cNORM (version 3.3.1)

ppvt: Vocabulary development from 2.5 to 17

Description

A dataset based on an unstratified sample of PPVT4 data (German adaption). The PPVT4 consists of blocks of items with 12 items each. Each item consists of 4 pictures. The test taker is given a word orally and he or she has to point out the picture matching the oral word. Bottom and ceiling blocks of items are determined according to age and performance. For instance, when a student knows less than 4 word from a block of 12 items, the testing stops. The sample is not identical with the norm sample and includes doublets of cases in order to align the sample size per age group. It is primarily intended for running the cNORM analyses with regard to modeling and stratification.

Usage

ppvt

Format

A data frame with 4542 rows and 6 variables:

age

the chronological age of the child

sex

the sex of the test taker, 1=male, 2=female

migration

migration status of the family, 0=no, 1=yes

region

factor specifying the region, the data were collected; grouped into south, north, east and west

raw

the raw score of the student, spanning values from 0 to 228

group

age group of the child, determined by the getGroups()-function with 12 equidistant age groups

A data frame with 5600 rows and 9 columns

References

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

Examples

if (FALSE) {
# Example with continuous age variable, ranked with sliding window
model.ppvt.sliding <- cnorm(age=ppvt$age, raw=ppvt$raw, width=1)

# Example with age groups; you might first want to experiment with
# the granularity of the groups via the 'getGroups()' function
model.ppvt.group <- cnorm(group=ppvt$group, raw=ppvt$raw) # with predefined groups
model.ppvt.group <- cnorm(group=getGroups(ppvt$age, n=15, equidistant = T),
                          raw=ppvt$raw) # groups built 'on the fly'


# plot information function
plot(model.ppvt.group, "subset")

# check model consistency
checkConsistency(model.ppvt.group)

# plot percentiles
plot(model.ppvt.group, "percentiles")
}