S651 | 10924-10925 | David James

TOPIC: HIERARCHICAL LINEAR MODELS Many research problems of interest to social scientists posit causal linkages at more than one level of analysis such as individuals and groups or cities and states. For example, the academic performance of students is a function of both (1) the individual capacities of students and (2) the characteristics of the schools they attend. Are differences in students’ academic performance due to differences in the qualities of schools or difference in the aptitudes of students who attend those schools? Questions of this kind, involving causal processes at 2 or more levels of analysis can be properly modeled and estimated using Hierarchical Linear Modeling (HLM) techniques. Pervious analyses of quantitative hierarchical data sets have often used ordinary least squares regression, which produces estimates with poor statistical properties. During the past 5 years, advances in statistical estimation techniques have finally made it possible to estimate hierarchical models properly in ways that take the hierarchical nature of the data into account. Given the wide variety of applications in the social sciences, Multilevel Modeling is perhaps the most important new statistical technique to appear in the past 5 or 10 years. Research papers using the technique are now quite common in professional journals. This is a graduate level course in applied statistics that builds upon the ordinary least squares regression techniques. Consequently, students should have completed S554 (Statistics for Sociology I) or an equivalent course in single-equation regression techniques. If you have not taken a course in linear regression, you should talk to me before enrolling in the course. Lectures will be followed immediately by a statistics lab. We will use the labs to gain practical experience in the estimation and interpretation of hierarchical linear models using the statistical program. Since the course will be conducted in part like a practicum, the labs are scheduled to make sure that students have access to the software needed to practice multilevel modeling techniques. Free lab time will be available to students to work on problem sets. Required Text: Tom A.B. Snijders and R.J. Bosker. 1999. Multilevel Analysis. Sage. Some recommended texts will also be available in the bookstore. These will include: Raudenbush and Bryk. 2002. Hierarchical Linear Models (2nd edition). Sage. Gellman and Hill. 2006. Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge