*Alexandra Lenhard, Wolfgang Lenhard & Sebastian Gary*

cNORM (W. Lenhard, Lenhard & Gary) is a package for the R environment for statistical computing that aims at generating continuous test norms in psychometrics and biometrics and to analyze the model fit. It is based on the approach of A. Lenhard, Lenhard, Suggate and Segerer (2016).

The method stems from psychometric test construction and was developed to create continuous norms for age or grade in performance assessment (e. g. vocabulary development, A. Lenhard, Lenhard, Segerer & Suggate, 2015; reading and writing development, W. Lenhard, Lenhard & Schneider, 2017). It can however be applied wherever test data like psychological (e. g. intelligence), physiological (e. g. weight) or other measures are dependent on continuous (e.g., age) or discrete (e.g., sex or test mode) explanatory variables. In addition, it can also be used for conventional norm score generation for individual groups, i.e. without modeling depending on other variables.

The package estimates percentile curves in dependence of the explanatory variable (e. g. schooling duration, age ...) via Taylor polynomials, thus offering several advantages:

- By optimizing the model on the basis of the total sample, small deviations from the representativeness of individual subsamples, for example due to incomplete data stratification, are minimized.
- Gaps between different discrete levels of the explanatory variable are closed. For example, in school performance tests, norm tables can be created not only for the discrete measurement point of the normative sample collection (e.g. midyear or end of the year), but also at any time of the school year with the desired accuracy.
- Norm tables are always determined on the basis of the entire normative sample, not only on the basis of a single cohort or class level. Therefore, in comparison to conventional norming, higher norming quality is achieved even with considerably smaller normative samples.
- The limits of the model fit can be evaluated graphically and analytically. For example, it is possible to determine where the model deviates strongly from the manifest data or where strong floor or ceiling effects occur. This makes it possible to specify at which points the test scores can no longer be interpreted in a meaningful way.
- cNORM does not require any distribution assumptions. Therefore, in most use cases the data can be modeled more precisely than with parametric methods (A. Lenhard, Lenhard, & Gary, 2019). This is particularly true for small samples (< 150 per age group or grade) and skewed raw score distributions. Moreover, it applies in particular to those areas that deviate rather strongly from the population average, but often represent precisely those areas that have the highest relevance in diagnostic practice. The following figure illustrates this:

Figure: Modelling of the manifest data (black dots) of the ELFE reading comprehension test at the beginning of the fifth grade with cNORM (green line) and a parametric method (modelling of location, dispersion and skewness with Box-Cox-transformation; red line). It can clearly be seen that parametric modeling in this case significantly overestimates the probability densities in the lower performance range. As a consequence, the normal scores of low performing children would also be overestimated and therefore the children would be identified too rarely as dyslexic. By contrast, cNORM provides reliable data modeling across the entire performance range.

For the mathematical background, please have a look at the mathematical derivation of the method.

On the following pages, we demonstrate the necessary steps for the application of the R package with real human performance data, namely, with the standardization sample of the sentence comprehension subtest of *ELFE 1-6*, a reading comprehension test in German language (W. Lenhard & Schneider, 2006). Essentially, there are five steps to complete:

- Installation of the R-Package
- Data Preparation
- Data Modeling
- Model Validation
- Generating Norm Tables
- Graphical User Interface Web-based or as a Jamovi Module

A quick guide to the essential cNORM functionality:

## Basic example code for modeling the sample dataset

library(cNORM)

# Start the graphical user interface (needs shiny installed)

# The GUI includes the most important functions. For specific cases,

# please use cNORM on the console.cNORM.GUI()

# If you prefer the console, you can use the syntax as well: Rank data within

# group and compute powers and interactions for the internal dataset 'elfe'

# and compute model. The resulting object includes the ranked data via

# object$data and model via object$model.cnorm.elfe <- cnorm(raw = elfe$raw, group = elfe$group)

# Plot R2 of different model solutions in dependence of the number

# of predictorsplot(cnorm.elfe, "subset", type=0) # plot R2

plot(cnorm.elfe, "subset", type=3) # plot MSE# NOTE! At this point, you usually select a good fitting model and rerun

# the process with a fixed number of terms, e. g. four. Avoid models

# with a high number of terms:cnorm.elfe <- cnorm(raw = elfe$raw, group = elfe$group, terms = 4)

# Visual inspection of the percentile curves of the fitted model

plot(cnorm.elfe, "percentiles")

# Visual inspection of the observed and fitted raw and norm scores

plot(cnorm.elfe, "norm")

plot(cnorm.elfe, "raw")# In order to check, how other models perform, plot series of percentile

# plots with ascending number of predictors, in this example up to 14

# predictors.plot(cnorm.elfe, "series", end=14)

# Cross validation of number of terms with 20% of the data for validation

# and 80% training. Due to the time intensity, max terms is restricted to

# 10 in this example; 3 repetitionscnorm.cv(cnorm.elfe$data, max=10, repetitions=3)

# Cross validation with pre-specified terms, e. g. of an already

# existing modelcnorm.cv(cnorm.elfe, repetitions=3)

# Print norm table (for grade 3, 3.2, 3.4, 3.6)

normTable(c(3, 3.2, 3.4, 3.6), cnorm.elfe)

# The other way round: Print raw table (for grade 3) together with 90%

# confidence intervalls for a test with a reliability of .94rawTable(3, cnorm.elfe, CI = .9, reliability = .94)

# cNORM can as well be used for conventional norming. In this case,

# the group variable has to be set to FALSE when ranking the data.

# please use an arbitrary value for age when generating the tables.d <- rankByGroup(elfe, raw="raw", group=FALSE)

d <- computePowers(d)

m <- bestModel(d)

rawTable(0, model = m)# start vignette for a complete walk through

vignette("cNORM-Demo", package = "cNORM")

Installation |

cNORM is licensed under GNU Affero General Public License v3 (AGPL-3.0). This means that copyrighted parts of cNORM may only be used free of charge in commercial and non-commercial projects that run under this same license, retain the copyright notice, provide their source code and correctly cite cNORM. Copyright protection includes, for example, the reproduction and distribution of source code or parts of the source code of cNORM or of graphics created with cNORM. The integration of the package into a server environment in order to access the functionality of the software (e.g. for online delivery of norm scores) is also subject to this license. However, a regression function determined with cNORM is not subject to copyright protection and may be used freely for commercial or non-commercial projects. If you want to apply cNORM in a way that is not compatible with the terms of the AGPL 3.0 license, please do not hesitate to contact us to negotiate individual conditions.

If you want to use cNORM for scientific publications, we would also ask you to quote the source.

CDC (2012). National Health and Nutrition Examination Survey: Questionaires, Datasets and Related Documentation. available: https://wwwn.cdc.gov/nchs/nhanes/OtherNhanesData.aspx. date of retrieval: 25/08/2018 |

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, A., Lenhard, W., Segerer, R. & Suggate, S. (2015). Peabody Picture Vocabulary Test - Revision IV (Deutsche Adaption). Frankfurt a. M.: Pearson Assessment. |

Lenhard, A., Lenhard, W., Suggate, S. & Segerer, R. (2016). A continuous solution to the norming problem. Assessment, Online first, 1-14. doi: 10.1177/1073191116656437 |

Lenhard, W., Lenhard, A. & Schneider, W. (2017). ELFE II - Ein Leseverständnistest für Erst- bis Siebtklässler. Göttingen: Hogrefe. |

Lenhard, W. & Schneider, W. (2006). ELFE 1-6 - Ein Leseverständnistest für Erst- bis Sechstklässler. Göttingen: Hogrefe. |

The World Bank (2018). Mortality rate, infant (per 1,000 live births). Data Source available https://data.worldbank.org/indicator/SP.DYN.IMRT.IN (date of retrieval: 02/09/2018) |

The World Bank (2018). Life expectancy at birth, total (years). Data Source World Development Indicators available https://data.worldbank.org/indicator/sp.dyn.le00.in (date of retrieval: 01/09/2018) |

In case you need a reference to this page in a scientific paper, please use the following citation:

Lenhard, A., Lenhard, W. & Gary, S. (2018).cNORM - Generating Continuous Test Norms.Retrieved from: https://www.psychometrica.de/cNorm_en.html. Dettelbach (Germany): Psychometrica. DOI: 10.13140/RG.2.2.25821.26082