Abstract
This volume has been written with the view that there are several larger perspectives that can be used (a) to throw light on the sometimes confusing array of models and data that one can find in the area of item response modeling, (b) to explore different contexts of data analysis than the ‘test data’ context to which item response models are traditionally applied, and (c) to place these models in a larger statistical framework that will enable the reader to use a generalized statistical approach and also to take advantage of the flexibility of statistical computing packages that are now available.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agresti, A., Booth, J., Hobert, J.P., & Caffo, B. (2000). Random-effects modeling of categorical data. Sociological Methodology, 30, 27–80.
Baker, F.B. (1992) Item Response Theory: Parameter Estimation Techniques. New York: Marcel Dekker.
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F.M. Lord & M.R. Novick (Eds), Statistical Theories of Mental Test Scores (pp. 395–479). Reading, MA: Addison-Wesley.
Bock, R.D. (1997). A brief history of item response theory. Educational Measurement: Issues and Practice, 16, 21–33.
Bond, T., & Fox, C. (2001). Applying the Rasch Model: Fundamental Measurement in Human Sciences. Mahwah, NJ: Lawrence Erlbaum.
Boomsma, A., van Dijn, M.A.J., & Snijders, T.A.B. (Eds) (2001). Essays and Item Response Theory. New York: Springer.
Breslow, N.E., & Clayton, D.G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 9–25.
Camilli, G. (1994). Origin of the scaling constant d = 1.7 in item response theory. Journal of Educational and Behavioral Statistics, 19, 293–295.
Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/correlation Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Cronbach, L.J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 672–684.
Davidian, M., & Giltinan, D.M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall.
Davis, C.S. (2002). Statistical Methods for the Analysis of Repeated Measurements. New York: Springer.
Embretson, S.E. (1983). Construct validity: Construct representation versus nomothetic span. Psychological Bulletin, 93, 179–197.
Embretson, S.E. (Ed.) (1985). Test Design: Developments in Psychology and Psychometrics. New York: Academic Press.
Embretson, S.E., & Reise, S. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Lawrence Erlbaum.
Fahrmeir, L., & Tutz, G. (2001). Multivariate Statistical Modeling Based on Generalized Linear Models (2nd ed.). New York: Springer.
Fischer, G.H., & Molenaar, I. (Eds) (1995). Rasch Models Foundations, Recent Developments and Applications. New York: Springer.
Goldstein, H. (2003). Multilevel Statistical Models (3rd ed.). London: Arnold.
Hambleton, R.K., Swaminathan, H., & Rogers, H.J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage.
Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational and Behavioral Statistics, 38, 79–93.
Kirk, R.E. (1995). Experimental Design. Procedures for the Behavioral Sciences (3rd ed.). Pacific Grove, CA: Brooks/Cole.
Kreft, I., & de Leeuw, J. (1998). Introducing Multilevel Modeling. London: Sage.
Longford, N.T. (1993). Random Coefficient Models. London: Oxford University Press.
Lord, F.M., & Novick, M. (1968). Statistical Theories of Mental Test Scores. Reading, MA: Addison Wesley.
McCullagh, P., & Neider, J.A. (1989). Generalized Linear Models (2nd ed.). London: Chapman & Hall.
McCulloch, C.E., & Searle, S.R. (2001). Generalized, Linear, and Mixed Models. New York: Wiley.
McDonald, R.P. (1999). Test Theory. Hillsdale, NJ: Lawrence Erlbaum.
Mellenbergh, G. (1994). Generalized linear item response theory. Psychological Bulletin, 115, 300–307.
Moustaki, I., & Knott, M. (2000). Generalized latent trait models. Psy-chometrika, 65, 391–441.
Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage.
Rijmen, F., Tuerlinkx, F., De Boeck, P., & Kuppens (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8, 185–205.
SAS Institute (1999). SAS Online Doc (Version 8) (software manual on CD-Rom). Cary, NC: SAS Institute Inc.
Snijders, T., & Bosker, R. (1999). Multilevel Analysis. London: Sage.
Spiegelhalter, D., Thomas, A., Best, N. & Lunn, D. (2003). BUGS: Bayesian inference using Gibbs sampling. MRC Biostatistics Unit, Cambridge, England,www.mrc-bsu.cam.ac.uk/bugs/
Spielberger, C.D. (1988). State-Trait Anger Expression Inventory Research Edition. Professional Manual. Odessa, FL: Psychological Assessment Resources.
Spielberger, C.D., & Sydeman, S.J. (1994). State-trait anxiety inventory and state-trait anger expression inventory. In M.E. Maruish (Ed.), The Use of Psychological Tests for Treatment Planning and Outcome Assessment (pp. 292–321). Hillsdale, NJ: Lawrence Erlbaum.
Sternberg, R.J. (1977). Component processes in analogical reasoning. Psychological Review, 84, 353–378.
Sternberg, R.J. (1980). Representation and process in linear syllogistic reasoning. Journal of Experimental Psychology: General, 109, 119–159.
Thissen, D., & Orlando, M. (2001). Item response theory for items scored in two categories. In D. Thissen & H. Wainer (Eds), Test Scoring (pp. 73–140). Mahwah, NJ: Lawrence Erlbaum.
Thissen, D., & Wainer, H. (Eds) (2001). Test Scoring. Mahwah, NJ: Lawrence Erlbaum.
van der Linden, W.J., & Hambleton, R.K. (Eds) (1997). Handbook of Modern Item Response Theory. New York: Springer.
Vansteelandt, K. (2000). Formal models for contextualized personality psychology. Unpublished doctoral dissertation, K.U.Leuven, Belgium.
Verbeke, G., & Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
Vonesh, E.F., & Chinchilli, V.M. (1997). Linear and Nonlinear Models for the Analysis of Repeated Measurements. New York: Dekker.
Wallenstein, S. (1982). Regression models for repeated measurements. Biometrics, 38, 849–853.
Wilson, M. (2005). Constructing Measures: An Item Response Modeling Approach. Mahwah, NJ: Lawrence Erlbaum.
Wilson, M., & Adams, R.J. (1992). A multilevel perspective on the ‘two scientific disciplines of psychology’. Paper presented in a Symposium on the Two Scientific Disciplines of Psychology at the XXV International Congress of Psychology, Brussels.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer Science+Business Media New York
About this chapter
Cite this chapter
De Boeck, P., Wilson, M. (2004). A framework for item response models. In: De Boeck, P., Wilson, M. (eds) Explanatory Item Response Models. Statistics for Social Science and Public Policy. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3990-9_1
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3990-9_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2323-3
Online ISBN: 978-1-4757-3990-9
eBook Packages: Springer Book Archive