Quality of Performance and Change-Sensitive ... - Nelson Education

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what magnitude? Aylward (2002; 1997) suggests that the degree of correction to accurately predictoutcomes of premature infants will require an algorithm based on the age of the infant, backgroundrisk factors, and, importantly, the domain of cognitive, motor, or language behavior being assessed.Recent research using the new MP-R will be reviewed to show progress made in examining theage correction dilemma.SUMMARYMore than a decade of research has been conducted to study and develop instrumentssensitive to developmental growth and decline in cognitive functioning. Many applications toimportant assessment problems in education, psychology, medicine, special education, and infantevaluation have been discussed. Many challenges remain for future researchers, including continuingstudies using true longitudinal research designs, experimental studies of premature infants “catchingup,” and studies of early-emerging cognitive abilities such as fluid reasoning. Possible technologydevelopments in the future may be promising, such as use of personal (“palm”) data-collectiondevices to test children more frequently across time. Finally, one of the promising advantages ofchange-sensitive assessment is the ability to show parents of children with special needs that theirchildren are making progress predicted by the patterns of documented growth curves.ReferencesAylward, G.P. (2002). Methodological issues in outcome studies of at-risk infants. Journal of Pediatric Psychology,27(1), 37-45.Aylward, G. P. (1997). Conceptual issues in developmental screening and assessment. Journal of Developmental andBehavioral Pediatrics, 18(5), 340-349.Bauer, D. J., & Estell, D. B. (2001). Cluster analysis of developmental profiles: Relations between trajectories ofaggression and popularity over adolescence. In L. Collins & A. Sayer (Eds.), New methods for the analysisof change. (pp. 385-387) Washington, DC: American Psychological Association.Carroll, J.B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York: Cambridge UniversityPress.Collins, L. M., & Sayer, A. G. (Eds.) (2001). New methods for the analysis of change. Washington, DC: AmericanPsychological Association.Embretson, S.E. (1996). The new rules of measurement. Psychological Assessment, 8, 341-349.Flanagan, D. (2000). Wechsler-based CHC cross-battery assessment and reading achievement. School PsychologyQuarterly, 15, 295-329.Horn, J.L., & Cattell, R.B. (1966). Refinement and test of the theory of fluid and crystallized intelligence. Journal ofEducational Psychology, 57, 253-270.Gulliksen, H. (1950). Theory of mental tests. New York: Wiley.Jackson, D. F. (1990). The use of sequence analysis algorithms in research on computer-assisted problemsolving strategies. Paper presented at the meetings of the American Educational Research Association,Boston, April.Krishnakumar, A., & Black, M. (2001). Estimating cognitive growth curves from environmental risk factors: Mediatingthe role of parenting and child factors. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change.(pp. 414-415) Washington, DC: American Psychological Association.Lems, W., Hopkins, B., & Samson, J. F. (1993). Mental and motor development in preterm infants: the issue of correctedage. Early Human Development, 34, 113-123.Lincacre, J.M., & Wright, B.D. (2000). WINSTEPS v. 3.00: Rasch item analysis computer program manual. Chicago:MESA Press.Lord, F.M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Erlbaum.Miller, L.J. & Roid, G.H. (1994) The T.I.M.E. Toddler and Infant Motor Evaluation. San Antonio, TX:The Psychological Corporation.
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