Question 1

The Uncertainty Coefficient is possible statistic to use with nominal level variables when there are serious table problems.

Accepted Answer

A) True 
 B)False  True

Question 2

The nominal-level concept of association is that

Accepted Answer

A)  as the number of cases in the table cells of one of the values increases, the corresponding number should decrease in cells of the other values 
B)  as the number of cells in the row of the independent variable increases, the corresponding number of cells should decrease in cells of the other rows 
C)  the rank of scores in the cells of the rows should match the rank of scores in the cells of the columns 
D)  all of the above 
A)  as the number of cases in the table cells of one of the values increases, the corresponding number should decrease in cells of the other values 
B)  as the number of cells in the row of the independent variable increases, the corresponding number of cells should decrease in cells of the other rows 
C)  the rank of scores in the cells of the rows should match the rank of scores in the cells of the columns 
D)  all of the above A

Question 3

Which of the variables below would be interpreted as a natural dichotomy?

Accepted Answer

A)  age measured by "young" and "old" 
B)  gender measured by "male" and "female" 
C)  income measured by "low" and "high" 
D)  type of crime measured by "nonserious" and "serious"
Testing for Association: Phi 
A)  age measured by "young" and "old" 
B)  gender measured by "male" and "female" 
C)  income measured by "low" and "high" 
D)  type of crime measured by "nonserious" and "serious"
Testing for Association: Phi B

Question 4

Any variable that can be split into two equal parts is, by definition, a natural dichotomy.

Accepted Answer

A) True 
 B)False

Question 5

Phi is sensitive to unequal marginal distributions.

Accepted Answer

A) True 
 B)False

Question 6

If you explained 25% of the variance in the dependent variable using the Phi statistic, what would the Phi coefficient be?

Accepted Answer

A)  .25 
B)  .40 
C)  .50 
D)  none of the above 
A)  .25 
B)  .40 
C)  .50 
D)  none of the above

Question 7

There are alternatives to the use of Phi; two of them are:

Accepted Answer

A)  Gamma and Lambda 
B)  Uncertainty Coefficient and Spearman's Rho 
C)  Chronbach's Alpha and McNemar's Q 
D)  none of the above 
A)  Gamma and Lambda 
B)  Uncertainty Coefficient and Spearman's Rho 
C)  Chronbach's Alpha and McNemar's Q 
D)  none of the above

Question 8

Under which of these circumstances would you prefer NOT to use Phi?

Accepted Answer

A)  the number of cases is large 
B)  the relationship is small 
C)  the coefficient produces a value less than .50 
D)  the table marginals are very different 
A)  the number of cases is large 
B)  the relationship is small 
C)  the coefficient produces a value less than .50 
D)  the table marginals are very different

Question 9

A Phi value of .40 would be interpreted as having explained

Accepted Answer

A)  4% of the variance in the dependent variable 
B)  40% of the variance in the dependent variable 
C)  1.6% of the variance in the dependent variable 
D)  16% of the variance in the dependent variable 
A)  4% of the variance in the dependent variable 
B)  40% of the variance in the dependent variable 
C)  1.6% of the variance in the dependent variable 
D)  16% of the variance in the dependent variable

Question 10

[Instructor note: Use this question if you are combining questions on measures of association.] An appropriate measure of association for determining the strength of the relationship between religious affiliation (Protestant, Other) and gender (Male, female) is:

Accepted Answer

A)  Somers' d 
B)  Phi 
C)  chi-square 
D)  Pearson's r 
A)  Somers' d 
B)  Phi 
C)  chi-square 
D)  Pearson's r

Question 11

A perfect relationship, as defined by Phi, is

Accepted Answer

A)  all cases in one row or column 
B)  all cases in two rows or columns 
C)  zero cases on the table diagonal 
D)  all cases on the table diagonal 
A)  all cases in one row or column 
B)  all cases in two rows or columns 
C)  zero cases on the table diagonal 
D)  all cases on the table diagonal

Question 12

Correlation coefficients for Phi range between zero and 1.0.

Accepted Answer

A) True 
 B)False

Question 13

Phi is based on an inferential statistic; that statistics is

Accepted Answer

A)  Analysis of Variance 
B)  t-test 
C)  Chi-square 
D)  Fisher's Exact Test 
A)  Analysis of Variance 
B)  t-test 
C)  Chi-square 
D)  Fisher's Exact Test

Question 14

If a two-variable table creates problems for Chi-square, there will also be problems with Phi.

Accepted Answer

A) True 
 B)False

Question 15

In order to interpret Phi, you must

Accepted Answer

A)  divide the correlation coefficient by N 
B)  square the correlation coefficient 
C)  multiply the correlation coefficient by 2 
D)  multiply the correlation coefficient by 100 
A)  divide the correlation coefficient by N 
B)  square the correlation coefficient 
C)  multiply the correlation coefficient by 2 
D)  multiply the correlation coefficient by 100

Question 16

One of the reasons that Phi is a popular statistic is that it has both symmetric and asymmetric versions.

Accepted Answer

A) True 
 B)False

Question 17

You can't tell the direction of a relationship by looking only at its Phi coefficient.

Accepted Answer

A) True 
 B)False

Question 18

Phi is designed to be used with square tables.

Accepted Answer

A) True 
 B)False

Question 19

Phi is a correlation statistic for which level of measurement below?

Accepted Answer

A)  nominal 
B)  ordinal 
C)  interval 
D)  nominal and ordinal 
A)  nominal 
B)  ordinal 
C)  interval 
D)  nominal and ordinal

Question 20

Assumptions made by the Phi statistic include

Accepted Answer

A)  normal distributions 
B)  equal variances 
C)  variables are natural dichotomies 
D)  all of the above 
A)  normal distributions 
B)  equal variances 
C)  variables are natural dichotomies 
D)  all of the above

The Uncertainty Coefficient is possible statistic to use with nominal level variables when there are serious table problems.

The nominal-level concept of association is that

Which of the variables below would be interpreted as a natural dichotomy?

Any variable that can be split into two equal parts is, by definition, a natural dichotomy.

Phi is sensitive to unequal marginal distributions.

If you explained 25% of the variance in the dependent variable using the Phi statistic, what would the Phi coefficient be?

There are alternatives to the use of Phi; two of them are:

Under which of these circumstances would you prefer NOT to use Phi?

A Phi value of .40 would be interpreted as having explained

[Instructor note: Use this question if you are combining questions on measures of association.] An appropriate measure of association for determining the strength of the relationship between religious affiliation (Protestant, Other) and gender (Male, female) is:

A perfect relationship, as defined by Phi, is

Correlation coefficients for Phi range between zero and 1.0.

Phi is based on an inferential statistic; that statistics is

If a two-variable table creates problems for Chi-square, there will also be problems with Phi.

In order to interpret Phi, you must

One of the reasons that Phi is a popular statistic is that it has both symmetric and asymmetric versions.

You can't tell the direction of a relationship by looking only at its Phi coefficient.

Phi is designed to be used with square tables.

Phi is a correlation statistic for which level of measurement below?

Assumptions made by the Phi statistic include

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Curves and Distributions

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: The T-Test

Testing for Significance in Multiple Groups: The Analysis of Variance Statistic

The Concept of Association

Testing for Association: Pearsons R and Regression

Doing Real Research: Elementary Multivariate Relationships

Statistics and Data Analysis

Filters

Exam 15: Testing for Association: Phi

The Uncertainty Coefficient is possible statistic to use with nominal level variables when there are serious table problems.

The nominal-level concept of association is that

Which of the variables below would be interpreted as a natural dichotomy?

Any variable that can be split into two equal parts is, by definition, a natural dichotomy.

Phi is sensitive to unequal marginal distributions.

If you explained 25% of the variance in the dependent variable using the Phi statistic, what would the Phi coefficient be?

There are alternatives to the use of Phi; two of them are:

Under which of these circumstances would you prefer NOT to use Phi?

A Phi value of .40 would be interpreted as having explained

[Instructor note: Use this question if you are combining questions on measures of association.] An appropriate measure of association for determining the strength of the relationship between religious affiliation (Protestant, Other) and gender (Male, female) is:

A perfect relationship, as defined by Phi, is

Correlation coefficients for Phi range between zero and 1.0.

Phi is based on an inferential statistic; that statistics is

If a two-variable table creates problems for Chi-square, there will also be problems with Phi.

In order to interpret Phi, you must

One of the reasons that Phi is a popular statistic is that it has both symmetric and asymmetric versions.

You can't tell the direction of a relationship by looking only at its Phi coefficient.

Phi is designed to be used with square tables.

Phi is a correlation statistic for which level of measurement below?

Assumptions made by the Phi statistic include

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Curves and Distributions

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: The T-Test

Testing for Significance in Multiple Groups: The Analysis of Variance Statistic

The Concept of Association

Testing for Association: Pearsons R and Regression

Doing Real Research: Elementary Multivariate Relationships

Statistics and Data Analysis

Filters