1128777

Course
Measuring the Unobservable: Scale Development, Confirmatory Factor Analysis, and Structural Equation Modeling - ONLINE

Faculty
Associate Professor Tobias Schäfers
Associate Professor Michel van der Borgh

Course Coordinator
Associate Professor Tobias Schäfers

Prerequisites
Students should have basic knowledge of conducting survey research and using quantitative methods of data analysis, such as regression and exploratory factor analysis. Knowledge of structural equation modeling or any particular software is not required.

With their applications, students are requested to submit a brief (no more than 750 words) description of themselves and their dissertation project containing the following:
• Research topic
• Data collection and data analysis 
• Experience in using quantitative methods

Please send your brief description to Tobias Schäfers ts.marktg@cbs.dk 

In order to receive the course diploma, students are required to attend the whole course and pass the exam.

Aim
The course introduces doctoral students to the fundamentals of measuring unobservable phenomena by using latent constructs. This includes the basic premises of measurement in the social sciences, steps of scale development (i.e., conceptualization and operationalization of latent constructs), and the use of confirmatory factor analysis (CFA) and structural equation modeling (SEM) for analyzing latent variable models. Through hands-on exercises, participants will be equipped with the knowledge required for assessing existing scales, developing new scales, and performing their own analyses.

Course content
Research projects in various areas of the social sciences often involve the measurement of abstract, non-observable phenomena, such as attitudes, perceptions, or preferences. These so-called latent constructs are commonly measured indirectly via self-reported multi-item scales. Due to their latent nature, however, several challenges arise with regards to the controllability, reliability, and validity of measurement, which need to be considered in the conceptualization and operationalization of a latent construct, as well as in the data analysis. Analyzing latent variable models requires that researchers account for the indirect nature of measurement. To this end, confirmatory factor analysis and structural equation modeling are used, which allow for estimating complex models that combine latent variables with, among others, mediated and moderated relationships, multi-group analyses, variability and change over time, and multilevel data.

This course covers the fundamental aspects of measuring unobservable phenomena with latent constructs, introduces key criteria for assessing the validity and reliability of existing scales, takes students through the process of developing new scales, and explains how confirmatory factor analysis and covariance-based structural equation modeling are used to specify, estimate, and interpret latent variable models. 

An extensive part of the course will consist of exercises in which participants will learn how to perform their own analyses. For these, the two programs JASP (free, open-source) and Mplus will be used. Participants who do not have a valid Mplus license will receive instructions on how to use the free demo version. Additionally, students will receive an overview of alternative SEM software packages (e.g., Stata) and an explanation for how to implement the analyses from the course.

As this course covers the general application of specific research methods, it will not focus on a single area of the social sciences (e.g., marketing, psychology), but will comprise topics that are relevant for every doctoral student intending to employ latent variable models in their analyses.

Teaching style
Mix of interactive lecture, exercises, and discussions

Lecture plan
Day 1: Measurement Issues and SEM Fundamentals
09:00-10:30
10:45-12:15
13:00-14:30
14:45-16:15
16:30-17:15
1. Welcome & Introduction
2. Fundamentals of Measurement
3. Latent Constructs
4. Reflective and Formative Measurement Models
5. Latent Variable Model Specification, Estimation, and Interpretation

 Tobias Schäfers
Michel van der Borgh
Ad de Jong
Day 2: Advanced Topics in SEM
09:00-10:30
10:45-12:15
13:00-14:30
14:45-16:15
16:30-17:15		 6. Exercise Session: CFA and SEM Models in Mplus
7. Mediation Analysis
8. Measuring Variability and Change
9. Moderation, nonlinear Effects, and Conditional Process Analysis
10. Miscellaneous		 Michel van der Borgh
Tobias Schäfers
Day 3: Advanced Topics in SEM (continued) & Scale Development
09:00-10:30
10:45-12:15
13:00-14:30
14:45-16:15
16:30-17:15		 11. Multilevel Analysis and Multilevel SEM
12. Finding and Assessing Existing Scales
13. Four Steps of Scale Development
14. Exercise Session: Reliability and Validity Assessment 
15. Summary and Outlook		 Ad de Jong
Tobias Schäfers




Learning objectives
Upon completion of this course, participants should have achieved the following:

1) Have a theoretical understanding of latent construct measurement.
2) Be able to perform basic and more advanced CFA and SEM analyses for latent variable models.
3) Be able to assess the reliability and validity of latent construct operationalizations.
4) Understand the procedures used to develop multi-item scales for measuring latent constructs.
5) Be able to interpret latent variable SEM estimation output, assess model fit, and compare competing models.
6) Have a theoretical understanding of advanced latent variable SEM topics, such as mediation, moderation, multi-group analyses, latent growth models, and multilevel models.

Exam
The course will conclude with an online exam that consists of analytical assignments in which students are required to demonstrate their understanding of the course contents and their ability to apply it to different data sets. This involves analytical procedures in Mplus and interpretation of analysis results. The exam will be distributed at the end of the course; students will have one week to submit their responses.

Other

Start date
22/02/2021

End date
24/02/2021

Level
PhD

ECTS
3

Language
English

Course Literature
Borsboom, D., Mellenbergh, G.J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203-219.
Davvetas, V., Diamantopoulos, A., Zaefarian, G., & Sichtmann, C. (2020). Ten basic questions about structural equations modeling you should know the answers to – but perhaps you don’t. Industrial Marketing Management, 90, 252-263.
Flatten, T.C., Engelen, A., Zahra, S.A., & Brettel, M. (2011). A measure of absorptive capacity: Scale development and validation. European Management Journal, 29(2), 98-116.
Haans, R. F., Pieters, C., & He, Z. L. (2016). Thinking about U: Theorizing and testing U‐and inverted U‐shaped relationships in strategy research. Strategic Management Journal, 37(7), 1177-1195.
Homburg, C., Schwemmle, M., & Kuehnl, C. (2015). New product design: Concept, measurement, and consequences. Journal of marketing, 79(3), 41-56.
Netemeyer, R.G., Bearden, W.O., & Sharma, S. (2003). Scaling Procedures: Issues and Applications. Thousand Oaks, CA: Sage. 
Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of school psychology, 48(1), 85-112.
Zhao, X., Lynch Jr., J.G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37(2), 197-206.


Suggested readings :
Bandalos, D.L. (2018). Measurement Theory and Applications for the Social Sciences. New York, NY: Guilford.
Blunch, N.J. (2013). Introduction to Structural Equation Modeling Using IBM SPSS Statistics and AMOS (Second Ed.). London: Sage.
Bollen, K.A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605-634.
Brown, T.A. (2015). Confirmatory Factor Analysis for Applied Research (Second Ed.). New York, NY: Guilford.
Byrne, B.M. (2016). Structural Equation Modeling with Amos: Basic Concepts, Applications, and Programming (Third ed.). New York: Routledge.
Churchill, G.A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73.
Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business and Psychology, 25(3), 325-334.
de Jong, M.G. & Pieters, R. (2019). Assessing sensitive consumer behavior using the item count response technique. Journal of Marketing Research, 56(3), 345-360.
DeVellis, R.F. (1991). Scale Development – Theory and Applications. Newbury Park, CA et al.: Sage.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of marketing research, 38(2), 269-277.
Geiser, C. (2013). Data Analysis with Mplus. New York, NY: Guilford.
Hancock, G.R. & Mueller, R.O. (Eds.) Structural Equation Modeling: A Second Course (2nd Ed).   Greenwich, CT: Information Age Publishing.
Hayes, A.F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). New York, NY: Guilford.
Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate behavioral research, 45(4), 627-660.
Hoyle, R.H. (Ed.). (1995). Structural Equation Modeling. Concepts, Issues, and Applications. Thousand Oaks, CA et al.: Sage.
Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 139-153.
Kline, R.B. (2016). Principles and Practices of Structural Equation Modeling (Fourth Ed.). New York: Guilford Press.
Noar, S.M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling, 10(4), 622 - 647.
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate behavioral research, 42(1), 185-227.
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological methods, 15(3), 209-233.
Spector, P.E. (1992). Summated rating scale construction. Newbury Park, CA et al.: Sage.
Spiller, S. A., Fitzsimons, G. J., Lynch Jr, J. G., & McClelland, G. H. (2013). Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. Journal of marketing research, 50(2), 277-288.
Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4-69.

Fee
DKK 1950 (online price Spring 2021)

Minimum number of participants
23

Maximum number of participants
25

Location
22 - 24 February 2021 - each day from 9 - 17:15

Location:
This course will take place fully online

Contact information
For the content of the course:
Tobias Schäfers - ts.marktg@cbs.dk

For the administration of the course:
Bente S. Ramovic - bsr.research@cbs.dk 

Registration deadline
31/01/2021

Please note that the registration is binding after the registration deadline.
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