Azen Walker Solutions 1

Azen & Walker Solutions 1

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Solutions

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make all solutions a vailable to the public/students.Categorical Data Analysis for the Behavioral and Social Sciences, 2e Razia Azen, Cindy Walker (Solutions Manual All Chapters, 100% Original Verified, A+ Grade) 1 / 4

Azen & Walker Solutions 2

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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.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

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 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 (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).

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 (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, 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. 2 / 4

Azen & Walker Solutions 3

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1.5 Answers can vary; scales should be described and match the level of measurement given in

the answer. For example:

• 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.

• The dependent variable in this scenario is income, and the independent variable in this

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 is an ordinal variable and procedures for analyzing categorical data are needed.

• 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.

• 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.

1.6 Answers can vary; scales should be described and match the level of measurement given in

the answer. For example:

• 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 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.

• 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.

• 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 $49,999, etc.) then it is an ordinal variable and procedures for analyzing categorical data are needed.

• 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.

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Azen & Walker Solutions 4

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1.7 Answers can vary; scales should be described and match the level of measurement given in

the answer. For example:

• The dependent variable in this scenario is reading proficiency, which is typically

measured as an ordinal variable (for example, when reading proficiency is differentiated into four categories such as minimal, basic, proficient, and advanced, as is commonly done). Thus, procedures for analyzing categorical data are needed.

• The dependent variable in this scenario is income and the independent variable is gender.

If income is measured by gross annual income, then it would be a ratio variable and procedures for analyzing categorical data are not needed. In this case an ANOVA analysis could be used to determine if the average annual income differs for males and females. 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 and procedures for analyzing categorical data are needed.

• The dependent variable in this scenario is once again income. The independent variable is

level of education. Both variables can be measured as either categorical or continuous variables. The following table provides the correct analytical procedure for all four

possible combinations:

Measurement of Income Measurement of Education Analytical Procedure Ratio Ratio Regression Ratio Ordinal ANOVA Ordinal Ratio Categorical Procedure Ordinal Ordinal Categorical Procedure

• In this scenario both variables are categorical and thus procedures for analyzing

categorical data are needed.

1.8 Answers will vary.

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