sjPlot (version 1.8.3)

sjt.itemanalysis: 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 sjt.pca as example for retrieving factor groups for a scale and see examples for more details).

Usage

sjt.itemanalysis(df, factor.groups = NULL, factor.groups.titles = "auto",
  scaleItems = FALSE, minValidRowMeanValue = 2, alternateRowColors = TRUE,
  orderColumn = NULL, orderAscending = TRUE, showShapiro = FALSE,
  showKurtosis = FALSE, showCompCorrMat = TRUE, file = NULL,
  encoding = NULL, CSS = NULL, useViewer = TRUE, no.output = FALSE,
  remove.spaces = TRUE)

Value

Invisibly returns

• df.list: List of data frames with the item analysis for each sub.group (or complete, iffactor.groupswasNULL)
• index.scores: List of standardized scale / index scores of each case (mean value of all scale items for each case) for each sub-group. Note that NA's are removed from this list. Usedf.index.scoresif you want to append the cases' related index scores to the original data frame.
• df.index.scores: A data frame with allindex.scoresas column variables. Whileindex.scoresdon't have NA's included, this data frame's row-length equals to the originals data frame's row-length (and thus can be appended)
• 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 was 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.

Details

See 'Details' in sjt.frq.

References

• Jorion N, Self B, James K, Schroeder L, DiBello L, Pellegrino J (2013) Classical Test Theory Analysis of the Dynamics Concept Inventory. (https://www.academia.edu/4104752/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 (http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6494.1986.tb00391.x/abstract{web})
• 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. (http://www.socialresearchmethods.net/kb/reltypes.php{web})

See Also

http://www.strengejacke.de/sjPlot/sjt.itemanalysis/{sjPlot manual: sjt.itemanalysis}

Examples

# -------------------------------
# Data from the EUROFAMCARE sample dataset
# -------------------------------
library(sjmisc)
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[, c(start:end)])
colnames(mydf) <- varlabs[c(start:end)]

sjt.itemanalysis(mydf)

# -------------------------------
# auto-detection of labels
# -------------------------------
sjt.itemanalysis(efc[, c(start:end)])

# ---------------------------------------
# Compute PCA on Cope-Index, and perform a
# item analysis for each extracted factor.
# ---------------------------------------
factor.groups <- sjt.pca(mydf, no.output = TRUE)$factor.index
sjt.itemanalysis(mydf, factor.groups)