IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p6117-d569727.html
   My bibliography  Save this article

Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan

Author

Listed:
  • Melissa Gladstone

    (International Child Health and Neurodevelopmental Paediatrics, Department of Women and Children’s Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L12 2AP, UK)

  • Gillian Lancaster

    (School of Medicine, Keele University, Keele ST5 5BG, UK)

  • Gareth McCray

    (School of Medicine, Keele University, Keele ST5 5BG, UK)

  • Vanessa Cavallera

    (Brain Health Unit in Department of Mental Health and Substance Use, World Health Organization (WHO), 1202 Geneva, Switzerland)

  • Claudia R. L. Alves

    (Pediatrics Department, Medicine School, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 30130-100, Brazil)

  • Limbika Maliwichi

    (Department of Psychology, University of Malawi, Zomba P.O. Box 280, Malawi)

  • Muneera A. Rasheed

    (Centre for International Health, Department of Global Public Health and Primary Care, University of Bergen, 5007 Bergen, Norway)

  • Tarun Dua

    (Brain Health Unit in Department of Mental Health and Substance Use, World Health Organization (WHO), 1202 Geneva, Switzerland)

  • Magdalena Janus

    (Offord Centre for Child Studies, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON L8S 4KI, Canada
    Co-last authors.)

  • Patricia Kariger

    (Center for Effective Global Action (CEGA), School of Public Health, University of California, Berkeley, CA 94704, USA
    Co-last authors.)

Abstract

Background: The early childhood years provide an important window of opportunity to build strong foundations for future development. One impediment to global progress is a lack of population-based measurement tools to provide reliable estimates of developmental status. We aimed to field test and validate a newly created tool for this purpose. Methods: We assessed attainment of 121 Infant and Young Child Development (IYCD) items in 269 children aged 0–3 from Pakistan, Malawi and Brazil alongside socioeconomic status (SES), maternal educational, Family Care Indicators and anthropometry. Children born premature, malnourished or with neurodevelopmental problems were excluded. We assessed inter-rater and test-retest reliability as well as understandability of items. Each item was analyzed using logistic regression taking SES, anthropometry, gender and FCI as covariates. Consensus choice of final items depended on developmental trajectory, age of attainment, invariance, reliability and acceptability between countries. Results: The IYCD has 100 developmental items (40 gross/fine motor, 30 expressive/receptive language/cognitive, 20 socio-emotional and 10 behavior). Items were acceptable, performed well in cognitive testing, had good developmental trajectories and high reliability across countries. Development for Age (DAZ) scores showed very good known-groups validity. Conclusions: The IYCD is a simple-to-use caregiver report tool enabling population level assessment of child development for children aged 0–3 years which performs well across three countries on three continents to provide reliable estimates of young children’s developmental status.

Suggested Citation

  • Melissa Gladstone & Gillian Lancaster & Gareth McCray & Vanessa Cavallera & Claudia R. L. Alves & Limbika Maliwichi & Muneera A. Rasheed & Tarun Dua & Magdalena Janus & Patricia Kariger, 2021. "Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan," IJERPH, MDPI, vol. 18(11), pages 1-19, June.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:6117-:d:569727
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/6117/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/11/6117/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    2. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    3. Dubeck, Margaret M. & Gove, Amber, 2015. "The early grade reading assessment (EGRA): Its theoretical foundation, purpose, and limitations," International Journal of Educational Development, Elsevier, vol. 40(C), pages 315-322.
    4. Adam Wyse, 2010. "R.J. DE AYALA (2009) The Theory and Practice of Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 778-779, December.
    5. Robert Mislevy, 1984. "Estimating latent distributions," Psychometrika, Springer;The Psychometric Society, vol. 49(3), pages 359-381, September.
    6. T. J. Cole, 1988. "Fitting Smoothed Centile Curves to Reference Data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 385-406, May.
    Full references (including those not matched with items on IDEAS)

    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. Ping Chen & Chun Wang, 2021. "Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 299-326, March.
    2. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
    3. Yoav Bergner & Peter Halpin & Jill-Jênn Vie, 2022. "Multidimensional Item Response Theory in the Style of Collaborative Filtering," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 266-288, March.
    4. Chanjin Zheng & Shaoyang Guo & Justin L Kern, 2021. "Fast Bayesian Estimation for the Four-Parameter Logistic Model (4PLM)," SAGE Open, , vol. 11(4), pages 21582440211, October.
    5. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    6. P. Bentler, 1986. "Structural modeling and psychometrika: An historical perspective on growth and achievements," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 35-51, March.
    7. Harold Doran, 2023. "A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 37-69, February.
    8. Yang Liu & Jan Hannig, 2017. "Generalized Fiducial Inference for Logistic Graded Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1097-1125, December.
    9. Isabel Gallego‐Alvarez & Eduardo Ortas & José Luis Vicente‐Villardón & Igor Álvarez Etxeberria, 2017. "Institutional Constraints, Stakeholder Pressure and Corporate Environmental Reporting Policies," Business Strategy and the Environment, Wiley Blackwell, vol. 26(6), pages 807-825, September.
    10. Yang Liu & Ji Seung Yang, 2018. "Bootstrap-Calibrated Interval Estimates for Latent Variable Scores in Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 333-354, June.
    11. Björn Andersson & Marie Wiberg, 2017. "Item Response Theory Observed-Score Kernel Equating," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 48-66, March.
    12. Steven P. Reise & Han Du & Emily F. Wong & Anne S. Hubbard & Mark G. Haviland, 2021. "Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 800-824, September.
    13. Christopher J. Urban & Daniel J. Bauer, 2021. "A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 1-29, March.
    14. Robert Mislevy, 1991. "Randomization-based inference about latent variables from complex samples," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 177-196, June.
    15. Paul A. Jewsbury & Peter W. van Rijn, 2020. "IRT and MIRT Models for Item Parameter Estimation With Multidimensional Multistage Tests," Journal of Educational and Behavioral Statistics, , vol. 45(4), pages 383-402, August.
    16. Michela Battauz, 2023. "Testing for differences in chain equating," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 134-145, May.
    17. Robert Mislevy, 1986. "Bayes modal estimation in item response models," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 177-195, June.
    18. Azevedo, Caio L.N. & Andrade, Dalton F. & Fox, Jean-Paul, 2012. "A Bayesian generalized multiple group IRT model with model-fit assessment tools," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4399-4412.
    19. Zhehan Jiang & Jonathan Templin, 2019. "Gibbs Samplers for Logistic Item Response Models via the Pólya–Gamma Distribution: A Computationally Efficient Data-Augmentation Strategy," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 358-374, June.
    20. J. Ramsay, 1989. "A comparison of three simple test theory models," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 487-499, 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:gam:jijerp:v:18:y:2021:i:11:p:6117-:d:569727. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.