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.

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.

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