This function performs an item analysis with certain statistics that are useful for scale or index development. The resulting tables are shown in the viewer pane resp. webbrowser or can be saved as file. Following statistics are computed for each item of a data frame:

percentage of missing values

mean value

standard deviation

skew

item difficulty

item discrimination

Cronbach's Alpha if item was removed from scale

mean (or average) inter-item-correlation

Optional, following statistics can be computed as well:

kurstosis

Shapiro-Wilk Normality Test

If `factor.groups`

is not `NULL`

, the data frame `df`

will be
splitted into groups, assuming that `factor.groups`

indicate those columns
of the data frame that belong to a certain factor (see return value of function `tab_pca`

as example for retrieving factor groups for a scale and see examples for more details).

## Usage

```
tab_itemscale(
df,
factor.groups = NULL,
factor.groups.titles = "auto",
scale = FALSE,
min.valid.rowmean = 2,
alternate.rows = TRUE,
sort.column = NULL,
show.shapiro = FALSE,
show.kurtosis = FALSE,
show.corr.matrix = TRUE,
CSS = NULL,
encoding = NULL,
file = NULL,
use.viewer = TRUE,
remove.spaces = TRUE
)
sjt.itemanalysis(
df,
factor.groups = NULL,
factor.groups.titles = "auto",
scale = FALSE,
min.valid.rowmean = 2,
alternate.rows = TRUE,
sort.column = NULL,
show.shapiro = FALSE,
show.kurtosis = FALSE,
show.corr.matrix = TRUE,
CSS = NULL,
encoding = NULL,
file = NULL,
use.viewer = TRUE,
remove.spaces = TRUE
)
```

## Arguments

- df
A data frame with items.

- factor.groups
If not

`NULL`

,`df`

will be splitted into sub-groups, where the item analysis is carried out for each of these groups. Must be a vector of same length as`ncol(df)`

, where each item in this vector represents the group number of the related columns of`df`

. If`factor.groups = "auto"`

, a principal component analysis with Varimax rotation is performed, and the resulting groups for the components are used as group index. See 'Examples'.- factor.groups.titles
Titles for each factor group that will be used as table caption for each component-table. Must be a character vector of same length as

`length(unique(factor.groups))`

. Default is`"auto"`

, which means that each table has a standard caption*Component x*. Use`NULL`

to suppress table captions.- scale
Logical, if

`TRUE`

, the data frame's vectors will be scaled when calculating the Cronbach's Alpha value (see`item_reliability`

). Recommended, when the variables have different measures / scales.- min.valid.rowmean
Minimum amount of valid values to compute row means for index scores. Default is 2, i.e. the return values

`index.scores`

and`df.index.scores`

are computed for those items that have at least`min.valid.rowmean`

per case (observation, or technically, row). See`mean_n`

for details.- alternate.rows
Logical, if

`TRUE`

, rows are printed in alternatig colors (white and light grey by default).- sort.column
Numeric vector, indicating the index of the column that should sorted. by default, the column is sorted in ascending order. Use negative index for descending order, for instance,

`sort.column = -3`

would sort the third column in descending order. Note that the first column with rownames is not counted.- show.shapiro
Logical, if

`TRUE`

, a Shapiro-Wilk normality test is computed for each item. See`shapiro.test`

for details.- show.kurtosis
Logical, if

`TRUE`

, the kurtosis for each item will also be shown (see`kurtosi`

and`describe`

in the`psych`

-package for more details.- show.corr.matrix
Logical, if

`TRUE`

(default), a correlation matrix of each component's index score is shown. Only applies if`factor.groups`

is not`NULL`

and`df`

has more than one group. First, for each case (df's row), the sum of all variables (df's columns) is scaled (using the`scale`

-function) and represents a "total score" for each component (a component is represented by each group of`factor.groups`

). After that, each case (df's row) has a scales sum score for each component. Finally, a correlation of these "scale sum scores" is computed.- CSS
A

`list`

with user-defined style-sheet-definitions, according to the official CSS syntax. See 'Details' or this package-vignette.- encoding
Character vector, indicating the charset encoding used for variable and value labels. Default is

`"UTF-8"`

. For Windows Systems,`encoding = "Windows-1252"`

might be necessary for proper display of special characters.- file
Destination file, if the output should be saved as file. If

`NULL`

(default), the output will be saved as temporary file and opened either in the IDE's viewer pane or the default web browser.- use.viewer
Logical, if

`TRUE`

, the HTML table is shown in the IDE's viewer pane. If`FALSE`

or no viewer available, the HTML table is opened in a web browser.- remove.spaces
Logical, if

`TRUE`

, leading spaces are removed from all lines in the final string that contains the html-data. Use this, if you want to remove parantheses for html-tags. The html-source may look less pretty, but it may help when exporting html-tables to office tools.

## Value

Invisibly returns

`df.list`

: List of data frames with the item analysis for each sub.group (or complete, if`factor.groups`

was`NULL`

)`index.scores`

: A data frame with of standardized scale / index scores for each case (mean value of all scale items for each case) for each sub-group.`ideal.item.diff`

: List of vectors that indicate the ideal item difficulty for each item in each sub-group. Item difficulty only differs when items have different levels.`cronbach.values`

: List of Cronbach's Alpha values for the overall item scale for each sub-group.`knitr.list`

: List of html-tables with inline-css for use with knitr for each table (sub-group)`knitr`

: html-table of all complete output with inline-css for use with knitr`complete.page`

: Complete html-output.

If `factor.groups = NULL`

, each list contains only one elment, since just one
table is printed for the complete scale indicated by `df`

. If `factor.groups`

is a vector of group-index-values, the lists contain elements for each sub-group.

## Note

The

*Shapiro-Wilk Normality Test*(see column`W(p)`

) tests if an item has a distribution that is significantly different from normal.*Item difficulty*should range between 0.2 and 0.8. Ideal value is`p+(1-p)/2`

(which mostly is between 0.5 and 0.8).For

*item discrimination*, acceptable values are 0.20 or higher; the closer to 1.00 the better. See`item_reliability`

for more details.In case the total

*Cronbach's Alpha*value is below the acceptable cut-off of 0.7 (mostly if an index has few items), the*mean inter-item-correlation*is an alternative measure to indicate acceptability. Satisfactory range lies between 0.2 and 0.4. See also`item_intercor`

.

## References

Jorion N, Self B, James K, Schroeder L, DiBello L, Pellegrino J (2013) Classical Test Theory Analysis of the Dynamics Concept Inventory. (web)

Briggs SR, Cheek JM (1986) The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x

McLean S et al. (2013) Stigmatizing attitudes and beliefs about bulimia nervosa: Gender, age, education and income variability in a community sample. International Journal of Eating Disorders. doi: 10.1002/eat.22227

Trochim WMK (2008) Types of Reliability.

## Examples

```
# Data from the EUROFAMCARE sample dataset
library(sjmisc)
library(sjlabelled)
data(efc)
# retrieve variable and value labels
varlabs <- get_label(efc)
# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")
# create data frame with COPE-index scale
mydf <- data.frame(efc[, start:end])
colnames(mydf) <- varlabs[start:end]
if (FALSE) {
if (interactive()) {
tab_itemscale(mydf)
# auto-detection of labels
tab_itemscale(efc[, start:end])
# Compute PCA on Cope-Index, and perform a
# item analysis for each extracted factor.
indices <- tab_pca(mydf)$factor.index
tab_itemscale(mydf, factor.groups = indices)
# or, equivalent
tab_itemscale(mydf, factor.groups = "auto")
}}
```