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

Comparing Means

Jack was discouraged by the results given in the preliminary evaluator's report. He couldn't believe that alcohol use increased from pretest to posttest. He'd heard through the grapevine that kids have not been drinking as much as they did before the program, especially the kids who attended every session.

Below is the participants' alcohol use in the past 30 days reported in the GPRA survey. The pretest scores range from 0 to 5 days and the posttest scores range from 0 to 7 days.

Participants' Frequency of Alcohol Use in the Past 30 Days

Kid #	Pretest (x)	Posttest (x)

It seems as though there is a lot of variation between scores. These scores may have affected the means, as you learned in previous modules. Now let's plot the means to get a visual representation.

As the figure shows, mean days of alcohol use increased from pretest to posttest by 0.21 (1.85 posttest mean - 1.64 pretest mean = 0.21 days).

As we learned in Modules 1 and 2, means can be a good way to summarize your data, but things do not happen in a vacuum. The average response in any test is influenced by several factors.

Jack asked himself, "What could explain the increase in the mean from pretest to posttest?"

He looked into his notes for the sessions. The activities for each session were carefully planned. As he looked at the sign-in sheets for the sessions, he noticed that attendance varied.

Some kids attended some sessions and others barely showed up to any. Thus, participants received different amounts of services. Some received more services because they attended more sessions.