nice_item_analysis.RdReturns a data.frame with detailed item analyses for the provided scale.
nice_item_analysis(
data,
scales,
labels = NULL,
round = 2,
ci = FALSE,
conf_level = 0.95,
check_key = TRUE,
keys = NULL,
keys_from_weights = FALSE,
RMSEA = FALSE,
difficulty = FALSE,
values = NULL,
fa = TRUE,
...
)A data Frame
A list with vectors with variable names that define each scale.
Optional labels for the items in the scale.
Rounds values to given decimal position.
If TRUE confidence intervals are calculated.
Confidence level (e.g. 0.95 for 95 percent).
Check_key for the psych::alpha function.
Optional key argument for the psych::alpha function.
If TRUE, tries to define keys from the weights dictionary attribute. These are only available when you defined them with the scaledic package.
If TRUE RMSEA is calculated.
If TRUE, the difficulty of the item is calculated.
Sets maximum and minimum valid values necessary to calculate item difficulty.
If TRUE, a one factor exploratory factor analyses is calculated and loadings are reported.
Further arguments passed to the nice_table() function.
A data frame with concise scale indices.
nice_item_analysis(
wmisc:::data_emo,
scale = wmisc:::data_emo_scales,
difficulty = TRUE,
values = c(1,5)
)
Table
Item analysis
Labels
M
SD
rit
Loadings
Difficulty
Grundeinstellung
n = 146; Alpha = .68; Std. alpha = .68; Homogeneity = .26
Resilienz
n = 146; Alpha = .60; Std. alpha = .60; Homogeneity = .23
Intuition
n = 145; Alpha = .80; Std. alpha = .81; Homogeneity = .41
Selbstwahrnehmung
n = 146; Alpha = .72; Std. alpha = .72; Homogeneity = .39
Sensibilität
n = 146; Alpha = .57; Std. alpha = .58; Homogeneity = .21
Aufmerksamkeit
n = 146; Alpha = .80; Std. alpha = .80; Homogeneity = .41