scaleDiagnosis provides a number of diagnostics for a scale (an aggregative measure consisting of several items).
scaleDiagnosis(dat=NULL, items=NULL, plotSize=180, sizeMultiplier = 1,
axisLabels = "none", scaleReliability.ci=FALSE, conf.level=.95,
powerHist=TRUE, ...)A dataframe containing the items in the scale. All variables in this dataframe will be used if items is NULL.
If not NULL, this should be a character vector with the names of the variables in the dataframe that represent items in the scale.
Size of the final plot in millimeters.
Allows more flexible control over the size of the plot elements
Passed to ggpairs function to set axisLabels.
TRUE or FALSE: whether to compute confidence intervals for Cronbach's Alpha and Omega (uses bootstrapping function in MBESS, takes a while).
Confidence of confidence intervals for reliability estimates (if requested with scaleReliability.ci).
Whether to use the default ggpairs histogram on the diagonal of the scattermatrix, or whether to use the powerHist version.
Additional arguments are passed on to powerHist.
An object with the input and several output variables. Most notably:
The results of scaleReliability.
A Principal Components Analysis
A Factor Analysis
Decriptive statistics about the items
A scattermatrix with histograms on the diagonal and correlation coefficients in the upper right half.
Function to generate an object with several useful statistics and a plot to assess how the elements (usually items) in a scale relate to each other, such as Cronbach's Alpha, omega, the Greatest Lower Bound, a factor analysis, and a correlation matrix.
# NOT RUN {
### Note: the 'not run' is simply because running takes a lot of time,
### but these examples are all safe to run!
# }
# NOT RUN {
### This will prompt the user to select an SPSS file
scaleDiagnosis();
### Generate a datafile to use
exampleData <- data.frame(item1=rnorm(100));
exampleData$item2 <- exampleData$item1+rnorm(100);
exampleData$item3 <- exampleData$item1+rnorm(100);
exampleData$item4 <- exampleData$item2+rnorm(100);
exampleData$item5 <- exampleData$item2+rnorm(100);
### Use a selection of two variables
scaleDiagnosis(dat=exampleData, items=c('item2', 'item4'));
### Use all items
scaleDiagnosis(dat=exampleData);
# }