Terms in Review, Making Research Decisions, Bringing Research to Life, Concept to Practice and From the Headlines for Chapters 15, 16, and 17. ATTN WAK SOLUTIONS

1

Define or explain:

1. Coding rules.

2

. Spreadsheet data entry.

3

. Bar codes.

4

. Precoded instruments.

5. Content analysis.

6

. Missing data.

7

. Optical mark recognition.

2

How should the researcher handle “don’t know” responses?

3

A problem facing shoe store managers is that many shoes eventually must be sold at markdown prices. This prompts us to conduct a mail survey of shoe store managers in which we ask, What methods have you found most successful for reducing the problem of high markdowns? We are interested in extracting as much information as possible from these answers to better understand the full range of strategies that store managers use. Establish what you think are category sets to code

50

0 responses similar to the

14

given here. Try to develop an integrated set of categories that reflects your theory of markdown management. After developing the set, use it to code the 14 responses.

1. Have not found the answer. As long as we buy style shoes, we will have markdowns. We use PMs on slow merchandise, but it does not eliminate markdowns. (PM stands for “push-money”—special item bonuses for selling a particular style of shoe.)

2. Using PMs before too old. Also reducing price during season. Holding meetings with salespeople indicating which shoes to push.

3. By putting PMs on any slow-selling items and promoting same. More careful check of shoes purchased.

4. Keep a close watch on your stock, and mark down when you have to—that is, rather than wait, take a small markdown on a shoe that is not moving at the time.

5. Using the PM method.

6. Less advance buying—more dependence on in-stock shoes.

7. Sales—catch bad guys before it’s too late and close out.

8

. Buy as much good merchandise as you can at special prices to help make up some markdowns.

9

. Reducing opening buys and depending on fill-in service. PMs for salespeople.

10

. Buy more frequently, better buying, PMs on slow-moving merchandise.

11

. Careful buying at lowest prices. Cash on the buying line. Buying closeouts, FDs, overstock, “cancellations.” (FD stands for “factory-discontinued” style.)

12

. By buying less “chanceable” shoes. Buy only what you need, watch sizes, don’t go overboard on new fads.

13

. Buying more staple merchandise. Buying more from fewer lines. Sticking with better nationally advertised merchandise.

14. No successful method with the current style situation. Manufacturers are experimenting, the retailer takes the markdowns—cuts gross profit by about 3 percent—keep your stock at lowest level without losing sales.

4

Select a small sample of class members, work associates, or friends and ask them to answer the following in a paragraph or two: What are your career aspirations for the next five years? Use one of the four basic units of content analysis to analyze their responses. Describe your findings as frequencies for the unit of analysis selected.

5

What data preparation process was Jason doing during data entry?

6

Data entry followed data collection in the research profiled during the opening vignette. What concerned Jason about this process?

7

Choose one of the cases from the text website that has an instrument (check the Case Abstracts section for a listing of all cases and an abstract for each). Code the instrument for data entry.

8

Your responses to the latest U.S. Census were used for two purposes. First, the Census Bureau tallied each response to produce an official population count. Second, it produced a 1-in-

20

sub-sample used for analysis by researchers. For those younger than 65, the estimates from the sample are similar to the full count. For those over age 65, the estimates disagree by as much as 15 percent. The sample data suggest that there are more very old men than very old women. And, the error jumbles the correlation between age and employment, age and marital status, and, possibly, other correlations as well. The Census Bureau has refused to correct the data.

1. Should the data in the 1-in-20 micro-sample be used to study people aged 65 and over?

2. What’s the source of the problem? Programming error, coding error, or manipulating the data to protect the identity of each individual?

Discussion Questions Chapter

16

Terms in Review

1
Define or explain:

1. Marginals.

2. Pareto diagram.

3. Nonresistant statistics.

4. Lower control limit.

5. The five-number summary.

Making Research Decisions

2

Suppose you were preparing two-way tables of percentages for the following pairs of variables. How would you run the percentages?

1. Age and consumption of breakfast cereal.

2. Family income and confidence about the family’s future.

3. Marital status and sports participation.

4. Crime rate and unemployment rate.

3

You study the attrition of entering college freshmen (those students who enter college as freshmen but don’t stay to graduate). You find the following relationships between attrition, aid, and distance of home from college. What is your interpretation? Consider all variables and relationships.

Aid

Home Near Receiving Aid

Home Far Receiving Aid

Yes (%)

No (%)

Yes (%)
No (%)
Yes (%)
No (%)

Drop Out

25

20

5

15

30

40

Stay

75

80

95

85

70

60

4

A local health agency is experimenting with two appeal letters, A and B, with which to raise funds. It sends out 400 of the A appeal and 400 of the B appeal (each subsample is divided equally among working-class and middle-class neighborhoods). The agency secures the results shown in the following table.

1. Which appeal is the best?

2. Which class responded better to which letter?

3. Is appeal or social class a more powerful independent variable?

Appeal A

Appeal B

Middle Class (%)

Working Class (%)

Middle Class (%)
Working Class (%)

Contribution

20
40
15
30

No Contribution

80
60
85
70
 

100

100
100
100
5

Assume you have collected data on sales associates of a large retail organization in a major metropolitan area. You analyze the data by type of work classification, education level, and whether the workers were raised in a rural or urban setting. The results are shown here. How would you interpret them?

Annual Retail Employee Turnover per 100 Employees

High Education

Low Education

Salaried

Hourly Wage

Salaried
Hourly Wage
Salaried
Hourly Wage

Rural

8
16
6

14

12

10

Distinguish between the following:

1. Parametric tests and nonparametric tests.

2. Type I error and Type II error.

3. Null hypothesis and alternative hypothesis.

4. Acceptance region and rejection region.

5. One-tailed tests and two-tailed tests.

6. Type II error and the power of the test.

Summarize the steps of hypothesis testing. What is the virtue of this procedure?

In analysis of variance, what is the purpose of the mean square between and the mean square within? If the null hypothesis is accepted, what do these quantities look like?

Describe the assumptions for ANOVA, and explain how they may be diagnosed.

Suggest situations where the researcher should be more concerned with Type II error than with Type I error.

1. How can the probability of a Type I error be reduced? A Type II error?

2. How does practical significance differ from statistical significance?

3. Suppose you interview all the members of the freshman and senior classes and find that 65 percent of the freshmen and 62 percent of the seniors favor a proposal to send Help Centers offshore. Is this difference significant?

What hypothesis testing procedure would you use in the following situations?

1. A test classifies applicants as accepted or rejected. On the basis of data on 200 applicants, we test the hypothesis that ad placement success is not related to gender.

2. A company manufactures and markets automobiles in two different countries. We want to know if the gas mileage is the same for vehicles from both facilities. There are samples of 45 units from each facility.

3. A company has three categories of marketing analysts: (1) with professional qualifications but without work experience, (2) with professional qualifications and with work experience, and (3) without professional qualifications but with work experience. A study exists that measures each analyst’s motivation level (classified as high, normal, and low). A hypothesis of no relation between analyst category and motivation is to be tested.

4. A company has

24

salespersons. The test must evaluate whether their sales performance is unchanged or has improved after a training program.

5. A company has to evaluate whether it should attribute increased sales to product quality, advertising, or an interaction of product quality and advertising.

You conduct a survey of a sample of 25 members of this year’s graduating marketing students and find that the average GPA is 3.2. The standard deviation of the sample is 0.4. Over the last 10 years, the average GPA has been 3.0. Is the GPA of this year’s students significantly different from the long-run average? At what alpha level would it be significant?

You are curious about whether the professors and students at your school are of different political persuasions, so you take a sample of 20 professors and 20 students drawn randomly from each population. You find that 10 professors say they are conservative and 6 students say they are conservative. Is this a statistically significant difference?

You contact a random sample of 36 graduates of Western University and learn that their starting salaries averaged $

28

,000 last year. You then contact a random sample of 40 graduates from Eastern University and find that their average starting salary was $28,800. In each case, the standard deviation of the sample was $1,000.

1. Test the null hypothesis that there is no difference between average salaries received by the graduates of the two schools.

2. What assumptions are necessary for this test?

A random sample of students is interviewed to determine if there is an association between class and attitude toward corporations. With the following results, test the hypothesis that there is no difference among students on this attitude.

Favorable

Neutral

Unfavorable

Freshmen

100
50
70

Sophomores

80
60
70

Juniors

50
50
80

Seniors

40
60

90

You do a survey of marketing students and liberal arts school students to find out how many times a week they read a daily newspaper. In each case, you interview 100 students. You find the following:

m =

4.5 times per week

Sm =

1.5

la =

5.6 times per week

Sla =

2.0

Test the hypothesis that there is no significant difference between these two samples.

One-Koat

Paint Company has developed a new type of porch paint that it hopes will be the most durable on the market. The R&D group tests the new product against the two leading competing products by using a machine that scrubs until it wears through the coating. One-Koat runs five trials with each product and secures the following results (in thousands of scrubs):

Trial

One-Koat

Competitor A

Competitor B

1

37

34

24
2
30
19
25
3
34

22

23

4
28

31

20
5

29

27

20

Test the hypothesis that there are no differences between the means of these products (a 5 .05).

A computer manufacturer is introducing a new product specifically targeted at the home market and wishes to compare the effectiveness of three sales strategies: computer stores, home electronics stores, and department stores. Numbers of sales by 15 salespeople are recorded here:

Electronics store: 5, 4, 3, 3, 3

Department store: 9, 7, 8, 6, 5

Computer store: 7, 4, 8, 4, 3

1. Test the hypothesis that there is no difference between the means of the retailers (α = .05).

2. Select a multiple comparison test, if necessary, to determine which groups differ in mean sales (α = .05).

Researchers at the University of Aberdeen found that when people were asked to recall past events or imagine future ones, the participants’ bodies subliminally acted out the metaphors we commonly conceptualized with the flow of time. With past years, the participants leaned backward, while when imagining the future, they leaned forward. The leanings were small, but the directionality was clear and dependable. Using this research as a base, if you have two groups (group A holds a cup of hot coffee, and group B holds iced coffee), what statistical hypothesis would you propose to test the groups’ perceptions of the personality of an imaginary individual holding coffee based on its temperature?