Jam N. answered • 09/03/23
Dynamic Academic Pathfinder: Excellence in Leadership and Learning
When you use SPSS to do a multilevel analysis to test a moderation hypothesis, the output includes several key tables and numbers that give you information about how true your hypothesis is. The sampling size, levels, and goodness-of-fit statistics are shown in the model summary table as a first look at the analysis. In the table of fixed effects coefficients, pay attention to the terms that show how the moderator variable and the predictor variables combine. These interaction terms show whether moderator levels change the strength or direction of the relationship between the independent and dependent factors. The p-values of interaction terms show whether or not they are statistically significant. If the p-values are less than the set significance level (e.g., 0.05), the interaction terms are statistically significant. Random effects variance components are listed to show the differences between groups. Even though these differences aren't directly related to moderation theories, knowing about them helps evaluate differences between groups. Likelihood ratio tests or Wald tests often show statistically significant results for interaction terms, which gives more evidence for moderation hypotheses. Graphs can help show how the link between the predictor and the dependent variable changes at different moderator levels. To make sense of the results of a multilevel analysis, it's important to understand the nested data structure and think about both statistical and theoretical effects.
