Module 4 Summary

This module emphasized approaches to analyzing the kinds of quantitative data often collected in intervention and evaluation research to answer questions about change and outcomes related to intervention. The chapter began with a review of some univariate and bivariate analysis topics learned in our prior course, applying these topics to intervention and evaluation research efforts. In addition to the descriptive analyses, you witnessed how chi-square, t-tests, and analysis of variance (Anova) might be utilized in intervention and evaluation research to answer group comparison questions. You learned a new test appropriate for use with repeated measures (longitudinal) data: the paired t-test. The importance of using appropriate repeated measures analysis strategies for analyzing data collected longitudinally was emphasized, as assumptions about independence of “groups” data are violated in longitudinal designs. You were reminded of the possible importance of nonparametric analytic approaches (when parametric data assumptions are violated) and were briefly introduced to logistic regression for the specific situation when the dependent variable is dichotomous and the independent variable is numeric. The final chapter examined approaches to analyzing data collected through single-system designs. This brings us to the next, concluding module for our two-course sequence.