Categorical Data Analysis
for the Behavioral and Social
ST
Sciences 2nd Edition
UV
SOLUTIONS
IA
MANUAL
_A
PP
Razia Azen
Cindy M. Walker
RO
Comprehensive Solutions Manual for
VE
Instructors and Students
D?
© Razia Azen & Cindy M. Walker
?
All rights reserved. Reproduction or distribution without permission is prohibited.
© DREAMSHUB
, Solutions Manual for Categorical Data Analysis for the Behavioral and
Social Sciences (2nd Edition)
Razia Azen & Cindy M. Walker
ISBN: 9780367352769
ST
UNIT 1: FOUNDATIONS OF CATEGORICAL DATA ANALYSIS
1. Introduction and Overview
2. Probability Distributions
UV
3. Proportions, Estimation, and Goodness-of-Fit
UNIT 2: ASSOCIATIONS AND MULTIVARIATE RELATIONSHIPS
4. Association between Two Categorical Variables
5. Associations between Three Categorical Variables
IA
UNIT 3: MODELING FRAMEWORKS IN CATEGORICAL ANALYSIS
6. Modeling and the Generalized Linear Model
_A
7. Log-Linear Models
UNIT 4: LOGISTIC REGRESSION METHODS
8. Logistic Regression with Continuous Predictors
9. Logistic Regression with Categorical Predictors
PP
10. Logistic Regression for Multicategory Outcomes
UNIT 5: ADVANCED MODELING APPROACHES
11. Generalized Linear Mixed Models
RO
VE
D?
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© DREAMSHUB
, Azen & Walker Solutions 2
Chapter 1
1.1 Several answers are possible, depending on justification provided.
a. Ordinal, interval or ratio can all be justified with explanation.
b. Ordinal is most probable, assuming there are more than two choices.
c. Ordinal is most probable.
1.2 Several answers are possible, depending on justification provided.
ST
a. Ordinal or interval are most likely; should be justified with explanation.
b. Nominal (no meaningful ordering).
c. Ratio (zero represents no income).
1.3 Answers can vary; scales should be described and match the level of measurement given in
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the answers. For example:
a. Dependent Variable = Mathematics proficiency measured by levels of proficiency,
ordinal scale (e.g., advanced, proficient, basic, and minimal); Independent Variable = Sex
measured by one demographic question, nominal scale (e.g., male or female).
b. Dependent Variable = Satisfaction with Life measured by a multiple item survey interval
IA
scale (e.g., each item measures on a Likert scale); Independent Variable = Relationship
Status measured by one dichotomous demographic question, nominal scale (e.g., single,
married, divorced, etc.).
c. Dependent Variable = Body Image measured by a multiple item survey, interval scale
_A
(e.g., each item measures on a Likert scale); Independent Variable = Sex measured by
one demographic question, nominal scale (e.g., male or female).
d. Dependent Variable = Level of Education measured by number of years attended school,
ratio scale; Independent Variable = Religious Affiliation measured by one demographic
survey item, nominal scale (e.g., Christian, Jewish, Muslim, Other).
PP
1.4 Answers can vary; scales should be described and match the level of measurement given in
the answer. For example:
a. Dependent Variable = Weight measured in kilograms or pounds, ratio scale; Independent
Variable = Country of residence measured by a demographic question, nominal scale
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(e.g., living in U.S. or not).
b. Dependent Variable = Cholesterol level measured by a blood test, ratio scale (e.g.,
amount of cholesterol in blood); Independent Variable = Sex measured by one
demographic question, nominal scale (e.g., male or female).
c. No distinction between dependent and independent variables. Political Affiliation
measured by a demographic question, nominal scale (e.g., Democratic, Republican,
VE
Other); Sex measured by one demographic question, nominal scale (e.g., male or female).
d. No distinction between dependent and independent variables. Grades in High School
measured by GPA, ratio scale (interval or ordinal also possible); Amount of sleep
measured by a survey item that asks respondents the number of hours they sleep each
night, ratio scale.
D?
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, Azen & Walker Solutions 3
1.5 Answers can vary; scales should be described and match the level of measurement given in
the answer. For example:
a. The dependent variable in this scenario is presidential choice, which is a nominal
variable. The independent variable in this scenario is income. If income is measured by
the gross annual income, then it would be a ratio variable. However, if income is
measured by a survey item that categorizes income (e.g., < 9,999; $10,000 to $29, 999,
$30,000 to $49,999, etc.) then it is an ordinal variable. Regardless, since the dependent
variable is a nominal variable, procedures for analyzing categorical data are needed.
b. The dependent variable in this scenario is income, and the independent variable in this
ST
scenario is presidential choice, which is a nominal variable. If income is measured by
gross annual income, then it would be a ratio variable and procedures for analyzing
categorical data are not needed. However, if income is measured by a survey item that
categorizes income (e.g., < 9,999; $10,000 to $29, 999, $30,000 to $49,999, etc.) then it
UV
is an ordinal variable and procedures for analyzing categorical data are needed.
c. The dependent variable in this scenario is fat content in diet. If this is measured by having
participants track their meals for a week and then counting up the grams of fat consumed
on an average day, this is a ratio variable. The independent variable is whether or not one
has had a heart attack, which is a nominal variable. Because the dependent variable is a
ratio variable, procedures for analyzing categorical data are not needed.
IA
d. The dependent variable is whether or not one has had a heart attack, which is a nominal
variable. The independent variable in fat content in diet, which can be measured as
described in 1.5(c) and is a ratio variable. Because the dependent variable is a nominal
variable, procedures for analyzing categorical data are needed.
_A
1.6 Answers can vary; scales should be described and match the level of measurement given in
the answer. For example:
a. The dependent variable in this scenario is whether or not one graduated from high school,
which is a dichotomous nominal variable. The independent variable in this scenario is
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grade point average, which can be considered a ratio variable. Because the dependent
variable is a nominal variable, procedures for analyzing categorical data are needed.
b. The dependent variable in this scenario is grade point average, which can be considered a
ratio (or interval) variable. Therefore, procedures for analyzing categorical data are not
needed.
RO
c. The dependent variable in this scenario is annual income. The independent variable is
whether or not a respondent attended college, which is a nominal (dichotomous) variable.
If income is measured by gross annual income, then it would be a ratio variable and
procedures for analyzing categorical data are not needed. But, if income is measured by a
survey item that categorizes income (e.g., < 9,999; $10,000 to $29, 999, $30,000 to
VE
$49,999, etc.) then it is an ordinal variable and procedures for analyzing categorical data
are needed.
d. The dependent variable in this scenario is whether or not one attended college, which is a
nominal (dichotomous) variable. Therefore, regardless of the manner in which income is
measured, procedures for analyzing categorical data are needed.
D?
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for the Behavioral and Social
ST
Sciences 2nd Edition
UV
SOLUTIONS
IA
MANUAL
_A
PP
Razia Azen
Cindy M. Walker
RO
Comprehensive Solutions Manual for
VE
Instructors and Students
D?
© Razia Azen & Cindy M. Walker
?
All rights reserved. Reproduction or distribution without permission is prohibited.
© DREAMSHUB
, Solutions Manual for Categorical Data Analysis for the Behavioral and
Social Sciences (2nd Edition)
Razia Azen & Cindy M. Walker
ISBN: 9780367352769
ST
UNIT 1: FOUNDATIONS OF CATEGORICAL DATA ANALYSIS
1. Introduction and Overview
2. Probability Distributions
UV
3. Proportions, Estimation, and Goodness-of-Fit
UNIT 2: ASSOCIATIONS AND MULTIVARIATE RELATIONSHIPS
4. Association between Two Categorical Variables
5. Associations between Three Categorical Variables
IA
UNIT 3: MODELING FRAMEWORKS IN CATEGORICAL ANALYSIS
6. Modeling and the Generalized Linear Model
_A
7. Log-Linear Models
UNIT 4: LOGISTIC REGRESSION METHODS
8. Logistic Regression with Continuous Predictors
9. Logistic Regression with Categorical Predictors
PP
10. Logistic Regression for Multicategory Outcomes
UNIT 5: ADVANCED MODELING APPROACHES
11. Generalized Linear Mixed Models
RO
VE
D?
?
© DREAMSHUB
, Azen & Walker Solutions 2
Chapter 1
1.1 Several answers are possible, depending on justification provided.
a. Ordinal, interval or ratio can all be justified with explanation.
b. Ordinal is most probable, assuming there are more than two choices.
c. Ordinal is most probable.
1.2 Several answers are possible, depending on justification provided.
ST
a. Ordinal or interval are most likely; should be justified with explanation.
b. Nominal (no meaningful ordering).
c. Ratio (zero represents no income).
1.3 Answers can vary; scales should be described and match the level of measurement given in
UV
the answers. For example:
a. Dependent Variable = Mathematics proficiency measured by levels of proficiency,
ordinal scale (e.g., advanced, proficient, basic, and minimal); Independent Variable = Sex
measured by one demographic question, nominal scale (e.g., male or female).
b. Dependent Variable = Satisfaction with Life measured by a multiple item survey interval
IA
scale (e.g., each item measures on a Likert scale); Independent Variable = Relationship
Status measured by one dichotomous demographic question, nominal scale (e.g., single,
married, divorced, etc.).
c. Dependent Variable = Body Image measured by a multiple item survey, interval scale
_A
(e.g., each item measures on a Likert scale); Independent Variable = Sex measured by
one demographic question, nominal scale (e.g., male or female).
d. Dependent Variable = Level of Education measured by number of years attended school,
ratio scale; Independent Variable = Religious Affiliation measured by one demographic
survey item, nominal scale (e.g., Christian, Jewish, Muslim, Other).
PP
1.4 Answers can vary; scales should be described and match the level of measurement given in
the answer. For example:
a. Dependent Variable = Weight measured in kilograms or pounds, ratio scale; Independent
Variable = Country of residence measured by a demographic question, nominal scale
RO
(e.g., living in U.S. or not).
b. Dependent Variable = Cholesterol level measured by a blood test, ratio scale (e.g.,
amount of cholesterol in blood); Independent Variable = Sex measured by one
demographic question, nominal scale (e.g., male or female).
c. No distinction between dependent and independent variables. Political Affiliation
measured by a demographic question, nominal scale (e.g., Democratic, Republican,
VE
Other); Sex measured by one demographic question, nominal scale (e.g., male or female).
d. No distinction between dependent and independent variables. Grades in High School
measured by GPA, ratio scale (interval or ordinal also possible); Amount of sleep
measured by a survey item that asks respondents the number of hours they sleep each
night, ratio scale.
D?
?
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, Azen & Walker Solutions 3
1.5 Answers can vary; scales should be described and match the level of measurement given in
the answer. For example:
a. The dependent variable in this scenario is presidential choice, which is a nominal
variable. The independent variable in this scenario is income. If income is measured by
the gross annual income, then it would be a ratio variable. However, if income is
measured by a survey item that categorizes income (e.g., < 9,999; $10,000 to $29, 999,
$30,000 to $49,999, etc.) then it is an ordinal variable. Regardless, since the dependent
variable is a nominal variable, procedures for analyzing categorical data are needed.
b. The dependent variable in this scenario is income, and the independent variable in this
ST
scenario is presidential choice, which is a nominal variable. If income is measured by
gross annual income, then it would be a ratio variable and procedures for analyzing
categorical data are not needed. However, if income is measured by a survey item that
categorizes income (e.g., < 9,999; $10,000 to $29, 999, $30,000 to $49,999, etc.) then it
UV
is an ordinal variable and procedures for analyzing categorical data are needed.
c. The dependent variable in this scenario is fat content in diet. If this is measured by having
participants track their meals for a week and then counting up the grams of fat consumed
on an average day, this is a ratio variable. The independent variable is whether or not one
has had a heart attack, which is a nominal variable. Because the dependent variable is a
ratio variable, procedures for analyzing categorical data are not needed.
IA
d. The dependent variable is whether or not one has had a heart attack, which is a nominal
variable. The independent variable in fat content in diet, which can be measured as
described in 1.5(c) and is a ratio variable. Because the dependent variable is a nominal
variable, procedures for analyzing categorical data are needed.
_A
1.6 Answers can vary; scales should be described and match the level of measurement given in
the answer. For example:
a. The dependent variable in this scenario is whether or not one graduated from high school,
which is a dichotomous nominal variable. The independent variable in this scenario is
PP
grade point average, which can be considered a ratio variable. Because the dependent
variable is a nominal variable, procedures for analyzing categorical data are needed.
b. The dependent variable in this scenario is grade point average, which can be considered a
ratio (or interval) variable. Therefore, procedures for analyzing categorical data are not
needed.
RO
c. The dependent variable in this scenario is annual income. The independent variable is
whether or not a respondent attended college, which is a nominal (dichotomous) variable.
If income is measured by gross annual income, then it would be a ratio variable and
procedures for analyzing categorical data are not needed. But, if income is measured by a
survey item that categorizes income (e.g., < 9,999; $10,000 to $29, 999, $30,000 to
VE
$49,999, etc.) then it is an ordinal variable and procedures for analyzing categorical data
are needed.
d. The dependent variable in this scenario is whether or not one attended college, which is a
nominal (dichotomous) variable. Therefore, regardless of the manner in which income is
measured, procedures for analyzing categorical data are needed.
D?
?
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