202: Got Survey Data? Tips and Tricks for Testing Interaction Effects

An evaluator’s methodological approach to survey analysis, and steps taken to prepare the data prior to analysis, can ultimately influence survey results. These results are often used for critical organizational decision-making and funding asks. This poster aims to build evaluator capacity to analyze quantitative survey data by demonstrating how evaluator choices in cleaning and analysis can influence survey results, specifically focusing on interactions between predictor variables. Using a simulation study based on a national survey of K-12 educators, the poster addresses the use of sum scores, variable centering, and quadratic terms. Results of analyses using descriptives, multiple regression, estimated factor scores, and structural equation modeling will also be compared.

Ken Fujimoto – Loyola University Chicago; Naomi Brown – George Mason University; Elizabeth Levine Brown – George Mason University; Kate Phillippo – Loyola University Chicago