Table with alpha values — nice_alpha

Returns a data.frame with item analyses for the provided scales.

nice_alpha_table(
  data,
  scales,
  labels = NULL,
  round = 2,
  ci = TRUE,
  conf_level = 0.95,
  check_key = TRUE,
  keys = NULL,
  keys_from_weights = FALSE,
  RMSEA = FALSE,
  difficulty = FALSE,
  values = NULL,
  fa = TRUE
)

alpha_table(
  data,
  scales,
  labels = NULL,
  round = 2,
  ci = TRUE,
  conf_level = 0.95,
  check_key = TRUE,
  keys = NULL,
  keys_from_weights = FALSE,
  RMSEA = FALSE,
  difficulty = FALSE,
  values = NULL,
  fa = TRUE
)

Arguments

data: A data Frame
scales: A list containing vectors with variable names. Each list element defines one scale. Named list elements are used as labels.
labels: Label names for scales (defaults to named list elements in 'scales').
round: Rounds values to given decimal position.
ci: If TRUE confidence intervals are calculated.
conf_level: Confidence level (e.g. 0.95 for 95 percent).
check_key: Check_key for the psych::alpha function.
keys: Optional key argument for the psych::alpha function.
keys_from_weights: If TRUE, tries to define keys from the weights dictionary attribute. These are only available when you defined them with the scaledic package.
RMSEA: If TRUE RMSEA is calculated.
difficulty: If TRUE, the difficulty of the item is calculated.
values: Sets maximum and minimum valid values necessary to calculate item difficulty.
fa: If TRUE, a one factor exploratory factor analyses is calculated and loadings are reported.

Value

A data frame with concise scale indices.

Examples

## Example needs packages scaledic and purrr installed and active
nice_alpha_table(
  data = wmisc:::ex_itrf,
  scales = wmisc:::ex_itrf_scales,
  labels = c("Inernalizing", "Externalizing"),
  keys_from_weights = TRUE,
  difficulty = TRUE,
  values = list(c(0, 3)),
  RMSEA = TRUE
)
#> Error in names(keys) <- colnames(x): 'names' attribute [19] must be the same length as the vector [0]
#> 
#> Cautions:
#> 1: Removed 1 rows because all items were missing.
#> 2: Weights from scaledic attributes are missing.

nice_alpha_table(
  wmisc:::data_emo,
  wmisc:::data_emo_scales,
  check_key = TRUE,
  difficulty = TRUE,
  value = list(c(0,4))
  )


  Table
Item analysis
    
Scale
      
        N
      
      
        Alpha [95% CI]
      
      Homogeneity
      Discriminations
      Difficulties
      Means
      SDs
      |Loadings|
    
items
      cases
      Raw
      Standardized
    
Grundeinstellung
6
[145, 146]
.68 [.59, .75]
.68 [.59, .75]
.26
[.29, .60]
[.47, .81]
[1.86, 3.23]
[0.92, 1.15]
[.33, .83]
Resilienz
5
[146, 146]
.60 [.49, .69]
.60 [.49, .69]
.23
[.30, .40]
[.43, .62]
[1.72, 2.49]
[1.06, 1.18]
[.41, .56]
Intuition
6
[145, 146]
.80 [.75, .85]
.81 [.75, .85]
.41
[.41, .68]
[.70, .80]
[2.79, 3.2]
[0.73, 1.05]
[.45, .78]
Selbstwahrnehmung
4
[145, 146]
.72 [.63, .78]
.72 [.64, .79]
.39
[.45, .63]
[.62, .70]
[2.47, 2.78]
[0.95, 1.09]
[.53, .83]
Sensibilität
5
[146, 146]
.57 [.45, .67]
.58 [.46, .68]
.21
[.20, .45]
[.64, .82]
[2.58, 3.27]
[0.87, 1.05]
[.29, .65]
Aufmerksamkeit
6
[146, 146]
.80 [.75, .85]
.80 [.75, .85]
.41
[.51, .69]
[.35, .56]
[1.41, 2.23]
[1.01, 1.25]
[.57, .81]
 Note. Values in brackets depict upper and lower bound of confidence intervals or [min,max] intervals. N cases is the min and max number of non-missing cases for the scale items.

Scale	N		Alpha [95% CI]		Homogeneity	Discriminations	Difficulties	Means	SDs	\|Loadings\|
Table Item analysis
Scale	items	cases	Raw	Standardized	Homogeneity	Discriminations	Difficulties	Means	SDs	\|Loadings\|
Grundeinstellung	6	[145, 146]	.68 [.59, .75]	.68 [.59, .75]	.26	[.29, .60]	[.47, .81]	[1.86, 3.23]	[0.92, 1.15]	[.33, .83]
Resilienz	5	[146, 146]	.60 [.49, .69]	.60 [.49, .69]	.23	[.30, .40]	[.43, .62]	[1.72, 2.49]	[1.06, 1.18]	[.41, .56]
Intuition	6	[145, 146]	.80 [.75, .85]	.81 [.75, .85]	.41	[.41, .68]	[.70, .80]	[2.79, 3.2]	[0.73, 1.05]	[.45, .78]
Selbstwahrnehmung	4	[145, 146]	.72 [.63, .78]	.72 [.64, .79]	.39	[.45, .63]	[.62, .70]	[2.47, 2.78]	[0.95, 1.09]	[.53, .83]
Sensibilität	5	[146, 146]	.57 [.45, .67]	.58 [.46, .68]	.21	[.20, .45]	[.64, .82]	[2.58, 3.27]	[0.87, 1.05]	[.29, .65]
Aufmerksamkeit	6	[146, 146]	.80 [.75, .85]	.80 [.75, .85]	.41	[.51, .69]	[.35, .56]	[1.41, 2.23]	[1.01, 1.25]	[.57, .81]
Note. Values in brackets depict upper and lower bound of confidence intervals or [min,max] intervals. N cases is the min and max number of non-missing cases for the scale items.