Course

Unit 1

Introduction to Psychometric Testing and Measurement

Definition and role of psychological testing. Latent vs. manifest variables. Levels of measurement. Basic concepts of measurement error. Visualisation of measurement models using Miro. Simulated error computation using Excel/Google Sheets

Unit 2

Classical Test Theory (CTT)

Axioms and assumptions of CTT. Models in classical test theory. Estimating model parameters: Statistical methods for model verification. Interactive model demonstration using JASP/Jamovi

Unit 3

Reliability and Factor Analysis

Concept and importance of reliability. Methods to determine reliability (test-retest, internal consistency, split-half, etc.). Exploratory factor analysis: assumptions, steps, and interpretation. Application in evaluating test structures. Hands-on reliability estimation using JASP or Jamovi. Excel templates for Cronbach’s alpha calculation. Kaggle dataset exploration for practice factor analysis. 

Unit 4

Test and Item Construction

Principles and guidelines for item writing. Scaling and scoring. Item analysis and item difficulty. Methods of single-case diagnostics. Conducting item analysis through Google Sheets. Collaborative test-construction using Padlet or Trello. Mentimeter live quiz to illustrate test discrimination and item difficulty

Unit 5

Validation and R-Based Analysis

Qualitative methods in item development. Validity: content, criterion, and construct validity. Validating questionnaires. Hands-on practice using R for psychometric analysis. Introduction to RStudio (guided mode) and Jamovi for validation exercises. JASP output interpretation for construct validity visualisation. Mini-project: Build a 10-item scale and validate it using R or Jamovi

Text Books / References

Textbooks

1. Cohen, R. J., & Swerdlik, M. E. (2018). Psychological testing and assessment: An introduction to tests and measurement (9th ed.). McGraw-Hill Education.
2. Devellis, R. F. (2016). Scale development: Theory and applications (4th ed.). Sage Publications.
3. Gregory, R. J. (2017). Psychological testing: History, principles, and applications (7th ed.). Pearson.
4. Kaplan, R. M., & Saccuzzo, D. P. (2017). Psychological testing: Principles, applications, and issues (9th ed.). Cengage Learning.
5. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.

Suggested Readings

1. Cohen, R. J., & Swerdlik, M. E. (2018). Psychological testing and assessment: An introduction to tests and measurement (9th ed.). McGraw-Hill Education.
2. Gregory, R. J. (2017). Psychological testing: History, principles, and applications (7th ed.). Pearson.
3. Kaplan, R. M., & Saccuzzo, D. P. (2017). Psychological testing: Principles, applications, and issues (9th ed.). Cengage Learning.

This course offers a comprehensive exploration of the theoretical and practical aspects of psychometric testing, with a particular focus on classical test theory (CTT) and its application in psychological assessment. Students are introduced to the foundational principles of measurement, which underpin the development and evaluation of psychological tests. The curriculum begins with an overview of psychometric testing and classical test theory, highlighting its axioms, models, and relevance for modern psychological measurement. A key component of the course is the study of latent and manifest variables. Latent variables represent underlying psychological traits or constructs that cannot be observed directly, while manifest variables are the observable outcomes measured through tests. Understanding the distinction and interplay between these variables is essential for designing effective assessment tools and interpreting test results accurately. The principles of measurement are discussed in detail, emphasising their importance in ensuring the validity and reliability of psychological assessments. Students learn to apply statistical methods to verify psychometric properties, including reliability estimation and model verification. Techniques such as reliability analysis enable students to assess the consistency and trustworthiness of measurement instruments, which are crucial for both research and practical applications in psychology. The course guides students through the process of test and item construction, providing guidelines and best practices for developing meaningful and effective test items. Both qualitative and quantitative methods are covered, including item development and validation techniques. Exploratory factor analysis is introduced as a tool for examining the structure of psychological constructs and refining measurement instruments. Students gain hands-on experience with statistical software, such as R, for data handling, reliability analysis, and exploratory factor analysis. The application of R enhances students’ ability to perform sophisticated statistical analyses and validate psychological tests with scientific precision. Throughout the module, emphasis is placed on scientific rigour and ethical standards in psychometric diagnostics. By the end of the course, students are equipped to design, construct, and validate psychological measurement tools, demonstrating an understanding of both theoretical foundations and practical applications. The course objectives and outcomes reinforce the development of essential skills in psychometric testing, preparing students and educators to engage confidently with psychological assessment and research.

Course Objectives:

• To introduce the basic principles of psychometric testing and measurement in psychology.
• To explain the concepts of latent and manifest variables in the context of psychological testing.
• To examine the axioms and models of classical test theory (CTT).
• To teach statistical methods for reliability estimation and model verification.
• To train students in item construction and validation using qualitative and quantitative techniques, including R programming.
• To familiarise students with beginner-friendly tools for data handling, reliability analysis, and exploratory factor analysis.

Course Outcomes:

• CO1: Understand and differentiate between latent and manifest variables in psychological measurement.
• CO2: Demonstrate knowledge of classical test theory and its axioms, including statistical verification of its models.
• CO3: Apply appropriate methods to estimate reliability and conduct exploratory factor analysis.
• CO4: Develop skills in single-case diagnostics and construct reliable and valid psychometric tools.
• CO5: Utilise qualitative methods for item development and validate self-made questionnaires using R software.

CO-PO Mapping