Item Response Theory

During the past 30 years or so, a new theoretical basis for educational and psychological testing and measurement has emerged. It has been variously referred to as latent trait theory, item characteristic curve theory, and, more recently, item response theory (IRT). Although this new test theory holds considerable promise as a successor to classical test theory, it has been underutilized by test practitioners. One important reason for this underutilization is that many test developers have not had sufficient time to devote to the study of the technical and mathematical intricacies involved in this new test theory and its mathematical models. This chapter is intended as an overview of IRT for individuals with some background in the basic methods of classical test theory. Readers are referred to Hambleton (1989) and Hambleton and Swaminathan (1985) for other overviews of IRT.

[1]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[2]  C. I. Mosier Psychophysics and mental test theory: Fundamental postulates and elementary theorems. , 1940 .

[3]  L. Guttman A basis for scaling qualitative data. , 1944 .

[4]  R. Cattell,et al.  Personality and creativity in artists and writers. , 1958, Journal of clinical psychology.

[5]  Melvin R. Novick,et al.  Some latent train models and their use in inferring an examinee's ability , 1966 .

[6]  M. R. Novick,et al.  Statistical Theories of Mental Test Scores. , 1971 .

[7]  David Lindley,et al.  THE ESTIMATION OF MANY PARAMETERS , 1970 .

[8]  R. Darrell Bock,et al.  Fitting a response model forn dichotomously scored items , 1970 .

[9]  D. Lindley,et al.  Bayes Estimates for the Linear Model , 1972 .

[10]  R. Darrell Bock,et al.  Estimating item parameters and latent ability when responses are scored in two or more nominal categories , 1972 .

[11]  R. Hambleton,et al.  ANALYSIS OF EMPIRICAL DATA USING TWO LOGISTIC LATENT TRAIT MODELS , 1973 .

[12]  F. Samejima Homogeneous case of the continuous response model , 1973 .

[13]  A. O'Hagan,et al.  On posterior joint and marginal modes , 1976 .

[14]  V. Avanesov The Problem of Psychological Tests , 1980 .

[15]  F. Lord Applications of Item Response Theory To Practical Testing Problems , 1980 .

[16]  M. Degroot,et al.  Information about Hyperparameters in Hierarchical Models , 1981 .

[17]  Georg Rasch,et al.  Probabilistic Models for Some Intelligence and Attainment Tests , 1981, The SAGE Encyclopedia of Research Design.

[18]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm , 1981 .

[19]  R. D. Bock,et al.  Adaptive EAP Estimation of Ability in a Microcomputer Environment , 1982 .

[20]  H. Swaminathan,et al.  Bayesian Estimation in the Rasch Model , 1982 .

[21]  Martha L. Stocking,et al.  Developing a Common Metric in Item Response Theory , 1982 .

[22]  Fritz Drasgow,et al.  Item response theory : application to psychological measurement , 1983 .

[23]  I. J. Good,et al.  The Robustness of a Hierarchical Model for Multinomials and Contingency Tables , 1983 .

[24]  Prem K. Goel,et al.  Information Measures and Bayesian Hierarchical Models , 1983 .

[25]  David J. Weiss,et al.  APPLICATION OF COMPUTERIZED ADAPTIVE TESTING TO EDUCATIONAL PROBLEMS , 1984 .

[26]  R. Hambleton,et al.  Item Response Theory , 1984, The History of Educational Measurement.

[27]  Mark D. Reckase,et al.  The Difficulty of Test Items That Measure More Than One Ability , 1985 .

[28]  H. Swaminathan,et al.  Bayesian estimation in the two-parameter logistic model , 1985 .

[29]  Melvin R. Novick,et al.  Bayesian Inference and Diagnostics for the Three Parameter Logistic Model. , 1985 .

[30]  W. D. Linden The changing conception of measurement in education and psychology , 1986 .

[31]  Frederic M. Lord MAXIMUM LIKELIHOOD AND BAYESIAN PARAMETER ESTIMATION IN ITEM RESPONSE THEORY , 1986 .

[32]  Robert J. Mislevy,et al.  Bayes modal estimation in item response models , 1986 .

[33]  Janice A. Gifford,et al.  Bayesian estimation in the three-parameter logistic model , 1986 .

[34]  Hsin Ying Lin,et al.  Bayesian estimation of item response curves , 1986 .

[35]  C. David Vale,et al.  Linking Item Parameters Onto a Common Scale , 1986 .

[36]  L. Crocker,et al.  Introduction to Classical and Modern Test Theory , 1986 .

[37]  Frank B. Baker,et al.  Methodology Review: Item Parameter Estimation Under the One-, Two-, and Three-Parameter Logistic Models , 1987 .

[38]  Wendy M. Yen,et al.  A comparison of the efficiency and accuracy of BILOG and LOGIST , 1987 .

[39]  Michael R. Harwell,et al.  Item Parameter Estimation Via Marginal Maximum Likelihood and an EM Algorithm: A Didactic , 1988 .

[40]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[41]  Robert J. Mislevy,et al.  A Consumer's Guide to LOGIST and BILOG , 1987 .

[42]  Deborah J. Harris Comparison of 1-, 2-, and 3-Parameter IRT Models , 1989 .

[43]  W. D. Linden,et al.  A maximin model for IRT-based test design with practical constraints , 1989 .

[44]  H. Swaminathan,et al.  Bias and the Effect of Priors in Bayesian Estimation of Parameters of Item Response Models , 1990 .

[45]  Tae-Je Seong Sensitivity of Marginal Maximum Likelihood Estimation of Item and Ability Parameters to the Characteristics of the Prior Ability Distributions , 1990 .

[46]  Robert J. Mislevy,et al.  BILOG 3 : item analysis and test scoring with binary logistic models , 1990 .

[47]  F. Baker,et al.  A Comparison of Two Procedures for Computing IRT Equating Coefficients , 1991 .

[48]  Michael R. Harwell,et al.  The Use of Prior Distributions in Marginalized Bayesian Item Parameter Estimation: A Didactic , 1991 .