scaleDiagnosis — scaleDiagnosis • ufs

scaleDiagnosis provides a number of diagnostics for a scale (an aggregative measure consisting of several items).

scaleDiagnosis(
  data = NULL,
  items = NULL,
  plotSize = 180,
  sizeMultiplier = 1,
  axisLabels = "none",
  scaleReliability.ci = FALSE,
  conf.level = 0.95,
  normalHist = TRUE,
  poly = TRUE,
  digits = 3,
  headingLevel = 3,
  scaleName = NULL,
  ...
)

# S3 method for scaleDiagnosis
print(x, digits = x$digits, ...)

scaleDiagnosis_partial(
  x,
  headingLevel = x$input$headingLevel,
  quiet = TRUE,
  echoPartial = FALSE,
  partialFile = NULL,
  ...
)

# S3 method for scaleDiagnosis
knit_print(
  x,
  headingLevel = x$headingLevel,
  quiet = TRUE,
  echoPartial = FALSE,
  partialFile = NULL,
  ...
)

Arguments

data: 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).
normalHist: Whether to use the default ggpairs histogram on the diagonal of the scattermatrix, or whether to use the normalHist() version.
poly: Whether to also request the estimates based on the polychoric correlation matrix when calling scaleStructure().
digits: The number of digits to pass to the print method for the descriptives dataframe.
headingLevel: The level of the heading (number of hash characters to insert before the heading, to be rendered as headings of that level in Markdown).
scaleName: Optionally, a name for the scale to print as heading for the results.
...: Additional arguments for scaleDiagnosis() are passed on to scatterMatrix(), and additional arguments for the print method are passed to the default print method.
x: The object to print.
quiet: Whether to be chatty (FALSE) or quiet (TRUE).
echoPartial: Whether to show the code in the partial (TRUE) or hide it (FALSE).
partialFile: The file with the Rmd partial (if you want to overwrite the default).

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
scatterMatrix: 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.

Author

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters gjalt-jorn@userfriendlyscience.com

Examples


### Note: the 'not run' is simply because running takes a lot of time,
###       but these examples are all safe to run!
if (FALSE) {
### 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(data=exampleData, items=c('item2', 'item4'));

### Use all items
scaleDiagnosis(data=exampleData);
}