Course Meeting Times

Lectures: 1 session / week, 3 hours / session


A large proportion of contemporary research on organizations, strategy and management relies on quantitative research methods. This course is designed to provide an introduction to some of the most commonly used quantitative techniques, including logit/probit models, count models, event history models, and pooled cross-section techniques.

This is a course about the research process. My explicit goal is to help you understand the relationship between theory, data and statistical methods. In that sense this is not a course in statistical theory; we will not spend a lot of time deriving likelihood functions, etc., although we will be explicit about the assumptions underlying the statistical procedures. Instead, this is a course in how to use statistical techniques to answer research questions. We will spend considerable time thinking about how theoretical insights can be translated into testable propositions, and how those propositions are best tested. We will do this through discussions of published research from leading journals in organizational research, and by having you work with data and estimate models. My primary goal in these skills is to help you increase your comfort with using statistical methods to ask and answer research questions, and to develop critical skills in evaluating others' research, such that you might apply those skills to your own.

Format and Requirements

The structure of the course involves (roughly) alternating lectures on the principles associated with a particular method in one week, followed the next week by the application of those models. We will pursue two types of application:

  • First, we will discuss working papers (found on the Internet and elsewhere) that use the particular methods in question, with an eye toward assessing whether the data and methods are appropriate for the research question.
  • Second, I will ask students to work with data to estimate models (in STATA®), write up an interpretation of the results, and then (occasionally) present the results in class.

Some words about the data assignments are in order. Most importantly, these assignments are deliberately open-ended. I will not specify what the dependent variable should be, what you are trying to explain, etc. Part of the goal of these exercises is to give you practice with working with data, estimating models and making sense of the results. In the language of 15.347, I want you to formulate a conceptual model of the data as well as a statistical model. The major constraint is that you apply the method discussed during that week's class (e.g., count models). I will make appropriate datasets available for this purpose, but you are also allowed to use your own data sources.

At the beginning of class in each "Application" week, I ask that you hand in a brief summary of the results of your analysis. This summary should be a maximum of two pages of text, plus any tables and figures, and should discuss the results in light of the conceptual model you have developed. These assignments may be done in groups of up to two people. I will ask two or three groups each week to present the results of their work, so that we may discuss them as a class.

The grading is broken down as follows:

Class participation 30%
Discussion leadership for one of the assigned working papers 20%
The five short assignments that involve working with data 50%


If you have not worked with STATA® before, I encourage you to do things during the first few weeks of class:

  1. Get yourself set up (e.g., in the computer lab) so that you know how to get it going and
  2. At least look through some of the manuals. It can be particularly helpful to look at "Getting Started with STATA®."

There are also resources available on the Internet, e.g., the STATA® Starter Kit provided through UCLA .


Aldrich, John H., and Forrest D. Nelson. Linear Probability, Logit and Probit Models. Newbury Park, CA: Sage, 1984. ISBN: 0803921330.

Singer, Judith D., and John B. Willett. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press, 2003. ISBN: 0195152964.