ShinyItemAnalysis-package

ShinyItemAnalysis: Test and Item Analysis via Shiny

The ShinyItemAnalysis package contains an interactive Shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters, which can be accessed using function startShinyItemAnalysis(). The shiny application covers a broad range of psychometric methods and offers data examples, model equations, parameter estimates, interpretation of results, together with a selected R code, and is therefore suitable for teaching psychometric concepts with R. It also allows the users to upload and analyze their own data and to automatically generate analysis reports in PDF or HTML.

Besides, the package provides its own functions for test and item analysis within classical test theory framework (e.g., functions gDiscrim(), ItemAnalysis(), DistractorAnalysis(), or DDplot()), using various regression models (e.g., plotCumulative(), plotAdjacent(), plotMultinomial(), or plotDIFLogistic()), and under IRT framework (e.g., ggWrightMap(), or plotDIFirt()).

Package also contains several demonstration datasets including the HCI

dataset from the book by Martinkova and Hladka (2023), and from paper by Martinkova and Drabinova (2018). package

Functions

• startShinyItemAnalysis()
• DDplot()
• DistractorAnalysis()
• plotDistractorAnalysis()
• fa_parallel()
• gDiscrim()
• ggWrightMap()
• ICCrestricted()
• ItemAnalysis()
• blis()
• plotAdjacent(), plotCumulative(), plotMultinomial()
• plotDIFirt(), plotDIFLogistic()
• plot_corr()
• recode_nr()

Datasets

• AIBS()
• Anxiety()
• AttitudesExpulsion()
• BFI2()
• CLoSEread6()
• CZmatura()
• CZmaturaS()
• dataMedical()
• dataMedicalgraded()
• dataMedicalkey()
• dataMedicaltest()
• HCI()
• HCIdata()
• HCIgrads()
• HCIkey()
• HCIlong()
• HCIprepost()
• HCItest()
• HCItestretest()
• HeightInventory()
• LearningToLearn()
• MSATB()
• MSclinical()
• NIH()
• TestAnxietyCor()

References

Martinkova, P., & Hladka, A. (2023). Computational Aspects of Psychometric Methods: With R. Chapman and Hall/CRC. tools:::Rd_expr_doi("10.1201/9781003054313")

Martinkova, P., & Drabinova, A. (2018). ShinyItemAnalysis for teaching psychometrics and to enforce routine analysis of educational tests. The R Journal, 10(2), 503--515, tools:::Rd_expr_doi("10.32614/RJ-2018-074")

See Also

Useful links:

• https://www.ShinyItemAnalysis.org
• Report bugs at https://github.com/patriciamar/ShinyItemAnalysis/issues

Author(s)

Patricia Martinkova

Institute of Computer Science of the Czech Academy of Sciences

Faculty of Education, Charles University

martinkova@cs.cas.cz

Adela Hladka (nee Drabinova)

Institute of Computer Science of the Czech Academy of Sciences

Jan Netik

Institute of Computer Science of the Czech Academy of Sciences