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 sjt.pca
as example for retrieving factor groups for a scale and see examples for more details).
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)A data frame with items.
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. See 'Examples'.
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.
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.
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.
Logical, if TRUE, rows are printed in
alternatig colors (white and light grey by default).
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.
Logical, if TRUE, a Shapiro-Wilk normality test is computed for each item.
See shapiro.test for details.
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.
A list with user-defined style-sheet-definitions,
according to the official CSS syntax.
See 'Details' or this package-vignette.
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.
Destination file, if the output should be saved as file.
If NULL (default), the output will be saved as temporary file and
openend either in the IDE's viewer pane or the default web browser.
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.
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.
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.
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. 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. 10.1002/eat.22227
Trochim WMK (2008) Types of Reliability. (web)
# NOT RUN {
# 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 {
sjt.itemanalysis(mydf)
# auto-detection of labels
sjt.itemanalysis(efc[, start:end])
# Compute PCA on Cope-Index, and perform a
# item analysis for each extracted factor.
factor.groups <- sjt.pca(mydf)$factor.index
sjt.itemanalysis(mydf, factor.groups)
# }
# NOT RUN {
# }
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