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 = NULL,
      group = "group",
      raw = "raw",
      weights = NULL,
      method = 4,
      scale = "T",
      descend = FALSE,
      descriptives = TRUE,
      covariate = NULL,
      na.rm = TRUE
    )

### Arguments

`data` | data.frame with norm sample data. If no data.frame is provided, the raw score and group vectors are directly used
`group` | name of the grouping variable (default 'group') or numeric vector, e. g. grade, setting group to FALSE cancels grouping (data is treated as one group)
`raw` | name of the raw value variable (default 'raw') or numeric vector
`weights` | Vector or variable name in the dataset with weights for each individual case. It can be used to compensate for moderate imbalances due to insufficient norm data stratification. Weights should be numerical and positive. Please note, that this feature is currently EXPERIMENTAL and subject to ongoing work! Precision of weighting increases with sample size. On the other hand, in large samples, it is easy to stratificate and then weighting is not needed anymore.
`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 modeling, 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.
`na.rm` | remove values, where the percentiles could not be estimated, most likely happens in the context of weighting

### 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 modeling 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 modeling solutions. The question, if it is really reasonable to include covariates when norming a test is a decision beyond the pure data modeling. Please use with care or alternatively split the dataset into the two groups beforehand and model them separately.

### See Also

rankBySlidingWindow, computePowers

Other prepare: `computePowers()`, `prepareData()`, `rankBySlidingWindow()`

### Examples

    # Transformation with default parameters: RankIt and converting to T scores
    data.elfe <- rankByGroup(elfe, group = "group") # using a data frame with vector names
    data.elfe2 <- rankByGroup(raw=elfe$raw, group=elfe$group) # use vectors for raw score and group

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

    # cNORM can as well be used for conventional norming, in case no group is given
    d <- rankByGroup(raw = elfe$raw)
    d <- computePowers(d)
    m <- bestModel(d)
    rawTable(0, m) # please use an arbitrary value for age when generating the tables

[Package cNORM version 2.0.3 Index]