Chapter 14 Making Use of Associations Tests

1) What type of analysis would be used to answer a question such as "Which customer

demographic is most strongly related to product purchase/nonpurchase"?

A) association or relationship analysis

B) predictive analysis

C) predictive or relationship analysis

D) analysis of variance

E) canine/feline regression analysis

Answer: A

2) Pontiac wants to know what types of persons respond favorably to proposed style changes

in the Firebird. Frito-Lay wants to know what kinds of people buy from the Frito-Lay line.

These are questions that may be answered through:

A) relationship analysis.

B) chi-square analysis.

C) associative analysis.

D) analysis of variance.

E) regression analysis.

Answer: C

3) Associative analyses determine whether stable relationships exist between:

A) costs and expenses.

B) two variables.

C) 12 or more variables.

D) marketing and sales.

E) statistics and results.

Answer: B

4) When a scale has "labels" as opposed to "levels," we can normally assume the level of

measurement is:

A) nominal or categorical.

B) ratio.

C) interval or ordinal.

D) interval or ratio.

E) metric.

Answer: A

5) When a consistent and systematic relationship is found between two variables, the linkage

is:

A) causal.

B) statistical.

C) managerially significant.

D) important.

E) relevant.

Answer: B

6) You do not have a "relationship" that links the labels (or levels) for two variables unless

the relationship is:

A) causal and consistent.

B) consistent and systematic.

C) systematic and causal.

D) systematic and important.

E) consistent and important.

Answer: B

7) Associative analysis procedures are useful because they determine if there is a consistent

and systematic relationship between the presence (label) or amount of one variable and the:

A) nonpresence of a regressive relationship.

B) presence (label) or amount of another variable.

C) presence (label) or amount of the same variable.

D) covariance of the other variable.

E) presence of unobservable variables.

Answer: B

8) The four basic types of relationships between two variables are:

A) a non-monotonic, duotonic, linear, and curvilinear.

B) nonmonotonic, duotonic, sublinear, and curvilinear.

C) nonmonotonic, monotonic, linear, and curvilinear.

D) causal, consistent, systematic, and linear.

E) duotonic, linear, sublinear, and alinear.

Answer: C

9) A relationship in which the presence (or absence) of one variable is systematically

associated with the presence (or absence) of another is:

A) causal relationship.

B) linear relationship.

C) monotonic relationship.

D) nonmonotonic relationship.

E) alinear relationship.

Answer: D

10) We know that McDonald's customers drink coffee for breakfast and soft drinks at lunch.

This is an example of what type of relationship?

A) causal relationship

B) linear relationship

C) monotonic relationship

D) nonmonotonic relationship

E) curvilinear relationship

Answer: D

11) Which type of relationship would you have when you have a general direction assigned

to the relationship; that is, as one variable increases, the other variable may increase (or

decrease)?

A) causal relationship

B) duotonic relationship

C) monotonic relationship

D) nonmonotonic relationship

E) curvilinear relationship

Answer: C

12) The owner of a shoe store knows that as children increase in age, their shoe size tends to

get larger. This is an example of what type of relationship?

A) causal relationship

B) duotonic relationship

C) monotonic relationship

D) nonmonotonic relationship

E) linear relationship

Answer: C

13) Which type of relationship is a "straight-line" relationship between two variables and for

which allows us to know the knowledge of one variable if we have knowledge of the other?

A) causal relationship

B) linear relationship

C) monotonic relationship

D) nonmonotonic relationship

E) curvilinear relationship

Answer: B

14) Which type of relationship is described by the formula: y = a + bx?

A) causal relationship

B) linear relationship

C) monotonic relationship

D) nonmonotonic relationship

E) curvilinear relationship

Answer: B

15) Which type of relationship is described by relationships that may be S-shaped or Jshaped?

A) causal relationship

B) linear relationship

C) curvilinear relationship

D) nonmonotonic relationship

E) irregular relationship

Answer: C

16) The product life cycle curve that describes the sales pattern of a new product over time

(growing slowly during its introduction and then spurting upward rapidly during its growth

stage and finally plateauing or slowing down considerably as the market becomes saturated)

is an example of which type of relationship?

A) causal relationship

B) linear relationship

C) nonmonotonic relationship

D) monotonic relationship

E) curvilinear relationship

Answer: E

17) Which of the following were discussed by the authors as being ways that we may

characterize relationships?

A) presence, direction, concreteness

B) direction, strength, concreteness

C) presence, direction, strength

D) presence, strength, concreteness

E) presence, pattern, concreteness

Answer: C

18) Strength of association might be described as:

A) strong.

B) weak.

C) moderate.

D) all of the above

E) none of the above

Answer: D

19) Which of the following refers to the finding that a systematic relationship exists between

the two variables of interest in the population?

A) presence

B) direction

C) strength

D) pattern

E) concreteness

Answer: A

20) Which of the following is used when describing the general pattern of nonmonotonic

relationships?

A) presence

B) direction

C) strength

D) pattern

E) concreteness

Answer: D

21) What is used to determine whether a nonmonotonic relationship exists between two

nominal-scaled variables?

A) tabulation analysis and t tests

B) cross-tabulation and chi-square tests

C) cross-tabulation and t tests

D) tabulation analysis and chi-square tests

E) only t tests

Answer: B

22) A cross-tabulation table is sometimes referred to as a:

A) tabular table.

B) nonmonotonic display table.

C) r × c table.

D) t × n table.

E) c × x table.

Answer: C

23) The intersection of a row and column in a cross-tabulation table is called:

A) a cross-tabulation cell.

B) a dangerous intersection.

C) a chi-square.

D) a cross-cell interaction.

E) a row box.

Answer: A

24) The chi-square test is useful for determining:

A) if a nonmonotonic relationship exists between two nominal-scaled variables.

B) if a monotonic relationship exists between two nominal-scaled variables.

C) if a nonmonotonic relationship exists between two interval-scaled variables.

D) if a duotonic relationship exists between two variables.

E) if a duotonic relationship exists between three variables.

Answer: A

25) Which of the following is NOT a number that can be found in each cross-classification

table cell?

A) frequency

B) raw percentage

C) column percentage

D) overall percentage

E) row percentage

Answer: D

26) The logic of the chi-square test would argue that, for a significant relationship to exist:

A) there should be large differences between the observed and expected frequencies.

B) there should be few differences between the observed and expected frequencies.

C) there should be no differences between the observed and expected frequencies.

D) there should be negative differences between the observed and expected frequencies.

E) there should be only one difference between the observed and expected frequencies.

Answer: A

27) In chi-square analysis, the greater the number of cells, the larger the degrees of freedom.

The greater the number of cells, the more opportunity exists to calculate a large chi-square

value. In other words, the chi-square value can be "inflated" not due to a real association but

simply due to the fact that there are more cells in the analysis. This is why degrees of

freedom are used to:

A) determine how many cells you should analyze.

B) determine whether or not the computed chi-square value should be used for a post hoc

test.

C) determine whether or not the chi-square value has a probability high enough to support, or

not support, the null hypothesis.

D) all of the above

E) None of the above; degrees of freedom is not calculated with the chi-square test.

Answer: C

28) If we run the chi-square test and we get a .02 level of significance to support the null

hypothesis, this means:

A) there is adequate support for the null hypothesis.

B) there is no association between the two nominally scaled variables.

C) there is only a 2 percent chance that the two nominally scaled variables are systematically

related.

D) there is a significant association between the two nominally scaled variables, and this

information alone is adequate to explain the nature of the association.

E) there is a significant association between the two nominally scaled variables, but this

information alone is a "flag" that we need to look more closely at the variables to discern the

nature of the relationship.

Answer: E

29) In order to run a chi-square test using SPSS, the proper command sequence is:

A) ANALYZE; CHI-SQUARE; GO.

B) ANALYZE; SUMMARIZE; CROSSTABS; CHI-SQUARE.

C) ASSOCIATIONS; NONMONO; CHI-SQUARE.

D) ANALYZE; DESCRIPTIVE STATISTICS; CROSSTABS.

E) ASSOCIATIONS; STATISTICS; CROSSTABS.

Answer: D

30) In the textbook you were given an example of running a chi-square test using SPSS. The

output shows a "Pearson Chi-Square" value of 23.272. This value alone means:

A) there is a significant difference.

B) there is no significant difference.

C) the difference is associative.

D) the difference must immediately be rounded up.

E) none of the above

Answer: E

31) In the textbook you were given an example of running a chi-square test using SPSS. The

output shows a "Pearson Chi-Square" value of 23.272 and df =7. This information alone

means:

A) there is a significant difference.

B) there is no significant difference.

C) the difference is associative.

D) the test was run incorrectly.

E) none of the above

Answer: E

32) In the textbook you were given an example of running a chi-square test using SPSS. The

output shows a "Pearson Chi-Square" value of 23.272, df = 7 and the Asymp. Sig. = .002.

This means:

A) there is a significant association.

B) there is no significant association.

C) the difference is associative.

D) the test was run incorrectly.

E) none of the above

Answer: A

33) If you were to find a significant association between two nominally scaled variables, a

good way to present the findings in your cross-tabulation table would be to use:

A) a p value for each nonmonotonic relationship found.

B) a Sig. value for each nonmonotonic relationship found.

C) graphical presentations.

D) numerical presentations that clearly indicate the direction and strength of the relationship.

E) numerical presentations that clearly indicate the linear values in the relationship.

Answer: C

34) A correlation coefficient is an index number constrained to fall between the range of:

A) 0 and 1.00.

B) 0 and 100.

C) -1.00 and +1.00.

D) -1.00 and 0.

E) -100 and 0.

Answer: C

35) Assume that a researcher and client determine that a p value of .05 or less determines

significance. Listed below are several correlation coefficients and their respective

significance levels. Which correlation coefficient demonstrates an association not likely due

to chance; that is, is it significant?

A) .22, .06

B) .75, .05

C) .32, .15

D) .76, .95

E) .05, 1.00

Answer: B

36) Which of the following correlation coefficients would indicate a "moderate" association?

A) .85

B) -.85

C) -.75

D) .35

E) .60

Answer: C

37) Let's assume we find in a study that the correlation coefficient between number of years

of education and cigarette smoking is -.89. This means that as education level increases:

A) smoking tends to increase.

B) smoking tends to decrease.

C) smoking changes 89 percent.

D) smoking is nonexistent.

E) only 89 out of every 100 people in the study would not smoke.

Answer: B

38) If we were to graph two variables, let's say, height (in inches) and GPA, and the graph

showed points scattered about in a formless shape, we could say there is:

A) likely no significant relationship between height and GPA.

B) likely a positive relationship between height and GPA.

C) likely a negative relationship between height and GPA.

D) a need to re-graph the data.

E) likely a curvilinear relationship between height and GPA.

Answer: A

39) The Pearson product moment correlation measures the linear relationship between two:

A) nominal- or ordinal-scaled variables.

B) nominal- or interval-scaled variables.

C) interval- or ratio-scaled variables.

D) nominal- or ratio-scaled variables.

E) ordinal- or interval-scaled variables.

Answer: C

40) If you wished to compute a Pearson product moment correlation coefficient using SPSS,

which command sequence would you use?

A) CORRELATE; PEARSON; GO

B) ANALYZE; CORRELATE; PEARSON

C) ANALYZE; CORRELATE; PEARSON; R

D) ANALYZE; CORRELATE; PEARSON; GO

E) ANALYZE; CORRELATE; BIVARIATE

Answer: E

41) Let's assume we use SPSS to run a correlation analysis, and we get a Pearson correlation

coefficient of .941 and a Sig. value of .000. These figures mean:

A) there is 94.1 percent probability for supporting the null hypothesis.

B) there is little or no probability for supporting the null hypothesis.

C) there is little or no probability for supporting the null hypothesis, and there is a strong

association.

D) there is little or no probability for supporting the null hypothesis, and there is a strong,

positive association.

E) that more information is needed.

Answer: D

42) The manager of the city's professional hockey team conducted a large survey. He wanted

to know if there was an association between fans being "season ticket holders" versus

"nonseason ticket holders" and whether they "bought" versus "didn't buy" hockey team

merchandise at the game. Because his survey included these measurements, he used SPSS to

run a Pearson product moment correlation coefficient that turned out to be .88 with a Sig.

value of .001. This meant that:

A) there is no significant relationship.

B) there is a significant, strong, positive relationship.

C) there is a significant, strong, negative relationship.

D) there is a significant, moderate, positive relationship.

E) None of the above; Pearson's product moment correlation is not the appropriate measure

of association here because both variables are nominally scaled.

Answer: D

43) Which of the following are caveats of correlation?

A) Its use is limited to metric variables (interval or ratio scaled).

B) It examines the relationship between only two variables.

C) Do not assume cause and effect.

D) It is limited to linear relationships.

E) all of the above

Answer: E

44) A researcher runs a correlation analysis between two variables that she is certain are

associated but the analysis indicates the two variables are not associated. The researcher may

then want to:

A) run another association test and add three variables.

B) adopt a lower standard for determining significance, that is, a p value of .20.

C) do nothing; if the association is deemed insignificant it is inappropriate to run further

analyses.

D) run a scatter plot in search of a curvilinear relationship.

E) run a scatter plot in search of a linear relationship.

Answer: D

45) Associative analyses determine whether stable relationships exist between two variables.

Answer: True

46) When the descriptors on a scale measure levels or amounts (i.e., the level of sales

dollars), the scale is metric (interval or ratio).

Answer: True

47) When the descriptors on a scale measure levels or amounts (i.e., the level of sales

dollars), the scale is categorical (nominal or ordinal).

Answer: True

48) Significant association relationships are always causal.

Answer: False

49) Associative analysis procedures identify if there are consistent and systematic

relationships between variables.

Answer: True

50) The key to establishing a nonmonotonic relationship between two variables is

determining if one is present (or absent) when the other is present (or absent).

Answer: True

51) The fact that most tourists at sunny beach resorts use sun block is a curvilinear

relationship.

Answer: False

52) Coffee orders at a restaurant are present at breakfast and soft drink orders are present at

lunch. This is an example of a monotonic relationship.

Answer: False

53) The key to establishing a monotonic relationship between two variables is determining if

there is a general direction (increasing or decreasing) between the two variables.

Answer: True

54) If we determine a precise linear relationship between two variables, then by knowing the

value of one variable we should be able to predict the other variable.

Answer: True

55) Presence refers to the strength of a relationship between three or more variables.

Answer: False

56) The characteristic of direction, in describing the relationship between two variables, is

applicable only when the relationship is either monotonic or linear.

Answer: True

57) Both low-level and high-level scales can incorporate very precise linear relationships.

Answer: False

58) We would use cross-tabulation if we wanted to visualize the nonmonotonic relationship

between two nominal scaled variables.

Answer: True

59) Cross-tabulation tables allow us to look at two variables simultaneously and are arranged

in a row and column format.

Answer: True

60) Frequencies tables, part of cross-tabulation, contain raw data.

Answer: True

61) With the information used to construct a cross-tabulation table, a column percentages

table can also be constructed, but a row percentages table cannot.

Answer: False

62) Row cell percentage is calculated by dividing a cell frequency by the cell row total.

Answer: True

63) Chi-square analysis always begins with the assumption that no association exists between

the two nominal scaled variables under analysis.

Answer: True

64) The chi-square analysis would be appropriate to determine if there is an association

between the number of dollars spent on books and the number of years of education.

Answer: False

65) Generally speaking, the chi-square value is calculated by dividing the sum of the squared

differences between observed and expected frequencies by the expected frequencies.

Answer: True

66) In the chi-square analysis, the greater the differences between the observed frequencies

and the expected frequencies, the less likely it is that there will be a statistically significant

relationship.

Answer: False

67) Chi-square analysis is a form of a "goodness of fit" test.

Answer: True

68) The degrees of freedom in chi-square are calculated by multiplying the rows, minus one

times the columns, minus one.

Answer: True

69) It is more difficult for a chi-square value with high degrees of freedom (i.e., there are

more cells) to achieve "significance" than for a chi-square value with fewer degrees of

freedom (i.e., there are fewer cells).

Answer: True

70) The manager of the city's professional hockey team conducted a large survey. He wanted

to know if there was an association between fans being "season ticket holders" versus "nonseason ticket holders" and whether they "bought" versus "didn't buy" hockey team

merchandise at the game. Because his survey included these measurements, he used SPSS to

run a cross-tabulation analysis and selected the chi-square test to test for the significance of

any association. The Pearson Chi-Square turned out to be 81.6 with an Asymp. Sig. value of

.001. This meant there is a significant relationship.

Answer: True

71) To run chi-square analysis in SPSS, you should go to the Crosstabs command, click

Statistics, then select Chi-Square.

Answer: True

72) In chi-square, the null hypothesis states that there is no association. When the "Asymp.

Sig." on the SPSS output is low, this means there is low support for the null hypothesis.

Answer: True

73) When we present findings on cross-tabulations with chi-square analysis, we cannot use

the characteristics of direction or strength because we are dealing with categorical, or

nominal scaled, variables.

Answer: True

74) When we present findings on cross-tabulations with chi-square analysis, graphical

presentations such as bar or stacked bar charts are often useful.

Answer: True

75) A correlation coefficient is an index number constrained to fall between the range of -1.0

and +1.0 that communicates both the strength and the direction of association between three

or more variables.

Answer: False

76) Covariation is defined as the amount of change in one variable systematically associated

with a change in another variable.

Answer: True

77) To use a correlation, you must first establish that it is statistically significant from 1.

Answer: False

78) Regardless of the size of the correlation value, if it is not significant, it is meaningless.

Answer: True

79) When it comes to determining the statistical significance of the correlation coefficient,

there are rules of thumb. For example, .81 to 1.00 is considered to be "strong."

Answer: True

80) There is no connection between scatter diagrams and correlation coefficients.

Answer: False

81) If you plotted data between two variables and the points all fell precisely in a straight line

that was sloping upward to the right, your correlation coefficient would be equal to +1.0.

Answer: True

82) If you plotted data between two variables and the points all fell precisely in a straight line

that was sloping downward to the right, your correlation coefficient would be equal to +1.0.

Answer: False

83) The Pearson product moment correlation measures the linear relationship between two

interval-scaled and/or ratio-scaled variables.

Answer: True

84) We would use the Pearson correlation table if we wanted to visualize the nonmonotonic

relationship between two nominal scaled variables.

Answer: False

85) The Pearson product moment correlation measures the linear relationship between two

nominal and/or ordinal scaled variables.

Answer: False

86) The SPSS command used to execute the Pearson product moment correlation is:

ANALYZE; CORRELATE; BIVARIATE.

Answer: True

87) If you are examining the SPSS output for Pearson correlation, you will find the value of

"1" along the diagonal of the correlation matrix indicating that each variable is perfectly

correlated with itself.

Answer: True

88) If you have a significant and very strong correlation, greater than .90, you may assume

there is a causal relationship between the two variables.

Answer: False

89) You are an officer in your college's Student Marketing Association. You are looking for

ways to ensure that members will join again the following year. Students tend to join for one

semester or one year and then drop out. You decide to take a simple random sample of this

year's members and give them a survey. One of the questions asks: Will you join the SMA

next semester? Yes, No, Don't Know. Another question asks respondents to check all the

following that they feel provides them with "value" by virtue of being in the SMA: free food

at meetings, getting to socialize in a relaxed setting with fellow classmates, learning about

businesses through the guest speaker program, getting job search information through the

organization's "Career Search" program, and getting to know your professors on a more

personal basis. You want to know which of these are related to whether or not students will

join the SMA in the next semester. What analysis should you run?

A) association analysis between the question that asks if they will join and each one of the

remaining questions

B) differences analysis between the question that asks if they will join and each one of the

remaining questions

C) predictive analysis between the question that asks if they will join and each one of the

remaining questions

D) determinant analysis between the question that asks if they will join and each one of the

remaining questions

E) None of the above; there is no analysis that will help identify which of these issues are

related to rejoining the SMA.

Answer: A

90) You are an officer in your college's Student Marketing Association. You are looking for

ways to ensure that members will join again the following year. Students tend to join for one

semester or one year and then drop out. You decide to take a simple random sample of this

year's members and give them a survey. One of the questions asks: Will you join the SMA

next semester? Yes, No, Don't Know. Another question asks respondents to check all the

following that they feel provides them with "value" by virtue of being in the SMA: free food

at meetings, getting to socialize in a relaxed setting with fellow classmates, learning about

businesses through the guest speaker program, getting job search information through the

organization's "Career Search" program, and getting to know your professors on a more

personal basis. You want to know which of these are related to whether or not students will

join the SMA in the next semester. What analysis should you run?

A) Pearson product moment correlation analysis

B) independent samples t tests

C) cross-tabulation

D) cross-tabulation with chi-square tests

E) paired samples tabulation analysis

Answer: D

91) The advertising director in your firm announced her resignation this morning to take

another job, and she is leaving this afternoon. Your boss has asked you to take charge of

advertising. Unfortunately, you learn the former director was just beginning planning for an

upcoming promotion of one of the company's new products. Your immediate decision is to

determine a brand name for the product. As the former director leaves, she stops by to drop

off some marketing research reports she had just received, which includes several tests on

brand names that were proposed for the new brand. The research company tested 30 potential

brand names. For each brand name, they collected data on a number of variables such as

"intention to purchase" and "attitude toward the brand name." All these variables were

collected using 5-point intensity continuum scales. Thus, all the variables possess an interval

level of measurement. Just focusing on the two variables mentioned ("intention to purchase"

and "attitude toward the brand name"), what type of analysis would you conduct to help you

make the decision?

A) Pearson product moment correlation analysis

B) independent samples t tests

C) cross-tabulation

D) cross-tabulation with chi-square tests

E) paired samples tabulation analysis

Answer: A

92) Michelle Steward is a marketing professor at Wake Forest University. Michelle had been

asked by the administration to study a sample of classes at Wake to help the university

understand the student population better, particularly in terms of factors that differentiate

students with high versus low GPAs. One of the questions asked was: "Did you pass or fail

the last test you took?" and another question in the study asked "Did you study or not study

for the last test you took?" Michelle decided to run a cross-tabulation analysis on these two

questions. When she did she also ran the chi-square test. The result was a Pearson Chi-Square

Value of 8.64 and a p value reported as a "Sig." in SPSS of .03. Michelle knew that this

meant:

A) there was a significant, positive association between the two variables

B) there was the presence of an association; the probability of supporting the null hypothesis

that there is no association is only 3 percent

C) there was the presence of a negative association; the probability of supporting the null

hypothesis that there is no association is only 3 percent

D) there was the presence of a positive, "very strong" association because the probability of

supporting the null hypothesis that there is no association is only .03 percent

E) None of the above; Michelle should not have run a chi-square test because the two

variables are both metric.

Answer: B

93) Michelle Steward is a marketing professor at Wake Forest University. Michelle had been

asked by the administration to study a sample of classes at Wake to help the university

understand the student population better particularly in terms of factors that differentiate

students with high versus low GPAs. One of the questions asked was, "What score did you

earn (0 to 100) on the last test that you took?" and another question in the study asked, "How

much time, estimated in numbers of minutes, did you study for the last test you took?"

Michelle decided to run a cross-tabulation analysis on these two questions. When she did, she

also ran the chi-square test. The result was a Pearson Chi-Square Value of 8.64 and a p value

reported as a "Sig." in SPSS of .03. Michelle knew that this meant:

A) there was a significant, positive association between the two variables.

B) there was the presence of an association; the probability of supporting the null hypothesis

that there is no association is only 3 percent.

C) there was the presence of a negative association; the probability of supporting the null

hypothesis that there is no association is only 3 percent.

D) there was the presence of a positive, "very strong" association because the probability of

supporting the null hypothesis that there is no association is only .03 percent.

E) None of the above; Michelle should not have run a chi-square test because the two

variables are both metric.

Answer: E

94) Michelle Steward is a marketing professor at Wake Forest University. Michelle had been

asked by the administration to study a sample of classes at Wake to help the university

understand the student population better particularly in terms of factors that differentiate

students with high versus low GPAs. One of the questions asked was, "What score did you

earn (0 to 100) on the last test that you took?" and another question in the study asked, "How

much time, estimated in numbers of minutes, did you study for the last test you took?"

Michelle decided to run a Pearson product moment correlation analysis on these two

questions. When she did, SPSS generated the following output: Pearson Correlation .98; Sig.

(2 tailed) .0001. Michelle knew that this meant:

A) there was a significant, nonmonotonic association between the two variables.

B) there was the presence of an association because the probability of supporting the

alternative hypothesis is very low, less than 1 percent.

C) there was the presence of a negative association; the probability of supporting the null

hypothesis that there is no association is only .01 percent.

D) there was the presence (aka "significant") of a positive, "very strong" association between

the variables.

E) None of the above; Michelle should not have run a Pearson product moment correlation

because the two variables are both categorical (aka nominal).

Answer: D

Test Bank for Marketing Research

Alvin C. Burns, Ronald F. Bush

9780133074673, 9780134895406, 9780134167404