Julian Urban

• Norming of psychological tests is crucial for the accurate interpretation of test scores. Conventional norming, which relies on subgroups, may introduce bias and requires large samples (uneconomic) to achieve high precision (i.e., low standard errors) of the estimated norm scores. Continuous norming has been proposed as a solution to reduce bias and resolve the dilemma between economy and precision. Continuous norming estimates norm scores based on the entire normative samples – rather than subgroups – using (non-linear) regression. The aim of this dissertation is to examine continuous norming methods to improve both test development and psychological diagnostics. To this end, this dissertation comprises four research studies. This first study includes both a systematic review of continuous norming and an empirical study. The systematic review introduces different continuous norming methods and highlights their respective advantages and limitations. The empirical study compares the precision of conventional and continuous norms and investigates the presentation of continuous norms in classical norm tables. The second study provides a systematic overview of German-language tests, showing that continuous norming methods are rarely applied and that descriptions of the norming process in test manuals are often scarce. Such a scarce reporting hinders a critical evaluation of the estimated norm scores. To address this issue, the third study introduces guidelines for reporting on norm-referenced scores. The guidelines cover relevant aspects along the entire norming process and provide detailed information on each aspect. One aspect, where the guidance is limited, is determining the required sample size. The fourth study addresses this gap by providing empirical based guidance to determine the required sample size for continuous norming. For a comprehensive evaluation of continuous norming, I combined the findings of these studies with (1) an illustration of the effects of bias and precision on individual diagnostics and the financial costs of normative studies, (2) an updated systematic literature search, and (3) an examination of applied norming practices in recently published tests. This integration allowed the extraction of seven best practices for continuous norming. The first best practice is to favor continuous over conventional norming, as it effectively resolves the economy-precision dilemma: continuous norming produces less biased norms than conventional norming and requires smaller sample sizes to achieve high precision. Despite these advantages, continuous norming is still rarely applied in German-language tests. The guidelines and sample-size recommendations provided in this dissertation may facilitate a wider adoption of continuous norming methods, thereby improving both test development and psychological diagnostics.