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.
Astin et al. (Tue,) studied this question.