The linear logistic test model (LLTM) breaks down the item parameter of the Rasch model as a linear combination of some hypothesized elementary parameters. Although the original purpose of applying the LLTM was primarily to generate test items with specified item difficulty, there are still many other potential applications, which may be of use for psychometric research on various testing conditions. This article provides some examples of such applications. The examples include (a) position effect of item presentation (in particular, learning and fatigue effects); (b) content-specific learning effect; (c) effect of speeded item presentation; and (d) effect of item response format.
Dissauer, G. (1979). Lern-bzw. Übungseffekte innerhalb von Testreihen [Learning and training effects within psychological tests]. Unpublished doctoral dissertation, University of Vienna, Austria.
2.
Fischer, G.H. (1972). Conditional maximum-likelihood estimations of item parameters for a linear logistic test model (Research Bulletin 9). Vienna: University of Vienna, Psychological Institute .
3.
Fischer, G.H. (1995). Derivations of the Rasch model. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models (pp. 15-38). New York: Springer.
4.
Fischer, G.H., & Pendl, P. (1980). Individualized testing on the basis of the dichotomous Rasch model. In L.J.D. van der Kamp, W. F. Langerak, & D.N.M. de Gruijter (Eds.), Psychometrics for educational debates (pp. 171-188). New York: John Wiley.
5.
Fischer, G.H., & Ponocny-Seliger, E. (1998). Structural Rasch modeling. Handbook of the usage of LPCM-WIN 1.0. Groningen: ProGAMMA.
6.
Formann, A.K., & Piswanger, K. (1979). Wiener Matrizen-Test (WMT) [Viennese Matrices] . Weinheim: Beltz.
7.
Gittler, G., & Wild, B. (1989). Der Einsatz des LLTM bei der Konstruktion eines Itempools fur das adaptive Testen [Using LLTM for adaptive test construction] . In K. D. Kubinger (Ed.), Moderne Testtheorie-Ein Abriß samt neuesten Beiträgen [Modern psychometrics-A brief survey with recent contributions] (pp. 115-139). Munich: PVU.
8.
Hahne, J. (1999). Lerneffekte innerhalb von Leistungstests [Learning effects on achievement tests]. Unpublished master's thesis, University of Vienna, Austria.
9.
Kempf, W. (1977). Dynamic models for the measurement of traits in social behavior. In W. Kempf & B. H. Repp (Eds.), Mathematical models for social psychology (pp. 14-58). Berne: Huber.
10.
Kubinger, K.D. (1979). Das Problemlöseverhalten bei der statistischen Auswertung psychologischer Experimente. Ein Beispiel hochschuldidaktischer Forschung [Problem-solving behavior in the case of statistical analyses of psychological experiments. An example of research on university didactics] . Zeitschrift für Experimentelle und Angewandte Psychologie , 26, 467-495.
11.
Kubinger, K.D. (1980). Die Bestimmung der Effektivität universitärer Lehre unter Verwendung des Linearen Logistischen Testmodells von Fischer. Neue Ergebnisse [The evaluation of effectiveness of university lecturing with the help of the linear logistic test model by Fischer. New results]. Archiv für Psychologie, 133, 69-79.
12.
Kubinger, K.D. (2005). Psychological test calibration using the Rasch model-Some critical suggestions on traditional approaches. International Journal of Testing, 5, 377-394.
13.
Kubinger, K.D. (2006). Psychologische Diagnostik-Theorie und Praxis psychologischen Diagnostizierens [Psychological assessment-Theory and application]. Gottingen: Hogrefe.
Mair, P., & Hatzinger, R. (2006). eRm: Extended Rasch modeling. R package version 0.9.5 [Computer software]. Retrieved from http://r-forge.r-project.org/
16.
Poinstingl, H., Mair, P., & Hatzinger, R. (2007). Manual zum Softwarepackage eRm (extended Rasch modeling). Anwendung des Rasch-Modells (1-PL Modell)-Deutsche Version [Manual of eRm. To apply the Rasch model-German version]. Lengerich : Pabst.
17.
Rasch, D., & Kubinger, K.D. (2006). Statistik für das Psychologiestudium-Mit Softwareunterstützung zur Planung und Auswertung von Untersuchungen sowie zu sequentiellen Verfahren [Statistics for the study of psychology-Software support for the planning of studies and sequential procedures]. Munich: Spektrum.
18.
Wilson, M., & de Boeck, P. (2004). Descriptive and explanatory item response models . In P. de Boeck & M. Wilson (Eds.), Explanatory item response models (pp. 43-74). New York: Springer .
