Conventional methods for producing test norms are often plagued with “jumps” or “gaps” (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. We propose a new approach for producing continuous test norms to address these problems that also has the added advantage of not requiring assumptions about the distribution of the raw data: Norm values are established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The proposed method appears to minimize bias arising from sampling and measurement error, while handling marked deviations from normality—such as are commonplace in clinical samples. In addition to step-by-step instructions in how to apply this method, we demonstrate its advantages over conventional discrete norming procedures using norming data from two different psychometric tests, employing either age norms ( N = 3,555) or grade norms ( N = 1,400).

Diagnostic and prognostic uncertainty is one of the major psychological stressors for patients in acute and chronic illness, as well as for parents of children with disabilities or chronic disease. Whereas the parents' feeling of uncertainty is undoubtedly very strong shortly after the birth of a child with disabilities, the long-term effects on the parents of having or not having a precise genetic diagnosis, in terms of emotional stress, remain unclear. In this study, mothers of non-disabled children are compared to mothers of children with Down syndrome, and to mothers of children with a diagnostically unassigned mental retardation with regard to the level of anxiety, feelings of guilt, and emotional burden. While the mothers of children with Down syndrome score comparably to the mothers of non-disabled children, the results show broad psychoemotional disadvantages for mothers of children with a mental retardation of unknown etiology. Consequently, the value of genetic diagnosis of infantile disabilities encompasses, beyond clinical considerations like therapy planning and assignment of the recurrence risk for siblings, significant and long-lasting emotional relief for the parents.

Continuous norming methods have seldom been subjected to scientific review. In this simulation study, we compared parametric with semi-parametric continuous norming methods in psychometric tests by constructing a fictitious population model within which a latent ability increases with age across seven age groups. We drew samples of different sizes (n = 50, 75, 100, 150, 250, 500 and 1,000 per age group) and simulated the results of an easy, medium, and difficult test scale based on Item Response Theory (IRT). We subjected the resulting data to different continuous norming methods and compared the data fit under the different test conditions with a representative cross-validation dataset of n = 10,000 per age group. The most significant differences were found in suboptimal (i.e., too easy or too difficult) test scales and in ability levels that were far from the population mean. We discuss the results with regard to the selection of the appropriate modeling techniques in psychometric test construction, the required sample sizes, and the requirement to report appropriate quantitative and qualitative test quality criteria for continuous norming methods in test manuals.

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