blue_zscaling: This function can be used to do linear percentile scaling. It...

In jsaraviadrago/bluegrafir: Psychometric tools for structural equation models and scale comparisons

Description

It calculates the cumulative frequency of a continous variable, transforms this variable into percentiles by using a constant of .50 in order to calculate values over the 50 After that, it standardize scores and then transforms values with a mean of 500 and standard deviation of 100 (default).

Usage

blue_zscaling(x, data, sdev = 100, means = 500, type_scale = "CumulativeZ")

Arguments

x	vector of continous values
data	data from the continous values of x parameter
sdev	standard deviation which is to be assigned for the transformation, default value is 100.
means	mean which is to be assigned for the linear transformation. Default value is 500.
type_scale	logical parameter were TRUE assigns three thresholds. It is based on a normal distribution were values lower than -1 standard deviation from the mean are low, values higher than 1 standard deviation are high and values in between are medium (default). if logical parameter is FALSE, 5 thresholds are assigned where values bigger than 2 standard deviations are very high, higher than 1 standard deviation are high, higher than -1 are medium, higher than -2 are low and lower than -2 standard deviations are very low.

Value

The output is a tibble with raw scores, zscores and thresholds

Author(s)

Juan Carlos Saravia

Examples

data_example <- data.frame(ID = c(1,2,3,4,5,6,7,78,7,7,7,7,7,7,7,7,8,8,8,8,8),
puntaje = c(1,2,3,4,5,6,7,78,7,7,7,7,7,7,7,7,8,8,8,8,8))
blue_zscaling(data_example$puntaje,data_example,
type_scale = "CumulativeZ")

jsaraviadrago/bluegrafir documentation built on July 20, 2020, 3:01 a.m.