Question 1

A researcher has found a non-random relationship in a large random sample using hypothesis testing. Why would it be a good idea to also compute measures of association?

Accepted Answer

Measures of association provide information about the strength and direction of the relationship, which is not provided by hypothesis testing alone.

Question 2

Why are measures of association helpful in testing the validity of a theory?

Accepted Answer

Measures of association help determine the strength and direction of the relationship between variables, which is crucial for testing if one variable causes another as stipulated by a theory.

Question 3

Tests of significance reveal if results are non-random. What should a researcher use to investigate the strength and direction of relationships?

Accepted Answer

Measures of association are used to investigate the strength and direction of relationships between variables.

Question 4

Which element of research design influences tests of significance far more than it influences measures of association?

Accepted Answer

Sample size influences tests of significance more than measures of association because larger samples tend to produce more reliable estimates and can detect smaller effects, thus affecting the significance level.

Question 5

In what sense do measures of association help with prediction?

Accepted Answer

Measures of association help with prediction by providing a better estimate of a case's score on the dependent variable, indicating the strength and direction of the relationship between variables, which aids in making more accurate predictions.

Question 6

Which of these statements describes the variables if the distribution of the scores of one variable changes across the categories of another variable?

Accepted Answer

If the distribution of one variable's scores changes across the categories of another variable, it indicates some level of association between the two variables.

Question 7

If the independent (X) variable is located in the columns, and the dependent (Y) variable in the rows of a bivariate table, how would we inspect the table to understand a bivariate association?

Accepted Answer

To understand a bivariate association in this setup, you should look across the columns to compare the conditional distributions of the dependent variable (Y) for each level of the independent variable (X).

Question 8

Suppose chi square computed on a bivariate table is greater than zero. Which of these statements describes the variables?

Accepted Answer

A chi-square value greater than zero suggests some level of association between the variables, indicating they may be dependent.

Question 9

How would a researcher reveal if variables are associated when the variables are arranged in a bivariate table?

Accepted Answer

Computing percentages in the direction of the independent variable helps to reveal the association between variables in a bivariate table by showing how the dependent variable changes with different levels of the independent variable.

Question 10

Which percentages should be computed in order 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?

Accepted Answer

When the independent variable is arranged in the columns, percentages should be computed within each column to show the effect of the X variable on the Y variable.

Question 11

Suppose there is a perfect relationship between being convicted of a crime (X) and being sentenced to jail (Y) in a sample of criminal defendants. Which of the following statements can we conclude?

Accepted Answer

In a perfect relationship, all cases would fall into a single cell, indicating that being convicted directly leads to being sentenced to jail without exception.

Question 12

Suppose the maximum difference calculated in a bivariate table is 23 percentage points. How should we describe the relationship?

Accepted Answer

The answer of Suppose the maximum difference calculated in a...

Question 13

Which one of the following patterns describes a positive association between two variables?

Accepted Answer

A positive association between two variables means that as one variable increases, the other variable also increases.

Question 14

As age increases, memorization speed declines. What kind of relationship is this?

Accepted Answer

As age increases and memorization speed declines, it indicates a negative relationship, where one variable increases while the other decreases.

Question 15

What is the association between variables if they vary in opposite directions?

Accepted Answer

When variables vary in opposite directions, it indicates a negative association or correlation.

Question 16

A historical study of dietary patterns in the Middle Ages finds that, with increased wealth, people consumed more calories. How would this relationship be summarized statistically?

Accepted Answer

A positive association means that as one variable increases, the other variable also increases. In this case, as wealth increased, caloric intake also increased.

Question 17

A researcher is interested in the association between gender (measured in two categories) and income (measured in three ordered categories: low, medium, high). What variable should dictate what measure of association is used?

Accepted Answer

The answer of A researcher is interested in the association...

Question 18

What must be calculated before phi and Cramer's V can be computed?

Accepted Answer

Phi and Cramer's V are measures of association that require the chi-square statistic, which is calculated from a chi-square test, to determine the strength of the relationship between categorical variables.

Question 19

Which of the following types of bivariate tables are appropriate for calculating phi?

Accepted Answer

Phi is a measure of association for 2 × 2 contingency tables.

Question 20

How is phi similar to Cramer's V?

Accepted Answer

Both phi and Cramer's V require calculating the chi-square statistic as part of their computation process.

Quiz 8: Measures of Association for Variables Measured at the Nominal Level