This module extends what was learned about descriptive and comparative statistics in our previous course to understanding how various statistical approaches help answer questions about social work interventions. Using Excel software, we review earlier lessons about group comparisons as applied to comparing intervention groups (chi-square, t-tests, Anova, and correlations). We introduce for the first-time repeated measure analysis for comparing pre- and post-intervention longitudinal data (paired t-test), and address how single-system research data might be analyzed. We briefly re-introduce nonparametric statistical principles and briefly introduce the idea behind logistic regression to test outcomes in intervention or evaluation research.
After engaging with these reading materials and learning resources, you should be able to:
- Describe appropriate data analytic approaches for descriptive (mean, median, standard deviation) and group comparison questions based on the nature of the research questions and type of variables (single sample t-test, independent samples t-test, one-way analysis of variance, chi-square);
- Explain why repeated measures analysis (paired-t test) would be used on longitudinal data and describe the approach;
- Identify basic non-parametric and logistic regression principles relevant in analysis of intervention data;
- Recognize and explain approaches to analyzing single system design data for understanding social work interventions;
- Define key terms related to analyzing data for understanding social work interventions.