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

Strength of the Relationship: Pearson's Correlation Coefficient

At this point, we can identify the direction of a linear relationship (positive or negative). The next step is to figure out how STRONG the relationship is between the two variables.

The most popular way to measure the degree of association between two interval level variables is the Pearson's correlation coefficient. It is usually represented by the letter "r" (think "relationship"). This coefficient ranges from -1 to 1, including 0. Each level of measurement has an appropriate test of association. Ask your evaluator about this.

Values closer to +1 indicate a positive relationship. Values closer to -1 indicate a negative relationship. Values closer to 0 represent the absence of a relationship between two variables. We have learned that a perfect positive or negative correlation is very rare. In fact, even the strongest correlations we see in the real world fall short of the ranges suggested in textbooks.

Below is a reference on how to interpret correlation coefficients. Keep in mind that the same interpretation also applies to negative correlations.

Correlation Coefficient	Interpretation
.00 - .19 .20 - .39 .40 - .69 .70 - .89 .90 - 1.00	Slight, almost negligible correlation Low, quite small correlation Moderate correlation High correlation Very high correlation