Setting the Scope for Early Child Development Instrument (EDI): A Psychometric Re-examination of the Tool with Alberta Data
- 258 Downloads
The early child development instrument (EDI) has become an important tool for screening children at entry into kindergarten in order to assess their development in five areas. After more than 10 years of its initiation and widespread use, it’s time to rethink about the tool’s theoretical and empirical basis. In this study, we applied factor analytic methods to re-examine the factorial structure and test the goodness of fit of several alternative models, using the 2011 EDI data for the province of Alberta. We also analyzed all 103 items from a classical test theory perspective to investigate the relevance of items to the definition of vulnerability postulated by the EDI developers. Changes need to be made to accommodate alternative factorial structure and, if possible a short form of EDI is to be developed, as there is evidence that reliability is high even with fewer items. A number of questions are addressed, aligned with specific objectives.
KeywordsEarly child development Psychometric analysis Confirmatory factor analysis Item analysis Alberta
- Allen, M. J., & Yen, W. M. (1979). Introduction to measurement theory. Monterey, Calif: Brooks/Cole Pub. Co.Google Scholar
- Andrich, D., & Styles, I. (2004). Final report on the psychometric analysis of the early development instrument (EDI) using the rasch model: A technical paper commissioned for the development of the Australian early development instrument (AEDI). Royal Children’s Hospital.Google Scholar
- Commonwealth of Australia. (2013). Australian early development index 2012: Summary report (updated November 2013). Canberra: Department of Education.Google Scholar
- Fernald, L. C. H., Kariger, P., Engle, P., & Raikes, A. (2009). Examining early child development in low income countries: A toolkit for the assessment of children in the first five years of life. Washington, DC: The World Bank.Google Scholar
- Fletcher, T. D. (2010). Psychometric: Applied psychometric theory. R package version 2.2. http://CRAN.R-project.org/package=psychometric.
- Halfon, N., Russ, S., Oberklaid, F., Bertrand, J., & Eisenstadt, N. (2009). An International comparison of early childhood initiatives: From services to systems. Commonwealth Funnd pub. No. 1241 Retrieved June 18, 2013. http://www.commonwealthfund.org/Publications/Fund-Reports/2009/May/An-International-Compar.
- Janus, M., Duku, E., & Stat, P. (2005a). Development of the short early development instrument (S-EDI). Hamilton, CANADA: Offord Centre for Child Studies, Department of Psychiatry and Behavioural, Neurosciences, McMaster University.Google Scholar
- Janus, M., Walsh, C., & Duku, E. (2005b). Early development instrument: Factor structure, sub-domains and multiple challenge index. Department of Psychiatry and Biobehavioural Sciences, McMaster University, Annual Research Day.Google Scholar
- Janus, M., Willms, J. D., & Offord, D. R. (2000). Psychometric properties of the early development instrument (EDI): A teacher-completed measure of children’s readiness to learn at school entry. Unpublished manuscript.Google Scholar
- Jöreskog, K. G. (2005). Structural equation modeling with ordinal variables using LISREL (pp. 2002–2005). Lincolnwood, IL: Technical report, Scientific Software International, Inc.Google Scholar
- Krishnan, V. (2011). A comparison of principal components analysis and factor analysis for uncovering the early development instrument (EDI) domains. Community-University Partnership (CUP), Faculty of Extension, University of Alberta, Edmonton, Alberta, Canada.Google Scholar
- Krishnan, V. (2013). The early child development instrument (EDI): An item analysis using classical test theory (CTT) on Alberta’s data. Community-University Partnership (CUP), Faculty of Extension, University of Alberta, Edmonton, Alberta, Canada.Google Scholar
- Magnusson, D. (1967). Test theory. Reading, Mass: Addison-Wesley Pub. Co.Google Scholar
- Muthén, B., Du Toit, S. H., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Psychometrika, 75, 1–45.Google Scholar
- Muthén, L. K., & Muthén, B. O. (2012a). Mplus 7.2 for windows. Los Angeles, CA: Muthén & Muthén.Google Scholar
- Muthén, L. K., & Muthén, B. O. (2012b). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
- Offord Centre for Child Studies. (2013). Retrieved June 18, 2013. http://www.offordcentre.com/readiness/bibliography_abstracts.html.
- R Core Team. (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/.