https://github.com/p-gw/classicaltesttheory.jl

Classical Test Theory analysis in Julia
https://github.com/p-gw/classicaltesttheory.jl

classical-test-theory julia psychometrics

Last synced: 5 months ago
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Classical Test Theory analysis in Julia

• Host: GitHub
• URL: https://github.com/p-gw/classicaltesttheory.jl
• Owner: p-gw
• License: mit
• Created: 2022-08-31T14:58:25.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-02-20T12:49:30.000Z (over 2 years ago)
• Last Synced: 2025-07-09T17:09:28.010Z (12 months ago)
• Topics: classical-test-theory, julia, psychometrics
• Language: Julia
• Homepage:
• Size: 57.6 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ClassicalTestTheory.jl

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://p-gw.github.io/ClassicalTestTheory.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://p-gw.github.io/ClassicalTestTheory.jl/dev)

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[![Coverage](https://codecov.io/gh/p-gw/ClassicalTestTheory.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/p-gw/ClassicalTestTheory.jl)

ClassicalTestTheory.jl is a Julia package for data analysis using [Classical Test Theory](https://en.wikipedia.org/wiki/Classical_test_theory#:~:text=It%20is%20a%20theory%20of,the%20reliability%20of%20psychological%20tests.).

## Installation

```julia

] add https://github.com/p-gw/ClassicalTestTheory.jl.git

```

## Getting started

ClassicalTestTheory.jl provides two entry points to doing data analsis. 

The input data can either be a numeric `Matrix` or a [`PsychometricTest`](https://github.com/JuliaPsychometrics/PsychometricTests.jl). 

While `Matrix` methods provide full functionality, `PsychometricTest` methods provide some 

additional convenience such as scale analysis. 

For details on how to use ClassicalTestTheory.jl with `PsychometricTest` see [XXX](#).

Consider some input data `x`,

```julia-repl

julia> n_persons = 100;

julia> n_items = 8;

julia> x = rand(0:100, n_persons, n_items);

```

we can get some descriptive analysis of the items,

```julia

itemanalysis(x)

```

or estimate the internal consistency (e.g. using Cronbach's alpha)

```julia

reliability(x, Alpha())

```

The package will automatically calculate the coefficient from the data and construct appropriate confidence intervals.

To get multiple estimates of reliability just pass a vector of methods:

```julia

coefficients = [Alpha(), GLB(), Mu(2)]

reliability(x, coefficients)

```