IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v56y1991i3p495-515.html
   My bibliography  Save this article

A multidimensional latent trait model for measuring learning and change

Author

Listed:
  • Susan Embretson

Abstract

No abstract is available for this item.

Suggested Citation

  • Susan Embretson, 1991. "A multidimensional latent trait model for measuring learning and change," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 495-515, September.
    • Handle: RePEc:spr:psycho:v:56:y:1991:i:3:p:495-515
    DOI: 10.1007/BF02294487
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02294487
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02294487?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Erling Andersen, 1985. "Estimating latent correlations between repeated testings," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 3-16, March.
    2. Yoshio Takane & Jan Leeuw, 1987. "On the relationship between item response theory and factor analysis of discretized variables," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 393-408, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shiyu Wang & Yan Yang & Steven Andrew Culpepper & Jeffrey A. Douglas, 2018. "Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 57-87, February.
    2. Susan Embretson, 1999. "Generating items during testing: Psychometric issues and models," Psychometrika, Springer;The Psychometric Society, vol. 64(4), pages 407-433, December.
    3. Matthias Davier & Xueli Xu & Claus Carstensen, 2011. "Measuring Growth in a Longitudinal Large-Scale Assessment with a General Latent Variable Model," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 318-336, April.
    4. Michela Gnaldi, 2017. "A multidimensional IRT approach for dimensionality assessment of standardised students’ tests in mathematics," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1167-1182, May.
    5. Gerhard Fischer, 1995. "Some neglected problems in IRT," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 459-487, December.
    6. Chun Wang & Steven W. Nydick, 2020. "On Longitudinal Item Response Theory Models: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 339-368, June.
    7. Minjeong Jeon & Sophia Rabe-Hesketh, 2016. "An autoregressive growth model for longitudinal item analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 830-850, September.
    8. Sun-Joo Cho & Amanda P. Goodwin, 2017. "Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 846-870, September.
    9. Jeffrey Rouder & Jordan Province & Richard Morey & Pablo Gomez & Andrew Heathcote, 2015. "The Lognormal Race: A Cognitive-Process Model of Choice and Latency with Desirable Psychometric Properties," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 491-513, June.
    10. Yang Liu & Xiaojing Wang, 2020. "Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 274-296, June.
    11. José H. Lozano & Javier Revuelta, 2021. "A Bayesian Generalized Explanatory Item Response Model to Account for Learning During the Test," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 994-1015, December.
    12. Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    13. Zhengguo Gu & Wilco H. M. Emons & Klaas Sijtsma, 2018. "Review of Issues About Classical Change Scores: A Multilevel Modeling Perspective on Some Enduring Beliefs," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 674-695, September.
    14. Olsbjerg, Maja & Christensen, Karl Bang, 2015. "%lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(c02).
    15. Li Cai & Carrie R. Houts, 2021. "Longitudinal Analysis of Patient-Reported Outcomes in Clinical Trials: Applications of Multilevel and Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 754-777, September.
    16. Matthew J. Madison & Laine P. Bradshaw, 2018. "Assessing Growth in a Diagnostic Classification Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 963-990, December.
    17. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chun Wang & Steven W. Nydick, 2020. "On Longitudinal Item Response Theory Models: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 339-368, June.
    2. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    3. Christoph T. Weiss, 2012. "Persistent Attitudes and Behaviors," "Marco Fanno" Working Papers 0143, Dipartimento di Scienze Economiche "Marco Fanno".
    4. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    5. Molenaar, Dylan & Kő, Natasa & Rózsa, Sandor & Mészáros, Andrea, 2017. "Differentiation of cognitive abilities in the WAIS-IV at the item level," Intelligence, Elsevier, vol. 65(C), pages 48-59.
    6. Yang Yixin & Lü Xin & Ma Jian & Qiao Han, 2014. "A Robust Factor Analysis Model for Dichotomous Data," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 437-450, October.
    7. Yong Luo & Khaleel Al-Harbi, 2016. "The Utility of the Bifactor Method for Unidimensionality Assessment When Other Methods Disagree," SAGE Open, , vol. 6(4), pages 21582440166, October.
    8. Thum, Anna-Elisabeth, 2013. "Psychology in econometric models: conceptual and methodological foundations," MPRA Paper 52293, University Library of Munich, Germany.
    9. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    10. Edward Haertel, 1990. "Continuous and discrete latent structure models for item response data," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 477-494, September.
    11. Sun-Joo Cho & Allan Cohen & Brian Bottge, 2013. "Detecting Intervention Effects Using a Multilevel Latent Transition Analysis with a Mixture IRT Model," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 576-600, July.
    12. Royce Anders & William Batchelder, 2015. "Cultural Consensus Theory for the Ordinal Data Case," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 151-181, March.
    13. Yang Liu, 2020. "A Riemannian Optimization Algorithm for Joint Maximum Likelihood Estimation of High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 439-468, June.
    14. Robert Gibbons & R. Bock, 1987. "Trend in correlated proportions," Psychometrika, Springer;The Psychometric Society, vol. 52(1), pages 113-124, March.
    15. Anna Brown, 2016. "Item Response Models for Forced-Choice Questionnaires: A Common Framework," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 135-160, March.
    16. Alexander Robitzsch, 2024. "A Comparison of Limited Information Estimation Methods for the Two-Parameter Normal-Ogive Model with Locally Dependent Items," Stats, MDPI, vol. 7(3), pages 1-16, June.
    17. Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    18. Mayssun El-Attar, 2013. "Trust, child care technology choice and female labor force participation," Review of Economics of the Household, Springer, vol. 11(4), pages 507-544, December.
    19. Rolf Langeheine & Jeroen Pannekoek & Frank Van De Pol, 1996. "Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis," Sociological Methods & Research, , vol. 24(4), pages 492-516, May.
    20. Albert Yu & Jeffrey A. Douglas, 2023. "IRT Models for Learning With Item-Specific Learning Parameters," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 866-888, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:psycho:v:56:y:1991:i:3:p:495-515. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.