 506-517.

Hendriks, A. H. C., de Moor, J. M. H., Oud, J. H. L., Savelberg, M. M. W. H., & Bargeman, W. H. (2000). Sources and determinants of job stress among employees working in therapeutic toddler classes in Dutch rehabilitation centres. International Journal of Disability, Development, and Education, 47, 155-169.

Mathijssen, J. J. P., Koot, H. M., Verhulst, F.C., De Bruyn, E. E. J., & Oud, J. H. L. (1998). The relationship between mutual family relations and child psychopathology. Journal of Child Psychology and Psychiatry, 39, 477-487.

Mathijssen, J. J. P., Koot, H. M., Verhulst, F.C., De Bruyn, E. E. J., & Oud, J. H. L. (1997). Family functioning and child psychopathology: individual versus composite family scores. Family Relations: Interdisciplinary Journal of Applied Family Studies, 46, 247-255.

Haughton, D. M. A., Oud, J. H. L., & Jansen, R. A. R. G. (1997). Information and other criteria in structural equation model selection. Communications in Statistics: Simulation and Computation, 26, 1477-1516.

De Moor, J. M. H., van Waesberghe, B. T. M., & Oud, J. H. L. (1994). Effectiveness of play training with handicapped toddlers. In J. Hellendoorn, R. van der Kooij, & B. Sutton-Smith (Eds.), Play and intervention (pp. 144-156). Albany, NY: State University of New York Press.

Oud, J. H. L., & Bijleveld, C. C. J. H. (1992). Analysis of longitudinal time-dependent data in behavioral science: Dutch contributions. Statistica Applicata, 4, 719-734.

Mommers, M. J. C., & Oud, J. H. L. (1992). Modelling and predicting reading difficulties. In L. T. Verhoeven & J. H. A. L. de Jong (Eds.), The construct of language proficiency: Applications of psychological models to language assessment (pp. 49-60). Amsterdam: Benjamins.

Dumont, J. J., Oud, J. H. L., van Mameren-Schoehuizen, G. G. M., Jacobs, M. J. M. I., van Herpen, M. J., & van den Bekerom, F. L. M. (1990). Effectiveness of dyslexia treatment. In G. Th. Pavlidis (Ed.), Perspectives on Dyslexia: Vol. 2 (pp. 293-326). New York: Wiley

Mommers, M. J. C., van Leeuwe, J. F. J., Oud, J. H. L., & Janssens, J. M. A. M. (1986). Decoding skills, reading comprehension and spelling: A longitudinal investigation. Tijdschrift voor Onderwijsresearch, 11, 97-113.

Oud, J. H. L., & Sattler, J. M. (1984). Generalized Kappa coefficient: A Microsoft BASIC Program. Behavior Research Methods, Instruments and Computers, 16, 481.

 

CONSTRUCTION OF MONITORING SYSTEMS ON THE BASIS OF A LONGITUDINAL SEM MODEL

Oud, J. H. L. (2010). Second-order stochastic differential equation model as an alternative for the ALT and CALT models. Advances in Statistical Analysis, 94, 202-215.

Oud, J. H. L., & Delsing, M. J. M. H. (2010). Continuous time modeling of panel data by means of SEM. In K. van Montfort, J. Oud & A. Satorra (Eds.), Longitudinal research with latent variables (pp. 201-244). New York: Springer.

Nijhuis-Van der Sanden, M. W. G., Oud, J. H. L., Van der Sanden, W. A. M., & Oostendorp, R. A. B. (2005). Zorg in een nieuwe jas: Transparant maar uitgekleed. Fysiopraxis, 14 (4), 12-15.

Oud, J.H. L. (2004b). Volgen in continue tijd en aan de hand van geïndividualiseerde verwachte ontwikkelingscurven. In L. Verhoeven & M. Voeten (Eds.), Onderwijskunde in theorie en praktijk: Bijdragen aangeboden aan Cor Aarnoutse bij zijn afscheid als hoogleraar aan de Katholieke Universiteit Nijmegen (pp. 188-206). Tilburg: Zwijsen.

Oud, J. H. L. (2004a). SEM state space modeling of panel data in discrete and continuous time and its relationship to traditional state space modeling. In K. van Montfort, J. Oud & A. Satorra (Eds.), Recent developments on structural equations models: Theory and applications (pp. 13-40). Dordrecht: Kluwer Academic Publishers.

Spee, J., & Oud, H. (2004). De groep als geheel heeft zich wel in de gewenste richting ontwikkeld. Perspectief, 12(2), 21-22.

Oud, H., & Spee, J. (2002). Jeugdige delinquenten volgen met het Kalman filter. STAtOR, 3(4), 14-18.

Aarnoutse, C., van Leeuwe, J., Oud, H., Voeten, R., Manders, D., Hoffs, J., & van Kan, N. (2000). Leer in zicht: het leerlingvolgsysteem dat voorspelt [Learning in sight: the pupil monitoring system that predicts]. Lisse: Swets & Zeitlinger.

Oud, J. H. L., & Jansen, R. A. R. G. (2000). Continuous time state space modeling of panel data by means of SEM. Psychometrika, 65, 199-215.

Oud, J. H. L., Jansen, R. A. R. G., van Leeuwe, J.F.J., & Aarnoutse, C. A. J., & Voeten, M. J. M. (1999). Monitoring pupil development by means of the Kalman filter and smoother based upon SEM state space modeling. Learning and Individual Differences, 11, 121-136.

Oud, J. H., van den Bercken, J. H., & Essers, R. J. (1990). Longitudinal factor score estimation using the Kalman filter. Applied Psychological Measurement, 14, 395-418.

Oud, J. H. L., van Waesberghe, B. T. M., & Oud-de Glas, M. M. B. (1999). Leerlingvolgsystemen [Pupil monitoring systems]. In M. L. Krüger, H. P. M. Creemers, J. H. G. I. Giesbers, & C.A. van Vilsteren (Eds.), Handboek schoolorganisatie en onderwijsmanagement: Leiding geven in onderwijs (2e aanvulling; pp. 31-60). Alphen aan den Rijn: Samsom H.D. Tjeenk Willink.

 

Figure 1

Estimates of decoding ability for one pupil, computed by the pupil monitoring system and displayed against the background of the development of a nationwide sample of pupils

 

decoding
ability
liskal example
month 
grade 
 
Apr.
01
Oct.
00002
Apr. Oct.
00003
0Apr. Oct.
00004
0Apr. 0Oct.
000005
0Apr. 0Oct.
000006
Apr.
 

Figure 1 displays an example of a graph generated by the monitoring system, showing the Kalman filter results, based on a longitudinal SEM model. Decoding speed development of an individual pupil is compared with population mean development over the entire six year primary school period. This is done graphically without any numerical information being necessary. The bold line represents the population's mean developmental curve of decoding skill. The white band consists of the area plus and minus one standard deviation from the mean representing 68% of the population. The areas outside the white band each consist of, respectively, the 16% highest and lowest scoring pupils in the population. Pupils whose latent scores lie within the area below the white band are considered to be in the warning zone and needing special attention from the teacher. The latent developmental curve of the individual pupil is represented by the thin dotted line, based on 11 biannual measurements starting in the middle of the first school year. The lines around it represent the standard errors of estimation (i.e. plus and minus one standard error). In view of the standard errors, rather safely the pupil's true curve may be concluded to be at each time point below the population mean and above the population mean minus one standard deviation. Although some fluctuation over time can be observed, the pupil's relative position in the population is not changing dramatically. This pupil's decoding speed development does not seem to be a source of special concern at any point in time. In addition to the actual development, also the prediction with prediction intervals can be displayed for pupils not yet in grade 6 Apr. Instead of the general population development, also some specific subgroup development can be chosen as background information (boys, school, class etc.). up


Last update: 30-03-14