tab_itemscale: Summary of item analysis of an item scale as HTML table

In sjPlot: Data Visualization for Statistics in Social Science

Summary of item analysis of an item scale as HTML table

Description

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. (web)

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]

## Not run: 
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")
}
## End(Not run)

sjPlot documentation built on Aug. 17, 2023, 5:11 p.m.