Modelling norm scores with the cNORM package in R
Please always quote using this URN: urn:nbn:de:bvb:20-opus-284143
- In this article, we explain and demonstrate how to model norm scores with the cNORM package in R. This package is designed specifically to determine norm scores when the latent ability to be measured covaries with age or other explanatory variables such as grade level. The mathematical method used in this package draws on polynomial regression to model a three-dimensional hyperplane that smoothly and continuously captures the relation between raw scores, norm scores and the explanatory variable. By doing so, it overcomes the typical problems ofIn this article, we explain and demonstrate how to model norm scores with the cNORM package in R. This package is designed specifically to determine norm scores when the latent ability to be measured covaries with age or other explanatory variables such as grade level. The mathematical method used in this package draws on polynomial regression to model a three-dimensional hyperplane that smoothly and continuously captures the relation between raw scores, norm scores and the explanatory variable. By doing so, it overcomes the typical problems of classical norming methods, such as overly large age intervals, missing norm scores, large amounts of sampling error in the subsamples or huge requirements with regard to the sample size. After a brief introduction to the mathematics of the model, we describe the individual methods of the package. We close the article with a practical example using data from a real reading comprehension test.…
Author: | Sebastian Gary, Wolfgang Lenhard, Alexandra Lenhard |
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URN: | urn:nbn:de:bvb:20-opus-284143 |
Document Type: | Journal article |
Faculties: | Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Psychologie |
Language: | English |
Parent Title (English): | Psych |
ISSN: | 2624-8611 |
Year of Completion: | 2021 |
Volume: | 3 |
Issue: | 3 |
First Page: | 501 |
Last Page: | 521 |
Source: | Psych (2021) 3:3, 501-521. https://doi.org/10.3390/psych3030033 |
DOI: | https://doi.org/10.3390/psych3030033 |
Dewey Decimal Classification: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Tag: | continuous norming; curve fitting; data smoothing; inferential norming; percentile estimation; regression-based norming |
Release Date: | 2023/07/05 |
Date of first Publication: | 2021/08/30 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |