itemanal {rela}R Documentation

Item analysis function

Description

This function enables the user to evaluate the functioning of two or more items as a coherent scale. Among the traditionally used Cronbach's Alpha the function also produces standardized estimates as well as Duhachek and Iacobucci's (2004) proposed standrad errors and respective confidence intervals for the reliability coefficients. Further, the function provides a bootstrap estimate of the convidance interval of both the regular and standardized alpha values. The function will produce three plots: A density plot of the alpha and standardized alpha bootstrap simulations, a line plot of the "if item deleted" alpga values by each item and a star plot for all of the items for their respective "if item deleted" scale values.

Usage

itemanal(object, SE.par = 1.96, boots = 1000)

Arguments

object Numeric dataset (usually a coerced matrix from a prior data frame) containing all items of the scale. The dataset is arranged observations (rows) by measure items (columns).
SE.par Confidence interval corresponding Z-score. By default set to the 95 % confidence interval Z-score of 1.96.
boots Number of boot strap samples computed. By default 1,000 simulations are estimated.

Details

The function is sensitive to the how the dataset was compiled. Using the cbind function will often return a matirx that appears numeric but in reality functions as a numeric compiled list. If system error messages occur try transforming the dataset using as.matrix(data.frame(your.dataset)).

Value

Output consists of a list with the following values:

Variables General information about the entered items such as item type, number of cases used in the analysis, minimum, maximum values and item sum.
Tendency Contains the measures of central tendancy: The respective item mean, median, standard deviation (SD), standard error of the mean (SE.mean) , lower and upper values of a 95 % confidence interval of the mean and item variance.
Skewness Skewness, standard error of the skew, lower and upper values of the skew.
Kurtosis Kurtosis, standard error of kurtosis as well as its respective 95 % confidence interval values.
Covariance The covariance matrix of all items in the dataset.
Correlation The correlation matrix of all submitted items.
Alpha The number of items in the scale as well as the covariance based Cronbach's alpha estimate.
Conf.Alpha Standard error of Cronbach's alpha with the associated lower and upper bound confidence interval values.
Bootstrap.Simmulations The regular (covariance based) alpha bootstrap simulated estimates.
Alpha.Bootstrap Bootstrap mean, standard error and confidence interval lower and upper limits.
Std.Alpha The number of items in the scale as well as the correlation based Cronbach's alpha estimate.
Conf.Std.Alpha Standard error of the standardized Cronbach's alpha with the associated lower and upper bound confidence interval values.
Bootstrap.Std.Simmulations The standardized (correlation based) alpha bootstrap simulated estimates.
Alpha.Std.Bootstrap Standardized bootstrap mean, standard error and confidence interval lower and upper limits.
Scale.Stats Changes in scale statistics upon deletion of any one item in the scale. Contains scale mean and variance.
Alpha.Stats Changes in the scale's reliability estimate alpha upon deletion of any one item. Contains, corrected total item correlation, squared multiple correlation, adjusted alpha statistic without given item.
call Submitted function call.

Note

Under the current version of this function/package missing data is deleted listwise. Subsequently only full cases are used in determining scale reliability.

Author(s)

Michael Chajewski ( http://www.chajewski.com )

References

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.

Duhachek, A. & Iacobucci, D. (2004). Alpha's standard error (ASE): An accurate and precise confidence interval estimate. Journal of Applied Psychology, 89(5), 792-808.

Kim, J., & Mueller, C. W. (1978). Introduction to factor analysis: What it is and how to do it. SAGE Publications: Newbury Park, CA.

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3 ed.). McGraw-Hill: New York, NY.

Kaiser, H. F. & Cerny, B. A. (1979). Factor analysis of the image correlation matrix. Educational and Psychological Measurement, 39, 711-714.

Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. SAGE Publications: Thousand Oaks, CA.

Examples


library(rela)

Belts <- Seatbelts[,1:7]
Belts.item <- itemanal(Belts)
Belts.item

Belts2 <- Belts[,-5]
Belts2 <- Belts2[,-5] 
Belts.item2 <- itemanal(Belts2)
Belts.item2

[Package rela version 4.0 Index]