CONTENTS

Table of Contents
Introduction

1. Descriptive Statistics in Evaluation
2. Subgroup Analysis
3. Variable - Are They Related?
4. Correlation
5. The t-Test of Difference Between Means

Supplements
Resources
Glossary

Wading Through the Data Swamp:
Program Evaluation 201

Module Content Objectives Background Comparing Means Attrition Versus Loss to Followup New Information: Change Scores and Participation How To Construct Scatter Plots How To Interpret Scatter Plots Linear Relationship Strength of the Relationship: Pearson's Correlation Coefficient What Do All the Numbers and Plots Mean? Adding Points to a Scatter Plot Lessons Learned: What Jack Should Have Asked His Evaluator What Did Jack Learn About His Program From These Analyses? How Can Jack Use This Statistical Information to Improve His Program? How Can Jack Use This Statistical Information To Better Promote His Program? Module 4 Quiz

What Do All the Numbers and Plots Mean?

The scatter plot shows that the pattern of scores falls fairly well onto a straight line. The strength of the linear relationship is calculated using the Pearson's correlation Coefficient. This test is most easily conducted using Excel or SPSS.

Since it would be cruel and unusual to ask you to calculate the Pearson's correlation coefficient by hand, we did this one for you (well, Excel did). The calculated r value is 0.502. This means there is a moderate correlation between program participation and negative change in alcohol use from pretest to posttest. In conclusion, Jack's program seems to show some evidence of success. As the amount of services increased, alcohol use decreased.