IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v65y2017icp48-59.html
   My bibliography  Save this article

Differentiation of cognitive abilities in the WAIS-IV at the item level

Author

Listed:
  • Molenaar, Dylan
  • Kő, Natasa
  • Rózsa, Sandor
  • Mészáros, Andrea

Abstract

It is known that studying the differentiation of cognitive abilities is associated with many methodological challenges. In the recent years, methods have been developed to address these challenges. However, these methods require that the item scores of an intelligence test are combined into a composite score which may affect the power to detect the differentiation effect or even produce spurious results. Therefore, in this study, an item level approach is presented that can be used to simultaneously test for ability differentiation, age differentiation, and age differentiation-dedifferentiation. The new method is investigated in two small simulation studies, and applied to the standardization data of the Hungarian WAIS-IV. Results indicate that the ability differentiation effect is consistently present in the items of the WAIS-IV while there is no consistent age differentiation and/or age differentiation-dedifferentiation effect.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intell:v:65:y:2017:i:c:p:48-59
    DOI: 10.1016/j.intell.2017.10.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289617301599
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2017.10.004?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. 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. Feraco, Tommaso & Cona, Giorgia, 2022. "Differentiation of general and specific abilities in intelligence. A bifactor study of age and gender differentiation in 8- to 19-year-olds," Intelligence, Elsevier, vol. 94(C).

    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. 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.
    2. Christoph T. Weiss, 2012. "Persistent Attitudes and Behaviors," "Marco Fanno" Working Papers 0143, Dipartimento di Scienze Economiche "Marco Fanno".
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Thum, Anna-Elisabeth, 2013. "Psychology in econometric models: conceptual and methodological foundations," MPRA Paper 52293, University Library of Munich, Germany.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Jochen Ranger & Kay Brauer, 2022. "On the Generalized S − X 2 –Test of Item Fit: Some Variants, Residuals, and a Graphical Visualization," Journal of Educational and Behavioral Statistics, , vol. 47(2), pages 202-230, April.
    15. Yu-Wei Chang & Nan-Jung Hsu & Rung-Ching Tsai, 2017. "Unifying Differential Item Functioning in Factor Analysis for Categorical Data Under a Discretization of a Normal Variant," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 382-406, June.
    16. Njål Foldnes & Steffen Grønneberg, 2019. "On Identification and Non-normal Simulation in Ordinal Covariance and Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 1000-1017, December.
    17. Steffen Grønneberg & Jonas Moss & Njål Foldnes, 2020. "Partial Identification of Latent Correlations with Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 1028-1051, December.
    18. Ulf Böckenholt & William Dillon, 1997. "Modeling within-subject dependencies in ordinal paired comparison data," Psychometrika, Springer;The Psychometric Society, vol. 62(3), pages 411-434, September.
    19. Lippman, Sheri A. & Neilands, Torsten B. & Leslie, Hannah H. & Maman, Suzanne & MacPhail, Catherine & Twine, Rhian & Peacock, Dean & Kahn, Kathleen & Pettifor, Audrey, 2016. "Development, validation, and performance of a scale to measure community mobilization," Social Science & Medicine, Elsevier, vol. 157(C), pages 127-137.
    20. Dylan Molenaar & Conor Dolan & Paul Boeck, 2012. "The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 455-478, July.

    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:65:y:2017:i:c:p:48-59. 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.