IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v64y1999i4p407-433.html
   My bibliography  Save this article

Generating items during testing: Psychometric issues and models

Author

Listed:
  • Susan Embretson

Abstract

No abstract is available for this item.

Suggested Citation

  • Susan Embretson, 1999. "Generating items during testing: Psychometric issues and models," Psychometrika, Springer;The Psychometric Society, vol. 64(4), pages 407-433, December.
  • Handle: RePEc:spr:psycho:v:64:y:1999:i:4:p:407-433
    DOI: 10.1007/BF02294564
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02294564
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/BF02294564?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. Robert K. Tsutakawa & Michael J. Soltys, 1988. "Approximation for Bayesian Ability Estimation," Journal of Educational and Behavioral Statistics, , vol. 13(2), pages 117-130, June.
    2. 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.
    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. Susan Embretson & Xiangdong Yang, 2013. "A Multicomponent Latent Trait Model for Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 14-36, January.
    2. O.O Adedoyin & T Mokobi, 2013. "Using IRT Psychometric Analysis in Examining The Quality of Junior Certificate Mathematics Multiple Choice Examination Test Items," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 3(4), pages 992-1011, April.
    3. Minjeong Jeon & Sophia Rabe-Hesketh, 2012. "Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures," Journal of Educational and Behavioral Statistics, , vol. 37(4), pages 518-542, August.
    4. Javier Revuelta, 2009. "Identifiability and Equivalence of GLLIRM Models," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 257-272, June.
    5. Byron Gajewski & Larry Price & Valorie Coffland & Diane Boyle & Marjorie Bott, 2013. "Integrated analysis of content and construct validity of psychometric instruments," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 57-78, January.
    6. Hanneke Geerlings & Cees Glas & Wim Linden, 2011. "Modeling Rule-Based Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 337-359, April.
    7. Sun-Joo Cho & Paul Boeck & Susan Embretson & Sophia Rabe-Hesketh, 2014. "Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 84-104, January.
    8. Javier Revuelta, 2008. "The generalized Logit-Linear Item Response Model for Binary-Designed Items," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 385-405, September.
    9. Paul Boeck, 2008. "Random Item IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 533-559, December.

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. Robert Mislevy & Norman Verhelst, 1990. "Modeling item responses when different subjects employ different solution strategies," Psychometrika, Springer;The Psychometric Society, vol. 55(2), pages 195-215, June.
    10. 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.
    11. Gerhard Fischer, 1995. "Some neglected problems in IRT," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 459-487, December.
    12. 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.
    13. repec:jss:jstsof:25:i08 is not listed on IDEAS
    14. 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.
    15. 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.
    16. 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.
    17. 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).
    18. 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.

    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:64:y:1999:i:4:p:407-433. 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.