userfriendlyscience (version 0.1-2)

scaleReliability: scale.ic

Description

The scale.ic function computes a number of measures to assess scale reliability and internal consistency.

Usage

scaleReliability(dat=NULL, items = 'all', digits = 2, ci = TRUE,
                 conf.level=.95, silent=FALSE, samples=1000, bootstrapSeed = NULL,
                 omega.psych = FALSE)

Arguments

dat

A dataframe containing the items in the scale. All variables in this dataframe will be used if items = 'all'.

items

If not 'all', this should be a character vector with the names of the variables in the dataframe that represent items in the scale.

digits

Number of digits to use in the presentation of the results.

ci

Whether to compute confidence intervals as well (this can take a while!).

conf.level

The confidence of the confidence intervals.

silent

If computing confidence intervals, the user is warned that it may take a while, unless silent=TRUE.

samples

The number of samples ot compute for the bootstrapping of the confidence intervals.

bootstrapSeed

The seed to use for the bootstrapping - setting this seed makes it possible to replicate the exact same intervals, which is useful for publications.

omega.psych

Whether to also compute omega using the function 'omega' in the 'psych' package. This method differs from the one used by the MBESS package, and usually (perhaps always) results in higher estimates for omega.

Value

• An object with the input and several output variables. Most notably:
• input
  Input specified when calling the function
• intermediate
  Intermediate values and objects computed to get to the final results
• output
  Values of reliability / internal consistency measures, with as most notable elements:
• output$dat
  A dataframe with the most important outcomes
• output$omega
  Point estimate for omega
• output$glb
  Point estimate for the Greatest Lower Bound
• output$alpha
  Point estimate for Cronbach's alpha
• output$omega.ci
  Confidence interval for omega
• output$alpha.ci
  Confidence interval for Cronbach's alpha

Details

This function is basically a wrapper for functions from the psych, GPArotation, ltm, and MBESS packages that compute measures of reliability and internal consistency. For backwards compatibility, in addition to scaleReliability, scale.ic can still be used (but this is obsolete and therefore strongly discouraged).

Examples

### This will prompt the user to select an SPSS file
scaleReliability();

### Load data from simulated dataset testRetestSimData (which
### satisfies essential tau-equivalence).
data(testRetestSimData);

### Select some items in the first measurement
exampleData <- testRetestSimData[2:6];

### Use all items
scaleReliability(dat=exampleData, ci=FALSE);

### Use a selection of two variables
scaleReliability(dat=exampleData, items=c('t0_item2', 't0_item4'), ci=FALSE);

### Note that 'ci = FALSE' can be omitted to compute confidence intervals
### for scales of three items or more.