mtmm: Multitrait Multimethod approach of scale validation

In psy: Various Procedures Used in Psychometrics

Multitrait Multimethod approach of scale validation

Description

This function is destinated to assess the convergent and discriminant validity of subscales of a given scale. Items belonging to the same subscale should correlate highly amongst themselves. Items belonging to different subscales should not correlate highly. This approach is simpler and more robust than confirmatory factor analysis (CFA). It can be interesting to verify (at least approximately) the proposed structure of an existing instrument in a new population. Most psychometricians will however prefer CFA.

Usage

mtmm(datafile,x,color=FALSE,itemTot=FALSE,graphItem=FALSE,stripChart=FALSE,namesDim=NULL)
 

Arguments

datafile	name of datafile
x	a list of variable names (as many elements as there are subscales)
color	boxplot are in colour: FALSE = colourless just in grey and white (by default), TRUE = with colours
itemTot	if TRUE, for subscale i (i=1,...,n), boxplot of Pearson's correlations between total score of subscale i and the items of subscale j (j=1,...n). If j=i, the item is omited in the computation of the total score. If FALSE, for subscale i (i=1,...,n), boxplot of Pearson's correlations between the items of subscale i and the items of subscale j (j=1,...n)
graphItem	if TRUE represents graphically each correlation
stripChart	if TRUE, dot charts are preferred to boxplots. Used with small number of items
namesDim	Labels foreach boxplots

Value

For subscale i (i=1,...,n), displays the n boxplots of the distributions of the Pearson's correlations between items of subscale i and items of subscale j (j=1,...,n). If j=i, the correlation of a given item with itself is ommited. Boxplot for i=j (grey by default) should be above boxplots for i!=j. Likewise, the correlation of an item with the global score of its subscale should be above its correlations with the global score of the other subscales.

Author(s)

Adeline Abbe

Examples

data(ehd)

par(mfrow=c(1,5))
mtmm(ehd,list(c("e15","e18","e19","e20"),c("e4","e5","e6","e14","e17"),c("e11","e13","e16")
,c("e1","e10","e12"),c("e2","e3","e7","e8","e9")))

# Boxplots of the distributions of the Pearson's correlations between total score of
# subscale i and the items of subscale j
par(mfrow=c(1,5))
mtmm(ehd,list(c("e15","e18","e19","e20"),c("e4","e5","e6","e14","e17"),c("e11","e13","e16")
,c("e1","e10","e12"),c("e2","e3","e7","e8","e9")))

# Pearson's correlations between total score of subscale i and all items
par(mfrow=c(3,2))
mtmm(ehd,list(c("e15","e18","e19","e20"),c("e4","e5","e6","e14","e17"),c("e11","e13","e16")
,c("e1","e10","e12"),c("e2","e3","e7","e8","e9")),graphItem=TRUE)

psy documentation built on April 22, 2022, 1:08 a.m.