calculateReliability |
calculateReliability: determine the reliability and SEM per Type |

calculateVarCov |
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_delta |

checkDatasets |
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. |

computeCompositeReliability |
computeCompositeReliability: multivariate generalizability theory approach to estimate the composite reliability of student performance across different types of assessments. |

computeMaxCompositeReliability |
computeMaxCompositeReliability: multivariate generalizability theory approach to estimate the maximum composite reliability of student performance across different types of assessments. |

DStudy |
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. |

GStudy |
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. |

GStudyPerType |
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. |

mydata |
mydata |