sem: Standard error of measurement

In rel: Reliability Coefficients

Description

Calculates the standard error of measurement

Usage

sem(data = NULL, type = c("mse", "sd", "cpd"), conf.level = 0.95)

Arguments

data	A matrix with n subjects and m observations (n*m matrix).
type	The method used to compute sem with a character string specifying "sd" for the within-subject standard deviation, "mse" for the square root of the ANOVA error variance, or "cpd" for the consecutive pairwise difference.
conf.level	Confidence level of the interval.

Details

"sd" and "mse" includes complete cases only and have a confidence interval based on a t distribution. "cpd" includes all cases, derives sem from the difference between adjacent trials, and has a confidence interval based on a chi squared distribution (Hopkins 2015). "cpd" is computed both overall and separately for consecutive trials, the latter allowing one to assess whether habituation decreases sem (Hopkins 2015).

Value

method	Analysis name
obs	Number of observations
sample	Sample size
na	missing values
est	Point estimate
lb	Lower confidence boundary
ub	Upper confidence boundary
est.cpd	sem for adjacent columns
data	analyzed data

Author(s)

Riccardo Lo Martire

References

Nunnally, J. C., Bernstein, I. H. (1994). Psychometric theory. New York, NY: McGraw-Hill.

Hopkins, W. G. (2015). Spreadsheets for Analysis of Validity and Reliability. Sportscience 19, 36-42.

Examples

#Sample data: 200 subjects rated their weight twice.
data <- cbind(sample(50:100,200,replace=TRUE), sample(50:100,200,replace=TRUE))

#Standard error of measurement
sem(data=data, type="mse", conf.level=0.95)

Example output

Call:
sem(data = data, type = "mse", conf.level = 0.95)

      Estimate LowerCB UpperCB
Const   13.355  12.293  14.416

Confidence level = 95%
Observations = 2
Sample size = 200

rel documentation built on March 3, 2020, 9:07 a.m.