Course overview: Starting from the scale construction and gradually moving to the most recent statistical methods employed in measurement, the course provides a complete methodological framework for applied researchers. While the classical test theory ideas on reliability and validity, often used in the literature, are presented, the more methodologically sound item response theory counterparts are explained (difficulty and discrimination, item and total characteristic and information curves).For the measurement of the latent variable(s) the course presents exploratory and confirmatory factor analysis (EFA and CFA) for numerical data. For categorical data, both the item response theory approach (IRT; 2-parameter logistic, grated response and partial credit models) as well as the item factor analysis model (IFA; both with regard to EFA and CFA). The course also presents the methods to explore measurement differences (measurement invariance) between groups (MG CFA), measurement differences due to covariates (MIMIC models), and measurement differences between raters or time points (longitudinal IFA), for all types of data. Lectures and lab sessions will be based on the use of the Stata software.

Requirements: Potential students should already be familiar with and have a good understanding of regression and factor analysis. While no prior knowledge of Stata will be assumed, this will not be a formal Stata course. The course will be of interest to statisticians, researchers and PhD students of Psychology and Psychiatry, and mental health professional who wish to acquire and/or consolidate the knowledge and skills necessary for mental health research.

Learning outcomes:

Subject Specific: Knowledge, Understanding and Skills

On successful completion of this module the student should be able to:

• develop a psychometric tool, for a characteristic of interest (latent variable)
• apply classical test theory methods in psychometrics to test the reliability and the validity of a scale
• apply item response theory methods in psychometrics
• apply exploratory and confirmatory factor analysis for binary, categorical, and numerical data
• understand the concept of measurement invariance and be able to apply the corresponding methods across groups
• to choose the appropriate model for each type of data
• to understand the links between different schools of thought in psychometrics, their similarities and their differences
• to critically evaluate the quality of published psychometric assessments
• understand and explain the concept of latent constructs and their measurement

General: Knowledge, Understanding and Skills

On successful completion of this module the student should be able to:

• Interact effectively in a research team (group activities)
• Communicate ideas effectively and professionally by written, oral and visual means
• Select and properly manage information drawn from books, journals, and the internet
• Be able to report the results of their study in the form of a scientific paper
• Study autonomously and undertake activities/projects with minimum guidance
• Show confidence in the use of Stata to implement psychometric models for real life applications