JSBJ2280 Measurement and Factor Analysis (KATAJA) (5–8 op)

This intensive three-day course offers a comprehensive introduction to the foundations and advanced applications of measurement theory, with a strong emphasis on practical implementation using statistical software. Designed for researchers, graduate students, and professionals in the social sciences, business, and related fields, the course guides participants through core principles and state-of-the-art techniques essential for designing, evaluating, and refining measurement instruments.

Day 1 lays the groundwork with an in-depth overview of measurement theory, covering key concepts such as reliability and validity. Participants will learn to assess measurement quality through various techniques, and apply these concepts using statistical software in hands-on sessions. Group exercises will deepen understanding by challenging participants to evaluate and improve real or simulated measurement tools.

Day 2 focuses on Exploratory and Confirmatory Factor Analysis (EFA and CFA), two essential tools in the validation of measurement models. The day begins with the theoretical basis and practical techniques for EFA, including factor extraction and rotation methods, followed by a detailed look at CFA, including model specification, parameter estimation, and the use of modification indices. Participants will again engage in hands-on applications using software to practice and interpret both EFA and CFA models.

Day 3 explores advanced topics in measurement. Participants will learn how to diagnose and troubleshoot issues in measurement models, detect and control for common method variance, and test for measurement invariance across groups. The course will also introduce more complex models, such as bi-factor models, and present an overview of formative measurement. Finally, participants will learn how to integrate these advanced measurement approaches into broader CFA models. 

1: Understand fundamental measurement theories and their implications for research design

2: Differentiate between reflective and formative measurement approaches

3: Apply exploratory and confirmatory factor analysis techniques appropriately

4: Evaluate measurement reliability beyond basic Cronbach's alpha

5: Assess different forms of validity (convergent, discriminant, nomological)

6: Identify and address common method variance issues to a degree that they are addressable

7: Diagnose and resolve problems with non-convergent measurement models

8: Test for measurement invariance across groups

9: Address complex measurement challenges in research contexts

10: Critically evaluate published measurement instruments using contemporary standards

11: Leverage large language models to assist with statistical software applications

1: Some of the learning materials for this course are available on the instructor's YouTube channel, hosted by Mikko Rönkkö: https://www.youtube.com/@mronkko. The channel features instructional videos on measurement, factor analysis, and advanced statistical techniques that complement the topics covered in this course.

2: Course literature (may be updated)

a: Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press. (Chapter 5-9, 10, 12)

b: DeVellis, R. F. (2017). Scale development theory and applications (4th ed.). Thousand Oaks: Sage. (Chapters 1-4, 6-7)

c: Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061.

d: Rönkkö, M., & Cho, E. (2019). Discriminant validity: A synthesis of definitions and a test of techniques. Organizational Research Methods.