16 USING LINEAR REGRESSION PREDICTING THE FUTURE 16: MEDIA LIBRARY Premium Videos Core Concepts in Stats Video · Linear Regression Lightboard Lecture Video · Multiple Regression Time to Practice Video · Chapter 16: Problem 2 Difficulty Scale (as hard as they get!) WHAT YOU WILL LEARN IN THIS CHAPTER · Understanding how prediction works and how it can be used in the social and behavioral sciences · Understanding how and why linear regression works when predicting one variable on the basis of another · Judging the accuracy of predictions · Understanding how multiple regression works and why it is useful INTRODUCTION TO LINEAR REGRESSION You’ve seen it all over the news—concern about obesity and how it affects work and daily life. A set of researchers in Sweden was interested in looking at how well mobility disability and/or obesity predicted job strain and whether social support at work can modify this association. The study included more than 35,000 participants, and differences in job strain mean scores were estimated using linear regression, the exact focus of what we are discussing in this chapter. The results found that level of mobile disability did predict job strain and that social support at work significantly modified the association among job strain, mobile disability, and obesity. Want to know more? Go to the library or go online … Norrback, M., De Munter, J., Tynelius, P., Ahlstrom, G., & Rasmussen, F. (2016). The association of mobility disability, weight status and job strain: A cross-sectional study. Scandinavian Journal of Public Health, 44, 311–319. WHAT IS PREDICTION ALL ABOUT? Here’s the scoop. Not only can you compute the degree to which two variables are related to one another (by computing a correlation coefficient as we did in Chapter 5), but you can also use these correlations to predict the value of one variable based on the value of another. This is a very special case of how correlations can be used, and it is a very powerful tool for social and behavioral sciences researchers. The basic idea is to use a set of previously collected data (such as data on variables X and Y), calculate how correlated these variables are with one another, and then use that correlation and the knowledge of X to predict Y. Sound difficult? It’s not really, especially once you see it illustrated. For example, a researcher collects data on total high school grade point average (GPA) and first-year college GPA for 400 students in their freshman year at the state university. He computes the correlation between the two variables. Then, he uses the techniques you’ll learn about later in this chapter to take a new set of high school GPAs and (knowing the relationship between high school GPA and first-year college GPA from the previous set of students) predict what first-year GPA should be for a new student who is just starting out. Pretty nifty, huh? Here’s another example. A group of kindergarten teachers is interested in finding out how well ex.

1. 16 USING LINEAR REGRESSION PREDICTING THE
FUTURE
16: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Linear Regression
Lightboard Lecture Video
· Multiple Regression
Time to Practice Video
· Chapter 16: Problem 2
Difficulty Scale
(as hard as they get!)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding how prediction works and how it can be used in
the social and behavioral sciences
· Understanding how and why linear regression works when
predicting one variable on the basis of another
· Judging the accuracy of predictions
· Understanding how multiple regression works and why it is
useful
INTRODUCTION TO LINEAR REGRESSION
You’ve seen it all over the news—concern about obesity and
how it affects work and daily life. A set of researchers in
Sweden was interested in looking at how well mobility
disability and/or obesity predicted job strain and whether social
support at work can modify this association. The study included
more than 35,000 participants, and differences in job strain
mean scores were estimated using linear regression, the exact
focus of what we are discussing in this chapter. The results
found that level of mobile disability did predict job strain and
that social support at work significantly modified the
association among job strain, mobile disability, and obesity.
Want to know more? Go to the library or go online …
Norrback, M., De Munter, J., Tynelius, P., Ahlstrom, G., &

2. Rasmussen, F. (2016). The association of mobility disability,
weight status and job strain: A cross-sectional
study. Scandinavian Journal of Public Health, 44, 311–319.
WHAT IS PREDICTION ALL ABOUT?
Here’s the scoop. Not only can you compute the degree to which
two variables are related to one another (by computing a
correlation coefficient as we did in Chapter 5), but you can also
use these correlations to predict the value of one variable based
on the value of another. This is a very special case of how
correlations can be used, and it is a very powerful tool for
social and behavioral sciences researchers.
The basic idea is to use a set of previously collected data (such
as data on variables X and Y), calculate how correlated these
variables are with one another, and then use that correlation and
the knowledge of X to predict Y. Sound difficult? It’s not
really, especially once you see it illustrated.
For example, a researcher collects data on total high school
grade point average (GPA) and first-year college GPA for 400
students in their freshman year at the state university. He
computes the correlation between the two variables. Then, he
uses the techniques you’ll learn about later in this chapter to
take a new set of high school GPAs and (knowing the
relationship between high school GPA and first-year college
GPA from the previous set of students) predict what first-year
GPA should be for a new student who is just starting out. Pretty
nifty, huh?
Here’s another example. A group of kindergarten teachers is
interested in finding out how well extra help after for their
students aids them in first grade. That is, does the amount of
extra help in kindergarten predict success in first grade? Once
again, these teachers know the correlation between the amount
of extra help and first-grade performance from prior years; they
can apply it to a new set of students and predict first-grade
performance based on the amount of kindergarten help.
How does regression work? Data are collected on past events
(such as the existing relationship between two variables) and

3. then applied to a future event given knowledge of only one
variable. It’s easier than you think.
The higher the absolute value of the correlation coefficient,
regardless of whether it is direct or indirect (positive or
negative), the more accurate the prediction is of one variable
from the other based on that correlation. That’s because the
more two variables share in common, the more you know about
the second variable based on your knowledge of the first
variable. And you may already surmise that when the
correlation is perfect (+1.0 or −1.0), then the prediction is
perfect as well. If rxy = −1.0 or +1.0 and if you know the value
of X, then you also know the exact value of Y. Likewise,
if rxy = −1.0 or +1.0 and you know the value of Y, then you
also know the exact value of X. Either way works just fine.
What we’ll do in this chapter is go through the process of using
linear regression to predict a Y score from an X score. We’ll
begin by discussing the general logic that underlies prediction,
then review some simple line-drawing skills and, finally,
discuss the prediction process using specific examples.
Why the prediction of Y from X and not the other way around?
Convention. Seems like a good idea to have a consistent way to
identify variables, so the Y variable becomes the dependent
variable or the one being predicted and the X variable becomes
the independent variable and is the variable used to predict the
value of Y. And when predicted, the Y value is represented
as Y′ (read as Y prime)—the predicted value of Y. (To sound
like an expert, you might call the independent variable
a predictor and the dependent variable the criterion. Purists save
the terms independent and dependent to describe cause-and-
effect relationships, which we cannot assume when talking
about correlations.)THE LOGIC OF PREDICTION
Before we begin with the actual calculations and show you how
correlations are used for prediction, let’s understand the
argument for why and how prediction works. We will continue
with the example of predicting college GPA from high school
GPA.

4. Prediction is the computation of future outcomes based on a
knowledge of present ones. When we want to predict one
variable from another, we need to first compute the correlation
between the two variables. Table 16.1 shows the data we will be
using in this example. Figure 16.1 shows the scatterplot
(see Chapter 5) of the two variables that are being computed.
Table 16.1 ⬢ Total High School GPA and First-Year College
GPA
High School GPA
First-Year College GPA
3.50
3.30
2.50
2.20
4.00
3.50
3.80
2.70
2.80
3.50
1.90
2.00
3.20
3.10
3.70
3.40
2.70
1.90
3.30
3.70
Figure 16.1 ⬢ Scatterplot of high school GPA and college GPA
To predict college GPA from high school GPA, we have to
create a regression equation and use that to plot what is called
a regression line. A regression line reflects our best guess as to
what score on the Y variable (college GPA) would be predicted

5. by a score on the X variable (high school GPA). For all the data
you see in Table 16.1, the regression line is drawn so that it
minimizes the distance between itself and each of the points on
the predicted (Y′) variable. You’ll learn shortly how to draw
that line, shown in Figure 16.2.
What does the regression line you see in Figure 16.2 represent?
First, it’s the regression of the Y variable on the X variable. In
other words, Y (college GPA) is being predicted from X (high
school GPA). This regression line is also called the line of best
fit. The line fits these data because it minimizes the distance
between each individual point and the regression line. Those
distances are errors because it means the prediction was wrong;
it was some distance from the right answer. The line is drawn to
minimize those errors. For example, if you take all these points
and try to find the line that best fits them all at once, the line
you see in Figure 16.2 is the one you would use.
Second, it’s the line that allows us our best guess (at estimating
what college GPA would be, given each high school GPA). For
example, if high school GPA is 3.0, then college GPA should be
around (remember, this is only an eyeball prediction) 2.8. Take
a look at Figure 16.3 to see how we did this. We located the
predictor value (3.0) on the x-axis, drew a perpendicular line
from the x-axis to the regression line, then drew a horizontal
line to the y-axis, and finally estimated what the predicted value
of Y would be.
Figure 16.3 ⬢ Estimating college GPA given high school GPA
Third, the distance between each individual data point and the
regression line is the error in prediction—a direct reflection of
the correlation between the two variables. For example, if you
look at data point (3.3, 3.7), marked in Figure 16.4, you can see
that this (X, Y) data point is above the regression line. The
distance between that point and the line is the error in
prediction, as marked in Figure 16.4, because if the prediction
were perfect, then all the predicted points would fall where?
Right on the regression or prediction line.

6. Figure 16.4 ⬢ Prediction is rarely perfect: estimating the error
in prediction
Fourth, if the correlation were perfect (and the x-axis meets
the y-axis at Y ’s mean), all the data points would align
themselves along a 45° angle, and the regression line would
pass through each point (just as we said earlier in the third
point).
Given the regression line, we can use it to precisely predict any
future score. That’s what we’ll do right now—create the line
and then do some prediction work.CORE CONCEPTS IN
STATS VIDEOLinear RegressionDRAWING THE WORLD’S
BEST LINE (FOR YOUR DATA)
The simplest way to think of prediction is that you are
determining the score on one variable (which we’ll call Y—
the criterion or dependent variable) based on the value of
another score (which we’ll call X—the predictor or independent
variable).
The way that we find out how well X can predict Y is through
the creation of the regression line we mentioned earlier in this
chapter. This line is created from data that have already been
collected. The equations are then used to predict scores using a
new value for X, the predictor variable.
Formula 16.1 shows the general formula for the regression line,
which may look familiar because you may have used something
very similar in a high school or college math course. In
geometry, it’s the formula for any straight line:
(16.1)
Y'=bX+a,Y′=bX+a,
where
· Y ′ is the predicted score of Y based on a known value of X;
· b is the slope, or direction, of the line;
· X is the score being used as the predictor; and
· a is the point at which the line crosses the y-axis.
Let’s use the same data shown earlier in Table 16.1, along with
a few more calculations that we will need thrown in.

7. X
Y
X2
Y2
XY
3.5
3.3
12.25
10.89
11.55
2.5
2.2
6.25
4.84
5.50
4.0
3.5
16.00
12.25
14.00
3.8
2.7
14.44
7.29
10.26
2.8
3.5
7.84
12.25
9.80

8. 1.9
2.0
3.61
4.00
3.80
3.2
3.1
10.24
9.61
9.92
3.7
3.4
13.69
11.56
12.58
2.7
1.9
7.29
3.61
5.13
3.3
3.7
10.89
13.69
12.21
Total
31.4
29.3
102.50
89.99
94.75

9. From this table, we see that
· ∑ X, or the sum of all the X values, is 31.4.
· ∑Y, or the sum of all the Y values, is 29.3.
· ∑ X 2, or the sum of each X value squared, is 102.5.
· ∑ Y 2, or the sum of each Y value squared, is 89.99.
· ∑ XY, or the sum of the products of X and Y, is 94.75.
Formula 16.2 is used to compute the slope of the regression line
(b in the equation for a straight line):
(16.2)
b=ΣXY−(ΣXΣY/n)ΣX2−[(ΣX)2/n].b=ΣXY−(ΣXΣY/n)ΣX2−[(ΣX)
2/n].
In Formula 16.3, you can see the computed value for b, the
slope of the line:
(16.3)
b=94.75−[(31.4×29.3)/10]102.5−[(31.4)2/10],b=2.7493.904=0.7
04.b=94.75−[(31.4×29.3)/10]102.5−[(31.4)2/10],b=2.7493.904=
0.704.
Formula 16.4 is used to compute the point at which the line
crosses the y-axis (a in the equation for a straight line):
(16.4)
a=ΣY−bΣXn.a=ΣY−bΣXn.
In Formula 16.5, you can see the computed value for a, the
intercept of the line:
(16.5)
a=29.3−(0.704×31.4)10,a=7.1910=0.719.a=29.3−(0.704×31.4)10
,a=7.1910=0.719.
Now, if we go back and substitute b and a into the equation for
a straight line (Y = bX + a), we come up with the final
regression line:
Y'=0.704X+0.719.Y′=0.704X+0.719.
Why the Y ′ and not just a plain Y ? Remember, we are
using X to predict Y, so we use Y ′ to mean the predicted and
not the actual value of Y.
So, now that we have this equation, what can we do with it?
Predict Y, of course.
For example, let’s say that high school GPA equals 2.8 (or X =

10. 2.8). If we substitute the value of 2.8 into the equation, we get
the following formula:
Y'=0.704(2.8)+0.719=2.69.Y′=0.704(2.8)+0.719=2.69.
So, 2.69 is the predicted value of Y (or Y ′) given X is equal to
2.8. Now, for any X score, we can easily and quickly compute a
predicted Y score.
You can use this formula and the known values to compute
predicted values. That’s most of what we just talked about. But
you can also plot a regression line to show how well the scores
(what you are trying to predict) actually fit the data from which
you are predicting. Take another look at Figure 16.2, the plot of
the high school–college GPA data. It includes a regression line,
which is also called a trend line. How did we get this line?
Easy. We used the same charting skills you learned in Chapter
5 to create a scatterplot; then we selected Add Fit Line in the
SPSS Chart Editor. Poof! Done!
You can see that the trend is positive (in that the line has a
positive slope) and that the correlation is .6835—very positive.
And you can see that the data points do not align directly on the
line, but they are pretty close, which indicates that there is a
relatively small amount of error.
Not all lines that fit best between a bunch of data points are
straight. Rather, they could be curvilinear, just as you can have
a curvilinear relationship between your variables, as we
discussed in Chapter 5. For example, the relationship between
anxiety and performance is such that when people are not at all
anxious or very anxious, they don’t perform very well. But if
they’re moderately anxious, then performance can be enhanced.
The relationship between these two variables is curvilinear, and
the prediction of Y from X takes that into account. Dealing with
curvilinear relationships is beyond the scope of this book, but
fortunately, most relationships you’ll see in the social sciences
are essentially linear.
HOW GOOD IS YOUR PREDICTION?
How can we measure how good a job we have done predicting
one outcome from another? We know that the higher the

11. absolute magnitude of the correlation between two variables,
the better the prediction. In theory, that’s great. But being
practical, we can also look at the difference between the
predicted value (Y ′) and the actual value (Y) when we first
compute the formula of the regression line.
For example, if the formula for the regression line is Y ′ =
0.704X + 0.719, the predicted Y (or Y ′) for an X value of 2.8 is
0.704(2.8) + 0.719, or 2.69. We know that the actual Y value
that corresponds to an X value is 3.5 (from the data set shown
in Table 16.1). The difference between 3.5 and 2.69 is 0.81, and
that’s the size of the error in prediction.
Another measure of error that you could use is the coefficient of
determination (see Chapter 5), which is the percentage of error
that is reduced in the relationship between variables. For
example, if the correlation between two variables is .4 and the
coefficient of determination is 16% or .42, the reduction in
error is 16% since initially we suspect the relationship between
the two variables starts at 0 or 100% error (no predictive value
at all).
If we take all of these differences, we can compute the average
amount that each data point differs from the predicted data
point, or the standard error of estimate. This is a kind of
standard deviation that reflects average error along the line of
regression. The value tells us how much imprecision there is in
our estimate. As you might expect, the higher the correlation
between the two values (and the better the prediction), the lower
this standard error of estimate will be. In fact, if the correlation
between the two variables is perfect (either +1 or −1), then the
standard error of estimate is zero. Why? Because if prediction is
perfect, all of the actual data points fall on the regression line,
and there’s no error in estimating Y from X.
The predicted Y ′, or dependent variable, need not always be a
continuous one, such as height, test score, or problem-solving
skills. It can be a categorical variable, such as admit/don’t
admit, Level A/Level B, or Social Class 1/Social Class 2. The
score that’s used in the prediction is “dummy coded” to be a 0

12. or a 1 (or any two values) and then used in the same equation.
Yes, you are right that the level of measurement for this sort of
correlational stuff is supposed to be at the interval level, but a
variable with just two values works mathematically as if it has
equal-sized intervals because there is only one interval.
USING SPSS TO COMPUTE THE REGRESSION LINE
Let’s use SPSS to compute the regression line that predicts Y′
from X. The data set we are using is Chapter 16 Data Set 1. We
will be using the number of hours of training to predict how
severe injuries will be if someone is injured playing football.
There are two variables in this data set:
Variable
Definition
Training (X)
Number of hours per week of strength training
Injuries (Y)
Severity of injuries on a scale from 1 to 10
Here are the steps to compute the regression line that we
discussed in this chapter. Follow along and do it yourself.
1. Open the file named Chapter 16 Data Set 1.
2. Click Analyze → Regression → Linear. You’ll see the Linear
Regression dialog box shown in Figure 16.5.
3. Click on the variable named Injuries and then move it to the
Dependent: variable box. It’s the dependent variable because its
value depends on the value of number of hours of training. In
other words, it’s the variable being predicted.
4. Click on the variable named Training and then move it to the
Independent(s): variable box.
5. Click OK, and you will see the partial results of the analysis,
as shown in Figure 16.6.
We’ll get to the interpretation of this output in a moment. First,
let’s have SPSS overlay a regression line on the scatterplot for
these data like the one you saw earlier in Figure 16.2.
6. Click Graphs → Legacy Dialogs → Scatter/Dot.
7. Click Simple Scatter and then click Define. You’ll see the

13. simple Scatterplot dialog box.
8. Click Injuries and move it to the variable label to the Y Axis:
box. Remember, the predicted variable is represented by the y-
axis.
9. Click Training and move it to the variable label to the X
Axis: box.
10. Click OK, and you will see the scatterplot as shown
in Figure 16.7.
Now let’s draw the regression line.
11. If you are not in the chart editor, double-click on the chart
to select it for editing.
12. Click on the Add Fit Line at Total button (on the second
row of buttons, about fifth from the left) that looks a little like
this: .
13. Close the Properties box that opened when you selected the
Add Fit Line at Total button and then close the chart editor
window. The completed scatterplot, with the regression line, is
shown in Figure 16.8 along with the multiple regression
value R2, which equals 0.21. As you will read more about
shortly, the multiple regression correlation coefficient is the
regression of all the X values on the predicated value.
When you have the Properties dialog box open for drawing the
regression line, notice that there is a set of Confidence Intervals
options. When clicked, these show you a boundary within which
there is a specific probability as to how good the prediction is.
For example, if you click Mean and specify 95%, the graph will
show you the boundaries surrounding the regression line, within
which there is a 95% chance of the predicted scores occurring.
This idea of wanting to be within a certain range of error 95%
of the time is the same as wanting a .05 significance level for
statistical analyses.
Figure 16.5 ⬢ Linear Regression dialog box
Understanding the SPSS Output
The SPSS output tells us several things:
1. The formula for the regression line is taken from the first set
of output shown in Figure 16.6 as Y ′ = –0.125X + 6.847. This

14. equation can be used to predict level of injury given any number
of hours spent in strength training.
2. As you can see in Figure 16.8, the regression line has a
negative slope, reflecting a negative correlation (of –.458,
which is what Beta is in Figure 16.6) between hours of training
and severity of injuries. So it appears, given the data, that the
more one trains, the fewer severe injuries occur.
3. You can also see that the prediction is significant—in other
words, predicting Y from X is based on a significant
relationship between the two variables such that the test of
significance for both the constant (Training) and the predicted
variable (Injuries) is significantly different from zero (which it
would be if there was no predictive value for X predicting Y).
So just how good is the prediction? Well, the SPSS output
(which we did not show you) also indicates that the standard
error of estimate for Injuries (the predicted variable) is 2.182;
double that (4.36) and you’ll see that there is a 95% chance
(remember 1.96 or about 2 standard deviations away from the
mean creates a 95% confidence interval) the prediction will fall
between the mean of all injuries (which is 4.33) and ±4.46. So,
based on the correlation coefficient, the prediction is okay but
not great.
THE MORE PREDICTORS THE BETTER? MAYBE
All of the examples that we have used so far in the chapter have
been for one criterion or outcome measure and one predictor
variable. There is also the case of regression where more than
one predictor or independent variable is used to predict a
particular outcome. If one variable can predict an outcome with
some degree of accuracy, then why couldn’t two do a better job?
Maybe so, but there’s a big caveat—read on.
For example, if high school GPA is a pretty good indicator of
college GPA, then how about high school GPA plus number of
hours of extracurricular activities? So, instead of
Y'=bX+a,Y′=bX+a,
the model for the regression equation becomes
Y'=bX1+bX2+a,Y′=bX1+bX2+a,

15. where
· X1 is the value of the first independent variable,
· X2 is the value of the second independent variable,
· b is the regression weight for that particular variable, and
· a is the intercept of the regression line, or where the
regression line crosses the y-axis.
As you may have guessed, this model is called multiple
regression (multiple predictors, right?). So, in theory anyway,
you are predicting an outcome from two independent variables
rather than one. But you want to add additional predictor
variables only under certain conditions. Read on.
LIGHTBOARD LECTURE VIDEO
Multiple Regression
Any variable you add has to make a unique contribution to
understanding the dependent variable. Otherwise, why use it?
What do we mean by unique? The additional variable needs to
explain differences in the predicted variable that the first
predictor does not. That is, the two variables in combination
should predict Y better than any one of the variables would do
alone.
In our example, level of participation in extracurricular
activities could make a unique contribution. But should we add
a variable such as the number of hours each student studied in
high school as a third independent variable or predictor?
Because number of hours of study is probably highly related to
high school GPA (another of our predictor variables,
remember?), study time probably would not add very much to
the overall prediction of college GPA. We might be better off
looking for another variable (such as ratings on letters of
recommendation) rather than collecting the data on study time.
Take a look at Figure 16.9, which is the result of a multiple
regression analysis that adds the number of extracurricular
activity hours to the data you saw in Table 16.1. You can see
how both high school GPA and number of hours of
extracurricular activity are significant contributors to first-year
college GPA. This is a powerful way of examining what and

16. how more than one independent variable contribute to
prediction of another variable.
Figure 16.9 ⬢ A multiple regression analysis
The Big Rule(s) When It Comes to Using Multiple Predictor
Variables
If you are using more than one predictor variable, try to keep
the following two important guidelines in mind:
1. When selecting a variable to predict an outcome, select a
predictor variable (X) that is related to the criterion variable
(Y). That way, the two share something in common (remember,
they should be correlated).
2. When selecting more than one predictor variable (such
as X1 and X2), try to select variables that are independent or
uncorrelated with one another but are both related to the
outcome or predicted (Y) variable.
In effect, you want only independent or predictor variables that
are related to the dependent variable and are unrelated to each
other. That way, each one makes as distinct a contribution as
possible to predicting the dependent or predicted variable.
There are whole books on multiple regression, and much of
what one needs to learn about this powerful procedure is beyond
the scope of this book. Chapter 18 talks more about multiple
regression.
How many predictor variables are too many? Well, if one
variable predicts some outcome, and two are even more
accurate, then why not three, four, or
five predictor variables? In practical terms, every time you add
a variable, an expense is incurred. Someone has to go collect
the data, it takes time (which is $$$ when it comes to research
budgets), and so on. From a theoretical sense, there is a fixed
limit on how many variables can contribute to an understanding
of what we are trying to predict. Remember that it is best when
the predictor or independent variables are independent or
unrelated to each other. The problem is that once you get to
three or four variables, fewer things can remain unrelated.

17. Better to be accurate and conservative than to include too many
variables and waste money and the power of prediction.
Real-World Stats
How children feel about what they do is often very closely
related to how well they do what they do. The aim of this study
was to analyze the consequences of emotion during a writing
exercise. In the model this research follows, motivation and
affect (the experience of emotion) play an important role during
the writing process. Fourth and fifth graders were instructed to
write autobiographical narratives with no emotional content,
positive emotional content, and negative emotional content. The
results showed no effect regarding these instructions on the
proportion of spelling errors, but the results did reveal an effect
on the length of narrative the children wrote. A simple
regression analysis (just like the ones we did and discussed in
this chapter) showed a correlation and some predictive value
between working memory capacity and the number of spelling
errors in the neutral condition only. Since the model on which
the researchers based much of their preliminary thought about
this topic states that emotions can increase the cognitive load or
the amount of “work” necessary in writing, that becomes the
focus of the discussion in this research article.
Want to know more? Go online or to the library and find …
Fartoukh, M., Chanquoy, L., & Piolat, A. (2012). Effects of
emotion on writing processes in …
Chapter 9 Global Inequality and Poverty
ONE PHOTO CAPTURES A SHARP CONTRAST BETWEEN
RICH AND POOR IN THE DEVELOPING WORLD. The high-
rise buildings in the background are apartments for the wealthy.
Learning Objectives
1. 9.1Examine how widening gap between rich and poor
strengthens inequality-perpetuating institutions
2. 9.2Contrast between the viewpoints of globalists and
antiglobalists on the effects of globalization

18. 3. 9.3Examine the causes and the impact of domestic or global
inequality between nations
4. 9.4Examine the economic, social, and educational inequality
that exists within rich countries
5. 9.5Examine the inequalities that exist in different aspects of
life in poor countries
6. 9.6Review the six dimensions of poverty that can be used to
gauge poverty
7. 9.7Evaluate some of the measures for diminishing poverty
and reducing inequality
The richest eighty people in the world control as much wealth
as the poorest half of the world’s population. Thirty-five of
those eighty are Americans. The top 1 percent of the world’s
richest people control 48 percent of the world’s total wealth.
More than one billion people in the world live on less than
$1.25 a day.1 Inequality exists within the United States. The
richest four hundred Americans own more assets than the
poorest 150 million, or almost half the population. The bottom
15 percent, about forty-six million people, live in households
earning less than $22,000 per year. The top 5 percent of
households in Washington, D.C., make an average of more than
$500,000, while the bottom 20 percent make less than $9,500.
Conflict between rich and poor is now the greatest source of
tension in American society. Economic inequality has emerged
as a dominant global issue that has fueled massive protests and
popular uprisings. The global financial crisis and economic
recession have rekindled debates about inequality and its
consequences. Discussions about wealth and poverty and how to
achieve greater equality are as old as human society. They
demonstrate a perennial concern about the implications of
inequality for the security and well-being of communities.
Given the persistence of inequality among individuals, groups,
and nations over centuries, this debate is interminable.
Struggles to achieve equality are also endless. Issues pertaining
to global inequality and poverty permeate almost every
significant global problem, from trade to the environment, from

19. terrorism and criminal activities to democratization and human
rights, and from ethnic conflicts to the proliferation of weapons
of mass destruction. As we have seen, popular uprisings in the
Middle East and North Africa were strongly influenced by
widespread inequality and poverty. Consequently, as our
discussion shows, inequality and poverty are closely connected
to politics, economics, and culture.
A central question addressed in this chapter is whether
inequality matters. Human societies are inherently unequal due
to variations of abilities, opportunities, geographic location,
luck, personal characteristics, and so on. But why is it
important to address issues of inequality, something that
societies have struggled with historically? Globalization is
widely perceived as the major cause of global inequality. Yet,
as we have noted, unequal distributions of wealth existed
independent of the current wave of globalization and are present
in societies little affected by it. This chapter analyzes the
globalization and inequality debate as well as the current state
of global inequality. In addition to focusing on inequality
between rich and poor countries and inequality within both
developed and developing societies, we will examine the issue
of gender inequality. This chapter discusses the enduring issues
of global poverty, hunger and malnutrition, economic
development and poverty, and efforts to close the gap between
rich and poor and reduce the negative effects of inequality and
poverty. The chapter concludes with a case study of food
security and rising food prices.9.1: Does Inequality Matter?
1. 9.1 Examine how widening gap between rich and poor
strengthens inequality-perpetuating institutions
The existence of inequality is not automatically a major
problem, especially when the economy is growing and there are
many opportunities for upward mobility. As long as the standard
of living is improving for those on the bottom of the economic
ladder, concerns about inequality tend to diminish. The last two
decades of the twentieth century and the first decade of this
century were characterized by a widening gap between rich and

20. poor and the proliferation of millionaires and billionaires.
While economic disparities remained a serious problem in
developing countries, the forces of globalization created
conditions that helped widen the gap between rich and poor in
industrialized societies. When the economy deteriorates, the gap
between rich and poor tends to be narrower, but concerns about
inequality are heightened. During the global economic
recession, the wealthy lost money, but the poor lost their jobs,
houses, and health insurance. In the United States, the poverty
rate peaked at 15.1 percent in 2010, its highest level since 1993.
In 2013, the poverty rate was still high, at 15.0 percent.
Widespread demonstrations in the United States against
excessive executive compensation, especially those in
companies that received financial assistance from the
government, underscores the dangers of economic inequality.
The financial and economic crisis increased inequality and
heightened awareness of the concentration of wealth held by the
top 1 percent of Americans. That awareness led to “We are the
99 percent,” a battle cry of the Occupy Wall Street protests
against financial inequality that began in New York City and
spread around the world. The perception that economic
inequality is essentially transitory when opportunities for
economic advancement are widely available mitigates negative
effects of actual inequality.
However, persistent inequality and enduring poverty challenge
beliefs in the equality of opportunity and the possibility of
upward mobility. Eventually, the legitimacy of the economic
system and political and social institutions are challenged.
Extreme inequality is detrimental to sustainable economic
growth.
The legitimacy of the global economic system is likely to be
strengthened if a larger number of countries and individuals are
benefiting from it. Extreme inequality perpetuates poverty and
the concentration of economic and political power and reduces
economic efficiency. It strengthens inequality-perpetuating
institutions in three ways:

21. 1. Inequality discourages the political participation of poor
people, which, in turn, diminishes their access to education,
health care, and other services that contribute to economic
growth and development.
2. Inequality often prevents the building and proper functioning
of impartial institutions and observance of the rule of law.
3. Inequality enables the wealthy to refuse to compromise
politically or economically, which further weakens poor
societies in a global society that requires relatively fast
responses to economic developments.2
These consequences of inequality combine to ensure that poor
societies will remain poor and unequal, trapping most of their
inhabitants in a destructive cycle of poverty. Growing
inequality among as well as within nations has direct and
indirect implications for globalization. Inequality could
undermine globalization by influencing countries to adopt
protectionist policies and disengage, to the extent possible,
from the global economy. But the ramifications extend beyond
economic issues to problems such as terrorism, the environment,
and the spread of infectious diseases. Inequality influences
global perceptions of America and weakens its soft power, or its
cultural attraction.
As Chapter 4 shows, the democratization process and the
effective functioning of consolidated democracy depend largely
on a significant degree of economic and social equality. The
legitimacy of any democratic system is contingent upon the
voters’ belief that they have a vested interest in its preservation.
Their allegiance to the democracy is influenced partly by the
benefits they derive from the economic system. Inequality
undermines democracy by fostering despair and alienation
among workers and corruption and the abuse of power among
the wealthy. It corrodes trust and civility among citizens.
Inequality destroys the people’s will to engage in collective
solutions to political, social, and economic problems because it
weakens their sense of unity and common interests. Massive
protests globally against governments underscore this point.

22. The unequal distribution of wealth is often mitigated by
government redistributive policies. Extreme inequality
sometimes results in the voters pressuring governments to enact
trade protection legislation to safeguard their employment and
livelihoods. In this case, voters exercising their democratic
rights could inadvertently undermine the economic system that
supports democracy.
Global and domestic inequalities often directly affect many
areas. Terrorism is widely linked to poverty within developing
nations. Huge inequalities often fuel resentment, which finds
expression in global crime and a general disregard for the rules
and norms of global society. Those who are extremely poor are
often excluded from participation in decisions that negatively
impact their lives. They become vulnerable to being influenced
by radical minorities who are committed to violent change.
Poverty contributes to global and regional problems by fueling
ethnic and regional conflicts, creating large numbers of
refugees, and inhibiting access to resources, such as petroleum.
Finally, global and domestic inequality is perceived as
stimulating the global drug trade. For example, poor farmers in
Bolivia regard the cultivation of coca as essential to their
survival. More than three-quarters of the heroin sold in Europe
is refined from opium grown in Afghanistan by poor farmers.
The costs of fighting the war against drugs in poor countries,
such as Colombia and Afghanistan, are extremely high.9.2: The
Globalization and Inequality Debate
1. 9.2 Contrast between the viewpoints of globalists and
antiglobalists on the effects of globalization
The impact of globalization on income distribution and living
standards is a controversial topic. Preoccupation with
globalization to the exclusion of other factors often muddles the
debate about globalization and inequality. Would less
globalization produce more equality, and would more equality
among and within nations result in an improved quality of life
for the poor? There are two dominant, but sometimes
overlapping, viewpoints on this issue. The globalists argue that

23. globalization has increased economic growth and decreased
global inequality and poverty. The antiglobalists generally
perceive globalization as a negative and destructive force that is
responsible for the increasing global inequality and poverty and
the declining levels of human welfare.39.2.1: Globalists Make
Their Case
From the globalists’ perspective, the basic cause of inequality
and poverty is the relatively low level of globalization in some
countries. In other words, the poorest societies are the least
integrated into the global economy. Openness to foreign trade,
investments, and technology—combined with reforms such as
the privatization of the domestic economy—will ultimately
accelerate economic growth. The Organization for Economic
Cooperation and Development (OECD) calculated that countries
that are relatively open to trade grew about twice as fast as
those that are relatively closed to trade.4 China’s rapid
economic growth is an obvious example. On the other hand,
North Korea, Myanmar (formerly Burma), and Kenya are on the
margins of globalization and remain impoverished.
Globalists also argue that globalization has contributed to the
decline of inequality. Furthermore, poverty can be reduced even
as inequality increases. David Dollar and Aart Kraay found that
“a long-term global trend toward greater inequality prevailed
for at least 200 years; it peaked around 1975. But since then, it
has stabilized and possibly even reversed.”5 The accelerated
economic growth of China and India, the world’s two most
populous countries, which is seen as directly linked to
globalization, is given as the principal reason for the change.
Much of the inequality that persists within countries is due less
to globalization and more to policies dealing with education,
taxation, and social problems. Moreover, more economic growth
in China, for example, has been accompanied by a spectacular
reduction in poverty.6
Globalists emphasize that the number of people moving out of
poverty has increased. More than 800 million people have
abandoned the ranks of absolute poverty since 1990. The

24. number of people living in absolute poverty remains high—
around 1.2 billion. But given rapid population growth rates in
the poorest countries, the decline in global poverty is
impressive. The world’s poor are seen as getting to be less poor
in both absolute and relative terms.7 The more globalized poor
nations become, the better off their populations are in both
absolute and relative terms. Globalization has generally helped
the poor by contributing to reductions in the cost of numerous
consumer products. Less money has higher purchasing power in
a globalized economy. Finally, by facilitating migration,
establishing small businesses that rely on the Internet, and
improving access to jobs in telecommunications and computer
technologies in countries such as India and China, globalization
improves the quality of life for the poor.9.2.2: Antiglobalists
Make Their Case
Antiglobalists believe that globalization is widening the gap
between the haves and the have-nots. Concerned with making
global capitalism more equitable, they view globalization as
primarily benefiting the rich while making life more difficult
for the poor. Antiglobalists argue that globalization is a zero-
sum game, meaning that the rich are winning at the expense of
the poor. Antiglobalists also argue that globalization benefits
rich countries, such as the United States. China is one of the
few developing countries that is generally regarded as profiting
from free trade and open markets. The United States, the
locomotive of globalization, benefits the most from open
markets worldwide. George Soros—a leading financier,
philanthropist, and critic of globalization, though not an
antiglobalist—believes that globalization drains surplus capital
from periphery or developing countries to the United States,
thereby allowing Americans to spend more than they save and
import more than they export.8 Similarly, Jack Beatty contends
that the foundation of inequality resulting from globalization is
that rich countries do not play by the rules that they made to
govern the global economic system. Basically, the United States
and other Western countries require developing countries to

25. open their markets without reciprocating commensurably. To
support this argument, Beatty points out that although global
rules on trade discourage governments from subsidizing
industries, rich countries continue to provide subsidies to
agriculture.9
Critics also argue that globalization is like an “economic
temptress,” promising riches but not delivering. Global
communications have heightened awareness of the vast
disparities between rich and poor within the same society and
especially between rich and poor countries. Simultaneously,
global communications spawn aspirations of escaping poverty
and enjoying the good life. Unfortunately, globalization is
unable to make these dreams real. Countries integrated into the
global economic system are the most severely affected by
downturns in the economy. For example, Southeast Asia, which
depends on exports of steel, textiles, and electronic components,
suffers significantly in global economic crises and is unable to
generate enough jobs and sufficient wages for a population with
aspirations nurtured by television programs that depict
prosperity. Although conceding that globalization is not entirely
responsible for global poverty, antiglobalists generally view
globalization as a tide that lifts a few boats while leaving the
majority mired at the bottom. Even when global companies
create jobs within societies, the race to the bottom in labor
standards and wages inevitably results in the poor in developing
countries being unable to escape poverty while, at the same
time, reducing the wages for workers in rich countries or
depriving them of employment. This development is intertwined
with the precipitous decline of private sector labor unions. Kim
Phillips-Fein argues that unions mobilize their members to vote
for government policies that help redistribute wealth and
reinforce upward mobility, which strengthen the middle class.10
Antiglobalists contend that globalization compounds existing
inequalities and creates more inequality. By giving priority to
privatization, globalization weakens governments’ commitment
to the public sector. Vito Tanzi states that “even as the forces of

26. globalization boost the demand for strong social safety nets to
protect the poor, these forces also erode the ability of
governments to finance and implement large-scale social
welfare policies.”11 The emphasis on integrating poor nations
into the global economy diverts resources from more urgent
development needs, such as education, public health, industrial
capacity, and social cohesion. Many trade agreements impose
tight prerequisites on developing countries in exchange for
crumbs of enhanced market access. The African Growth and
Opportunity Act is an example. It provides increased access to
the U.S. market only if African apparel manufacturers use fabric
and yarns produced in the United States, instead of using their
own or supplies from less expensive sources. In other words,
the antiglobalists perceive globalization as perpetuating
inequality by impeding development. Furthermore, they argue,
countries such as South Korea and Taiwan, that globalists
frequently held up as models for the benefits of globalization
developed under radically different conditions. These countries
were not required to pay the costs that are now an integral
component of integration into global markets. During the 1960s
and 1970s, when they were rapidly growing, Taiwan and South
Korea did not face contemporary globalization’s pressures to
privatize their economies and open their borders to capital
flows. The demands of globalization undermine efforts essential
for a comprehensive development agenda.
9.3: Global Inequality
1. 9.3 Examine the causes and the impact of domestic or global
inequality between nations
Discussions of global inequality remind us of many of the
reasons some societies created powerful and prosperous
civilizations while others did not. Western Europe emerged as
the most prosperous region of the world. Areas that are now the
United States, Canada, Australia, and New Zealand were
conquered and settled by Europeans, many of whom embodied
the characteristics that contributed to Europe’s rise to global

27. prominence and economic prosperity. The advantages Europeans
enjoyed have been consolidated. This, in turn, contributes to
global inequality today. Several factors combined to produce
Europe’s economic success and profound global economic
inequality. A major factor is freedom of expression. Societies
that encouraged people to have their own ideas, to be
innovative, and to interact with each other eventually surpassed
societies that were totalitarian or authoritarian. The latter
generally stifled innovation because of their preoccupation with
traditions, conformity, and respect for authority. Initiative was
often equated with heresy. Another factor encompasses social
values. Chief among these is an emphasis on economic
opportunity and social equality. In his Wealth and Poverty of
Nations, David S. Landes stresses that China’s restrictions on
women hampered its growth, whereas women in Europe, who
were less confined to the home and were free to find
employment in certain occupations, were instrumental in that
region’s industrial development and expansion.12 A third factor
is the functioning of a free market and institutionalized property
rights. Chinese authorities became antagonistic toward free
enterprise and eventually regulated it out of existence. Muslim
countries failed to develop institutions that would have enabled
businesses to expand. Islamic partnership law and inheritance
law worked against the growth of large corporations. In Europe,
a partner in a business could designate heirs, thereby providing
continuity in the business after the partner’s death. Islamic law
did not provide mechanisms for partnerships to be easily
reconstituted following a partner’s death. Similarly, Islamic law
prescribed in rigid detail both immediate and extended family
members who had to inherit property. Europe, on the other
hand, allowed property to be inherited by one person, thereby
minimizing the chances that a business would disintegrate and
be prevented from getting larger. Virginia Postrel points out
that “the fragmentation produced by inheritance law, combined
with the structures of partnership law, kept Middle Eastern
enterprises small. That, in turn, limited the pressure to evolve

28. new economic forms.”13 However, increasing wealth from
petroleum has significantly strengthened many companies in the
Persian Gulf area, especially those involved in finance.
A final factor undergirding Europe’s economic success and
setting the foundation for global inequality is the separation of
the secular from the religious. Whereas Islam became
inseparable from the state, the origins of Christianity and its
spread to Rome forced it to compromise with secular authority,
a compromise encapsulated in the warning that Christians
should give to Caesar what belongs to him and give God what is
God’s. However, Muslim societies prospered when religion was
less restrictive. Muslims, commanded by the Koran to seek
knowledge, became leading scientists, physicians, artists,
mathematicians, philosophers, architects, and builders. For
more than five hundred years, Arabic was the language of
scholars and scientists. Muslims transmitted Chinese scientific
inventions, Greek and Persian texts, and their own impressive
scientific discoveries and inventions to Europe. From the tenth
to the thirteenth centuries, Europeans translated Arabic works
into Hebrew and Latin, thereby giving impetus to a rebirth of
learning that ultimately transformed Western civilization.
9.3.1: Inequality between Developed and Developing Countries
Despite rising living standards throughout most of the world,
the gap between rich and poor countries has steadily
widened. Tables 9.1 and 9.2 show some of those disparities in
greater detail. Historic trends suggest that most of the richest
countries will maintain their lead over most of the poorest
countries. The gap between the richest country and the poorest
country was 3 to 1 in 1820, 11 to 1 in 1913, 35 to 1 in 1950, 44
to 1 in 1973, and 72 to 1 in 1992. By the end of the twentieth
century, the richest 20 percent of the world’s population had
eighty-six times as much income as the poorest 20 percent. At
the beginning of the twenty-first century, the average income in
the richest twenty
Table 9.1 Income Inequality among Countries, 2011 (in terms of
GDP per capita)

29. Adapted from UN Development Programme, Human
Development Report 2013: The Rise of the South. Human
Progress in a Diverse World (New York: UN Development
Program, 2013). GDP per capita is given in international dollars
using purchasing power parity rates (PPP).
Some Rich Countries
Qatar
77,987
Luxembourg
68,458
Singapore
53,591
Norway
46,982
Brunei Darussalam
45,507
Hong Kong, China (SAR)
43,844
United States
42,486
United Arab Emirates
42,293
Switzerland
37,979
Netherlands
37,251
Australia
34,548
Japan
30,660
Republic of Korea (South Korea)
27,541
Some Poor Countries
Ethiopia
979

30. Mali
964
Togo
914
Mozambique
861
Madagascar
853
Malawi
805
Sierra Leone
769
Central African Republic
716
Niger
642
Burundi
533
Eritrea
516
Liberia
506
Democratic Republic of the Congo
329
countries was thirty-seven times that in the poorest twenty
countries.14 As Table 9.1 indicates, income disparities between
developed and developing countries are very wide. Economic
development, while dramatically improving the standard of
living in most countries, has not significantly closed the gap
because of differential growth rates between rich and poor
countries. Rich countries have experienced higher economic
growth rates than poor countries. Furthermore, per capita
income actually declined in more than one hundred of the
world’s poorest countries, many of them in Africa. Even
developing countries that have enjoyed unprecedented economic
growth, such as China and India, have failed to close the gap

31. between themselves and rich countries. It is estimated that it
would take China and India a hundred years of constant growth
rates higher than those now experienced by industrialized
countries just to reach current American income levels.
However, given the extraordinarily high standard of living in
the United States, both China and India would be relatively
prosperous if they achieved half the income level of Americans.
Furthermore, globalization is profoundly altering many old
assumptions. Because the income gap between rich and poor
countries has widened historically, it does not necessarily
follow that this will always be the case. Singapore and Kuwait,
two high-income countries, illustrate that poor countries can
become prosperous by implementing astute political, social, and
economic policies (in the case of Singapore) or by having
valuable natural resources (in the case of Kuwait). Economic
disparities between the developed and the developing world
have focused on the global digital divide. But access to the
Internet and improved telecommunications are not automatic
panaceas for solving the problems of developing societies.
9.3.2: Causes of Inequality between Rich and Poor Countries
In this section, we will briefly discuss some causes of the
widening gap between rich and poor countries. It is important to
remember that several factors combine to contribute to
inequality: (1) geography, (2) colonialism and its legacies, (3)
the structure of the global economy, (4) population growth, (5)
government policies, (6) political instability, and (7) natural
disasters.
Geography
Countries that are poor, some argue, have certain geographic
characteristics that contribute to their economic problems. For
example, they are in tropical regions or face high transportation
costs in accessing global markets because of their location.
Apart from the prevalence of tropical diseases, which have been
controlled to a large extent by modern medicines and practices,
countries in the Southern Hemisphere also tend to suffer from
being landlocked. Countries with extensive coastlines and good

32. harbors tend to be better off economically than landlocked
countries that lack the physical infrastructure (i.e., systems such
as roads and railroads) essential for gaining access to navigable
rivers and the sea. Landlocked countries or countries located far
from global markets are disadvantaged by high transportation
costs.
Colonialism
Many argue that European colonization of Africa, Asia, and
Latin America laid the foundation for economic disparities
between rich and poor nations. Inequality breeds inequality.
Just as wealth tends to perpetuate wealth, poverty tends to
perpetuate poverty. Countries that grew rich two hundred years
ago, partly because of their colonization of the developing
world, are generally still rich today. European groups that
migrated to Australia, Canada, the United States, South Africa,
New Zealand, and throughout Latin America continue to enjoy
significant advantages
Table 9.2 Health Inequalities
Adapted from UN Development Programme, Human
Development Report 2013: The Rise of the South. Human
Progress in a Diverse World (New York: UN Development
Program, 2013).
Physicians, 2005–2010 (per 1,000 people)
Life Expectancy at Birth, 2012 (years)
Maternal Mortality Ratio, 2010 (deaths per 100,000 live births)
Country
Rich Countries
Norway
4.1

33. 81.3
7
Switzerland
4.1
82.5
8
Netherlands
3.9
80.8
6
Sweden
3.6
81.6
4
Germany
3.5
80.6
7
Ireland
3.2
80.7
6
Australia
3.0
82
7
United States
2.7
78.7
21
New Zealand
2.4
80.8
15
Japan
2.1

34. 83.6
5
Republic of Korea (South Korea)
2.0
80.7
16
Poor Countries
Guinea
0.1
54.5
610
Central African Republic
0.1
49.1
890
Burkina Faso
0.1
55.9
300
Democratic Republic of Congo
0.1
48.7
540
Sierra Leone
0.0
48.1
890
Burundi
0.0
50.9
800
PPOL 650

35. Discussion Board Grading Rubric
Student:
Criteria
Points
Possible
Points Earned
Instructor’s Comments
Structure
· Presentation is strong and focused.
· Paragraphs are organized and coherent.
· Transitions are clear and maintain flow of thought.
· Conclusion is objective and rises from entry.
5
Content
· Thread is substantive, well-developed, and fully addresses all
aspects of the task.
· Replies demonstrate analysis of classmates’ posts.
· Replies extend meaningful discussion by building on previous
posts.
· Replies demonstrate an understanding of subject.
· Assertions are clearly supported and/or illustrated.
· Biblical integration is evident in threads and replies.
· Uses factually correct references to weekly readings,
presentations, and other scholarly sources to support comments.
30
Grammar and Mechanics
· Sentence structure is complete, clear, and concise.
· Spelling and punctuation are correct.
· Word choice is precise, unambiguous, and appropriate.
5

36. Submission
· Meets the required word limit (at least 400 words for the
thread and at least 250 words for each reply).
5
Format
· Pleasing general appearance.
· Correct and current Turabian style in internal citations and
references.
5
Total
50
CHAPTER
14
Communicating Findings
Strategic managers and entrepreneurial researchers know that
unless research findings are reported effectively nothing
happens. You may report findings at meetings, in press releases,
brochures, project reports, annual reports, and academic papers.
In this chapter we argue for clear, focused presentations tailored
to the needs of a particular audience. At the end of the chapter
we touch on ethical concerns that occur in connection with
reporting data.
VARIATIONS IN AUDIENCES AND THEIR NEEDS
Whether you plan to make an oral presentation or write a report,
the first steps are to focus on your purpose and the
characteristics of the intended audience(s). First, you need to
get the audience’s attention. Virtually everyone is overloaded

37. with information. Effective administrators and policy makers
may be particularly adept at protecting their time and ignoring
information that they do not need or want. Second, you want to
prevent having the listeners or readers miss your main point. If
you aren’t clear and don’t get their attention they can
misunderstand or ignore important findings. Third, you may
want to teach your audience members something, influence their
thinking, or motivate them to act.
Identifying the audience for an oral presentation is
straightforward. Your first question should be “why will people
attend the presentation?” Once you have the answer you can
tailor the presentation to the audience’s concerns, its level of
knowledge, and its motivation to act. Identifying potential
readers is more difficult. A report may be passed on to
supervisors, staff, agency analysts, interest-group members,
professional acquaintances, legislators, or students. Reports
may be placed in an agency library or posted on a Web site. To
satisfy diverse readers, reports must be clearly written and
research procedures should be fully documented. On the other
hand, including full details, especially about the methodology,
can diminish a report’s readability or an audience’s attention.
You can resolve this apparent conflict by putting important
information, such as the report’s findings and recommendations,
first and placing complicated or technical details in footnotes or
appendices. You may also direct an audience to Web sites or
other easily accessed sources for additional information.
If you are conducting a study for an organization that you don’t
work for, you may want to learn how it normally organizes and
presents information. You may attend oral briefings or ask
potential audience members to identify presentations that they
thought were especially effective. You can use this information
to infer what features generate audience interest and
involvement. When you read reports, save ones that seem
particularly well done.
ORAL PRESENTATIONS OF RESEARCH FINDINGS
No matter whether you see yourself as a strategic manager or an

38. analyst, you need to hone your oral presentation skills. As
teachers we have observed talented students who avoid making
oral presentations. These students lose valuable opportunities to
practice presenting their ideas, listening to others, and phrasing
and answering questions. Whether you normally speak to one
person, a small group, or a large, formal audience, your ability
to explain your work clearly will serve you well. Professionals
who feel pressed for time may prefer to hear about a study
rather than read through a report. Some people are “oral
learners,” that is, they efficiently absorb and understand
information they hear. Others value debating information and
discussing it with investigators and colleagues.
An oral presentation provides an excellent opportunity to have
an impact. Audience members may feel compelled to pay
attention. Their interactions may motivate the group or
individuals to discuss and follow up on the findings. You can
prepare by asking yourself, “Why will people attend this
presentation? What do I want them to learn? What action do I
want them to take? How can I convince them to take it?” The
answers should guide how you organize a presentation.
You should not discount the importance of one-on-one informal
discussions of your research. Their informality can be
deceptive—don’t overlook the opportunities they provide. They
offer an important occasion for others to develop interest in
your project. What would you say if your agency head were to
ask what you are working on? Wouldn’t you want to generate
interest in your current project? Wouldn’t you want to lay the
groundwork for a decision based on your findings? A trick that
some researchers employ is to prepare an “elevator speech.”
That is, they prepare a very short description of what they are
working on that is short enough to be said during an elevator
ride. You may never be caught in an elevator with a person you
want to impress, but if you are prepared you will not waste an
unexpected opportunity to sell your project.
An effective presentation requires planning and practice. Select
the points you want to emphasize, the evidence you will use to

39. support these points, the order in which the information will be
presented, and visual aids. The traditional order for a research
presentation—background, methodology, findings, and
discussion—usually works well. It develops the material
logically. People with training in the sciences, including the
social and behavioral sciences, have come to expect it. If
audience members are already informed about the program or
policy, identifying the study’s purpose may be sufficient.
Otherwise, you should describe the program or policy to put the
information in context and to help audience members follow the
presentation. Usually a brief discussion of the methodology is
sufficient. Except for specialized audiences, you can skip the
technical details. Remember that while you have learned to pay
careful attention to detail and to examine findings from various
perspectives, these skills can translate into tedious, unfocused
presentations. Avoid trying to cover too much information.
Instead concentrate on a few important points and encourage the
audience to ask about the details, especially those details that
may affect their willingness to accept the findings.
Visual aids may be used throughout a presentation. PowerPoint
slides, tables, or graphs focus the presenter and the audience.
To select a visual aid, consider whether it communicates the
information clearly and effectively, requires special equipment,
or slows down the presentation. Too many visuals can bore an
audience. Detailed tables and graphics leave people in the back
rows squinting or feeling left out. Wordy slides focus the
audience on trying to decipher the slide instead of listening to
you.
The slide shown in Figure 14.1 is from a presentation on the
legislative history of a U.S. health care policy. You might
rightly point out the slide is too wordy, but even more
confusing is that it lacks coherence. We might start our revision
by focusing only on the Social Security Act and Wagner-
Murray-Dingell Bill. We would give the year of the Wagner-
Murray-Dingell bill. The citations probably could be deleted
from the slide. Figure 14.2 shows an improved version. Using

40. PowerPoint you might try your hand at further improving the
slide in Figure 14.1.
If your presentation contains a number of tables or slides, or if
you expect the audience to take notes from the PowerPoints,
you should prepare a handout containing the same information.
Alternately you may post the slides on a Web site or e-mail
them to participants.
Presentations with lively graphics can be fun to put together,
but make sure that they don’t draw attention away from the
presentation’s content. Similarly, fumbling around with
unfamiliar equipment creates a serious distraction.
Inexperienced presenters may overlook the importance of
practice. A researcher who has poured over a study may feel
confident in her ability to ad lib the presentation.
Unfortunately, she may bog down on the study’s minutiae or
move erratically from point to point. Typically, one should
practice with an audience of colleagues, team members, or
friends. Practice-session observers should make sure that the
major points are clearly presented, the statement of key points
does not become repetitious or condescending, the transitions
are smooth, and the equipment operates correctly. The observers
should ask questions about the methodology and the
interpretation of the findings. Preparing answers to “hard”
questions avoids the embarrassment of stumbling around during
the actual presentation. If questions challenging the credibility
of the study or its findings go unanswered or are poorly
answered, the written report may never be read and its potential
impact may be undermined.
FIGURE 14.1
An Ineffective Slide
FIGURE 14.2
A More Effective Slide.

41. WRITTEN PRESENTATIONS
A written research report should cover the study’s purpose,
relevant background articles and reports, its methodology,
findings, discussion, and recommendations. An executive
summary, a report summary that goes at the beginning of the
report, is actually the last part of the report you write. The
written report should be a permanent record of what was done,
why and how it was done, and what was found. Although the
number of people who actually read the report may be small,
this written record remains and may be available to all
interested parties.
Research findings may be summarized on handouts and Web
sites, in brochures or press releases. Typically, to prepare such
summaries you extract material from the research report.
Readers of summaries miss the details they need to judge the
credibility of the findings, to pick up information that may be
pertinent to them, or to justify making a decision or taking
action. However, the summaries may be the only part of the
report that some policy makers and others read. The executive
summary provides an overview of the important aspects of the
research report. It often is included as the first part of the
research report and can also be used as a separate document. We
first discuss the structure and content of the research report and
then discuss the executive summary.
Background Information
You should begin the report by identifying the question you are
asking and the value of answering it. As appropriate, a report
may discuss the program or policy’s origins, implementation
history, goals, relevant stakeholders, resources, and activities.
The specific information included depends on the report’s
audience and its purpose.
To develop the background information, you may cite
interviews, documents, and the research literature. You are most
likely to include a formal literature review in program
evaluations. The literature may justify the study’s design, the
variables you chose, the relationships you examined, and how

42. you interpreted the findings. You may present previous research
in chronological order or you may organize the discussion
around key variables or concepts. You may weave information
from the literature into the background presentation, assign it to
an appendix, or include it in an annotated bibliography.
Methodology Section
The final project report should be comprehensive enough for
others to use the report, verify its findings, or replicate the
research. The methodology section is key to providing this
information. At a minimum it should have enough detail so that
readers can decide if the findings are credible and policy
makers can use them as evidence. You should discuss how you
defined and measured the study’s variables, any intervention
you introduced, your sample, when you collected the data, and
how often you collected them.
If your study design was an experiment or a quasi-experiment
you should describe the intervention, the study population, and
how you created the study group(s). Your goal is to provide
sufficient information on the design and its implementation so
readers can assess the study’s internal validity, and subsequent
investigators and policy makers can assess how the findings
might apply in other settings. When reporting performance
measures or survey results you can limit the methodological
discussion to writing about the measures and samples.
In the measurement section you should identify the operational
definitions, how you categorized or assigned numerical values,
how you grouped values and combined variables to create
indicators, and evidence supporting the reliability and
operational validity of the measures. Customarily, in
quantitative studies researchers report only the findings from
mathematical tests of reliability and empirical evidence of
operational validity. To illustrate what is included we give a
hypothetical example of how to report the operational
definition. If an analyst divided the scale into categories, such
as high trust, somewhat trustful, and low trust, she could
include the information in a footnote.

43. REPORTING ON A MEASURE
To measure trust we asked respondents to rate the following
statements using a 7-point scale where 1 = strongly disagree and
7 = strongly agree; the alpha coefficient was .88.
• The people who represent the funder are trustworthy.
• My organization can count on the funder to meet its
obligations to the program.
• My organization feels it worthwhile to continue to work with
the funder. ■
In the discussion of the sample you should identify the target
population, sampling frame, sampling design, response rate, and
when the data were collected. To avoid ambiguity, you should
report the initial sample size, how many members of the sample
were contacted, how many of those contacted belonged to the
target population, how many refused to provide data, and how
many supplied incomplete data. If possible you should compare
respondents and nonrespondents. Any other sources of
nonsampling error should be identified.
Findings
Whether you are writing a report or preparing an oral
presentation the key considerations of how to present your
findings are the same. You need to (1) organize the findings
into a coherent presentation, (2) focus on the important findings
and avoid overwhelming the audience with unnecessary detail,
and (3) decide on how to present the data. Presented with an
uninteresting analysis or an overwhelming amount of detail,
audience members may stop listening or reading.
Your graphs and tables should complement the verbal
presentation and exhibit data efficiently. Attractive graphics
and clear explanations allow readers to assess the richness of
the data. The location of graphics and explanations and the
amount of space devoted to them signal the importance of the
information they contain. You should not waste space on
graphics that illustrate unimportant or trivial findings; they do
not deserve major emphasis.
Tables may show exact numerical values. They are effective

44. when you want to encourage many specific comparisons. Graphs
are especially effective for time series and to make simple
comparisons. They permit an audience: to pick out long-term
trends, cycles, and seasonal fluctuations; to compare different
groups or organizations; and to see differences before and after
an intervention.
You should take care to avoid ambiguous labels. Spell words
out and avoid abbreviations. The following summarizes
practices associated with constructing effective tables and
graphs.
1. Tables or graphs should have a precise, descriptive title. A
title may list by name the dependent variable by independent
variable by control variable (if any). Alternatively, a title may
summarize a major finding supported by a graphic, for example,
“City homicide rates have dropped over the past twenty years.”
All variables and their corresponding categories should be
clearly labeled and appropriate units (e.g., years) should be
indicated.
2. The independent variable normally heads the columns of a
table and the dependent variable heads the rows.
3. If percents are used, the percent sign (%) should be entered
at the top of each column.
4. The number of cases on which percent figures are based
should be indicated. The total number of cases used in the
analysis also should be indicated.
5. Statistical measures, if any, should be placed at the bottom
of the table.
6. Definitions of key terms should appear as a table or graph
footnote.
7. Data source(s) should be identified in the table or graph’s
footnote.
8. A good table supplements, not duplicates, the text. The table
and its data should be referred to in the text, but you need to
discuss only the highlights. As well, tables and graphs should
be able to stand alone, that is, readers should be able to grasp
the essential information without referring to the text.

45. 9. As you work on preparing tables and graphs, remember to
date them; you may even want to note the time. This is because
as you analyze the data you may note and correct errors and you
may decide on a different, more effective way to group your
data. Unless your graphs or tables are dated you may not
remember which represents the most recent version.
Discussion
In the findings section you report objective, verifiable
information. In the findings section you organize and present
the quantitative and qualitative data; in the discussion section
you discuss what you observed about the information and
interpret the findings. You may note
■ what seems important;
■ how the findings compare with the literature or stakeholders’
perceptions;
■ findings that were unexpected and your thoughts about why
they occurred;
■ implications of the findings for policy making, action, or
further research.
Recommendations
Program evaluations, policy analyses, and other studies done for
a legislative or administrative body may include
recommendations. Recommendations are normative statements
about changes that should be made in the program or policy.
Although you may feel ill-equipped or uncomfortable in making
normative statements or telling clients what they should do, the
study’s sponsors may expect recommendations.
You may find that recommendations focus decision makers on
what needs to be done and increase the utilization of your
findings. Recommendations should naturally follow from the
research findings, that is, a reader should be able to figure out
from the report’s content why the recommendations were made.
In making recommendations you should address only those
changes the agency can make; for example, recommending a
change in federal program requirements will not be of any value
to a local social service agency. In some cases the costs and

46. benefits of adopting a recommendation may be identified and
included. Alternatively, you may suggest several options for
agencies to consider.
Executive Summary
The executive summary highlights a report’s content. The
intended audience is the executive who has little time to read
complete reports. An executive summary is also useful for many
different audiences. Busy administrators and policy makers scan
an executive summary to decide if and when to read the entire
report or to refer it to an associate. Administrators with a
limited interest in the topic skim a summary to keep themselves
current. Policy actors may distribute summaries to communicate
and endorse the report’s findings. Investigators doing literature
reviews can infer if the report is relevant and if they should
read it.
Although usually included at the front of the research report,
the executive summary is the last part of a report to be written.
It includes only information contained in the report, but it can
be read and understood independently of the report. In writing
an executive summary, you decide what you want a reader to
know. For example, you may visualize the impatient
administrator who asks “What’s the headline?” You can go
through the report and find sentences that concisely describe
why the study was done, who the subjects were, how the data
were collected, major limitations in the methodology or its
implementation, and what the major findings were. You should
include any recommendations that were part of the report.
You should use clear direct sentences and visual cues to allow
an individual to read the summary quickly. Keep its length and
degree of detail consistent with the length and complexity of the
report, agency expectations, and the importance of the findings.
In preparing an executive summary, you should avoid including
too many details; otherwise, its benefits are defeated. The
following sample executive summary may serve as a model of
how to organize and summarize a report.
ETHICAL ISSUES

47. Completing a research project, presenting findings, and storing
information have ethical dimensions. A joint committee
convened by the National Academy of Sciences, the National
Academy of Engineering, and the Institute of Medicine
identified three sets of ethical issues: research misconduct,
questionable research practices, and other
misconduct.1 Research misconduct consists of acts of
fabrication, falsification, or plagiarism. Questionable practices
refer to decisions with regard to data retention and sharing,
record quality, authorship, statistical analysis, and release of
information. Other misconduct refers to acts that are
unacceptable but not unique to researchers—for example,
misuse of funds, vandalism, violations of government research
regulations, and conflicts of interest. In this section we focus on
research misconduct, handling research errors, and record-
keeping issues as these are ones that you are most likely to
encounter in the course of your work.
Research Misconduct
Fabrication is defined as making up data or results, and
falsification as changing data or results. We assume that you
know that fabrication is wrong. Falsification can be a bit more
ambiguous. An easy way to falsify results is to drop cases from
a dataset. Dropping selected cases can strengthen your
statistical evidence or even rescue a weak statistical model. For
example, you might eliminate cases that you think have
measurement errors, such as when you suspect that incorrect
data were reported. If you think that measurement error
occurred, you should try to confirm it. However, if you cannot
confirm the error you may decide to remove the cases, in which
case you must report that you removed the cases, explain why
you removed the cases, and indicate how their elimination
affected the results. The greater the effect, the more diligent
you must be in reporting the decision. A decision that markedly
affects the findings should not be buried in fine print.
AN EXECUTIVE SUMMARY
A Program Evaluation of the Vocational Training Programs at

48. Portal
Portal [pseudonym] is a community-based rehabilitation facility
whose mission is “to help people with vocational disabilities
achieve a sense of self-worth by optimizing their potential to
earn their own wages through work.”The facility has two
programs to provide persons with disabilities vocational
training suited to their needs and abilities.
Study Questions
■ How successful are Portal trainees in obtaining permanent
jobs?
■ What characteristics are associated with successful job
placement?
■ Is one of the training programs more successful than the
other?
Findings
A customer satisfaction survey of trainees of both programs
indicated that 22 of the 23 contacted were satisfied with the
services they received; and were currently working in
permanent positions.
An examination of Portal’s databases found that based on case
closure its success rate has declined in the past 3 years. In the
first year it had 100 percent successes as compared to the state
average of 87.2 percent. By the third year it had 81.3 percent
successes as compared to the state average of 82.7 percent. The
decrease may be attributed to a change in definition of the
term success, which no longer considers probationary
employment as a success. Analysis disclosed no differences in
race, sex, or disability of clients in either program between
those who were successful and those who were unsuccessful.
Inconsistencies in Portal’s databases limited the evaluators’
ability to find information and clarify definitions. A substantial
amount of useful information was missing from the databases.
Recommendations
■ Developing a centralized database for the entire agency with
clear definitions of database fields, leaving little need for
interpretation of information by data entry staff.

49. ■ Collecting more information on processes that could lead to
better service for clients and employers.
■ Connecting billing sheets to database to monitor hours of job
development and job coaching for each client.
■ Surveying or interviewing clients who have been placed in a
permanent job to monitor their long-term success. ■
You need not be overly concerned about avoiding charges of
falsification. You simply need to be careful in documenting
your decisions and why you made them. The documentation
makes your decisions accessible for peer review.
Plagiarism is falsely presenting another’s ideas or words as
one’s own. Quoted material should be placed in quotation marks
and references cited. Closely following another author’s diction
is wrong. You should either use your own words and sentence
structure or quote directly from your sources. Relying on the
works of others is inevitable in research. No one knows this
better than a textbook writer. We have referenced sources that
we relied on to write segments of this text or that provided a
unique or valuable perspective on the material. We have not
referenced sources for ideas and perspectives that we know are
part of the common knowledge of social science researchers.
Diligent referencing and use of your own words should be
adequate to avoid charges of plagiarism. In our experience, the
most common instances of plagiarism are using information
from Web sites without citing sources, or editing and presenting
another’s work as one’s own. Changing words and dropping
sentences also constitutes plagiarism. If a report is to be
published, you need to pay attention to copyright laws. You
must get permission from the copyright holder to reproduce
graphs, tables, long quotes, and other materials, including song
lyrics, poetry, and cartoons. However, government documents
are not covered by copyright, and their contents can be
reproduced without obtaining permission. Nevertheless, you
should use standard referencing procedures to cite a government
document.
Handling Research Errors

50. Error is inevitable in research. The joint committee convened by
the National Academy of Sciences, the National Academy of
Engineering, and the Institute of Medicine identified four
potential sources of error: the accuracy and precision of
measurements, the generalizability of experiments, the quality
of the experimental design, and the interpretation of the
practical significance of the findings. To reduce the diffusion of
incorrect knowledge, you should fully disclose your research
procedures, and acknowledge and correct errors.
Full disclosure allows others to scrutinize the research. They
may find errors by examining the research documents or by
attempting to replicate the research. Concealing limitations in
your methodology amounts to deception. Research reports
should clearly identify and evaluate the limitations. In fact, the
more troublesome a limitation, the more emphasis it should
receive.
Complete information on research procedures can overwhelm
readers with details and seriously diminish a report’s
effectiveness. The professional standards for program
evaluation may serve as a useful guide. To provide useful
information, the standards advise evaluators to write clearly,
present information that their audiences can understand, and
indicate the relative importance of their findings and
recommendations. To achieve full disclosure, the standards
advise evaluators to state their assumptions, their constraints,
and how readers may obtain full information on research
procedures, including data analysis.2 The standards relieve
evaluators of the burden of providing complete research
information in every report, but they must take reasonable
actions to ensure the accessibility of the database and
documentation.
SAVING DATA
Data must be saved and be accessible to allow research audits,
replication of results, refinement of the analysis, additional
analyses, or incorporation of data into other research designs
Research data include completed data collection instruments,

51. protocols for collecting and entering data, descriptions of
experimental procedures, data files, computer printouts, field
notes, videotapes, audio tapes, CDs, and DVDs. With this
information the research can be reconstructed or replicated.
Audits may be a component of ensuring integrity; auditors can
investigate charges of falsification or fabrication.
Misunderstandings can be avoided if you and other involved
parties agree on who will retain the data, how long they will be
kept, and the conditions governing …