Research in Higher Education

, Volume 50, Issue 4, pp 354-367

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Multi-Campus Studies of College Impact: Which Statistical Method is Appropriate?

  • Alexander W. AstinAffiliated withHigher Education Research Institute, University of California, Los Angeles Email author 
  • , Nida DensonAffiliated withLearning and Teaching at UNSW, University of New South Wales


In most multi-campus studies of college impact that have been conducted over the past four decades, investigators have relied on ordinary least squares (OLS) regression as the analytic method of choice. Recently, however, some investigators have advocated the use of Hierarchical Linear Modeling (HLM), a method specifically designed for analyses that involve both individual (student) and aggregate (institutional) level measures. Cross-validation analyses using a national database show that the two methods yield an equally good “fit” with empirical data. Existing OLS software has the advantage of enabling one to perform path analytical causal modeling; HLM has the advantage of yielding a more conservative estimate of the significance of institution-level effects.


Ordinary least squares Hierarchical linear modeling Methodology College effects Stepwise regression