The Elaboration Model: Testing Theories by Analyzing Variable Relationships

The Elaboration Model
Introduction
The elaboration model refers to a protocol for analyzing
relationships among variables for the purpose of testing
theories.This protocol is common to all sciences.In some ways
it is applied differently in the social sciences compared with the
life and physical sciences because the social sciences:work
more with abstract variables.cannot physically control social
variables to analyze their effects on other variables.
Introduction (Continued)
Earl Babbie uses the term, “Elaboration Model,” to refer to this
protocol for investigating cause and effect among
variables.Other names:Interpretation model.Lazersfeld
method.Columbia school.or simply, Scientific Method.
This presentation will:Describe the origin of the

elaboration model. Explain the rationale for using the
model.Explain the steps in using the model.Explain each type of
relationship between variables that should be investigated with
the model.Provide an example of an application of the model.
Origin of the Model
The American Soldier
Stouffer et al. sought to understand how to increase the
motivation and morale of soldiers in the U.S. Army.
The researchers sought ways to explain seemingly non-rational
behavior among U.S. soldiers during their training for duty
during WWII.
Origin of the Model
The American Soldier (Continued)
Consider these three hypotheses:
The greater the promotions, the greater the morale.
African-American soldiers trained in northern camps are more
likely to have high morale than African-American soldiers
trained in southern camps.
Soldiers with more education are more likely to resent being
drafted than soldiers with low education.
Origin of the Model

The American Soldier (Continued)
Although each hypothesis seems intuitively reasonable, each
one is not supported by observation.
Therefore, research scientists require a protocol for finding
indications of true cause or no cause and rejecting false
indications of cause or no cause.
Rationale for the Elaboration Model
Introduction
Scientists must be very careful when investigating cause and
effect to accurately identify the correct cause of an
outcome.Relying upon misleading results:wastes
money.influences the development of flawed technologies or
policies.might result in harm to individuals or societies.
Experimental Control
In the physical and life sciences, it is possible to physically
control most variables.In the social sciences, one cannot
physically control variables (e.g., one cannot “take out”
religious beliefs from a person to isolate the effects of
education on income, controlling for religious
beliefs).Therefore, the social sciences rely more so than the life
and physical sciences upon statistical control.

Aggregated Data
Social science research often must rely upon aggregated data
because of cost constraints and issues of
confidentiality.Analysis of aggregated data can yield misleading
results.Disaggregation of data examines how results vary when
changing the unit of analysis to the individual level.
SummaryScientists seek to understand cause and effect to
improve human well-being.They use the elaboration model to
accurately identify cause and effect.Because social scientists
often cannot physically control their variables of interest, and
because they must rely upon aggregated data more so than they
would like to do, they must be especially careful about using
the elaboration model to statistically control their variables of
interest.
Steps in the Elaboration Model
A relationship is observed to exist between two variables.The
researcher begins by examining frequencies and bivariate
relationships (e.g., the greater education, the greater the
income).
A third variable is held constant by subdividing the cases
according to the attributes of this third variable.The cases are
divided into the groupings of the third variable (e.g., education
and income are divided by males and females).

The original two-variable relationship is recomputed within
each of the subgroups.What is the relationship between
education and income for males and what is this relationship for
females?
The comparison of the original relationship with the
relationships found within each subgroup provides a fuller
understanding of the original relationship itself.Does the
relationship between education and income differ between
males and females?
Example of Steps
Consider this simplistic example of how the elaboration model
can be used to gain a better understanding of cause and
effect.We will examine hypothetical relationships among three
variables:education.income.sex of the subject.
Example of Steps (Continued)Step 1, Examine a bivariate
relationship:
Step 2: Examine this relationship within categories of a third
variable:
Education
(x)

Income
(y)
Education
Males: (x1)
Income
(y)
Education
Females:(x2)
Income
(y)
Males
Females
Example of Steps (Continued)
Step 3, Re-examine the original relationship:
Males
Females
Income
Education
Example of Steps (Continued)

When the relationship between education and income is
examined with respect to a third variable—sex of the subject—
then one can obtain a more accurate understanding of cause and
effect.The use of the elaboration model can thereby clarify
cause and effect as well as alert the scientist to the need for
further research.
Application of the Elaboration Model
Introduction
The purpose of the elaboration model is to obtain, as accurately
as possible, an understanding of cause and effect among
variables.The elaboration procedure is to attempt to find either
a false indication of causality or a better understanding of
causality by examining the effect of a third variable on a
relationship between two variables.
When an attempt to reveal a different understanding of causality
fails, that is, when the original relationship between the two
variables of interest is not altered by the introduction of a third
variable, then the original relationship is replicated.A
replication of the original relationship, despite attempts to
elaborate upon or discredit it, gives the researcher a sense of
confidence that it is not a false indication of causality.

When the attempt to elaborate upon or discredit causality
succeeds, then the researcher:gains a better understanding of
cause and effect among variables.learns more about the social
problem being investigated.gains awareness about avenues for
future research.
Introduction (Continued)We will review six kinds of
relationships among variables that can hinder accurate
interpretations of causality:Aggregation bias.Ecological
fallacy.Explanation (Moderating):Spurious
relationship.Suppressor relationship.Misspecified
relationship.Interpretation (Mediating).
Aggregation Bias
Aggregation bias occurs when aggregated data do not accurately
reflect the underlying causal conditions between the
independent variable (x) and the dependent variable (y).
The following example shows how aggregated data can distort
the real relationship between two variables.
Aggregation Bias: Example
Theory: The greater the human capital investment, the greater
the achievement.

Hypothesis: The greater the amount of teacher attention given to
students, the greater their academic achievement.
Note how one gains a different interpretation of the hypothesis
with two sets of data collected at different levels of analysis.
Aggregation Bias: Example
If data are collected at the individual level, then one observes
the true relationship between teacher attention and student
performance.
If only aggregated data are available (i.e., average performance
of high, medium, and low achievers) then one observes the
opposite and incorrect relationship between attention and
performance.
Aggregation Bias: Complete Data
x
x
Grades
Teacher Attention
High Achievers
(n = 57)
Medium Achievers
(n = 209)
Low Achievers
(n = 144)
x
The “x” represents the average grade for each group of students.

Aggregation Bias: Aggregated Data
x
x
Grades
Teacher Attention
x
The “x” represents the average grade for each group of students.
All Students
(n = 3, the average
for each group)
Ecological Fallacy
One commits an ecological fallacy when one assumes that
statistics calculated using aggregated data reflect individual
traits.That is, one mistakenly assumes that all individuals in a
group behave in the same manner as the group average.Consider
the following example about the racial composition of
classrooms and student performance.
Ecological Fallacy: Example
Theory: The greater the human capital investment, the greater
the achievement.
Hypothesis: The greater the percentage of whites compared with
blacks in a classroom, the greater the academic achievement.

This hypothesis assumes that whites will have more human
capital (e.g., background education, motivation to succeed) than
will black students and therefore will perform better
academically.
Classroom A (n = 10 students)White students = 80%Students
with a GPA of 3.5+ = 30%
Classroom B (n = 10 students)White Students = 50%Students
with a GPA of 3.5+ = 20%
Consider the potential explanations of these data and the social
policy implications of each:
Black students are less well prepared.Black students are more
disruptive.Black people and white people are not meant to
associate with one another.Students might perform better if
black students and white students attended different schools.
Suppose we look at the data at the individual level.

Classroom A (n = 10; White = 80%)Students with a GPA of 3.4
or less:
W, W, W, W, W, W, WStudents with a GPA of 3.5+:
B, B, W
Classroom B (n = 10; White = 50%)Students with a GPA of 3.4
or less:
B, B, B, B, W, W, W, WStudents with a GPA of 3.5+:
B, W
All StudentsBlack students with a GPA of 3.5+ = 42.9%White
students with a GPA of 3.5+ = 15.4%
Ecological Fallacy: ExampleFrom the aggregated data, we
conclude that the greater the percent of black students in the
classroom, the lower the GPA.From the disaggregated data, we
learn that the few black students in the classroom are the ones
who are making the best grades.This explanation implies
different social policies than the ones inferred from analysis of
the aggregated data.
Explanation (Moderating Relationship)
A moderating variable can alter the relationship between two

variables such that:
The original relationship is shown to be a false indication of
causality (i.e., spurious relationship).
The original relationship is shown to be a false indication of no
causality (i.e., suppressor relationship).
Explanation (Moderating Relationship)
The strength of the relationship differs across categories of a
third variable (i.e., misspecified relationship).
The direction of causality in the original relationship is
reversed (i.e., distorted relationship).
Moderating Relationship (Continued)
A third variable (z) moderates (or causally affects) both the
independent variable (x) and the dependent variable (y).
Note: X might or might not cause Y, depending upon the pattern
of causality related to Z.
Independent
Variable (x)
Moderating
Variable (z)

Dependent
Variable (y)
Spurious Relationship
A spurious relationship is a false indication of causality
between x (the independent variable) and y (the dependent
variable).A third, external, variable causes both x and y.The x
and y variables appear to be causally related, but they are not.
Independent
Variable (x)
External
Variable (z)
Dependent
Variable (y)
+
+
Spurious Relationship: Silly Example
The greater the rate of ice cream consumption, the higher
the rate of violent crime.
External variable: Air Temperature.

Ice-Cream
Consumption
Air
Temperature
Violent
Crime
+
+
Spurious Relationship: Actual Example
Theory: The greater the available resources, the greater the
productivity.
Hypothesis: The greater the expenditures per pupil, the greater
the academic achievement.
Independent variable (x): Expenditures per pupil.
Dependent variable (y): Academic achievement.
Moderating variable (z): Educated parents.
Spurious Relationship: Actual Example
Explanation: Educated parents, who value education, have
higher incomes and therefore contribute more to schools. They
also motivate their children to perform well academically.

School
Expenditures
Educated
Parents
Student
Performance
+
+
Suppressor Relationship
A false indication of no causality between x (the independent
variable) and y (the dependent variable).A third, moderating,
variable has, for example, a negative effect on x and a positive
effect on y. These two relationships “cancel out” the indication
of causality between x and y.The x and y variables appear not to
be causally related, but they are.
Suppressor Relationship (Continued)
A third variable (z) moderates (or causally affects) both the
independent variable (x) and the dependent variable (y).X does
not appear to cause Y, but it does.

Independent
Variable (x)
Moderating
Variable (z)
Dependent
Variable (y)
-
+
Suppressor Relationship: Example
Theory: The greater the self-actualization, the greater the life
satisfaction.
Hypothesis: The greater the marital satisfaction, the greater the
life satisfaction.
Independent variable (x): Marital satisfaction.
Dependent variable (y): Life satisfaction.
Moderating variable (z): Presence of children.
Suppressor Relationship: Example
Explanation: The presence of children can decrease marital
satisfaction but increase life satisfaction, making it appear that
marital satisfaction is not related to life satisfaction.

Marital
Satisfaction
Presence
of Children
Life
Satisfaction
-
+
Misspecified RelationshipAfter the introduction of a third
variable:The causality between two variables is supported by
the analysis and the direction of causality remains the same.But
the strength of the causality varies across levels of the third
variable.Example: When examining the effects of teacher
attention on student performance, we might find that the
positive relationship varies in strength among high, medium,
and low performing students.
Example of Specification
Grades
Teacher Attention

High Achievers
Medium Achievers
Low Achievers
Mediating RelationshipThe causal sequence goes from x to z to
y.Thus, x has an “indirect effect” on y.The independent variable
might also have a direct effect on y.In interpretation, the
relationship between x and y is mediated by the third variable,
z.
Mediating Relationship: ExampleSelf-esteem (x) has a direct
effect on marital satisfaction (z) and marital satisfaction has a
direct effect on life satisfaction (y).
Self-Esteem
Marital
Satisfaction
Life
Satisfaction

Summary
Scientists use the elaboration model to accurately identify cause
and effect.Social scientists must be especially careful about
using the elaboration model to statistically control their
variables of interest.In practice, scientists often work with many
variables, with many different potential misinterpretations of
causality, within a large model that contains many variables.
Practice in Using the Elaboration ModelConsider this diagram
of a series of possible causal relationships among a set of
variables. The + and – signs indicate the direction of the zero-
order correlations among the variables. The “0” represents a
very weak correlation.
X
Y
Z
Q
W
+
+
+
-
+
0

Practice in Using the Elaboration ModelWhich set of
relationships might be spurious?Which set of three variables
might have zero-order correlations and causal links that result
in a spurious relationship? What is the “external” or “spurious”
variable that creates the potential spurious relationship?
X
Y
Z
Q
W
+
+
+
-
+
0
relationships might be spurious?The relationship between Y and
Z might be spurious—a false indication of causality—because
all three zero-order correlations are in the same direction and X
(the external variable) causes Y and Z.
X
Y
Z
Q

W
+
+
+
-
+
0
relationships might be suppressed?Which set of three variables
might have zero-order correlations and causal links that result
in a suppressed relationship? What is the “external” or
“suppressor” variable that creates the potential suppressed
relationship?
X
Y
Z
Q
W
+
+
+
-
+
0

relationships might be suppressed?The relationship between W
and Q might be suppressed—a false indication of no causality—
because Z (the external variable) has a - correlation with W and
a + correlation with Q.
X
Y
Z
Q
W
+
+
+
-
+
0
An Application of the Elaboration ModelSocial Problem:
Community DevelopmentIssue: Success of small
businesses.Fact: Most new small businesses are owned by
women.Fact: Women-owned businesses are less successful than
men-owned businesses.Question: Is this a social problem?
Perhaps. But only if one can rule out other reasonable
explanations that do not imply discrimination against women.
An Application of the Elaboration ModelPossible explanations
for women-owned businesses being less successful:
Lower profit motive.

Fewer skills.
Less education.
Less networking.
More family responsibilities.
Industry sector.
Newer businesses.
Less access to credit.
An Application of the Elaboration ModelProcedure:
Collect data on possible explanations for lower business
success.
Use the elaboration procedure to determine the most important
causes of this lower success.
Statistically control for other explanations besides
discrimination against women.
An Application of the Elaboration ModelData
657 small businesses.
423 in rural towns (pop. < 10,000).
234 in urban towns (pop. ≥ 10,000).
Businesses owned by one or two males.
Total = 526 (Urban = 194; Rural = 332).
Businesses owned by one or two females.
Total = 131 (Urban = 40; Rural = 91).
An Application of the Elaboration ModelThe two most
important indicators of business success, after controlling for

many other factors that might influence success, are shown in
RED on the following two slides.The most important indicator
of success, as measured by gross sales, is business size. This
finding is not interesting because it is expected that the greater
the number of employees in a business, the greater the gross
sales.Can you guess which variable is the next most important
in explaining success?
Structure-Functional Model of Business Success
Sharon Bird and Steve Sapp
Dependents
Sector
Retail Pull
Sex of Owner
Age of Owner
Marital Status
Work
Experience
Ownership
Experience
Professional
Development
Civic
Involvement
Hours Worked
Profit Motive
Business
Size
Business
Age

Access
To Credit
Locale
Gross
Sales
Structure-Functional Model of Business Success
Sharon Bird and Steve Sapp
Sex of Owner
Age of Owner
Marital Status
Work
Experience
Ownership
Experience
Professional
Development
Civic
Involvement
Hours Worked
Profit Motive
Business
Size
Business
Age
Access
To Credit
Locale

Gross
Sales
An Application of the Elaboration Model
Summary
After controlling for many possible alternative explanations for
less success by women-owned businesses, we found that the
most important determinant of success is the sex of the owner.
Given the limitations of social science research, this is as close
as we can get in a single study to observing gendered
preferences.
Analyzing Survey Data: Guidelines
Before you begin this exercise, which is worth 100 points, you
should have read all the materials about survey research and
official statistics within the folder called Part 3, Strategies of
Social Research -- Survey Research," and have completed quiz
3.
The General Social Survey (GSS)
You will be conducting analysis of data from the GSS. Follow
the steps outlined bellow:
1. Find out what the General Social Survey (GSS) is, and figure
out why it is such an important source of data for understanding
American society. Write a brief paragraph that describes the
GSS. Here is a link that provides you with background about the
GSS. Do NOT use this site for analysis of the data.
·
· http://www.norc.org/projects/general+social+survey.htm

2. To analyze GSS data, we will be using a site at University of
California, Berkeley. This site is maintained as part of the
Survey Documentation and Analysis project that brings together
and archives publicly accessible data from a variety of studies.
SDA also provides online statistical analysis software to
conduct simple and complex analysis of data. We will use their
software to analysis GSS data for this assignment. Here's the
URL:
http://sda.berkeley.edu/cgi-bin/hsda?harcsda+gss06
3. Formulate three logically connected hypotheses. You may
formulate hypotheses about whatever topic you wish. However,
remember that GSS provides us with survey data, that is, it
consists of responses that people in a sample of adult American
population have given to sets of questions designed measure
what American think about a variety of topics -- mostly
political, social and religious. However, for specific years,
modules that deal in some detail with special topics are
available; for example, there are two modules on environment,
one on social support networks, science and other topics.
Your hypotheses should be accompanied by a brief rationale,
that is, your reasoning behind each of your hypotheses. Here's
an example of the format that this section of your exercise
should follow. Remember you may select any topic for which
there are data from the GSS.
A SIMPLE ILLUSTRATION (Yours might be more
complicated)
I will use a topic that has been researched thoroughly --
attitudes about abortion. We are learning about social research
methods, so our task is to locate our topic in society. Following
Durkheim's suggestions, we think level of education, racial

identity, gender, wealth and other background considerations
(variables) influence how individuals think about a given topic.
In this case, the topic is attitude about abortion. The GSS has
been measuring attitudes about abortion since 1977. Attitude
about abortion is my dependent variable. I hypothesize that
where a person is located in society (gender, age, social class,
etc.) should predict how they think about abortion. My task, and
yours, is to find variables in the GSS that test hypotheses. Here
are my hypotheses:
Hypothesis One: Educational level should be associated with
attitude toward abortion. Specifically, the more education a
person has achieved, the more likely he or she will be accepting
of a women's decision to have an abortion.
Rationale: Education expands a person's ability to understand
complex situations and increases their analytic skills. Education
also increases a person's ability to "take on the role of the
other" and, hence, understand how abortion might be a part of a
reasonable way to make sense out of being pregnant. Or, from
the literature, I know that education has a "liberalizing effect in
the US," and liberals are more likely to favor a women's right to
a legal abortion than conservatives.
Hypothesis Two: Gender should be associated with attitude
toward abortion. Women should be more accepting of abortion
than men.
Rationale: Women also are more attentive to the politics of
abortion than men because the issue may directly affect their
lives. Their responses to questions about legal abortion should
exhibit a distinctive pattern, that is, be different from how men
respond.
Hypothesis Three: A person's religious beliefs should be
associated with their attitude about abortion.

Rationale: Religion is a powerful social force in the lives of
some members of society. They use their understanding of the
teachings of their religion to make sense out of controversial
subjects. The more religious a person, the more likely their
responses will form a distinctive pattern.
Mitigating Circumstance (Control or Test): Some social facts
are more powerful influences than others. In American society,
given the long history of racial discrimination and economic
exploitation of American Africans, racial identity is a very
powerful social fact. Therefore, the above three hypotheses
might be influenced by whether or not the person who
responded to the question about abortion is black or white.
Now, the above hypotheses and the qualifying circumstance
form a theory about attitudes toward abortion. It is not a very
sophisticated theory and it should be better grounded in
literature, that is, in fully developed research project, I would
cite previously published findings and I would review existing
theories about the relationships between social facts and
attitudes. Not necessary for this exercise since we are two
goals: 1) learn how to use the GSS, and 2) appreciate the value
of survey research for finding patterns and testing theories.
STEP ONE:
Find variables (questions asked of people who were part of the
GSS sample) that fit with our theoretical problem (hypotheses).
My dependent variable is attitude toward abortion:
The variable I found is called ABANY (this is code, also called
a mnemonic) that stands for the question asked. ABANY is my
dependent variable.

ABANY: ABORTION IF WOMAN WANTS FOR ANY
REASON - 206. Please tell me whether or not you think it
should be possible for a pregnant woman to obtain a legal
abortion if: g. The woman wants it for any reason? 0 NAP 1
YES 2 NO 8 DK 9 NA
My independent variables are education, gender and religion. It
turns out that GSS has several ways to measure a person's
education. EDUC is coded in years of schooling for 0 to 21. If
you use this variable, you will have to recode it into categories,
for example HS, College and Graduate. (There are instructions
on how to recode at the SDA analysis site). It is helpful to
remember that there are different levels of measurement:
nominal or categorical and continuous (forget about rank order
for this exercise). If you try to cerate a table using EDUC and
do not recode it, you will get a table with 22 columns and two
rows. The table is unreadable. Therefore, you should recode
into a three or four categories.
I will be using cross-tabulation as my analytic tool. DEGREE is
another measure of education and it is already coded for cross
tabulation. I will use DEGREE.
Gender is easy. The mnemonic is SEX and it is coded for cross
tabulations as Male and Female (the variable is nominal or
categorical).
The GSS has many questions about religion, and quite
sophisticated analysis is possible. Let's keep it simple. People
were asked if they thought of themselves and fundamentalists,
moderate or liberals in their religious beliefs. I will use this
variable for "religion." It is called FUND in the GSS.
RACE is the mnemonic for racial identity. It is coded Black,
White and Other. When an interviewer enters a subject's home,
the code the respondent as black, white or other. If they can't

tell, they ask the respondent. Interestingly, the proportion of the
sample that is Black is very close to US census data (12%).
Build a table of your variables. Here is my table:
Variables
Question
Independent Variables:
DEGREE
SEX
FUND
Coded: Highest degree obtained by interviewee. LS than HS;
HS, junior college, college, graduate).
Coded: Male or Female
Coded: Fundamentalist, Moderate and Liberal
Dependent Variable:
ABANY
"Woman has right to legal abortion for any reason."
Coded as "yes" or "no"
Control or Test Variables:
RACE
White, Black, Other
Table One: Variables Used to Test Hypotheses (GSS 2010)

STEP TWO:
Discover the patterns for the variables you have selected. First
make sure all the variables you have selected are available for
the year you want to analyze. For example, in my example all
the variables are available for 2010, which is the most recent
data for the GSS. You can check which years your variable is
available by creating a table with your variable as the row and
"YEAR" as the column.
Create a descriptive Table for your variables: My looks like this
because I am using nominal variables. If you use continuous
variables, you will have means and standard deviations:
Variables
Descriptive Statistic (%)
DEGREE
LH = 15.1%; HS = 50.2%; JC = 7.2%; Bachelor = 17.5%;
Graduate = 10%. n= 2,045.
SEX
Male = 45.2%; Female = 54.8%. n=2,045.
FUND
Fundamentalists = 24.6%; Moderate = 42.1; Liberal = 31.5%. n=
1,939
ABANY
Yes = 42.9%: no = 57.1%. n= 1,234.
RACE
White = 75.7%; Black = 14.6%; Other = 9.7%. n = 2,045.
Table Two: Descriptions for Variables Use to Test Theory
STEP THREE:
Now create tables (or charts, if you feel creative) for each first
order association: ABANY by DEGREE, ABANY by FUND,

and ABANY by SEX. From the outputs you get, create your own
table or chart that shows the results. Here's my example:
DEGREE
Less than High School = 32.4%
High School = 34.3%
Junior College = 40.2%
Bachelors degree = 52.7%
Graduate degree = 57.6%
FUND
Fundamentalist = 30.8%
Moderate = 35.8%
Liberal = 60%
SEX
Male = 45.2%
Female = 40.9%
TABLE THREE: Percentage Yes, to "Woman should have right
to legal abortion for any reason" (GSS 2010, n= 1,234)
STEP FOUR:
Run cross tabulation again with RACE as a control variable.
Create a table showing the results. This can be complicated.
Remember that you are applying the Elaboration Model, so you
will want to pay attention to how the relationships change with
different racial identifications.
Figure One: Percent Yes for Blacks and Whites by Degree (GSS
2010)

Figure Two: Percent Yes for Blacks and Whites by Religious
Belief (GSS, 2010)
Figure Three: Percent Yes for Blacks and Whites by Gender
(GSS 2010)
STEP FIVE:
Now comes the interesting part. Interpret your results. In my
little study, I found that education and religion are associated
with attitude toward abortion. The more education a person has
the more likely they are to agree that a woman should have a
right to a legal abortion for any reason. And, if a person says
their religious beliefs are liberal, they are much more likely to
agree with the question. While males are more likely to say they
agree, the difference between men and women is very small and
probably not significant. These associations occur within ranges
of percentages from 30% of fundamentalists agreeing to a high
of nearly 60% of liberals. The range for the other two variables
is less wide (32% to 58% for education and only 41% to 45%
for gender). These are the first order or initial associations that
I discovered.
What happens when a test or control variable is introduced (the
elaboration model)? For DEGREE, I discovered the same
association for blacks and Whites, but a stronger one for blacks
than whites. For religious beliefs, I found a different pattern.
Black fundamentalists are much more likely to agree with a
legal abortion for a women for any reason, than white
fundamentalists. For moderates and liberals, the association is
similar. For gender, blacks and whites have pretty the same

association, that is, black men and black women show a similar
pattern and as do black and white men.
Partial Relations Compared to Original
TEST OR CONTROL VARIABLE
Antecedent Intervening
Same Relations
Replication
Replication
Less or none
Explanation
Interpretation
Split
Specification
Specification
Following the elaboration model (above) we can reason that
racial identity is an antecedent condition, and for degree we
replicated the original association, for religious belief, we
specified the original, and for gender we replicated a very weak
association.
SUMMARY
Your exercise should follow the above STEPS. It should consist
to
1. A brief paragraph or two about the GSS. What is it, what
does it do, why is it important for social research, how will you
use it?
2. Your theory, three hypotheses with accompanying rationales.
3. Your variables, descriptions with table.
4. Your original or first order findings with chart or table.

5. Your control variable and results (charts or tables).
6. Conclusions about what you find and how your control
variable affected the results.
Here's a list of possible independent and dependent variables
that you can use for your analysis. You may use other variables
if your hypotheses are not covered by any of the variables listed
below.
Independent Variables (information from the GSS that reflects
the conditions of urbanization that Wirth outlines in his article):
FINRELA: Opinion about family income. Is your family's
income far above average, above average, average, below
average, far below average? This is a Likert scale. It correlates
closely with actual income and can be used as a measure of
inequality.
AGE: ranges from 18 to 89. This is a continuous variable
which you will have to recode, e.g., age (r: 18 - 30 "young"; 31-
50 "adult"; 51 - 89 "Elderly") or whatever categories best
reflect your theoretical reasoning about the effects of age.
RACE: This variable is coded as white, black and other.
The interviewer codes the interviewee accordingly, asking only
if not obvious. There are other more sensitive measures of race
on the GSS, but this one is the simplest and most widely used. It
also is consistent with the US census; for example, about 12%
of the GSS sample is coded as "black," and that is the
percentage of the US population that is black according to the
census.
SEX: Dichotomous variable (male or female).

DEGREE: highest degree obtained by interviewee (LS than
HS; HS, junior college, college, graduate).
SRCBELT: This is a code that stands for where the
respondent lived at the time of the interview
SRC BELTCODE - 0 NOT ASSIGNED 1 12 LRGST
SMSA'S 2 SMSA'S 13-100 3 SUBURB, 12 LRGST 4 SUBURB,
13-100 5 OTHER URBAN 6 OTHER RURAL
XNORCSIZ: This variable is a code that NORC used for the
size of the place where the respondent lived at the time of the
interview.
EXPANDED N.O.R.C. SIZE CODE - 0 NOT ASSIGNED 1
CITY GT 250000 2 CITY,50-250000 3 SUBURB, LRG CITY 4
SUBURB, MED CITY 5 UNINC,LRG CITY 6 UNINC,MED
CITY 7 CITY,10-49999 8 TOWN GT 2500 9 SMALLER
AREAS 10 OPEN COUNTRY
RES16: This variable gives a rough indication of where a person
grew up.
TYPE OF PLACE LIVED IN WHEN 16 YRS OLD - 25. Which
of the categories on this card comes closest to the type of place
you were living in when you were 16 years old? 0 NAP 1
COUNTRY,NONFARM 2 FARM 3 TOWN LT 50000 4 50000
TO 250000 5 BIG-CITY SUBURB 6 CITY GT 250000 8 DK 9
NA
COMTYPE: TYPE OF COMMUNITY IN WHICH R LIVES.
Would you describe the place where you live as... 0 NAP 1 BIG
CITY 2 SUBURBS, OUTSKIRTS 3 SMALL TOWN 4
COUNTRY VILLAGE 5 FARM, CNTRY HOME 8 DK 9 NA
Dependent Variables: You will have to figure out which

dependent variable you want to use based on how you interpret
the Wirth hypothesis. Here are some that might be useful. Of
course there are many questions on the GSS that should reflect
urbanism as a way of life. When you find a variable you want to
use, make sure you know the year that it was asked. Your
independent and dependent variables must have been asked in
the same year in order the get a cross tabulation.
ABANY: ABORTION IF WOMAN WANTS FOR ANY
REASON - 206. Please tell me whether or not you think it
should be possible for a pregnant woman to obtaina legal
abortion if: g. The woman wants it for any reason? 0 NAP 1
YES 2 NO 8 DK 9 NA
GUNLAW: FAVOR OR OPPOSE GUN PERMITS - 86.
Would you favor or oppose a law which would require a person
to obtain a police permit before he or she could buy a gun? 0
NAP 1 FAVOR 2 OPPOSE 8 DK 9 NA
TEENSEX: SEX BEFORE MARRIAGE -- TEENS 14-16 -
217a. What if they are in their early teens, say 14 to 16 years
old? In that case, do you think sex relations before marriage are
always wrong, almost always wrong, wrong only sometimes, or
not wrong at all? 0 NAP 1 ALWAYS WRONG 2 ALMST
ALWAYS WRG 3 SOMETIMES WRONG 4 NOT WRONG AT
ALL 5 OTHER 8 DK 9 NA
PREMARSX : SEX BEFORE MARRIAGE - 217. There's
been a lot of discussion about the way morals and attitudes
about sex are changing in this country. If a man and woman
have sex relations before marriage, do you think it is always
wrong, almost always wrong, wrong only sometimes, or not
wrong at all? 0 NAP 1 ALWAYS WRONG 2 ALMST ALWAYS
WRG 3 SOMETIMES WRONG 4 NOT WRONG AT ALL 5
OTHER 8 DK 9 NA

BIBLE: FEELINGS ABOUT THE BIBLE - 120a. Which of
these statements comes closest to describing your feelings about
the Bible? 1. The Bible is the actual word of God and is to be
taken literally, word for word. 2. The Bible is the inspired word
of God but not everything in it should be taken literally, word
for word. 3. The Bible is an ancient book of fables, legends,
history, and moral precepts recorded by men. 0 NAP 1 WORD
OF GOD 2 INSPIRED WORD 3 BOOK OF FABLES 4 OTHER
8 DK 9 NA
You can search of variable in a number of different ways. There
is a search engine on the SDA cite. It is literal; you can use a
wildcard (*). There are folder labeled by topic which you can
open and see the variable names.
White (n =952) Less Than HS High School Junior College
College Graduate 35.1 39.6 41.9 51.4 54.8 Black (n
=167) Less Than HS High School Junior College College
Graduate 35 39.54 35.979999999999997 69.3
72.7
White (n = 917) Fundamentalist Moderate Liberal 26.5
36.799999999999997 61.3 Black (n= 158)
Fundamentalist Moderate Liberal 41.9 38.4 55 White
(n= 952) Male Female 45.6 41.3 Black (n =167) Male Female
47.6 41

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