calculateReliability

calculateReliability: determine the reliability and SEM per 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

calculateReliability function

<dl><dt>mydata</dt>
<dd>A dataframe containing columns ID, Type, Score (numeric)</dd>
<dt>n</dt>
<dd>A vector containing for each Type the number of scores or assessments, e.g. averages, requirements.</dd></dl>

Arguments

calculateReliability: determine the reliability and SEM per Type — calculateReliability

<dl>

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


<dt>n</dt>
<dd>A vector containing for each Type the number of scores or assessments, e.g. averages, requirements.</dd>

</dl>

calculateReliability: calculateReliability: determine the reliability and SEM per Type

Description

Usage

Value

Arguments

Examples