userfriendlyscience (version 0.1-2)

scaleDiagnosis: scaleDiagnosis

Description

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)

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).

Value

• An object with the input and several output variables. Most notably:
• scaleReliability
  The results of scaleReliability.
• pca
  A Principal Components Analysis
• fa
  A Factor Analysis
• describe
  Decriptive statistics about the items
• ggpairs.combined
  A scattermatrix with histograms on the diagonal and correlation coefficients in the upper right half.

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.

Examples

### 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 all items
scaleDiagnosis(dat=exampleData);

### Use a selection of two variables
scaleDiagnosis(dat=exampleData, items=c('item2', 'item4'));