Velocity Norms for Academic Growth

Shuttleworth (1934) suggested that growth standards for stature should be expressed in terms of progress rather than status. Tanner (1952) provided a theoretical framework for the development of clinical standards for growth and advocated velocity standards. Bayley (1956) made the first effort to produce standards for height that took account of tempo. Her paper foreshadowed the landmark paper by Tanner, Whitehouse and Takaishi (1966) on longitudinal standards for height velocity and weight velocity. Incremental growth charts for height and weight have since been produced for use in the United States (Baumgartner, Roche & Himes, 1986; Roche & Himes, 1980).

Have you ever heard of growth velocity norms for academic growth—i.e., the growth rate of reading ability or mathematical understanding? There are three reasons you haven’t, which persisted for most of the 20th century: (a) the absence of sufficient longitudinal data on which to base investigations of academic growth; (b) the analytical methods available to educational researchers who wished to study growth; and, (c) challenges of educational measurement (e.g., dimensionality, lack of scale comparability and common units across instruments). Yet, I submit at the dawn of the 21st century, these obstacles have been overcome.

The most recent two reauthorizations of the Elementary and Secondary Education Act (ESEA) required states to assess reading and mathematics in multiple grades. States have been accumulating data for more than a decade. So, longitudinal data are now feasible for reading and mathematics.

Rogosa, Brandt and Zimowski (1982) advocated the use of longitudinal data collection designs gathering more than two waves of serial measures on the same individuals, accompanied by an analytical methodology focused on the individual growth curve. In their landmark book, Raudenbush and Bryk (2002) included a chapter on formulating models for individual change. Singer and Willett (2003) gave book-length treatment to the modeling of individual change. Perhaps the most enabling resource for the educational research community was Singer’s (1998) article demonstrating how to implement multilevel (including growth) models using one of the most widely available general-purpose statistical packages.

Finally, near the end of the 20th century, a new scale was developed for measuring reading ability. Its significant advantage over previous scales was a new kind of general objectivity, attained by calibrating the scale to an external text-complexity continuum and double-anchoring the scale at two substantively important points, much as temperature scales are anchored at the freezing and boiling points of water (Williamson, 2015).

Combining longitudinal data, multilevel modeling and state-of-the-art measurement scales from The Lexile® Framework for Reading and The Quantile® Framework for Mathematics, Williamson (2016) premiered incremental velocity norms for average reading growth and average mathematics growth. Based on an individual growth model, the incremental velocities reflect the long-term developmental growth of students in a well-established reference population (n > 100,000). Now, it is possible to refer the reading or mathematics growth rates of students observed during schooling to a clearly defined population of growth curves derived from serial measures of students whose reading ability and mathematical understanding were systematically assessed over time.

References
Baumgartner, F. N., Roche, A. G., & Himes, J. H. (1986). Incremental growth tables: Supplementary to previously published charts. The American Journal of Clinical Nutrition, 43, 711-722.
Bayley, N. (1956). Growth curves of height and weight by age for boys and girls, scaled according to physical maturity. Journal of Pediatrics, 48, 187-194.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd  ed.). Thousand Oaks, CA: Sage Publications.
Roche, A. F., & Himes, J. H. (1980). Incremental growth charts. The American Journal of Clinical Nutrition, 33, 2041-2052.
Rogosa, D. R., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92, 726-748.
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 24(4), 323-355.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.
Shuttleworth, F. K. (1934). Standards of development in terms of increments. Child Development, 5, 89-91.
Tanner, J. M. (1952). The assessment of growth and development in children. Archives of Disease in Childhood, 27, 10-33.

Summer Math Challenge Kicks Off on June 19th!

On average, all students, regardless of socio-economic status, lose approximately 2.6 months of grade level equivalency in their mathematical repertoire over the summer months each year. This means students can enter a new school year in August or September having lost about a third of the ground they covered the year before. Fight the summer slide, and keep math skills sharp with the Quantile Summer Math Challenge, a FREE math skills maintenance program based on grade-level standards that help prepare students for college and careers.

For the past several years, MetaMetrics has tried to help stave off the erosion in learning that can occur during the summer months with the Summer Math Challenge. Last year, almost 20 State Department of Educations and over 26,000 students across all 50 states signed up to take the challenge. This year, we have expanded our successful program to include those students who have just finished 8th grade. Now, the program is targeted to students who have just completed grades 1 through 8 and is designed to help kids retain math skills learned during their previous school year.

The Summer Math Challenge lasts for six weeks. The challenge focuses on one math concept per week, so as to help the student remain sharp but not feel overburdened during the summer. From June 19th through July 28th parents will receive daily emails with fun activities and links to educational resources. Activities will be grounded in everyday life and be engaging for both parents and children. This program also helps parents to understand that they do not need to be math experts to talk about math with their kids! When the program ends parents can print an award certificate to celebrate their child’s summer math accomplishment! To learn more, visit www.quantiles.com/summer-math.

MetaMetrics is an educational measurement organization. Our renowned psychometric team develops scientific measures of student achievement that link assessment with targeted instruction to improve learning.