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

How To Construct Scatter Plots

You know the saying, "A picture is worth a thousand words." Well, it is as true in evaluation as anywhere else. An easy way to make the data talk about relationships is to construct scatter plots. Let's plot Jack's data on change in alcohol use against amount of services received.

In our example, Jack speculates that the amount of services affects the change in alcohol use, so the values for the amount of services go on the horizontal (x) axis (the independent variable). The scores for change in alcohol use go on the vertical (y) axis (the dependent variable).

When you repeat this for each participant, you are going to have a pattern of dots that summarizes the relationship between two variables. Amount of Services Received and Change in Attitudes are plotted for each participant.

Let's look at the data for one participant. (See red data point.) This particular kid was coded as a 5 (he attended all days) and had a change score of -2 (0 - 2). The red diamond shows the point at which these two scores intersect.