4.4 Exercises#
Dichotomous IRT#
We will now solve a few exercises on Item Response Theory with Dichotomous Data.
1️⃣ Dimensionality assessment#
Instructions:
Download and load the dataset
Test unidimensionality on the addition items of the
zareki
dataset.What conclusion can you draw from the results of the analysis?
Are there any items that seem to violate unidimensionality? If so, remove them and run the analysis again. What has changed?
# Data handling
# Princals
# EFA tetrachoric correlation
# ICA
2️⃣: The Rasch model#
Instructions:
Analyze the item parameters of a Rasch model on all the mathematical items in the
zareki
datasetAssess general model fit
Assess misfit at the item level
Clean the dataset and re-fit the Rasch model
Interpret the result
Plot the item response functions (ICC)
# Data handling
# FIT testing
# Cleaning and comparison
# ICC
3️⃣: 2-PL model#
Instructions:
Replicate the analysis above, but this time use a 2-PL model
Extract item difficulties and item discrimination into a python pandas.dataframe
Create 2 subplots.
1st subplot: barplot showing item difficulties
2nd subplot: barplot showing item discrimination
add xlabels and ylabels and a title
# 2-PL model
# Item parameters extraction
# Plotting