Abstract
Every test or questionnaire, constructed with either classical test theory or modern IRT, is ultimately meant as a tool to do further research. Most often the test is used to evaluate treatments or therapies or to see whether the abilities underlying the test are associated to other constructs, and sometimes test scores are used to make individual predictions or decisions. Whatever the ultimate goal, the immediate interest is usually to estimate correlations between the abilities underlying the test and other important variables.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abramowitz, M. and Stegun, I.A. (1965). Handbook of Mathematical Functions. New York: Dover Publications.
Albert, A. and Anderson, J.A. (1984). On the existence of maximum like- lihood estimates in logistic regression models. Biometrika 71, 1–10.
Andersen, E.B. (1973). Conditional inference for multiple choice questionnaires. British Journal of Mathematical and Statistical Psychology 26, 31–44.
Anderson, J.A. (1984). Regression and ordered categorical variables. Journal of the Royal Statistical Society, Series B 46, 1–30.
Diggle, P. and Kenward, M.G. (1994). Informative drop-out in longitudinal data analysis. Applied Statistics 43, 49–94.
Dixon, W.J., Brown, M.B., Engelman, L., and Jennrich, R.I. (1990). BMDP Statistical Software Manual, Vol. 2. Berkeley: University of California Press.
Egret (1985). Egret Reference Manual. Seattle: Statistics and Epidemiology Research Corporation and Cytel Software Corporation.
Fischer, G.H. (1981). On the existence and uniqueness of maximum likelihood estimates in the Rasch model. Psychometrika 46, 59–77.
Fischer, G.H. (1983). Logistic latent trait models with linear constraints. Psychometrika 48, 3–26.
Fischer, G.H. (1990, September). On the existence and uniqueness of a CML solution in the polytomous Rasch model. Paper presented at the 21st Meeting of the European Mathematical Psychology Group, Bristol.
Fischer, G.H. and Parzer, P. (1991). An extension of the rating scale model with an application to the measurement of change. Psychometrika 56, 637–651.
Glas, C.A.W. (1988). The derivation of some tests for the Rasch model from the multinomial distribution. Psychometrika 53, 525–546.
Glas, C.A.W. (1989). Contributions to Estimating and Testing Rasch Models. Unpublished doctoral dissertation, University of Twente, Enschede.
Glas, C.A.W. and Verhelst, N.D. (1989). Extensions of the partial credit model. Psychometrika 54, 635–659.
Glas, C. and Ellis, J. (1993). RSP User’s Manual. Groningen, the Netherlands: I. E.C. Progamma.
Hoijtink, H. (1993). Linear Models with a Latent Dependent Variable: Non-Parametric Error Term Density Functions. Manuscript submitted for publication.
Isaacson, E. and Keller, H.B. (1966). Analysis of Numerical Methods. New York: Wiley.
Liang, K.-Y., Zeger, S.L., and Qaqish, B. (1992). Multivariate regression analyses for categorical data. Journal of the Royal Statistical Society, Series B 54, 3–40.
Little, R.J.A. and Rubin, D.B. (1987). Statistical Analysis with Missing Data. New York: Wiley.
Mislevy, R.J. (1987). Exploiting auxiliary information about examinees in the estimation of item parameters. Applied Psychological Measurement 11, 81–91.
Molenaar, I.W. and Verhelst, N.D. (1988). Logit based parameter estimation in the Rasch model. Statistica Neerlandica 42, 273–296.
Prentice, R.L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics 44, 1033–1048.
Rao, C.R. (1973). Linear Statistical Inference and Its Applications. New York: Wiley.
van Houwelingen, J.C., le Cessie, S., and Zwinderman, A.H. (1991, July). Modelling Dependency for binary random variables. Paper presented at the Sixth Workshop on Statistical Modelling, Utrecht, The Netherlands.
Verhelst, N.D. and Eggen, T.J.H.M. (1989). Psychometrische en Statistische Aspecten van Peilingsonderzoek (PPON rapport 4) (in Dutch). Arnhem: Cito.
Verhelst, N.D. and Glas, C.A.W. (1993). A dynamic generalization of the Rasch model. Psychometrika 58, 395–416.
Zeger, S.L. and Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42, 121–130.
Zeger, S.L., Liang, K.-Y., and Albert, P.S. (1988). Models for longitudinal data: a generalized estimation equation approach. Biometrics 44, 1049 1060.
Zwinderman, A.H. (1991). A generalized Rasch model for manifest predictors. Psychometrika 56, 589–600.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer Science+Business Media New York
About this chapter
Cite this chapter
Zwinderman, A.H. (1997). Response Models with Manifest Predictors. In: van der Linden, W.J., Hambleton, R.K. (eds) Handbook of Modern Item Response Theory. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2691-6_14
Download citation
DOI: https://doi.org/10.1007/978-1-4757-2691-6_14
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2849-8
Online ISBN: 978-1-4757-2691-6
eBook Packages: Springer Book Archive