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Prof. Martin Schulz

2010

Class Hours: Wed 14:00-17:00 in HA 328

Abstract

This course provides an introduction to selected quantitative approaches currently prominent in organizational research. The main focus will be on dynamic models, in particular, event history models – a group of models that capture change of discrete outcomes in continuous time. Event history models have become the model of choice in organization studies because they overcome limitations inherent in older models (cross-sectional models, panel models) and because they provide a rich set of tools to model many different types of change. The course is not limited to event history analysis – neighboring models will be explored, in particular, Logit and Probit models which allow analysis of discrete outcomes in discrete time, and models of counting processes which allow analysis of event count data in discrete time.

Course Description

Change is a key characteristic of the empirical world. Individuals, organizations, and societies – be it in part or whole – experience various degrees of change. Change is not only omnipresent, it is also puzzling. Why do some things change fast and others at a much slower pace? Which events at which place and time trigger which other events? Why does history proceed in the way it does? Such questions are not entirely futile. The subject of change is at the heart of many theories and disciplines. In the organization sciences, change has found attention under such labels as "inertia", "advantage", "learning", "momentum", "newness", "fairness", "exchange", "escalation", "deviance", "entrepreneurship", "transaction", "differentiation" "innovation" – just to name a few.

This course offers a chest of tools that can help us to crack the puzzles of change in organization sciences and related fields. It rests on the potentially useful fiction that change is produced by units that experience ‘events' as they flow through networks of states. It offers tools that estimate, analyze, and predict the intensities at which units flow between states. It can model how units act (e.g., ‘comply', ‘decide'), change (e.g., ‘maturate', ‘adapt'), and interact with other units (e.g., ‘infect', ‘compete with') as they travel through time and different states. It provides techniques to study how units emerge (e.g., ‘birth', ‘founding'), transform (‘revisions'), and die (‘suspensions'). And it allows us to study how different units act and react in different ways during their life history.

Of course, this fiction is not without difficulties. How do we specify the units and states? Which characteristics of units do we need to consider? Which can we ignore? Which behavioral mechanisms do we include in our models? Guidance for such questions can be gleaned from theories of change and empirical research that has tackled puzzles of change in organizations. For these reasons, this seminar puts emphasis on both, the mastery of the statistical tools and on reflection on the structures and processes that give rise to change. Most of the reading materials of this course present theories and research on change alongside applications of the statistical tools. By combining the technical aspects of dynamic models with their theoretical background, students will gain a deeper understanding of dynamic phenomena, learn to avoid common pitfalls of dynamic analysis, and develop an appreciation of the enormous potential that these approaches offer.