Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Selection of Item Response Model by Genetic Algorithm

Selection of Item Response Model by Genetic Algorithm A method of selecting an item response model with a genetic algorithm is proposed, where a model indicator variable is regarded as a chromosome to distinguish other individuals. This scheme enables a model for each item to be selected automatically. The genetic algorithm with the set of techniques that is implemented here is called the simple genetic algorithm, and the results obtained from simulation studies were satisfactory. An issue with the graded response model and the generalized partial credit model was examined using simulation studies and numerical examples was to find which was the more useful of these two prevailing kinds. The results obtained from simulation studies proved the graded response model fit the data more flexibly, since it fit the data generated under the generalized partial credit model more frequently than for the opposite case. However, the generalized partial credit model was more suitable for two real data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behaviormetrika Springer Journals

Selection of Item Response Model by Genetic Algorithm

Shojima, Kojiro
Behaviormetrika , Volume 34 (1) – Jan 1, 2007
Download PDF
26 pages

Loading next page...
 
/lp/springer-journals/selection-of-item-response-model-by-genetic-algorithm-AbOpEaLRza
Publisher
Springer Journals
Copyright
Copyright © 2007 by The Behaviormetric Society
Subject
Statistics; Statistical Theory and Methods; Statistics for Business, Management, Economics, Finance, Insurance; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
ISSN
0385-7417
eISSN
1349-6964
DOI
10.2333/bhmk.34.1
Publisher site
See Article on Publisher Site

Abstract

A method of selecting an item response model with a genetic algorithm is proposed, where a model indicator variable is regarded as a chromosome to distinguish other individuals. This scheme enables a model for each item to be selected automatically. The genetic algorithm with the set of techniques that is implemented here is called the simple genetic algorithm, and the results obtained from simulation studies were satisfactory. An issue with the graded response model and the generalized partial credit model was examined using simulation studies and numerical examples was to find which was the more useful of these two prevailing kinds. The results obtained from simulation studies proved the graded response model fit the data more flexibly, since it fit the data generated under the generalized partial credit model more frequently than for the opposite case. However, the generalized partial credit model was more suitable for two real data sets.

Journal

Behaviormetrika – Springer Journals

Published: Jan 1, 2007

References

  • A new look at the statistical model identification
    Akaike, H
  • A rating formulation for ordered response categories
    Andrich, D
  • Item Response Theory: Parameter Estimation Techniques
    Baker, FB
  • Proceedings of the 1st International Conference on Genetic Algorithms and Their Applications
    Baker, JE
  • A comparison of graded response and Rasch patial credit models with subjective well-being
    Baker, JG; Rounds, JB; Zevon, MA
  • Estimating item parameters and latent ability when responses are scored in two or more ominal categories
    Bock, RD
  • Handbook of Modern Item Response Theory
    Bock, RD
  • Marginal maximum likelihood estimation of item parameters: An application of an EM algorithm
    Bock, RD; Aitkin, M
  • Fitting a response model for n dichotomously scored items
    Bock, RD; Lieberman, M
  • Model selection and Akaike’s information criteria (AIC)
    Bozdogan, H
  • Coefficient alpha and the internal structure of tests
    Cronbach, LJ
  • Proceedings of the 3rd International Conference on Genetic Algorithms
    Davis, L
  • A comparison of the partial credit model and graded response models in computerized adaptive testing
    Ayala, RJ; Dodd, BG; Koch, WR
  • Maximum likelihood from incomplete data via the EM algorithm (with discussion)
    Dempster, AP; Laird, NM; Rubin, DB
  • Item selection using a genetic algorithm
    Fujimori, S
  • Genetic algorithms in search, optimization, and machine learning
    Goldberg, DE
  • Item Response Theory
    Hambleton, RK; Swaminathan, H
  • Stochastic ordering using the latent trait and the sum score in polytomous IRT models
    Hemker, BT; Sijtsma, K; Molenaar, IW; Junker, BW
  • Adaptation in Natural and Artificial Systems
    Holland, H
  • Paper presented at the Annual Meeting of the National Council on Measurement in Education
    Jiang, H; Tang, KL
  • LISREL 8: Structural Equation Modeling with the SIM-PLIS Command Language
    Jöreskog, KG; Sörbom, D
  • On information and sufficiency
    Kullback, S; Leibler, RA
  • Applications of Item Response Theory to Practical Testing Problems
    Lord, FM
  • New Developments and Techniques in Structural Equation Modeling
    Marcoulides, GA; Drezner, Z
  • A Rasch model for partial credit scoring
    Masters, GN
  • A comparison of several goodness-of-fit statistics
    McKinley, RL; Mills, CN
  • Bayes modal estimation in item response models
    Mislevy, RJ
  • A generalized partial credit model: Application of an EM algorithm
    Muraki, E
  • Handbook of Modern Item Response Theory
    Muraki, E
  • A factor analysis model for a mixture of various types of variables
    Ogasawara, H
  • Homogeneous case of the continuous response model
    Samejima, F
  • Normal ogive model on the continuous response level in the multidimensional latent space
    Samejima, F
  • Evaluation of mathematical models for ordered polychotmous responses
    Samejima, F
  • Estimating the dimension of a model
    Schwartz, G
  • Evolution and optimum seeking
    Schwefel, HP
  • Item parameter estimation when a test contains different item response models
    Shojima, K; Toyoda, H
  • Adaptive probabilities of crossover and mutation in genetic algorithms
    Srinivas, M; Patnaik, LM
  • Proceedings of the 3rd International Conference on Genetic Algorithms
    Syswerda, G
  • Tools for statistical inference: Methods for the exploration of posterior distributions and likelihood functions
    Tanner, MA
  • MULTILOG User’s Guide-Version 6
    Thissen, D
  • A taxonomy of item response models
    Thissen, D; Steinberg, L
  • Estimation of two-parameter logistic item response curves
    Tsutakawa, RK
  • Bayesian estimation of item response curves
    Tsutakawa, RK; Lin, HY
  • Sequential item response models with an ordered response
    Tutz, G
  • Relationships and properties of polytomous item response theory models
    Ark, LA
  • Computerized Adaptive Testing: Theory and Practice
  • Handbook of Modern Item Response Theory
  • Handbook of Modern Item Response Theory
    Verhelst, ND; Glas, CAW; Vries, HH
  • The Simple Genetic Algorithm: Foundations and Theory
    Vose, MD
  • Item parameter estimation for a continuous response model using an EM algorithm
    Wang, T; Zeng, L
  • Comparison of selection methods for evolutionary optimization
    Zhang, B-T; Kim, J-J