Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis. In this study, three centering methods (uncentered, group mean, and grand mean) were compared using Read93 and Lunch Status as Level-1 predictor variables of Iowa Tests of Basic Skills (ITBS) 1994 reading test scores (for 5,638 ninth graders enrolled in 26 high schools within a large urban school district). The reliability estimates, or how accurately the sample estimate represents the population value differed among the three centering methods. It was found that the group mean centering method provided the best reliability estimate. When using outcome measures based on these three centering methods in a Level-2 analysis using two predictors, graduation rate (Gradrate) and percent in advanced diploma plans (%Advdip), the group mean centering method indicated a more reliable estimate, but the grand mean centering method explained more between-school variance. In fact, the gamma coefficients were markedly different, and the amount of variance explained was no longer consistent across the centering methods. These findings indicate that centering effects in Level-1 predictor variables can affect both theoretical and empirical findings in HLM. Five tables present study findings. (Contains 11 references.) (Author/SLD)