Question 1

When applying chi square to a 3 by 5 contingency table, Yates must be used whenever any fe is less than 10.

Accepted Answer

A) True 
 B)False

Question 2

When a single group of subjects is allocated among any number of categories, the chi square analysis is

Accepted Answer

A)  r by k 
B)  r by 1 
C)  2 by 2 
D)  1 by k 
A)  r by k 
B)  r by 1 
C)  2 by 2 
D)  1 by k

Question 3

The coefficient of contingency (C) may be applied when

Accepted Answer

A)  using a 2 by 2 contingency table 
B)  using a 3 by 4 contingency table 
C)  both of these 
D)  neither of these 
A)  using a 2 by 2 contingency table 
B)  using a 3 by 4 contingency table 
C)  both of these 
D)  neither of these

Question 4

When fe's are obtained by multiplying row totals by column totals, and then dividing by N, the chi square is a (n)

Accepted Answer

A)  1 by k 
B)  r by k 
C)  1 by r 
D)  all of these, depending on the degrees of freedom 
A)  1 by k 
B)  r by k 
C)  1 by r 
D)  all of these, depending on the degrees of freedom

Question 5

Chi square may only be used to compare frequencies, never sample means.

Accepted Answer

A) True 
 B)False

Question 6

When applying chi square to a 2 by 2 contingency table, each fo must be at least equal to 10.

Accepted Answer

A) True 
 B)False

Question 7

The effect of the Yates correction on the chi square value is that chi square is

Accepted Answer

A)  slightly decreased 
B)  slightly increased 
C)  greatly increased 
D)  none of these, since Yates does not influence the chi square value 
A)  slightly decreased 
B)  slightly increased 
C)  greatly increased 
D)  none of these, since Yates does not influence the chi square value

Question 8

The chi square is a

Accepted Answer

A)  parametric test of mean differences 
B)  a non-parametric test of frequency differences 
C)  a parametric test of frequency differences 
D)  an interval test of rank differences 
A)  parametric test of mean differences 
B)  a non-parametric test of frequency differences 
C)  a parametric test of frequency differences 
D)  an interval test of rank differences

Question 9

Regardless of the fo or fe values, the Yates correction need only be applied when

Accepted Answer

A)  the chi square is significant 
B)  the difference between a given fo and fe is less than 10 
C)  the df equals 1 
D)  none of these 
A)  the chi square is significant 
B)  the difference between a given fo and fe is less than 10 
C)  the df equals 1 
D)  none of these

Question 10

If one group of 35 subjects is being sorted into 8 independent categories, the chi square must have 7 degrees of freedom.

Accepted Answer

A) True 
 B)False

Question 11

Whenever the chi square is applied to interval data, the resulting value will be

Accepted Answer

A)  inflated 
B)  lowered 
C)  the same as when applied to nominal data 
D)  none of these, since chi square may not be applied to interval data 
A)  inflated 
B)  lowered 
C)  the same as when applied to nominal data 
D)  none of these, since chi square may not be applied to interval data

Question 12

With chi square, the more categories there are, then

Accepted Answer

A)  the fe's must each equal the fo's 
B)  the fe's must all equal each other 
C)  no single fe may ever equal a given fo 
D)  none of these 
A)  the fe's must each equal the fo's 
B)  the fe's must all equal each other 
C)  no single fe may ever equal a given fo 
D)  none of these

Question 13

When analyzing research data via the chi square, the independent variable should be placed in the rows.

Accepted Answer

A) True 
 B)False

Question 14

When any expected frequency is less than 10, some statisticians argue that Yates should be applied when df=1).

Accepted Answer

A) True 
 B)False

Question 15

The use of (C), the correlational technique cited in the text for nominal data, first requires

Accepted Answer

A)  a manipulated independent variable 
B)  an a priori hypothesis 
C)  that the constant, .50, be subtracted from the chi square 
D)  that the chi square be significant 
A)  a manipulated independent variable 
B)  an a priori hypothesis 
C)  that the constant, .50, be subtracted from the chi square 
D)  that the chi square be significant

Question 16

Applying the Yates correction, makes it somewhat easier to accept Ho.

Accepted Answer

A) True 
 B)False

Question 17

Use the following: Hypothesis: Police citations for traffic violations are a function of the age of the driver. A random sample of 50 drivers was selected from the population of cited traffic violators during a one-month period. The drivers were categorized according to age, and the frequencies for each category were established. AGE OF DRIVER
$ \begin{array}{lllll}18-27 & 28-37 & 38-47 & 48-57 & 58 \text { and over }\
\text { fo } &35 & 25 & 15 & 13 & 12
\end{array} $

-State the probability value of alpha.

Accepted Answer

The answer of Use the following: Hypothesis: Police citations for...

Question 18

In order to use the chi square

Accepted Answer

A)  all data must be in nominal form 
B)  the categories must be independent of each other 
C)  the sum total of all fo's must equal N 
D)  all of these 
A)  all data must be in nominal form 
B)  the categories must be independent of each other 
C)  the sum total of all fo's must equal N 
D)  all of these

Question 19

Whenever a single group of subjects is being allocated among four independent categories, the chi square analysis would be

Accepted Answer

A)  1 by 3 
B)  1 by 4 
C)  2 by 2 
D)  cannot tell from the above information 
A)  1 by 3 
B)  1 by 4 
C)  2 by 2 
D)  cannot tell from the above information

Question 20

The major mathematical rule underlying nominal scaling is

Accepted Answer

A)  the greater-than, or less-than rule 
B)  the equality versus non-equality rule 
C)  the equal-appearing-intervals rule 
D)  none of these, since nominal scaling is not mathematical 
A)  the greater-than, or less-than rule 
B)  the equality versus non-equality rule 
C)  the equal-appearing-intervals rule 
D)  none of these, since nominal scaling is not mathematical

When applying chi square to a 3 by 5 contingency table, Yates must be used whenever any fe is less than 10.

When a single group of subjects is allocated among any number of categories, the chi square analysis is

The coefficient of contingency (C) may be applied when

When fe's are obtained by multiplying row totals by column totals, and then dividing by N, the chi square is a (n)

Chi square may only be used to compare frequencies, never sample means.

When applying chi square to a 2 by 2 contingency table, each fo must be at least equal to 10.

The effect of the Yates correction on the chi square value is that chi square is

The chi square is a

Regardless of the fo or fe values, the Yates correction need only be applied when

If one group of 35 subjects is being sorted into 8 independent categories, the chi square must have 7 degrees of freedom.

Whenever the chi square is applied to interval data, the resulting value will be

With chi square, the more categories there are, then

When analyzing research data via the chi square, the independent variable should be placed in the rows.

When any expected frequency is less than 10, some statisticians argue that Yates should be applied when df=1).

The use of (C), the correlational technique cited in the text for nominal data, first requires

Applying the Yates correction, makes it somewhat easier to accept Ho.

In order to use the chi square

Whenever a single group of subjects is being allocated among four independent categories, the chi square analysis would be

The major mathematical rule underlying nominal scaling is

Introduction to Statistics

Percentages, Graphs and Measures of Central Tendency

Measures of Variability

The Normal Curve and Z Score

Z Scores, T Scores and Other Normal Curve Transformations

Probability

Statistics and Parameters

Parameter Estimates

The Fundamentals of Research Methodology

The Hypothesis of Difference

The Hypothesis of Association: Correlation

Analysis of Variance

Regression Analysis

Repeated-Measures and Matched-Subjects Designs Interval Data

Non-Parametrics Revisited: the Ordinal Case

Tests and Measurements

Filters

Exam 13: Nominal Categorical Data and the Chi Square

When applying chi square to a 3 by 5 contingency table, Yates must be used whenever any fe is less than 10.

When a single group of subjects is allocated among any number of categories, the chi square analysis is

The coefficient of contingency (C) may be applied when

When fe's are obtained by multiplying row totals by column totals, and then dividing by N, the chi square is a (n)

Chi square may only be used to compare frequencies, never sample means.

When applying chi square to a 2 by 2 contingency table, each fo must be at least equal to 10.

The effect of the Yates correction on the chi square value is that chi square is

The chi square is a

Regardless of the fo or fe values, the Yates correction need only be applied when

If one group of 35 subjects is being sorted into 8 independent categories, the chi square must have 7 degrees of freedom.

Whenever the chi square is applied to interval data, the resulting value will be

With chi square, the more categories there are, then

When analyzing research data via the chi square, the independent variable should be placed in the rows.

When any expected frequency is less than 10, some statisticians argue that Yates should be applied when df=1).

The use of (C), the correlational technique cited in the text for nominal data, first requires

Applying the Yates correction, makes it somewhat easier to accept Ho.

In order to use the chi square

Whenever a single group of subjects is being allocated among four independent categories, the chi square analysis would be

The major mathematical rule underlying nominal scaling is

Introduction to Statistics

Percentages, Graphs and Measures of Central Tendency

Measures of Variability

The Normal Curve and Z Score

Z Scores, T Scores and Other Normal Curve Transformations

Probability

Statistics and Parameters

Parameter Estimates

The Fundamentals of Research Methodology

The Hypothesis of Difference

The Hypothesis of Association: Correlation

Analysis of Variance

Regression Analysis

Repeated-Measures and Matched-Subjects Designs Interval Data

Non-Parametrics Revisited: the Ordinal Case

Tests and Measurements

Filters