rankByGroup {cNORM}R Documentation

Determine the norm scores of the participants in each subsample

Description

This is the initial step, usually done in all kinds of test norming projects, after the scale is constructed and the norm sample is established. First, the data is grouped according to a grouping variable and afterwards, the percentile for each raw value is retrieved. The percentile can be used for the modeling procedure, but in case, the samples to not deviate too much from normality, T, IQ or z scores can be computed via a normal rank procedure based on the inverse cumulative normal distribution. In case of bindings, we use the medium rank and there are different methods for estimating the percentiles (default RankIt).

Usage

rankByGroup(data, group = "group", raw = "raw", method = 4,
  scale = "T", descend = FALSE, descriptives = TRUE,
  covariate = NULL)

Arguments

data

data.frame with norm sample data

group

name of the grouping variable (default 'group'), e. g. grade, setting group to FALSE cancels grouping (data is treated as one group)

raw

name of the raw value variable (default 'raw')

method

Ranking method in case of bindings, please provide an index, choosing from the following methods: 1 = Blom (1958), 2 = Tukey (1949), 3 = Van der Warden (1952), 4 = Rankit (default), 5 = Levenbach (1953), 6 = Filliben (1975), 7 = Yu & Huang (2001)

scale

type of norm scale, either T (default), IQ, z or percentile (= no transformation); a double vector with the mean and standard deviation can as well, be provided f. e. c(10, 3) for Wechsler scale index points

descend

ranking order (default descent = FALSE): inverses the ranking order with higher raw scores getting lower norm scores; relevant for example when norming error scores, where lower scores mean higher performance

descriptives

If set to TRUE (default), information in n, mean, median and standard deviation per group is added to each observation

covariate

Include a binary covariate into the preparation and subsequently modelling, either by specifying the variable name or including the variable itself. BEWARE! Not all subsequent functions are already prepared for it. It is an experimental feature.

Value

the dataset with the percentiles and norm scales per group

Remarks on using covariates

So far the inclusion of a binary covariate is experimental and far from optimized. The according variable name has to be specified in the ranking procedure and the modelling includes this in the further process. At the moment, during ranking the data are split into the according cells group x covariate, which leads to small sample sizes. Please take care to have enough cases in each combination. Additionally, covariates can lead to unstable modelling solutions. The question, if it is really reasonable to include covariates when norming a test is a decision beyond the pure data modelling. Please use with care or alternatively split the dataset into the two groups beforehand and modell them seperately.

See Also

rankBySlidingWindow, computePowers

Examples

# Transformation with default parameters: RandIt and converting to T scores
data.elfe <- rankByGroup(elfe, group = "group")

# Transformation into Wechsler scores with Yu & Huang (2001) ranking procedure
data.elfe <- rankByGroup(elfe, group = "group", method = 7, scale = c(10, 3))

# cNORM can as well be used for conventional norming
# The group variable has to be set to NULL when ranking the group in this case
d <- rankByGroup(elfe, raw = "raw", group = FALSE)
d <- computePowers(d)
m <- bestModel(d)
rawTable(0, model = m) # please use an arbitrary value for age when generating the tables

[Package cNORM version 1.2.0 Index]