GRIMMER_test {rsprite2}

GRIMMER test for standard deviation

Description

This function tests whether a given standard deviation (with a specific precision) can result from a sample of a given size based on integer responses to one or more items. The test was first proposed by Anaya (2016); here, the algorithm developed by Allard (2018) is used, extended by Aurélien Allard to support multi-item scales.

Usage

GRIMMER_test(
  mean,
  sd,
  n_obs,
  m_prec = NULL,
  sd_prec = NULL,
  n_items = 1,
  min_val = NULL,
  max_val = NULL
)

Arguments

mean

The mean of the distribution

sd

The standard deviation of the distribution

n_obs

The number of observations (sample size)

m_prec

The precision of the mean, as number of digits after the decimal point. If not provided, taken based on the significant digits of mean - so only needed if reported mean ends in 0

sd_prec

The precision of the standard deviation, again only needed if reported standard deviation ends in 0.

n_items

Number of items in scale, if distribution represents scale averages. Defaults to 1, which represents any single-item measure.

min_val

(Optional) Scale minimum. If provided alongside max_val, the function checks whether the SD is consistent with that range.

max_val

(Optional) Scale maximum.

Value

Logical TRUE/FALSE indicating whether given standard deviation is possible, given the other parameters

References

Anaya J (2016). “The GRIMMER test: A method for testing the validity of reported measures of variability.” PeerJ Preprints, 4, e2400v1.

Examples

# A sample of 18 integers with mean 3.44 cannot have an SD of 2.47. This is shown by
GRIMMER_test(mean = 3.44, sd = 2.47, n_obs = 18)