alpha {epiDisplay}

Cronbach's alpha

Description

Calculate reliability coefficient of items in a data frame

Usage

alpha (vars, dataFrame, casewise = FALSE, reverse = TRUE, 
    decimal = 4, vars.to.reverse = NULL, var.labels = TRUE, 
    var.labels.trunc =150)   
alphaBest (vars, dataFrame, standardized = FALSE) 

Arguments

vars

a vector containing at least three variables from the data frame

dataFrame

data frame where items are set as variables

casewise

whether only records with complete data will be used

reverse

whether item(s) negatively correlated with other majority will be reversed prior to computation

decimal

number of decimal places displayed

var.labels

presence of descriptions of variables in the last column of the output

var.labels.trunc

number of characters used for variable descriptions, long labels can be truncated

vars.to.reverse

variable(s) to reverse prior to computation

standardized

whether choosing the best subset of items is based on the standardized alpha coefficient, if FALSE then the unstandardized alpha coefficient is used

Details

This function is based on the 'reliability' function from package 'Rcmdr', which computes Cronbach's alpha for a composite scale.

There must be at least three items in 'vars' specified by their names or their index in the data frame.

The argument 'reverse' (default = TRUE) automatically reverses items negatively correlated with other majority into negative and reports the activities in the first column of the last result section. This can be overwritten by the argument 'vars.to.reverse'

Similar to the 'reliability' function, users can see the effect of removing each item on the coefficents and the item-rest correlation.

'alphaBest' is a variant of 'alpha' for successive removal of items aiming to reach the highest possible Cronbach alpha. The resultant values include variable indices of excluded and remaining items, which can be forwarded to 'tableStack' to achieve total and mean scores of the best selected items. However, there is no promise that this will give the highest possible alpha. Manual attemps may also be useful in making comparison.

Value

A list.

'alpha' returns an object of class "alpha"

alpha

unstandardized alpha coefficient

std.alpha

standardized alpha coefficient

sample.size

sample size

use.method

method for handling missing values

rbar

the average inter-item correlation

items.selected

names of variables included in the function

alpha.if.removed

a matrix of unstandardized and standardized alpha coefficients and correlation of each item with the rest of the items

result

as above but includes a column showing the items that were reversed (if TRUE) and a column of item description. As a matrix, it could be sent to a spreadsheet software using 'write.csv'

decimal

decimal places

item.labels

a character vector containing descriptions of the items

'apha.Best' returns a list of the following elements

best.alpha

the possible highest alpha obtained from the function

removed

indices of items removed by the function

remaining

indices of the remaining items

items.reversed

names of items reversed

Author(s)

Virasakdi Chongsuvivatwong [e-mail]

See Also

'cronbach' from 'psy' package and 'reliability' from 'Rcmdr' package and 'tableStack' and 'unclassDataframe' of Epicalc

Examples

data(Cars93, package="MASS")
.data <- Cars93
attach(.data)
alpha(vars=c(Min.Price:MPG.highway, EngineSize), .data)
detach(.data)

data(Attitudes)
.data <-Attitudes
attach(.data)
alpha(qa1:qa18, .data)  # Needs full screen of Rconsole
alpha(qa1:qa18, var.labels.trunc=30, .data) 
                 # Fits in with default R console screen

alpha(qa1:qa18, reverse=FALSE, .data)

alphaBest(qa1:qa18, .data) -> best.alpha
best.alpha # .7621
tableStack(best.alpha$remaining, dataFrame=.data, reverse=TRUE)

# Manual attempts by trial and error give the following
alpha(c(qa1:qa9, qa15,qa18), .data) # .7644
detach(.data)
rm(list=ls())