Impactful Insights: Exploring the Importance and Implications of Centering in Evaluation.

Educational program evaluation frequently employs quantitative analysis of hierarchical data, such as students within schools. Multilevel modeling is well-suited for this, as it effectively partitions variance across levels. Crucially, predictor variables, like outcome variables, also exhibit multilevel variation and, potentially, distinct effects across levels, requiring appropriate variable centering. Properly accounting for this variation in the estimation and interpretation of their effects requires appropriate variable centering. However, many researchers either neglect to center predictors or apply an incorrect form of centering in their evaluation models. These errors can lead to misinterpretations and flawed inferences, ultimately undermining the validity of evaluation’s crucial outcomes. As evaluators of a state-funded preschool program, we investigate and demonstrate the implications of this subtle oversight on the interpretations and inferences derived from our multilevel models. Specifically, we explore these impacts in two-level cross-sectional and three-level longitudinal data structures.

Hope Akaeze; Guanqi Lu; Ellen Searle; Shimeng Dai; Steve Pierce