IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v82y2020ics0160289620300465.html
   My bibliography  Save this article

Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach

Author

Listed:
  • Lozano, José H.
  • Revuelta, Javier

Abstract

The present study aimed to investigate practice effects associated with the abstract rules involved in the Raven's Advanced Progressive Matrices (RAPM) under standard administration conditions. To that end, a linear logistic test modeling approach was used in combination with Carpenter, Just, and Shell's (1990) taxonomy of rules. Several operation-specific learning models were used in order to test different contingent and non-contingent learning hypotheses. The models were fitted to a sample of responses from 293 participants to Sets I and II of the RAPM. A Bayesian framework was adopted for model estimation and evaluation. The perceptual variables involved in the items were included in the analyses in order to control their influence on performance on the RAPM. The results did not provide evidence of rule learning during the RAPM. Instead, they suggested the existence of fatigue effects associated with each of the rules. Interestingly, the results revealed the existence of learning effects associated with the items' perceptual properties.

Suggested Citation

  • Lozano, José H. & Revuelta, Javier, 2020. "Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach," Intelligence, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:intell:v:82:y:2020:i:c:s0160289620300465
    DOI: 10.1016/j.intell.2020.101468
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289620300465
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2020.101468?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. William Mollenkopf, 1950. "An experimental study of the effects on item-analysis data of changing item placement and test time limit," Psychometrika, Springer;The Psychometric Society, vol. 15(3), pages 291-315, 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. 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.

    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. Matthias Trendtel & Alexander Robitzsch, 2021. "A Bayesian Item Response Model for Examining Item Position Effects in Complex Survey Data," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 34-57, February.
    2. Pushkin Kachroo & Sheen Kachen, 2018. "Item placement for questionnaire design for optimal reliability," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(4), pages 120-126, 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:eee:intell:v:82:y:2020:i:c:s0160289620300465. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    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.