A norm-referenced score expresses the position of an individual test taker in the reference population, thereby enabling a proper interpretation of the test score. Such normed scores are derived from test scores obtained from a sample of the reference population. Typically, multiple reference populations exist for a test, namely when the norm-referenced scores depend on individual characteristic(s), as age (and sex). To derive normed scores, regression-based norming has gained large popularity. The advantages of this method over traditional norming are its flexible nature, yielding potentially more realistic norms, and its efficiency, requiring potentially smaller sample sizes to achieve the same precision. In this tutorial, we introduce the reader to regression-based norming, using the generalized additive models for location, scale, and shape (GAMLSS). This approach has been useful in norm estimation of various psychological tests. We discuss the rationale of regression-based norming, theoretical properties of GAMLSS and their relationships to other regression-based norming models. Based on 6 steps, we describe how to: (a) design a normative study to gather proper normative sample data; (b) select a proper GAMLSS model for an empirical scale; (c) derive the desired normed scores for the scale from the fitted model, including those for a composite scale; and (d) visualize the results to achieve insight into the properties of the scale. Following these steps yields regression-based norms with GAMLSS for a psychological test, as we illustrate with normative data of the intelligence test IDS-2. The complete R code and data set is provided as online supplemental material. Translational Abstract Standardized psychological tests are widely used. Examples include intelligence, developmental, and neuropsychological tests. They are used for purposes as monitoring, selection, and diagnosing individuals. High-quality standardized tests have normed scores, like the well-known IQ scores for intelligence tests. Normed scores allow for properly interpreting an individual's test score. They are derived in the test construction phase, based on scores in a large normative sample. Normed scores express the position of an individual test taker in the reference population. The reference population for a test typically depends on individual characteristic(s), like age and possibly sex. This tutorial introduces the reader to a method to compute normed scores that depend on individual characteristic(s), making optimal use of all background knowledge and the scores in the whole normative sample. Therefore, the method yields potentially more realistic norms, and more precise norms than traditional methods, using the same amount of data. This is an important asset, because gathering sufficient data is difficult and costly. In this tutorial, we explain the technical background of the method, called regression-based norming with the generalized additive models for location, scale, and shape (GAMLSS), and explain how to apply it based on six steps. Following these steps yield regression-based norms with GAMLSS for a psychological test, as we illustrate with normative data of the intelligence test IDS-2. The complete R code and data set is provided as online supplemental material, so that test developers can apply the method to derive high-quality norms for their own test.