Assessment Instruments and Intervention Programs: Development and Psychometruc Analysis

Recommended Prerequisites

Some advanced knowledge of statistics is recommended, such as multiple regression models, semi- and non-parametric regression models, exploratoty and confirmatory factor analysis, structure equation models, and growth curve models.  

Teaching Methods

This course will use a multitude of strategies and activities, using oral presentations, in-class discussions, group work, problem solving and data analysis using open source statistical software such as R (https://www.r-project.org/) and RStudio (https://rstudio.com/), and critical analysis of the different applications of the methodologies covered.

Learning Outcomes

This course’s main objective is to enable students to develop assessment instruments and intervention programs, together with their psychometric analysis within the perspective of Classic Test Theory.

Work Placement(s)

No

Syllabus

Module 1 - Development of assessment instruments and intervention programs:

- Development methodologies.

- Construct to be evaluated, objectives of the intervention plan, and target population.

- Development of new items that ensures the representativeness of the construct/intervention program.

- Focus groups.

Module 2 - Psychometric analysis:

- Classical Test Theory.

- Difficulty and item discrimination measures, error and reliability measures.

- Determination of the optimum cut-off point(s) in diagnostic tests or continuous markers through the use of ROC (Receiver Operating Characteristic) curves, ROC predictive curves (PROC) and accuracy measures such as reliability / specificity, positive and negative predictive values and likelihood ratios.

- Interpretation of tests: use of normative data, confidence intervals, profile analysis, statistical versus clinical significance, trend analysis, analysis of change and discriminant functions.

Head Lecturer(s)

Bruno Cecílio de Sousa

Assessment Methods

Assessment
Laboratory work or Field work: 50.0%
Frequency: 50.0%

Bibliography

Adams,K.,& Waldron-Perrine,B.(2014).Psychometrics, test design, and essential statistics. In K. Stucky et al (Eds.), Clinical Neuropsychology study guide & Board Review (pp.79-114).OUP.

Crocker, L. (2005). Introduction to Classical & Modern Test Theory. Cengage Learning.

Koziol,L., et al. (2016).Large-Scale Brain Systems and Neuropsychological Testing.Springer.

Iliescu, D. (2017). Adapting tests in linguistic and cultural situations.Cambridge University Press.

Lopez-Raton, M.,Rodriguez-Alvarez, M.X, Cadarso-Suarez, C. & Gude-Sampedro, F.(2014). Optimal Cut points: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests. Journal of Statistical Software 61(8), 1–36.

Metzger,F.(Ed.)(2013). Neuropsychology: New Research.Nova Science.

Miller, L., & Lovler, R. (2019). Foundations of psychological testing.Sage.

Tate, R.L. & Perdices, M.(2019).Single-Case Experimental Designs for Clinical Research and Neurorehabilitation Settings: Planning, Conduct, Analysis and Reporting.Routledge.