Chapter 9

Question 1

cross-tabulatio

Answer 1

n A technique for analyzing the relationship between two nominal or ordinal variables that
have been organized in a table.

Question 2

bivariate analysis

Answer 2

A statistical method designed to detect and describe the relationship between two
nominal or ordinal variables.

Question 3

bivariate table

Answer 3

A table that displays the distribution of one variable across the categories of another variable

Question 4

A bivariate table displays what and how is it obtained

Answer 4

the distribution of one variable across the categories of another
variable. It is obtained by classifying cases based on their joint scores on two nominal or
ordinal variables. It can be thought of as a series of frequency distributions joined to make
one table

Question 5

Column variable

Answer 5

A variable whose categories are the columns of a bivariate table

Question 6

Row variable

Answer 6

A variable whose categories are the rows of a bivariate table

Question 7

Cell

Answer 7

The intersection of a row and a column in a bivariate table.

Question 8

Marginals

Answer 8

The row and column totals in a bivariate table.

Question 9

Finally, it is important to understand that ultimately what guides the construction and interpretation
of bivariate tables is

Answer 9

the theoretical question posed by the researcher.

Question 10

In the preceding section, we saw how to establish whether an association exists in a
bivariate table. If it does, how do we determine the strength of the association between the
two variables? A quick method is to

Answer 10

examine the percentage difference across the different
categories of the independent variable. The larger the percentage difference across the
categories, the stronger the association.

Question 11

Percentage differences are

Answer 11

a rough indicator of the

strength of a relationship between two variables.

Question 12

Positive relationship

Answer 12

A bivariate relationship between two variables measured at the ordinal level or higher
in which the variables vary in the same direction.

Question 13

Negative relationship

Answer 13

A bivariate relationship between two variables measured at the ordinal level or higher
in which the variables vary in opposite directions.

Question 14

Elaboration

Answer 14

A process designed to further explore a bivariate relationship; it involves the introduction of
control variables.

Question 15

Control variable

Answer 15

An additional variable considered in a bivariate relationship. The variable is controlled for
when we take into account its effect on the variables in the bivariate relationship.

Question 16

The introduction of additional control variables into a bivariate relationship serves three
primary goals in data analysis.

Answer 16

1. Elaboration allows us to test for nonspuriousness. Establishing cause-and-effect
   relations requires not only showing that an independent and a dependent variable are
   associated but also establishing the time order between them and providing
   theoretical and empirical evidence that the association is nonspurious—that is, it
   cannot be “explained away” by other variables.
2. Elaboration clarifies the causal sequence of bivariate relationships by introducing
   variables hypothesized to intervene between the independent and dependent
   variables.
3. Elaboration specifies the different conditions under which the original bivariate
   relationship might hold.

Question 17

Direct causal relationship

Answer 17

A bivariate relationship that cannot be accounted for by other theoretically
relevant variables.

Question 18

Spurious relationship

Answer 18

A relationship in which both the independent and dependent variables are influenced
by a causally prior control variable, and there is no causal link between them. The relationship between the
independent and dependent variables is said to be “explained away” by the control variable.

Question 19

The introduction of the control variable size of fire into the original bivariate relationship
between number of firefighters and amount of damage illustrates the process of elaboration.
These are the three steps:

Answer 19

1. Divide the observations into subgroups on the basis of the control variable. We have
   as many subgroups as there are categories in the control variable. (In our case, there
   were two subgroups: small and large fires.)
2. Reexamine the relationship between the original two variables separately for the
   control variable subgroups. The separate tables are called partial tables; they display
   the partial relationship between the independent (number of firefighters) and
   dependent (amount of damage) variables within each specific category of the control
   variable (small vs. large fire size).
3. Compare the partial relationships with the original bivariate relationship for the total
   group. In a direct causal pattern, the partial relationships will be very close to the
   original bivariate relationship. In a spurious pattern, the partial relationship will be
   much weaker than the original bivariate relationship.

Question 20

Partial tables

Answer 20

Bivariate tables that display the relationship between the independent and dependent variables
while controlling for a third variable.

Question 21

Partial relationship

Answer 21

The relationship between the independent and dependent variables shown in a partial
table.

Question 22

Intervening variable

Answer 22

A control variable that follows an independent variable but precedes the dependent
variable in a causal sequence.

Question 23

Intervening relationship

Answer 23

A relationship in which the control variable intervenes between the independent
and dependent variables.

Question 24

Conditional relationship

Answer 24

A relationship in which the control variable’s effect on the dependent variable is
conditional on its interaction with the independent variable. The relationship between the independent and
dependent variables will change according to the different conditions of the control variable.

Question 25

Most often there is a
perilous gap between theory and analysis. This does not mean that you have to abandon
your effort to untangle bivariate relationships, only that you should

Answer 25

be aware of both the
importance of theory as a guide to your analysis and the limitations of the statistical
analysis.

Question 26

In our examples, when the control variable was introduced, the
real nature of the relationship is revealed. It’s not

Answer 26

always that easy.

Question 27

A bivariate table displays the

Answer 27

e distribution of one variable across the categories of another variable

Question 28

a bivariate table is obtained by

Answer 28

It

is obtained by classifying cases based on their joint scores for two variables. P

Question 29

Percentaging bivariate

tables are used to examine

Answer 29

the relationship between two variables that have been organized in a
bivariate table. The percentages are always calculated within each category of the independent
variable

Question 30

A relationship is said to exist when c

Answer 30

certain values of one variable are associated with certain values of
the other variable

Question 31

Bivariate tables are interpreted by

Answer 31

comparing percentages across different
categories of the independent variable. A relationship is said to exist if the percentage distributions
vary across the categories of the independent variabl

Question 32

Variables measured at the ordinal or intervalratio levels may be p

Answer 32

be positively or negatively associated

Question 33

+ n - associations

Answer 33

With a positive association, higher values of
one variable correspond to higher values of the other variable. When there is a negative association
between variables, higher values of one variable correspond to lower values of the other variable.

Question 34

Elaboration

Answer 34

s a technique designed to clarify bivariate associations. It involves the introduction of
control variables to interpret the links between the independent and dependent variables

Question 35

In a

spurious relationship,

Answer 35

both the independent and dependent variables are influenced by a causally
prior control variable, and there is no causal link between them.

Question 36

In an intervening relationship,

Answer 36

the
control variable follows the independent variable but precedes the dependent variable in the causal
sequence.

Question 37

In a conditional relationship,

Answer 37

the bivariate relationship between the independent and

dependent variables is different in each of the partial tables.