Question 1

The relationship between measures of association and causal relations between variables may be summarized as
A)association proves causation.
B)association does not prove causation.
C)association and causation are unrelated concepts.
D)measures of association can be used only for prediction,never as evidence of a causal relationship between variables.

Accepted Answer

Measures of association cannot prove causation between two variables. While they can provide evidence of a relationship between variables, other factors beyond the measured variables may be influencing the relationship. Therefore, one cannot assume that just because two variables are associated, one causes the other.

Question 2

To prove that one variable causes another,we use
A)measures of association.
B)tests of significance.
C)ANOVA.
D)None of these;neither measures of association or tests of significance can prove causal relationships.

Accepted Answer

Neither measures of association nor tests of significance can prove causal relationships because correlation does not equal causation. To prove causality, we need to use experimental designs with control groups, random assignment, and manipulation of the independent variable.

Question 3

The distribution of scores on the dependent variable for a specific category of the independent variable is called
A)associated categories.
B)the distribution correlation.
C)the conditional distribution of Y.
D)the marginal response category.

Accepted Answer

The conditional distribution of Y refers to the distribution of scores on the dependent variable for a specific category of the independent variable. Therefore, C is the correct answer. A, B, and D are incorrect as they do not accurately describe this distribution.

Question 4

To display the effects of the X variable on the Y variable in a bivariate table when the independent variable has been arranged in the columns,compute percentages
A)within each column.
B)between each column.
C)within each row.
D)using N as the denominator.

Accepted Answer

Computing percentages within each column (also known as column percentages) helps to show the relationship between the independent variable (X) and the dependent variable (Y) within each level of the independent variable. This allows for a clearer interpretation of the strength and direction of the relationship between the variables, as well as any patterns or trends that may be present. Computing percentages between columns (option B) would compare the independent variable levels to each other, rather than examining the relationship within each level. Computing percentages within each row (option C) would be appropriate if the independent variable was arranged in the rows, rather than the columns. Using N as the denominator (option D) would simply show the raw counts of participants for each level of the independent variable, without providing any information about the relationship between the variables.

Question 5

If the distribution of the scores of one variable changes across the categories of another variable,the variables
A)are associated to some extent
B)are related in a way that is statistically significant
C)have a cause and effect relationship
D)are perfectly associated

Accepted Answer

A change in the distribution of scores in one variable across categories of another variable suggests that there is some association between the two variables, but it does not necessarily indicate a causal or statistically significant relationship. Therefore, option A is the most appropriate choice.

Question 6

A researcher has found a strong relationship between level of education and income and concludes that "higher levels of education cause higher income." What error has been made?
A)None,the argument is sound.
B)Income is only nominal in level of measurement and education is ordinal so they cannot be causally related.
C)Since these variables are continuous and interval ratio,they cannot have a causal relationship.
D)By itself,association does not prove causation.

Accepted Answer

The researcher has made the error of assuming that correlation implies causation. Just because there is a strong relationship between level of education and income does not mean that one variable causes the other. There could be other factors at play, such as innate ability, social connections, or luck. Therefore, the argument is unsound.

Question 7

Measures of association help social scientists accomplish two important goals:
A)prediction and forecasting.
B)understanding causation and measuring concepts.
C)prediction and correlation.
D)prediction and understanding causation.

Accepted Answer

Measures of association are useful in predicting and understanding causation between different variables. The measures help to establish if two variables are related and if there is a cause-and-effect relationship between them. Therefore, the best choice is D, which indicates that measures of association help social scientists predict and understand causation.

Question 8

Conventionally,each column of a bivariate table represents
A)the highest scores of each variable.
B)a category of the independent variable (X).
C)a category of the dependent variable (Y).
D)the lowest scores on each variable.

Accepted Answer

The answer of Conventionally,each column of a bivariate table represents
A)the...

Question 9

If there is an association between two variables,
A)the scores of the cases are approximately equal.
B)the distributions of the scores approach normality.
C)all of the scores are in the same row in the bivariate table.
D)the conditional distributions of Y change.

Accepted Answer

When there is an association between two variables, the conditional distributions of Y (the variable being predicted) will change based on the values of X (the predictor variable). This means that the relationship between the two variables is not a random chance occurrence and that there is a predictable pattern between them. Choices A and B are not necessarily true when there is an association between two variables, and choice C is not relevant to determining an association between two variables.

Question 10

Measures of association
A)increase our understanding of causal relationships among variables.
B)provide proof of causal relationships among variables.
C)enhance our ability to predict from one variable to another.
D)both increase our understanding of causal relationships among variables and enhance our ability to predict from one variable to another

Accepted Answer

Measures of association do not provide proof of causal relationships, but they can help us understand the strength and direction of relationships between variables, which can inform our understanding of causal factors. In addition, measures of association can be used to make predictions about one variable based on the value of another variable, which can be useful in many fields of research. Therefore, the best answer is D, as measures of association can both increase our understanding of causal relationships among variables and enhance our ability to predict from one variable to another.

Question 11

With the independent variable in the columns,the "maximum difference" is the largest difference
A)in column percents in any row.
B)in row percents in any column.
C)between the marginals.
D)between any two cell frequencies.

Accepted Answer

The answer of With the independent variable in the columns,the...

Question 12

Tests of significance and measures of association provide information about sample data that is
A)complementary.
B)contradictory.
C)unnecessary.
D)redundant.

Accepted Answer

Tests of significance and measures of association provide different but complementary information about sample data. Tests of significance help determine if there is a significant difference or relationship between variables in the population, while measures of association quantify the strength and direction of the relationship between variables. Both are important for understanding the data and making informed conclusions.

Question 13

Tests of significance tell us if our results are non-random.To investigate the strength and direction of relationships,we must use
A)Chi square tests.
B)ANOVA.
C)percentages.
D)measures of association.

Accepted Answer

Measures of association (such as correlation coefficients) are used to determine the strength and direction of relationships between variables. Tests of significance, such as chi-square tests and ANOVA, only tell us if the results are statistically significant, but do not provide information about the strength or direction of the relationship. Percentages can be useful in describing the frequency of events within a sample, but do not provide information about relationships between variables.

Question 14

Measures of association provide the researcher with information that
A)indicates the strength of a relationship between variables.
B)assists him or her in making predictions from one variable to another.
C)facilitates his or her understanding of causal relationships between variables.
D)all of these

Accepted Answer

Measures of association can provide information about the strength and direction of the relationship between variables, allowing researchers to make predictions about one variable based on the information from another variable. Additionally, measures of association can aid researchers in understanding potential causal relationships between variables. Therefore, all of these choices are correct.

Question 15

If variables are arranged in a bivariate table,we can see if they are associated by
A)adding their scores vertically.
B)subtracting their scores horizontally.
C)computing percentages in the direction of the independent variable.
D)computing percentages in the direction of the dependent variable.

Accepted Answer

The answer of If variables are arranged in a bivariate...

Question 16

Measures of association ____ the importance of a relationship between variables.
A)nullify
B)quantify
C)prove
D)maximize

Accepted Answer

Measures of association quantify the strength and direction of a relationship between variables, indicating its importance. They do not nullify or prove the relationship, and they do not aim to maximize it.

Question 17

A researcher can demonstrate a strong association between gender and income.Which variable is independent?
A)gender
B)both
C)income
D)neither

Accepted Answer

In this scenario, the variable "gender" is independent, meaning that it is not influenced by any other factors. However, income can be influenced by a variety of independent variables such as education, occupation, and experience. Therefore, gender is the independent variable in this case.

Question 18

If social class is a cause of a person's political ideology,then ____ is the independent variable and ____ is dependent.
A)class;ideology
B)ideology;class
C)person;ideology
D)information is not sufficient to tell which is independent and which dependent

Accepted Answer

According to the statement "If social class is a cause of a person's political ideology", the cause is social class and the effect or outcome is political ideology. Therefore, social class is the independent variable and political ideology is the dependent variable.

Question 19

If the conditional distributions of Y change,the variables are
A)independent.
B)not associated.
C)significantly related.
D)associateD.

Accepted Answer

If the conditional distributions of Y change, it indicates that the distribution of Y is related to the value(s) of another variable. Hence, the variables are associated.

Question 20

In a bivariate table,the categories of the dependent variable are placed in the
A)rows.
B)columns.
C)marginals.
D)far right-hand column.

Accepted Answer

In a bivariate table, the independent variable is usually placed in the columns and the dependent variable is placed in the rows. This allows for easy comparison and analysis of how the dependent variable is affected by the independent variable.

Quiz 12: Introduction to Bivariate Association and Measures of Association for Variables Measured at the Nominal Level

The relationship between measures of association and causal relations between variables may be summarized as

To prove that one variable causes another,we use

The distribution of scores on the dependent variable for a specific category of the independent variable is called

To display the effects of the X variable on the Y variable in a bivariate table when the independent variable has been arranged in the columns,compute percentages

If the distribution of the scores of one variable changes across the categories of another variable,the variables

A researcher has found a strong relationship between level of education and income and concludes that "higher levels of education cause higher income." What error has been made?

Measures of association help social scientists accomplish two important goals:

Conventionally,each column of a bivariate table represents

If there is an association between two variables,

Measures of association

With the independent variable in the columns,the "maximum difference" is the largest difference

Tests of significance and measures of association provide information about sample data that is

Tests of significance tell us if our results are non-random.To investigate the strength and direction of relationships,we must use

Measures of association provide the researcher with information that

If variables are arranged in a bivariate table,we can see if they are associated by

Measures of association ____ the importance of a relationship between variables.

A researcher can demonstrate a strong association between gender and income.Which variable is independent?

If social class is a cause of a person's political ideology,then is the independent variable and is dependent.

If the conditional distributions of Y change,the variables are

In a bivariate table,the categories of the dependent variable are placed in the