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.

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 in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM).

Joyce Moonen - van Loon

compositeReliabilityInNestedDesigns

Optimizing the Composite Reliability in Multivariate Nested
Designs

GStudy function

<dl><dt>mydata</dt>
<dd>A dataframe containing columns ID, Type, Score (numeric)</dd>
<dt>nrDigitsOutput</dt>
<dd>Integer, number of digits in the output</dd></dl>

Arguments

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

<dl>

<dt>mydata</dt>
<dd>A dataframe containing columns ID, Type, Score (numeric)</dd>


<dt>nrDigitsOutput</dt>
<dd>Integer, number of digits in the output</dd>

</dl>

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: 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.

Description

Usage

Value

Arguments

Examples