What conventional norming does, and where it breaks
For a test administered across an age range, conventional norm-table construction proceeds in three steps. First, the standardization sample is stratified into age bands (often six-month or one-year bands for child tests, larger bands for adult tests). Second, within each band, raw scores are ranked and converted to percentiles, then to standardized norm scores (T-scores, IQ scores, scaled scores, etc.). Third, the resulting age-band-by-raw-score lookup tables are published and used to convert future raw scores into normed scores.
The procedure is exact when each band has a large, representative sample at every relevant raw-score value. In practice it rarely does, and three pathologies emerge. Boundary discontinuities: a child whose age places her at the upper boundary of one band gets one normed score; a child one day older is reassigned to the next band and may receive a substantially different score for the same raw performance. Within-band sampling noise: rare raw scores within a band may have only a handful of observations, producing percentile estimates that are noisy and sometimes non-monotonic (a higher raw score yielding a lower percentile, because of which other participants happen to be in the band). Extrapolation impossibility: if the standardization sample under-covers some age range, the conventional method has no principled way to extrapolate norms into that range; the test cannot be normed there.
The combined consequence is that conventional norming requires large standardization samples—often 1,000 to 2,000 cases per age band for clinical tests—to suppress these pathologies to acceptable levels. Test publishers absorb this as the cost of doing business, but the cost is substantial and limits how often tests can be re-normed.
The continuous-norming alternative
Continuous norming approaches replace the within-band percentile-rank computation with a regression model that treats the norm distribution as a smooth function of the explanatory variable (typically age, but the framework generalizes to any continuous covariate). The published norms are then defined by the fitted model rather than by discrete tables, which automatically eliminates boundary discontinuities and within-band sampling noise.
The longest-established continuous-norming framework is the LMS method developed by Cole and Green (1992) for pediatric growth charts. LMS estimates three age-varying parameters of a Box-Cox transformation of the raw score: the skewness coefficient (L), the median (M), and the coefficient of variation (S). Each parameter is fitted as a smoothed function of age using cubic splines under penalized likelihood, and percentile curves are derived analytically from the fitted (L, M, S) functions. The LMS approach is dominant in growth-chart applications (height, weight, BMI by age) and has been extended to psychometric applications, but it makes a parametric assumption about the form of the raw-score distribution that is sometimes inappropriate for cognitive-ability data.
The GAMLSS framework introduced by Stasinopoulos and Rigby (2007) generalizes LMS by allowing arbitrary distributional families (not just Box-Cox), arbitrary link functions, and additive smoothing terms in any of the distributional parameters. GAMLSS is implemented in a comprehensive R package and is the most flexible parametric continuous-norming approach available.
Semiparametric continuous norming (SPCN), the Lenhard and Lenhard approach, takes a different route. Rather than parametrizing the raw-score distribution and then modeling parameters as functions of age, SPCN models the joint relationship of raw score, percentile, and the explanatory variable as a Taylor polynomial. The fitted polynomial is a three-dimensional surface from which any norm score can be read off for any combination of raw score and age. The method makes minimal assumptions about the raw-score distribution—the distribution is allowed to take whatever shape the data imply—at the cost of slightly higher computational complexity than parametric alternatives. The implementation is available in the open-source cNORM R package, which Lenhard and colleagues maintain on CRAN.
The 2020 simulation
Lenhard and Lenhard’s (2020) simulation generated a synthetic standardization population of approximately 840,000 cases using an item-response-theory model, with parameters chosen to span realistic test conditions. Test scales varied in number of items (short to long), item difficulty distribution, and discrimination. From this large population, the authors drew repeated standardization samples of varying size, applied both SPCN and conventional banded norming, and assessed how accurately each method recovered the population norm distribution.
The accuracy criteria were the standard psychometric ones: bias of the recovered norm score relative to the population value, root-mean-square error across the raw-score range, and consistency of the recovered norms across age values. The simulation also tested both methods on missing-data and incomplete-coverage scenarios that conventional norming handles poorly.
The findings, summarized:
- SPCN reaches optimal accuracy with substantially smaller samples than conventional norming. For comparable RMSE in the recovered norms, SPCN required roughly half the sample size that conventional banding required across most simulated test conditions.
- SPCN handles age-boundary regions and missing data without breakdown. Where conventional norming exhibits norm-score discontinuities at band boundaries and produces unreliable estimates at sparsely sampled ages, SPCN’s smooth regression interpolates and extrapolates with bounded error.
- SPCN’s relative advantage is largest where conventional norming fails hardest. Small standardization samples, narrow age bands, missing age coverage, and tests with floor or ceiling effects—the regimes where banding produces the noisiest norms—are precisely the regimes where SPCN’s smoothing assumption pays off most.
The authors’ companion 2019 paper in PLoS ONE (Lenhard, Lenhard, & Gary) made a similar comparison between SPCN and parametric continuous norming (LMS-style approaches), finding that the two perform comparably in well-behaved cases but that SPCN is more robust when raw-score distributions deviate from the parametric assumptions of LMS or GAMLSS.
Why this changes test-development economics
The practical implication of the Lenhard and Lenhard results is that test publishers can achieve equivalent norm precision with roughly half the standardization sample if they adopt SPCN instead of conventional banding. For a clinical test that traditionally requires 2,000 cases for adequate norms, this is a difference of 1,000 cases worth of recruitment, administration, scoring, and quality-control effort. For tests targeting populations that are difficult or expensive to recruit—clinical samples, geriatric samples, low-incidence diagnostic populations—the sample-size reduction may be the difference between a publishable test and an under-normed one.
A secondary economic implication is that re-norming becomes more tractable. Tests are typically re-normed every 10-15 years to address Flynn effects and changing population characteristics. The standardization-sample cost of re-norming has been a barrier to more frequent updates; halving that cost makes more frequent re-norming feasible, which in turn improves the contemporaneousness of norms.
Constraints and open questions
SPCN is not a free lunch. The method’s polynomial smoothing assumption can introduce error if the true norm distribution has features (sharp inflections, distinctly multimodal regions) that polynomial smoothing cannot capture. The cNORM implementation includes diagnostic procedures for detecting such features, but the analyst still bears responsibility for inspecting fitted norm curves and identifying regions where the smoothing is over-aggressive.
The method also depends on the explanatory variable being continuous and reasonably well-distributed across the standardization sample. For tests in which the only meaningful stratifier is a discrete categorical variable (sex, language version), SPCN reduces to within-category banding and loses its advantage. For tests where the explanatory variable is continuous but heavily skewed (most of the sample at narrow ages, sparse coverage at others), SPCN performs better than banding but not optimally; the smooth interpolation degrades where data are sparse, just less catastrophically than band-based estimation does.
Finally, the SPCN method has not yet been formally compared to GAMLSS-based parametric continuous norming under a unified simulation framework. The 2019 PLoS ONE paper made a partial comparison; a comprehensive head-to-head benchmark across realistic cognitive-test conditions would strengthen the case for either approach as the field-default continuous-norming method.
The bigger methodological shift
The deeper argument running through Lenhard and Lenhard’s program is that norm-score derivation is a regression problem—the question is how raw scores map to population percentiles as a function of relevant covariates—and that treating it as anything else throws away information. Conventional banding implicitly imposes a step-function structure on a relationship that is in fact smooth, and the cost of that mis-specification is paid in standardization-sample size and in the boundary artifacts that clinicians have learned to tolerate as background noise.
If the field accepts the regression framing, the question is no longer whether to use continuous norming but which form of continuous norming to use. The Lenhard work positions SPCN as the distribution-free option in a toolkit that also includes LMS, GAMLSS, and other parametric formulations. The cNORM R package makes the choice operational rather than aspirational; subsequent test-development projects can pick a continuous-norming approach off the shelf rather than building one from scratch. The 2020 EPM paper is the empirical case for that toolkit choice being the right default for psychometric applications.
Frequently asked questions
What is continuous norming?
Continuous norming replaces conventional age-band norm tables with a regression model that treats the norm distribution as a smooth function of an explanatory variable, typically age. Published norms are defined by the fitted model rather than by discrete tables, which automatically eliminates boundary discontinuities and within-band sampling noise.
What are the failure modes of conventional banded norming?
Three pathologies emerge. Boundary discontinuities: a one-day age difference can move a child from one band to another and produce a substantially different normed score. Within-band sampling noise: rare raw scores within a band may have only a handful of observations, producing percentile estimates that are noisy and sometimes non-monotonic. Extrapolation impossibility: where the standardization sample under-covers some age range, the conventional method has no principled way to extrapolate norms.
What is semiparametric continuous norming (SPCN)?
SPCN, developed by Lenhard and Lenhard, models the joint relationship of raw score, percentile, and the explanatory variable as a Taylor polynomial. The fitted polynomial is a three-dimensional surface from which any norm score can be read off for any combination of raw score and age. The method makes minimal assumptions about the raw-score distribution and is implemented in the open-source cNORM R package.
How much does SPCN reduce sample-size requirements?
For comparable RMSE in the recovered norms, SPCN required roughly half the sample size of conventional banding across most simulated test conditions. For a clinical test that traditionally requires 2,000 cases for adequate norms, the sample-size reduction translates into 1,000 fewer cases of recruitment, administration, scoring, and quality control.
How does SPCN compare to LMS and GAMLSS?
LMS (Cole & Green, 1992) and GAMLSS (Stasinopoulos & Rigby, 2007) are parametric continuous-norming approaches that model raw-score distributional parameters as smooth functions of age. SPCN takes a semiparametric route, modeling the joint raw-score-by-percentile-by-age surface directly. The 2019 Lenhard, Lenhard, and Gary comparison found SPCN more robust when raw-score distributions deviate from the parametric assumptions of LMS or GAMLSS.
What does SPCN not solve?
SPCN’s polynomial smoothing assumption can introduce error if the true norm distribution has sharp inflections or distinctly multimodal regions. It also depends on the explanatory variable being continuous and reasonably well-distributed; for tests stratified only by a discrete category, SPCN reduces to within-category banding and loses its advantage. The cNORM implementation includes diagnostics for detecting these regions.
References
- Cole, T. J., & Green, P. J. (1992). Smoothing reference centile curves: The LMS method and penalized likelihood. Statistics in Medicine, 11(10), 1305-1319. https://doi.org/10.1002/sim.4780111005
- Lenhard, A., Lenhard, W., & Gary, S. (2019). Continuous norming of psychometric tests: A simulation study of parametric and semi-parametric approaches. PLOS ONE, 14(9), e0222279. https://doi.org/10.1371/journal.pone.0222279
- Lenhard, W., & Lenhard, A. (2021). Improvement of norm score quality via regression-based continuous norming. Educational and Psychological Measurement, 81(2), 229-261. https://doi.org/10.1177/0013164420928457
- Stasinopoulos, D. M., & Rigby, R. A. (2007). Generalized additive models for location, scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1-46. https://doi.org/10.18637/jss.v023.i07