scaleDiagnosis
scaleDiagnosis
scaleDiagnosis provides a number of diagnostics for a scale (an aggregative measure consisting of several items).
Usage
scaleDiagnosis(dat=NULL, items=NULL, plotSize=180, sizeMultiplier = 1,
axisLabels = "none", scaleReliability.ci=FALSE, conf.level=.95,
powerHist=TRUE, ...)Arguments
- dat
A dataframe containing the items in the scale. All variables in this dataframe will be used if items is NULL.
- items
If not NULL, this should be a character vector with the names of the variables in the dataframe that represent items in the scale.
- plotSize
Size of the final plot in millimeters.
- sizeMultiplier
Allows more flexible control over the size of the plot elements
- axisLabels
Passed to ggpairs function to set axisLabels.
- scaleReliability.ci
TRUE or FALSE: whether to compute confidence intervals for Cronbach's Alpha and Omega (uses bootstrapping function in MBESS, takes a while).
- conf.level
Confidence of confidence intervals for reliability estimates (if requested with scaleReliability.ci).
- powerHist
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.
Details
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.
Value
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.
Examples
# 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);
# }