Abstract
The main purpose of this paper is to demonstrate how to apply the Hierarchical Linear Modeling (HLM) technique to multi-wave Curriculum-Based Measurement (CBM) measures in modeling academic growth and assessing its relations to student- and instruction-related variables. HLM has advantages over other statistical methods (e.g., repeated measures ANOVA, Structural Equation Modeling) in modeling academic growth. The advantages include allowing more flexible research designs in collecting multiple data points and estimating growth rates and their relations to correlates in more reliable, accurate ways. CBM, as a multi-wave progressmonitoring system, also has distinctive psychometric features that facilitate longitudinal research on academic skill development. These features include provision of multiple data points within short time periods, good validity and reliability, and sensitivity for detecting small degrees of change. Finally, research questions related to assessing the academic growth of students with learning difficulties and using assessment results to improve educational practices for them are discussed
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References
Bast, J., & Reitsma, P. (1998). Analyzing the development of individual differences in terms of Matthew effects in reading: Results from a Dutch longitudinal study.Developmental Psychology, 34, 1373–1399.
Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change.Psychological Bulletin, 101, 147–158.
Bryk, A. S., & Raudenbush, S. W. (1992).Hierarchical linear models. Newsbury Park, CA: Sage.
Deno, L. S. (1985). Curriculum-based measurement: The emerging alternative.Exceptional Children, 52, 219–232.
Deno, L. S., & Fuchs, L. S. (1987). Developing curriculumbased measurement systems for data-based special education problem solving.Focus on Exceptional Children, 19, 1–16.
Deno, L. S., Fuchs, L. S., Marston, D., & Shin, J. (in press). Using Curriculum-Based Measurement to Establish Growth Standards for Students with Learning Disabilities.School Psychology Review.
Francis, D. J., Shaywitz, S. E., Stuebing, K. K., Shaywitz, B. A., & Fletcher, J. M. (1996). Developmental lag versus deficit models of reading disability: A longitudinal, individual growth curves analysis.Journal of Educational Psychology, 88, 3–17.
Francis, H. (1992). Patterns of reading development in the first school.British Journal of Educational Psychology, 62, 225–232.
Fuchs, L. S., Fuchs, D., Hamlett, C. L., Walz, L., & Germann, G. (1993). Formative evaluation of academic progress: How much growth can we expect?School Psychology Review, 22, 27–48.
Good, R. H., & Jefferson, G. (1998). Contemporary perspectives on curriculum-based measurement validity. In M. R. Shinn (Ed.),Advanced applications of curriculum-based measurement (pp. 61- 88). NY: Guilford.
Hertzog, C., & Rovine, M. (1985). Repeated measures analysis of variance in developmental research: Selected issues.Child Development, 56, 787–809.
Labouvie, E. W. (1982). The concept of change and regression toward the mean.Psychological Bulletin, 92, 251–257.
Loehlin, J. C. (1998).Latent variable models: An introduction to factor, path, and structural analysis (3rd Ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
McCall, R. B., & Appelbaum, M. (1973). Bias in the repeated measures analysis of variance: Some alternative approaches.Child Development, 44, 333–344.
Marston, D. (1989). A curriculum-based measurement approach to assessing academic performance: What it is and why it is. In M. R. Shinn (Ed.),Curriculum-based measurement: Assessing special children (pp. 18- 78). NY: Guilford.
Marston, D., Deno, S. L., & Tindal, G. (1983).A comparison of standardized achievement tests and direct measurement techniques in measuring pupil progress (Research Report No. 126). Minneapolis, MN: University of Minnesota, Institute for Research on Learning Disabilities.
Marston, D., & Magnusson, D. (1985). Implementing curriculum-based measurement in special and regular education settings.Exceptional Children, 52, 266–276.
Maruyama, G. M. (1998).Basics of structural equation modeling. Thousand Oaks, CA: Sage.
Raudenbush, S. W., & Bryk, A. S. (1989). Methodological advances in analyzing the effects of schools and classrooms on student learning.Review of Research in Education, 15, 423–475.
Rogosa, D. R., & Willet, J. B. (1983). Demonstrating the reliability of the difference scores in the measurement of change.Journal of Educational Measurement, 20, 335- 343.
Salvia, J., & Ysseldyke, J. E. (1995).Assessment (6th Ed.). Boston, MA: Houghton Mifflin Company.
Shaywitz, B. A., & Shaywitz, S. E. (1994). Measurement and analyzing change. In Lyon, G. R. (Ed.),Frames of reference for the assessment of learning disabilities (pp. 59–67). Baltimore, MD: Brookes.
Shin, J. (1999).Reading-skill development and instructional practices facilitating reading growth for students with and without learning disabilities: A one-year longitudinal study. Unpublished doctoral dissertation, University of Minnesota, Minneapolis.
Shin, J., Deno, S. L., & Espin, C. A. (2000). Technical adequacy of the maze task for curriculum-based measurement of reading growth.The Journal of Special Education, 34, 164–172.
Shin, J., Deno, S. L., Robinson, S. L., & Marston, D. (2000). Predicting classroom achievement from active responding on a computer-based groupware system.Remedial and Special Education, 21, 53–60.
Stanovich, K. E., Nathan, R. G., & Zolman, J. E. (1988). The developmental lag hypothesis in reading: Longitudinal and matched reading-level comparisons.Child Development, 59, 71–86.
Willet, J. B. (1989a). Questions and answers in the measurement of change.Review of Research in Education, 15, 345–421.
Willet, J. B. (1989b). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth.Educational and Psychological Measurement, 49, 587–603.
Willet, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time.Psychological Bulletin, 116, 363–381.
Zimmerman, D. W., & Williams, R. H. (1982). Gain scores in research can be highly reliable.Journal of Educational Measurement, 19, 149–154.
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Shin, J., Espin, C.A., Deno, S.L. et al. Use of hierarchical linear modeling and curriculum-based measurement for assessing academic growth and instructional factors for students with learning difficulties. Asia Pacific Educ. Rev. 5, 136–148 (2004). https://doi.org/10.1007/BF03024951
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DOI: https://doi.org/10.1007/BF03024951