Foundations in Applied Measurement in Education

FAME 2 Classical Test Theory and Item Response Theory

Introduces the foundational concepts of educational assessment and measurement. Examines key concepts of reliability, validity, latent variables and measurement error. Covers basic statistical concepts used in educational measurement and reporting.

FAME 3 Item and test analysis using ACER ConQuest®

Involves conducting and interpreting a psychometric analysis of a multiple-choice assessment using ACER ConQuest® software to inform refinements to an existing measure. It analyses assessment items where partial credit scoring applies. Participants apply both the Rasch model and the one parameter logistic (IRT) model. Participants will work with models for dichotomous and for polytomous data.

FAME 4 Differential Item Functioning (DIF)

Explores important validity and comparability questions through an Item Response Theory paradigm by introducing participants to Differential Item Functioning (DIF) and requiring them to undertake a DIF analysis using ACER ConQuest®.

FAME 5 Test equating

Explores test equating methods using the one parameter logistic (IRT) model. Participants will learn to equate 2 assessments on a common measurement scale through common item equating.

FAME 6 Automated psychometric analysis reports with conquestr

Introduces participants to the conquestr package that interfaces with ACER ConQuest® software. Participants will use R and RStudio with conquestr output to produce an automated and reproducible psychometric analysis and reporting workflow for quality assuring multiple-choice assessments.

FAME 7 Working with plausible values

Introduces the theory behind different item response models and case ability estimation routines that can be used to estimate population parameters reliably. Participants will apply this knowledge to fit a combined item response and population model, similar to those seen in large-scale assessment programs such as NAPLAN and the Programme for International Student Assessment, and to correctly apply the law of total variance to undertake secondary analysis to yield unbiased population parameter estimates.