Package: CompositeReliability 1.0.3.9000

CompositeReliability: Determine the Composite Reliability of a Naturalistic, Unbalanced Dataset

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts.

Authors:Joyce Moonen - van Loon [aut, cre]

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CompositeReliability.pdf |CompositeReliability.html✨
CompositeReliability/json (API)
NEWS

# Install 'CompositeReliability' in R:
install.packages('CompositeReliability', repos = c('https://jmoonen.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jmoonen/compositereliability/issues

Datasets:

On CRAN:

2.70 score 210 downloads 9 exports 49 dependencies

Last updated 1 years agofrom:e53ad76c7f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 23 2024
R-4.5-winOKSep 23 2024
R-4.5-linuxOKSep 23 2024
R-4.4-winOKSep 23 2024
R-4.4-macOKSep 23 2024
R-4.3-winOKSep 23 2024
R-4.3-macOKSep 23 2024

Exports:%>%calculateReliabilitycalculateVarCovcheckDatasetscomputeCompositeReliabilitycomputeMaxCompositeReliabilityDStudyGStudyGStudyPerType

Dependencies:bootclicolorspacecpp11dplyrfansifarvergenericsggplot2glueGPArotationgtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixmgcvminqamnormtmunsellnlmenloptrpillarpkgconfigplyrpsychpurrrR6RColorBrewerRcppRcppEigenreshape2rlangRsolnpscalesstringistringrtibbletidyrtidyselecttruncnormutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
calculateReliability: determine the reliability and SEM per TypecalculateReliability
calculateVarCov: Estimate variance and covariance components of assessee p S_p and mean assessment scores i nested in assessees S_iINp, and determine the error scores S_deltacalculateVarCov
checkDatasets: assert that the given datasets adhere to the assumptions and requirements of this package i.e. the data set 'mydata' is a dataframe with 3 columns, named "ID", "Type" and "Score", column "Score" contains numeric data, and each combination of "ID" and "Type" exists at least once, data set n contains a numerical value for each "Type", and data set weights contains a numerical value for each "Type" and the sum of all values is equal to 1.checkDatasets
computeCompositeReliability: multivariate generalizability theory approach to estimate the composite reliability of student performance across different types of assessments.computeCompositeReliability
computeMaxCompositeReliability: multivariate generalizability theory approach to estimate the maximum composite reliability of student performance across different types of assessments.computeMaxCompositeReliability
DStudy: the program presents the reliability coefficient and the SEM for different numbers of assessments per type. Both the reliability coefficient and the SEM are presented in graphs for differing numbers of assessments, given insight in the impact on the reliability if more or less assessments per type were required or advised.DStudy
GStudy for a dataset in which every student p has a potentially differing number of scores i on each assessment type m. i.e. model i: (p x m). The output gives descriptive statistics, reliability coefficient and SEM for each assessment type.GStudy
GStudyPerType: This function is mainly used within calculateVarCov.R, but can be executed on its own to determine the reliability coefficient and SEM for a dataset with a single type of assessment.GStudyPerType
mydatamydata