Cooperative Colloquium "Latent Curve Models" by Prof. Friedmann, Prof. Maass, Dr. Martin Becker and Dr. Stefan Klößner (DokSem13/14), Master & Promotion

Semester: WS 2013/2014
Study Courses: Open
Credit Points: Master (12 CP)
Contact person: Sabine Janzen


Empirical work with methods derived from psychology and sociology became a central research paradigm in several fields of economic research. An increasing number of scientific publications utilizes basic as well as complex methods of empirical research and statistical modeling and evaluation. In the context, the examination of latent variables is of great interest, which enables analyzing cognitive factors and hidden social factors that cannot be observed directly. Alternatively, explorative and confirmatory methods are applied. The statistic modeling with the structural equation model, for instance Partial Least Squares Regression (PLS), dominates research in information systems for more than two decades. Although, PLS is preferred, because it enables valid results even for small samples. Furthermore, Infrequently non-linear models and methods are used, since psychological and sociological coherencies are not regularly linear by nature. World’s leading universities focus on learning and utilization of these empirical and statistical methods in economic research and especially in research of information systems / business informatics as well as marketing. Within this seminar, relevant topics concerning structural equation models (LISREL, PLS) and their application in empirical research will be discussed from the perspective of empirical research as well as of statistic modeling and analysis.

Dates and Rooms



  • Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective (Vol. 467). Wiley. com.
  • Kaplan, D. (2000) Structural equation modeling: Foundations and extensions. Thousand Oaks, CA, Sage.
  • Kline, R.B. (2010) Principles and practice of structural equation modeling, NY, Guildford Press.

Discussion of Structural Equation Modeling and PLS in Information Systems

  • Chin, W. (1998) The Partial Least Squares Approach to Structural Equation Modeling, MIS Quarterly 22(1), pp. vii-xvi.
  • Gefen, D., Straub, D., and Boudreau, M. (2000) Structural Equation Modeling Techniques and Regression: Guidelines for Research Practice, Comm. of AIS, 7(7), p.1-78.
  • Goodhue, D., Lewis, W., and Thompson, R. (2012) Does PLS have advantages for small sample size or non-normal data?, MIS Quarterly, 36(3), pp. 981-1001.


Master or Ph.D. students can participate in the seminar. Please, registrate via ISS eLearning platform until 17.07.2013. The first preliminary discussion will take place at 17.07.2013, 2 p.m. (c.t.) in room 2.03 in building A5 3.

The number of participants is limited to 6 - 10 students. The course will take place weekly in room 2.03 in building A5 3. Concrete day of week and time will be discussed in the seminar. Attendance is compulsory. Every week, one topic out of 6-10 topics will be discussed.

Master students have to prepare a topic by writing a paper and presenting the results. Paper and presentations will represent 45 and 40 percent of the final grad. Additionally, the active participation in discussions during all sessions is will be calculated as 15% of the final grade.

Ph.D. students have to prepare all topics to be able to discuss. Furthermore, they will lead the discussion of one empirical or statistical topic that they worked out in detail. Grading of this course consists of leadership-performance (60%) as well as general participation in the course (40%).

Attention: This seminar gives no introduction into specialized software packages, for instance SPSS AMOS or Smart PLS. Nevertheless, within this course each participants should be able to become acquainted with these software packages by self-study.