Practical Item Analysis: Understanding Easiness and Discrimination in Cognitive Assessments
This post provides a hands-on guide to performing item analysis on cognitive assessment items using Python. Learn how to simulate responses, evaluate item easiness, and calculate discrimination indices. By the end, you’ll be able to assess the quality of your items without relying on complex IRT models, making this approach useful for both item construction and the evaluation of existing item banks. Perfect for practitioners looking to enhance the effectiveness of recruitment assessments or other cognitive tests.
Sentiment Shifts and Topic Clusters: Dissecting the 2024 Presidential Debate
Using sentiment analysis and embeddings to explore the 2024 Presidential Debate.
Can We Predict Employee Happiness? Exploring Work Enjoyment with Random Forests
Using a combination of psychometric data and machine learning, we attempt to predict employee happiness.
Simulating the Effects of Item Drift on CATs
This simulation investigates the impact of item drift on computerised adaptive testing. It demonstrates how item compromise can lead to biased ability estimates and overexposed items, affecting the precision and validity of CATs.
AI Gets a Tinfoil Hat: Testing Conspiracy Beliefs with a Sunny Twist
Using Generative AI to determine who’s the biggest conspiracy theorist in the gang.
Exploring New Article Posts: Part 1
A quick exploration of news articles I scraped.