•CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 623
Changing the Visual Image EXHIBIT 25.10
Contracting or expanding vertical (amount) scale or horizontal
(time) scale tends to change the visual picture Distortion by Alternating
Scales
Original Scale Contracting Expanding Horizontal Expanding Vertical
Arrangement Expanding Vertical Horizontal and Contracting
Horizontal
Contracting Vertical Contracting Vertical and Expanding Horizontal
Source: Adapted with permission from Spear, Mary Eleanor, Practical Charting Techniques (© New York:
McGraw-Hill, 1969), p. 56.
Another common way of introducing distortion is to have the vertical scale start at some value
larger than zero or not reflect the entire range of the scale. Exhibit 25.11 shows how this exaggerates
difference between measures.The means presented in Exhibit 25.8 were gathered using a scale from
1 to 10.The exhibit shows how virtually no difference can be detected when the vertical axis includes
the entire scale range. However, if the vertical scale is restricted, even these very trivial differences can
appear large.The vertical axis of a graph should reflect the scale used for the data collection.
10.0 EXHIBIT 25.11
9.5
9.0 Distortion from Changing
8.5 Vertical Scale
8.0
7.5 Web Survey
7.0 Mail Survey
6.5 Telephone Survey
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
Customer Satisfaction
7.79
7.78
7.77 Web Survey
Mail Survey
7.76 Telephone Survey
7.75 © Cengage Learning 2013
7.74
Customer Satisfaction
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•624 PART SIX Data Analysis and Presentation
Pie Charts
One of the most useful kinds of charts is the pie chart, which shows the composition of some
total quantity at a particular time. As shown in the examples in Exhibits 25.12 and 25.13, each
angle, or “slice,” is proportional to its percentage of the whole. Exhibit 25.12 shows a simple pie
chart representing the percentage of sales at three different price levels. Exhibit 25.13 illustrates
how pie charts can be paired to illustrate differences. Companies often use pie charts to show how
revenues were used or the composition of their sales. Each of the segments should be labeled with
its description and percentage.The writer should not try to include too many small slices; about
six slices is a typical maximum.
Line Graphs
Line graphs are useful for showing the relationship of one variable to another.The dependent vari-
able generally is shown on the vertical axis, and the independent variable on the horizontal axis.
The most common independent variable for such charts is time, but it is by no means the only one.
Exhibit 25.14 depicts a simple line graph.
Variations of the line graph also are useful. The multiple-line graph, such as the example in
Exhibit 25.15, shows the relationship of more than one dependent variable to the independent
variable.The line for each dependent variable should be in a different color or pattern and should
be clearly labeled.The writer should not try to squeeze in too many variables; this can quickly lead
to confusion rather than clarification.
A second variation is the stratum chart, which shows how the composition of a total quantity
changes as the independent variable changes. Exhibit 25.16 provides an example. The same cau-
tions mentioned in connection with multiple-line graphs apply to stratum charts.
EXHIBIT 25.12 Units Sold
A Simple Pie Chart
32% 49%
19%
Full Price © Cengage Learning 2013
20% Off
50% Off
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•CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 625
U.S. Energy Consumption, 2004 U.S. Electricity Generation, 2004 EXHIBIT 25.13
Total 5 100 quadrillion Btu Total 5 3,953 billion kWh
Pie Charts
6% 9% 3%
8%
18%
23%
40% 20%
23% 50%
Petroleum Nuclear Power
Natural Gas Renewable Energy
Coal & Coal Coke
Source: Energy Information Administration, U.S. Department of Energy, “Renewable Energy Sources:
A Consumer’s Guide,” EIA Brochures, http://www.eia.gov, accessed March 28, 2006.
Spending on Prescription Drugs EXHIBIT 25.14
600
Simple Line Graph
500
Billions of Dollars
400
300
200
100
0
1960 1970 1980 1990 2000 2010* 2020
Year
*Projected data for 2004–2014.
Source: Data from U.S. Census Bureau, Statistical Abstract of the United States, 2006, Table 118, p. 98.
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
•626 PART SIX Data Analysis and Presentation
EXHIBIT 25.15 Median Age of Motor Vehicles
10 Cars
Multiple-Line Graph 8
6 Light Trucks
4
2
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Median Age (Years)*
*Age calculated as of July 1.
Source: Data from Dolliver, Mark “Aging of America, Vehicular Division,” Adweek, March 6, 2006,
downloaded from InfoTrac at http://web2.infotrac.galegroup.com.
EXHIBIT 25.16 Costs of Regulation
Stratum Chart
$800 Environmental Regulation Economic Regulation
700
600 Other Social Regulation Transfer Costs
500 Paperwork
400 Economic Regulation
300 Efficiency Costs
200
100 Costs of Regulation
0 ‘80 ‘82 ‘84 ‘86 ‘88 ‘90 ‘92 ‘94 ‘96 ‘98 2000 © Cengage Learning 2013
‘77
Bar Charts
A bar chart shows changes in the value of a dependent variable (plotted on the vertical axis) at
discrete intervals of the independent variable (on the horizontal axis).A simple bar chart is shown
in Exhibit 25.17.
Like the line graph, the bar chart format has variations.A common variant is the subdivided-bar
chart (see Exhibit 25.18). It is much like a stratum chart, showing the composition of the whole quan-
tity. The multiple-bar chart (see Exhibit 25.19) shows how multiple variables are related to the pri-
mary variable. In each of these cases, each bar or segment of the bar needs to be clearly identified with
a different color or pattern.The writer should not use too many divisions or dependent variables.Too
much detail obscures the essential advantage of charts, which is to make relationships easy to grasp.
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•CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 627
EXHIBIT 25.17
Simple Bar Chart
Teeth whitening, bonding, or Adults Who Have Undergone Cosmetic Treatments
other cosmetic dental work 7%
Lasik surgery to correct vision 3%
Type of Treatment Cosmetic surgery* 3%
Bariatric surgery for weight loss 1%
Facial skin resurfacing treatment** 1%
Laser treatment for veins, 1%
hair removal, etc.
0 1 2 3 4 5 6 7 8 9 10
Percent
*Includes face lift, chin implant, tummy tuck, etc.
**Includes chemical peels, laser abrasion, etc.
Source: Data from Harris Interactive, “Despite Risks, Adults Not Shying Away from Cosmetic Surgery and Other
Treatments,” news release, F ebruary 13, 2006.
2011 Average EXHIBIT 25.18
Retail Price: $3.68/gallon
Subdivided Bar Chart
11%
2000 Average 11%
Retail Price: $1.48/gallon 12%
12%
14%
28%
Distribution & Marketing 66%
Costs & Profits
Refining Costs & Profits
46%
Federal & State Taxes
Crude Oil
Source: Energy Information Administration, U.S. Department of Energy, “What We Pay For in a Gallon of
Regular Gasoline (June 2011),” http://www.eia.gov/oog/info/gdu/gasdiesel.asp, accessed August 11, 2011.
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
•628 PART SIX Data Analysis and Presentation
EXHIBIT 25.19 Recent Best Selling Cars in the U.S.A.
Multiple-Bar Chart
700,000
600,000
500,000
Units Sold 400,000
300,000
200,000
100,000
0 2007 2008
Chevy Impala Honda Civic Toyota Camry Honda Accord
Chevy Silvarado Ford F-150 Dodge Ram
Source: “Forbes.com—10 Best Selling Vehicles of 2007,” http://www.autospies.com/news/Forbes-com-10-Best-S elling-
Vehicles-of-2007-23985/, accessed August 22, 2011; Mitchell, Jacqueline, “The Year’s Best- And Worst-Selling Cars,”
http://www.forbes.com/2008/12/03/2008-car-sales-forbeslife-cx_jm_1203cars.html, accessed August 22, 2011.
The Oral Presentation
oral presentation The conclusions and recommendations of most research reports are presented orally as well as
in writing. The purpose of an oral presentation is to highlight the most important findings of a
A spoken summary of the research project and provide clients or line managers with an opportunity to ask questions.The oral
major findings, conclusions, presentation may be as simple as a short video conference with a manager at the client organiza-
and recommendations, given tion’s location or as formal as a report to the company board of directors.
to clients or line managers In either situation, the key to effective presentation is preparation. The Research Snapshot
to provide them with the “The 10/20/30 Rule of PowerPoint” provides some recommendations that can help you as a pre-
opportunity to clarify any senter. Communication specialists often suggest that a person preparing an oral presentation begin
at the end.3 In other words, while preparing a presentation, a researcher should think about what
ambiguous issues by asking he or she wants the client to know when it has been completed.The researcher should select the
questions. three or four most important findings for emphasis and rely on the written report for a full sum-
mary. The researcher also needs to be ready to defend the results of the research. This is not the
same as being defensive; instead, the researcher should be prepared to deal in a confident, compe-
tent manner with the questions that arise. Remember that even the most reliable and valid research
project is worthless if the managers who must act on its results are not convinced of its importance.
As with written reports, a key to effective oral presentation is adapting to the audience.
Delivering an hour-long formal speech when a ten-minute discussion is called for (or vice versa)
will reflect poorly on both the presenter and the report.
Lecturing or reading to the audience is sure to impede communication at any level of for-
mality. Presenters should refrain from reading prepared text word for word. By relying on brief
notes, familiarity with the subject, and as much rehearsal as the occasion calls for, presenters will
foster better communication. Presenters should avoid research jargon and use short, familiar words.
Presenters should maintain eye contact with the audience and repeat the main points. Because
the audience cannot go back and replay what the speaker has said, an oral presentation often is
organized around a standard format:“Tell them what you are going to tell them, tell them, and tell
them what you just told them.”
Graphic and other visual aids can be as useful in an oral presentation as in a written one.
While presenters can choose from a variety of media, most professional presentations are based on
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RESEARCH
SNAPSHOT
The 10/20/30 Rule of PowerPoint © Spencer Grant/PhotoEdit
Many business researchers find themselves being asked to P owerPoint presentations use, as a minimum, 30-point font
provide a presentation of research findings to different stake- size. Readability is more important than an overkill of informa-
holders. Given today’s reliance on visual technology, it is natural tion that is unreadable to the audience. Kawasaki even provides
to develop a presentation using Microsoft’s PowerPoint presen- a more flexible corollary to the 30 rule—take the age of the
tation software. But a poorly developed PowerPoint presenta- oldest person in the audience, and divide by two! Regardless
tion can limit your impact. One way to avoid this is to use Guy of the size of the font or the length of the presentation, com-
Kawasaki’s 10/20/30 Rule of PowerPoint. municating research should be done carefully, to maximize the
impact of the results you obtain. Perhaps the 10/20/30 rule can
The 10 refers to the number of optimal slides to have for work for you!
your presentation. More than 10 slides can cause your audi-
ence to lose interest, causing them to disregard your research Source: Kawasaki, Guy, “The 10/20/30 Rule of PowerPoint,” How to Change
findings. The 20 part of the rule is related to the time to actu- the World (December 30, 2005), http://blog.guykawasaki.com/2005/12/
ally present the results. In a typical hour-long meeting, any the_102030_rule.html, accessed August 22, 2011.
presentation over 20 minutes will start to lose your audience
as well, and in many instances the opportunity to discuss or
ask questions regarding your research is more valuable than
the presentation itself. Finally, Kawasaki recommends that
PowerPoint or comparable presentation software. For smaller audiences, the researcher may put 629
the visual aids on posters or flip charts.Another possibility is to make copies of the charts for each
participant, possibly as a supplement to one of the other forms of presentation.
Whatever medium is chosen, each visual aid should be designed to convey a simple, attention-
getting message that supports a point on which the audience should focus its thinking.As they do
in written presentations, presenters should interpret graphics for the audience.The best slides are
easy to read and interpret. Large typeface, multiple colors, bullets that highlight, and other artistic
devices can enhance the readability of charts.
Using gestures during presentations also can help convey the message and make presentations
more interesting. Here are some tips on how to gesture:4
■■ Open up your arms to embrace your audience. Keep your arms between your waist and
shoulders.
■■ Drop your arms to your sides when not using them.
■■ Avoid quick and jerky gestures, which make you appear nervous. Hold gestures longer than
you would in normal conversation.
■■ Vary gestures. Switch from hand to hand and at other times use both hands or no hands.
■■ Don’t overuse gestures.
Some gestures are used to draw attention to points illustrated by visual aids. For these, gesturing
with an open hand can seem more friendly and can even release tension related to nervousness. In
contrast, a nervous speaker who uses a laser pointer may distract the audience as the pointer jumps
around in the speaker’s shaky hand.5
Reports on the Internet or Intranet
Many clients want numerous employees to have access to research findings. One easy way to share
data is to make executive summaries and reports available on a company intranet. In addition, a com-
pany can use information technology on the Internet to design questionnaires, administer surveys,
analyze data, and share the results in a presentation-ready format. Real-time data capture allows for
beginning-to-end reporting. A number of companies offer fully web-based research management
systems—for example,WebSurveyor’s online solution for capturing and reporting research findings.
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•630 PART SIX Data Analysis and Presentation
The Research Follow-Up
research follow-up Research reports and oral presentations should communicate research findings so that managers
can make business decisions. In many cases, the manager who receives the research report is unable
Recontacting decision makers to interpret the information and draw conclusions relevant to managerial decisions. For this rea-
and/or clients after they have son, effective researchers do not treat the report as the end of the research process.They conduct
had a chance to read over a a research follow-up, in which they recontact decision makers and/or clients after the latter have
had a chance to read over the report.The purpose is to determine whether the researchers need to
research report in order to provide additional information or clarify issues of concern to management.
determine whether additional
information or clarification is
necessary.
SUMMARY
1. Discuss the research report from the perspective of the communications process. A research
report is an oral or written presentation of research findings directed to a specific audience to accom-
plish a particular purpose. Report preparation is the final stage of the research project. It is important
because the project can guide management decisions only if it is effectively communicated. The
theory of communications emphasizes that the writer (communicator) must tailor the report (mes-
sage) so that it will be understood by the manager (audience), who has a different field of experience.
2. Define the parts of a research report following a standard format. The consensus is that
the format for a research report should include certain prefatory parts, the body of the report, and
appended parts. The report format should be varied to suit the level of formality of the particu-
lar situation. The prefatory parts of a formal report include a title page, letters of transmittal and
authorization, a table of contents, and a summary. The summary is the part of a report most often
read and should include a brief statement of the objectives, results, conclusions, and (depending
on the research situation) recommendations. The report body includes an introduction that gives
the background and objectives, a statement of methodology, and a discussion of the results, their
limitations, and appropriate conclusions and recommendations. The appendix includes various
materials too specialized to appear in the body of the report.
3. Explain how to use tables for presenting numerical information. Tables present large
amounts of numerical information in a concise manner. They are especially useful for presenting
several pieces of information about each item discussed. Short tables are helpful in the body of the
report; long tables are better suited for an appendix. Each table should include a number, title,
stubheads and bannerheads, footnotes for any explanations or qualifications of entries, and source
notes for data from secondary sources.
4. Summarize how to select and use the types of research charts. Charts present numerical
data in a way that highlights their relationships. Each chart should include a figure number, title,
explanatory legends, and a source for secondary sources. Pie charts show the composition of a total
(the parts that make up a whole). Line graphs show the relationship of a dependent variable (on
the vertical axis) to an independent variable (horizontal axis). Most commonly, the independent
variable is time. Bar charts show changes in a dependent variable at discrete intervals of the inde-
pendent variable—for example, comparing one year with another or one subset of the population
with another. Variants of these charts are useful for more complex situations.
5. Describe how to give an effective oral presentation. Most research projects are reported
orally as well as in writing, so the researcher needs to prepare an oral presentation. The presenta-
tion should defend the results without being defensive. The presentation must be tailored to the
situation and the audience. The presenter should practice delivering the presentation in a natural
way, without reading to the audience. Graphic aids are useful supplements when they are simple
and easy to read. Gestures also add interest and emphasis.
6. Discuss the importance of Internet reporting and research follow-up. Posting a summary
of results online gives clients ready access to that information. Some online survey software pro-
cesses the data and displays results in a presentation-ready format. In the follow-up stage of a
research project, the researchers recontact decision makers after submitting the report. This helps
the researchers determine whether they need to provide further information or clarify any issues
of concern to management.
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Chapter 25: Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 631
KEY TERMS AND CONCEPTS
communication process, 611 introduction section, 618 research methodology section, 618
conclusions and recommendations oral presentation, 628 research report, 613
report format, 613 results section, 619
section, 619 research follow-up, 630
graphic aids, 620
QUESTIONS FOR REVIEW AND CRITICAL THINKING
1. Why is it important to think of the research report from a In preparing for a presentation to the Hi Time Board, the c lient
c ommunications perspective? tells the researcher that the chart doesn’t seem to reflect the
improvements made since 2006. Therefore, the researcher prepares
2. As a manager, what degree of formality would you want from the chart as shown here:
your research department?
250 So Cool
3. What types of tables might be used to describe some of the 245 Hi Time
statistical tests discussed in previous chapters? 240
235 2011
4. What is the difference between a basic business research paper 230
and an applied research report? 225
5. What is a pie chart? What is a bar chart? When might one be 2006
p referable over the other?
a. What has reformatting the bar chart accomplished?
6. What are some basic business research journals? Find some b. Was it ethical for the client to ask for the bar chart to be
published research reports in these journals. How do they meet
the standards set forth in this chapter? redrawn?
c. Would it be ethical for the researcher to use the new chart
7. What rules should be followed when preparing slides for
computer-generated presentations? in the presentation?
8. ETHICS What ethical concerns arise when you prepare (or read)
a report?
9. ETHICS A researcher working for Hi Time prepares a bar chart
comparing the number of customers visiting two c ompeting
booths at a fashion trade show. One booth is the Hi Time
booth, and the other is for a competing company, So Cool.
First, the chart is prepared as shown here:
250
200
150 So Cool
Hi Time
100
50
0 2011
2006
RESEARCH ACTIVITIES of the articles that actually presents some research reports, such as
consumer reactions to a new product. Prepare P owerPoint slides
1. ‘NET Input “Starbucks” or “McDonald’s” in an Internet search that contain appropriate charts to present the results.
engine like Google News. Look at the news and articles for that
company. Limit the search by using the word “report.” Find one
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632 Part 6: Data Analysis and Presentation
Annenberg Public Policy Center
CASE A study by the Annenberg Public Policy Center and practices related to price discrimination and the targeting of
25.1 investigated one major area of business decisions: consumers according to their shopping behaviors. Respondents
pricing practices.6 Specifically, the study addressed were asked whether each of these statements was true or false. Case
Exhibit 25.1-1 Exhibit 25.1-4 summarize some of the results from
consumer knowledge and attitudes about the practice of online this study.
retailers adjusting their prices according to customer characteristics, Questions
1. The information provided here is not detailed enough for a for-
such as how frequently they buy from the retailer. For example, a
mal report, but assume that you are making an informal report
website selling cameras charged different prices for the same model in a preliminary stage of the reporting process. Which of these
findings do you want to emphasize as your main points? Why?
depending on whether the visitor to the site had previously visited 2. Prepare a written summary of the findings, using at least two
tables or charts.
sites that supply price comparisons. In general, charging different 3. Prepare two tables or charts that would be suitable to accompany
an oral presentation of these results. Are they different from the
prices is called price discrimination and is legal unless it discriminates visual aids you prepared for question 2? Why or why not?
by race or sex or involves antitrust or price-fixing laws (such as two
competitors agreeing to charge certain prices).
The Annenberg study consisted of telephone interviews con-
ducted with a sample of 1,500 adults, screened to find persons
who had used the Internet in the preceding 30 days.The question-
naire gathered demographic data and data about Internet usage.
In addition, the interviewer read 17 statements about basic laws
CASE EXHIBIT 25.1-1 CASE EXHIBIT 25.1-2
Selected Information about the Sample Responses to Selected Knowledge Questions
Sex Response*
Male 48% Statement True Don’t
Female 52% False Know
Companies today have the ability to 80% 8% 12%
follow my activity across many sites
Online Connection at Home on the web.
Dial-up connection only 31% It is legal for an online store to 38% 29% 33%
Cable modem (with/without dial-up) 18% charge different people different
DSL (with/without dial-up) 25% prices at the same time of day.
Cable or DSL with another method 13%
Don’t know By law, a site such as Expedia or 37% 32% 31%
No connection at home 4% Orbitz that compares prices on
9% different airlines must include the
lowest airline prices.
It is legal for an offline store to 29% 42% 29%
charge different people different
prices at the same time of day.
Self-Ranked Expertise Navigating the Internet When a website has a privacy policy, 59% 25% 16%
it means the site will not share my
Beginner 14% information with other websites or
companies.
Intermediate 40%
Advanced 34% * When the numbers do not add up to 100%, it is because of a rounding error.
Boldface type indicates the correct answer.
Expert 12%
Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation:
Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, p. 20, downloaded at
American Shoppers Online and Offline,” APPC report, June 2005, p. 15, downloaded at http://www.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_
http://www.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_ appc_report_web_final.pdf, accessed August 22, 2011.
appc_report_web_final.pdf, accessed August 22, 2011.
(Continued)
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Chapter 25: Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 633
Case 25.1 (Continued )
CASE EXHIBIT 25.1-3 CASE EXHIBIT 25.1-4
Responses to Selected Attitude Questions Predicting Knowledge Score from Selected Demographics
Response* Unstandardized Standardized
Don’t Regression Regression
Coefficient (B) Coefficient ()
Agree Disagree Neutral Know
Statement Education 0.630* 0.200
It’s okay if a store charges 8% 91% — 1% Income 0.383* 0.150
me a price based on what it
knows about me. Self-perceived ability to navigate 0.616* 0.149
Internet
It’s okay if an online store 11% 87% 1% 1%
Constant
I use charges different 2.687
people different prices for
the same products during
the same hour. R2 0.148
It would bother me to learn 76% 22% 1% 1% * Significance < 0.001 level.
that other people pay less
than I do for the same Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation:
products. American Shoppers Online and Offline,” APPC report, June 2005, p. 29, downloaded at
http://www.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_
It would bother me if 57% 41% 2% 1% appc_report_web_final.pdf, accessed August 22, 2011.
websites I shop at keep
detailed records of my
buying behavior.
It’s okay if a store I shop at 50% 47% 2% 1%
frequently uses information
it has about me to create a
picture of me that improves
the services it provides for me.
* When the numbers do not add up to 100%, it is because of a rounding error.
Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American
Shoppers Online and Offline,” APPC report, June 2005, p. 22, downloaded at http://www
.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_appc_report_
web_final.pdf, accessed August 22, 2011.
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PART SEVEN
Comprehensive Cases
with Computerized
Databases
CASE 1
Running the Numbers: Does It Pay?
CASE 2
Attiring Situation
CASE 3
Values and the Automobile Market
CASE 4
TABH, INC., Automotive Consulting
CASE 5
The Atlanta Braves
CASE 6
Knowing the Way
© Mark Harwood/Iconica/Getty Images
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COMPREHENSIVE CASES
Running the Numbers: Does It Pay?
CASE (Download the data sets for this case from fit this profile. The invitation explained that the research was about
1 www.cengagebrain.com or request them from various employee attitudes and indicated that employees would not
your instructor.) Dr. William Ray, a research con- be required to identify themselves during the survey. Respondents
were informed that all responses would be strictly confidential.
sultant, has received a government grant of $75,000 to fund research The e-mail provided a click-through questionnaire which directed
respondents to a website where the survey was conducted using
examining how aspects of a student’s college experiences relate to an online survey provider. Each invitation was coded so that the
actual respondents could be identified by both e-mail address and
his or her job performance. Senator B. I. G. Shot is being lobbied name. Dr. Ray, however, kept this information confidential so the
company could not identify any particular employee’s response.
by his constituents that employers are discriminating against people
The following table describes the variables that were collected.
who do not like math by giving them lower salaries. Senator Shot
has obtained $50,000 of the $75,000 grant from these constituents.
The senator was also instrumental in the selection of Dr. Ray as the
recipient and hopes the research supported by the grant will help
provide a basis to support the proposed legislation making discrimi- Variables Available from Company Records
nation against people who do not like math illegal. Variable Name Variable Type Coding
The research questions listed in this particular grant proposal include:
RQ1: D oes a student’s liking of quantitative coursework in col- PROM Nominal indicating 1 5 Promoted
lege affect his or her future earnings? whether the employee 0 5 Not Promoted
has been promoted
RQ2: Do people with an affinity for quantitative courses get
promoted more quickly than those who do not? GPA Self-reported GPA in 0 (lowest) to 4 (highest)
last year of college
Dr. Ray has gained the cooperation of a Fortune 500 service firm Sex Nominal 1 5 Female
that employs over 20,000 employees across eight locations. The 0 5 Male
company allows Dr. Ray to survey employees who have been out of School Nominal
college for three years. Three hundred responses were obtained by Salary Ratio School initials
sending an e-mail invitation to approximately 1,000 employees who
Actual annual salary
from last year
Questions from Survey
Coding Strongly Disagree Disagree Neutral Agree Strongly Agree
(1) (2) (3) (4) (5)
X1 The quantitative courses I took in school were the □ □ □□ □
most useful courses. □ □ □□
□ □ □□ □
X2 Very few topics can be understood if you do not □ □ □□
understand the arithmetic. □ □ □□ □
□
X3 I hated going to math classes in college.
X4 I learned a great deal from the quantitative □
(Continued )
projects assigned to me in college.
X5 Students do not need to study quantitative topics
in college to succeed in their careers.
636
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Comprehensive Cases 637
Case 1 (Continued )
Please use the following items to describe your undergraduate college experience. For each pair of items, choose the check box closest to the adjective
that best describes your experience.
Coding (−3) (−2) (−1) (0) (1) (2) (3)
S1 Dull □ □ □
S2 Laborious □ □ □ □ □ □ □ Exciting
S3 Stressful □ □ □
S4 Boring □ □ □ □ □ □ □ Playful
S5 Carefree □ □ □
□ □ □ □ Relaxing
Questions
1. Does this grant present Dr. Ray with an ethical dilemma(s) in □ □ □ □ Fun
any way? □ □ □ □ Responsible
2. Derive at least one hypothesis for each research question listed
5. List another hypothesis (unrelated to the research questions in
above. Provide a sound rationale or theoretical explanation that the grant) that could be tested with this data.
leads to the hypothesis.
3. Use the data that corresponds to this case to perform an ade- 6. Test that hypothesis.
quate test of each hypothesis. Interpret the results. 7. Considering employees’ attitudes about their college experi-
4. Is there evidence supporting the discrimination claim? Explain.
ence, does the amount of fun that students had in college or the
degree to which they thought quantitative classes were a posi-
tive experience relate more strongly to salary?
8. Would the “problem” that led to the grant be a better candidate
for ethnographic research? Explain.
Attiring Situation
CASE RESERV is a national level placement firm special- Mr. Neil decides the problem can best be attacked by conduct-
2 izing in putting retailers and service providers together ing a laboratory experiment. In the experiment, two variables
with potential employees who fill positions at all levels are manipulated in a between-subjects design. The experiment
includes two experimental variables that are controlled by the
of the organization. This includes entry-level positions and senior researcher and by the subjects’ biological sex, which was recorded
and included as a blocking variable. The experimental variables
management positions. One international specialty clothing store (and blocking variable) are:
chain has approached them with issues involving key characteristics
of retail employees. The two key characteristics of primary interest
involve the appearance of potential employees and problems with Name Description Values
X1 A manipulation of the attire
customer integrity. of the service-providing 0 5 Professional Attire
X2 employee (Neatly groomed w/
Over the last five years, store management has adopted a very Gender business attire)
The manner with which the 1 5 Unprofessional Attire
relaxed dress code that has allowed employees some flexibility service-providing employee (Unkempt hair w/jeans
tries to gain extra sales—or and t-shirt)
in the way they dress for work. Casual attire was permitted with simply, the close approach
Subject’s biological sex 0 5 Soft Close
the idea that younger customers could better identify with store 1 5 Hard Close
employees, most of whom are younger than average. However, 0 5 Male
1 5 Female
senior management had just become aware of how some very suc-
cessful companies tightly control the appearance of their sales force.
The Walt Disney Company, for example, has strict grooming poli-
cies for all employees, provides uniforms (or costumes) for most
cast members, and does not permit any employees to work if they
have visible tattoos. Disney executives discuss many positive ben-
efits from this policy and one is that customers are more respon- Three dependent variables are included:
sive to the employees.Thus, it just may be that the appearance of
employees can influence the behavior of customers.This influence Name Description Range
TIME 0–10 minutes
can be from the greater identity that employees display—meaning How much time the subject spent with the
SPEND employee beyond what was necessary to $0–$25
they stand out better and may encourage acquiescence through choose the slacks and the shirt
KEEP $0–$25
friendliness. How much of the $25 the subject spent on (Continued )
extra products offered for sale by the retail
Senior research associate Michael Neil decides to conduct an service provider
experiment to examine relevant research questions including: How much of the $25 the subject kept
rather than returning to the researcher
RQ1: H ow does employee appearance affect customer pur-
chasing behavior?
RQ2: H ow does employee appearance affect customer ethics?
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
638 Part 7: Comprehensive Cases with Computerized Databases
Case 2 (Continued ) 2. Dressed unprofessionally and used a soft close.
3. Dressed professionally and used a hard close (i.e., “You really
Additionally, several variables were collected following the experi-
ment that tried to capture how the subject felt during the exercise. All need to match this up with some coordinated accessories which
of these items were gathered using a 7-item semantic differential scale. happen to be on sale today only”) in trying to sell merchandise
beyond the slacks and shirt.
Name Description 4. Dressed unprofessionally and used a hard close.
Low Quality–High Quality
SD1 Dislike–Like RESERV wishes to use this information to explain how
SD2 Unfavorable–Favorable employee appearance encourages shoppers to continue shopping
SD3 Negative–Positive (TIME) and spend money (SPEND). Rather than simply ask pur-
SD4 Easy–Difficult chase intentions, researchers gave each subject $25 (in one-dollar
SD5 Restful–Tiring bills), which they were allowed to spend on accessories.This allowed
SD6 Comfortable–Uncomfortable each subject to participate in an actual transaction. In addition, the
SD7 Calm–Tense experiment did not provide explicit instructions on what was to be
SD8 done with the money that was left over. Once the simulated shop-
ping trip was complete, subjects were taken to another small room
The experiment was conducted in a university union. Subjects were where they answered a questionnaire containing the semantic dif-
recruited from the food court area. RESERV employees approached ferential scales and demographic information, which they completed
potential subjects and requested their participation in a study that while alone and at their own pace. Because the instructions did not
examined how customers really bought things. Subjects would each specifically tell subjects what to do with the money they possessed
receive vouchers that could be exchanged for merchandise in return following the experiment, this allowed the researchers to operation-
for their participation. Each potential subject was informed that the alize a behavioral dependent variable (KEEP) that simulated ques-
participation could take between 20 and 40 minutes to complete. tionable behavior based on the implied assumption that the money
Upon agreeing to participate, subjects were escorted to a waiting was to be either handed to the research assistant when the shopping
area where they were provided with further instructions and min- trip was complete or turned in along with the questionnaire. In
gled with other participants before entering a small room that was other words, subjects who kept money were considered as behaving
set up to resemble an actual retail clothing counter. less ethically than those who left the money behind or turned it in
to a member of the research team.
Each subject was told to play the role of a customer who had
just purchased some dress slacks and a shirt.The employee was to Questions
complete the transaction. Once the subject entered the mock retail 1. Develop at least three hypotheses that correspond to the research
environment, a research assistant who was playing the role of the
retail employee entered the room.As a retail sales associate, one questions.
important role was to suggest add-on sales. Several dozen accessory 2. Test the hypotheses using an appropriate statistical approach.
items ranging from socks and handkerchiefs to small jewelry items 3. Suppose the researcher is curious about how the feel-
were displayed at the counter.
ings captured with the semantic differentials influence the
As a result of this experimental procedure, each subject was ran- dependent variables SPEND and KEEP. Conduct an analy-
domly assigned to one of four conditions, each corresponding to a sis to explore this possibility. Are any problems present in
unique combination of the experimental variables described above. testing this?
In other words, the employee fit into one of the following categories: 4. Is there a role for factor analysis in any of this analysis?
5. Critique the experiment from the viewpoint of internal and
1. Dressed professionally and used a soft close (i.e.,“Perhaps you external validity.
would like to see some additional accessories”) in trying to sell 6. What conclusions would be justified by management regarding
merchandise beyond the slacks and shirt. their employee appearance policy?
Values and the Automobile Market
CASE (Download the data sets for this case from better target market segments and better position their products via
3 www.cengagebrain.com or request them from more effective advertising. Insight into the foreign-domestic luxury
your instructor.) In the last decade, the luxury car car choice may result from examining owners’ personal values in
addition to their evaluations of car attributes, because luxury cars,
segment became one of the most competitive in the automobile like many other conspicuously consumed luxury products, may be
purchased mainly for value-expressive reasons.
market. Many American consumers who purchase luxury cars prefer
Industry analysts believe it would be important to assess whether
imports from Germany and Japan. personal values of consumers could be used to explain ownership of
American, German, and Japanese luxury cars. Further, they believe
A marketing vice president with General Motors once com- they should also assess whether knowledge of owners’ personal values
provides any additional information useful in explaining ownership
mented,“Import-committed buyers have been frustrating to us.”
(Continued )
This type of thinking has led industry analysts to argue that to suc-
cessfully compete in the luxury car segment, U.S. carmakers need
to develop a better understanding of consumers so that they can
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Comprehensive Cases 639
Case 3 (Continued ) a Japanese luxury car (Infiniti or Lexus) within the last year. A cover
letter explained that the survey was part of an academic research
of American, German, and Japanese luxury cars beyond that obtained project. People were asked to return the questionnaires anonymously
from their evaluations of the cars’ attributes. to a university address. (A postage-paid envelope was provided with
each survey.) A notice was included that stated that the project was
Personal values are likely to provide insights into reasons for approved by the University Internal Review Board and emphasized
ownership of luxury cars for at least two reasons. First,Americans have the fact that participation was voluntary. Beyond an appeal to help the
always had very personal relationships with their cars and have used researchers, respondents were not offered any other incentive to com-
them as symbols of their self-concepts. For instance, people who value plete the surveys. Of the 498 questionnaires originally sent, 17 were
a sense of accomplishment are quite likely to desire a luxury car that they returned by the post office as undeliverable. One hundred fifty-five
feel is an appropriate symbol of their achievement, whereas people completed surveys were received, for a response rate of 32.2 percent.
who value fun, enjoyment, and excitement are likely to desire a luxury car
that they perceive as fun and exciting to drive.An advertiser trying to THE SURVEY INSTRUMENT
persuade the former segment to purchase a luxury car should position
the car as a status symbol that will help its owners demonstrate their The survey included questions on (1) various issues that people con-
accomplishments to others. Similarly, an advertiser trying to persuade sider when purchasing new cars, (2) importance of car attributes, (3)
the latter segment to purchase a luxury car should position the car importance of different values, and (4) demographics (sex, age, educa-
as a fun and exciting car to drive. In other words, effective advertis- tion, and family income). Questions relating to the issues that people
ing shows consumers how purchasing a given product will help them consider when purchasing new cars were developed through initial
achieve their valued state, because brands tied to values will be per- interviews with consumers and were measured with a 7-point Likert
ceived more favorably than brands that deliver more mundane benefits. scale with end anchors of “strongly agree” and “strongly disagree.” (See
Case Exhibit 3.1.) A list of 12 car attributes was developed from the ini-
Second, when a market is overcrowded with competing brands tial interviews with consumers and by consulting Consumer Reports. (See
offering very similar options—as is the case with the luxury car Case Exhibit 3.2.) The importance of each attribute was measured with
m arket—consumers are quite likely to choose between brands on a 7-point numerical scale with end points labeled “very important” and
the basis of value-expressive considerations. “very unimportant.” The List of Values (LOV) scale in Case Exhibit 3.3
was used to measure the importance of values. Respondents were asked
METHOD to rate each of the eight values—we combined fun, enjoyment, and
excitement into one value—on a 7-point numerical scale with end
Data were collected via a mail survey sent to 498 consumers chosen points labeled “very important” and “very unimportant.”
at random from a list obtained from a syndicated research company
located in an affluent county in a southern state. The list contained
names of people who had purchased either an American luxury car
(Cadillac or Lincoln), a German luxury car (Mercedes or BMW), or
CASE EXHIBIT 3.1
Issues That Consumers Consider When Buying Luxury Automobiles
Having a luxury car is a major part of my fun and excitement.a (Issue 1) When I buy a new luxury car, my family’s opinion is very important to
me. (Issue 12)
Owning a luxury car is a part of “being good to myself.” (Issue 2)
My family usually accompanies me when I am shopping for a new
When I was able to buy my first luxury car, I felt a sense of luxury car. (Issue 13)
accomplishment. (Issue 3)
I usually rely upon ads and salespersons for information on cars. (Issue 14)
I enjoy giving my friends advice about luxury cars. (Issue 4)
I usually rely upon friends and acquaintances for information on cars.
Getting a good deal when I buy a luxury car makes me feel better (Issue 15)
about myself. (Issue 5)
When I shop for a car, it is important that the car dealer make me feel
I seek novelty and I am willing to try innovations in cars. (Issue 6) at ease. (Issue 16)
I tend to buy the same brand of the car several times in a row. (Issue 7) Most of my friends drive luxury import cars. (Issue 17)
I tend to buy from the same dealer several times in a row. (Issue 8) Most of my friends drive luxury domestic cars. (Issue 18)
I usually use sources of information such as Consumer Reports in I think celebrity endorsers in ads influence people’s choices of luxury
deciding on a car. (Issue 9) cars. (Issue 19)
I usually visit three or more dealerships before I buy a car. (Issue 10) I would not buy a luxury car if I felt that my debt level were higher
than usual. (Issue 20)
I would read a brochure or watch a video about defensive driving.
(Issue 11)
a Note: Subjects’ responses were measured with 1 as “strongly agree” and 7 as “strongly disagree.”
(Continued )
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
640 Part 7: Comprehensive Cases with Computerized Databases
Case 3 (Continued )
CASE EXHIBIT 3.2
Car Attributes
Attribute Code Attribute Code
Comfort Comfort Low maintenance cost Lomc
Safety Safety Reliability Rely
Power Power Warranty Warrant
Speed Speed Nonpolluting Nonpol
Styling Styling High gas mileage Gasmle
Durability Durabil Speed of repairs Repairs
CASE EXHIBIT 3.3 Code Value Code
Fun Sense of accomplishment Accomp
List of Values Belong Warm relationship Warm
Respect Security Security
Value Selfful Self-respect Selfres
Fun-enjoyment-excitement
Sense of belonging
Being well respected
Self-fulfillment
THE SAMPLE consumers (64 percent were college graduates), and economically
well-off consumers (87.2 p ercent earned $65,000 or more).
Of the 155 respondents in the sample, 58 (37.4 percent) owned
an American luxury car, 38 (24.5 percent) owned a European CODING
luxury car, and 59 (38.1 percent) owned a Japanese luxury
car. The m ajority of the sample consisted of older consumers Case Exhibit 3.4 lists the SPSS variable names and identifies codes
(85 percent were 35 years of age or above), more educated for these variables. (Note that this data set is also available in
Microsoft Excel.)
CASE EXHIBIT 3.4
List of Variables and Computer Codes
ID—Identification number
Age (categories are 2 5 35 years and under, 3 5 36–45 yrs, 4 5 46–55 yrs, 5 5 56–65 yrs, 6 5 651 yrs)
Sex (1 5 male, 0 5 female)
Educ—Education (1 5 less than high school, 2 5 high school grad, 3 5 some college, 4 5 college grad, 5 5 graduate degree)
Income (1 5 less than $35,000, 2 5 $35–50,000, 3 5 $50,001–65,000, 4 5 $65,0011)
Car—Type of luxury car (American car, European car, Japanese car)
Issues—The sequence of issues listed in Case Exhibit 3.1. (Strongly agree 5 1; strongly disagree 5 7)
Attributes—The sequence of car attributes listed in Case Exhibit 3.2. (Very important to you 5 1; very unimportant to you 5 7)
Values—The sequence of values listed in Case Exhibit 3.3. (Very important 5 1; very unimportant 5 7)
ADDITIONAL INFORMATION values variables. Do any of the values variables show significant
differences between American, Japanese, and European car
Several of the questions will require the use of a computerized data- owners?
base. Your instructor will provide information about obtaining the 3. Are there any significant differences on importance of attributes?
VALUES data set if the material is part of the case assignment. 4. Write a short statement interpreting the results of this research.
Questions Advanced Questions
1. Is the sampling method adequate? Is the attitude-measuring scale 5. Are any of the value scale items highly correlated?
6. Should multivariate analysis be used to understand the data?
sound? Explain.
2. Using the computerized database with a statistical software pack-
age, calculate the means of the three automotive groups for the
Case materials based on research by Ajay Sukhdial and Goutam Chakraborty, Oklahoma State University.
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Comprehensive Cases 641
TABH, INC., Automotive Consulting
CASE (Download the data sets for this case from study, Michelle decides to contact the traffic department at Cal Poly
4 www.cengagebrain.com or request them from University in Pomona, California, and at University of Central Mis-
your instructor.) TABH Consulting specializes souri in Warrensburg, Missouri. Michelle wishes to obtain data from
the students’ automobile parking registration records.Although both
in research for automobile dealers in the United States, Canada, schools are willing to provide anonymous data records for a limited
number of students, Cal Poly offers Michelle a chance to visit during
Mexico, and Europe. Although much of their work is done on a the registration period, which just happens to be the following week.
As a result, not only can Michelle get data from students’ registration
custom basis with customers such as dealerships and dealership net- forms, but she can obtain a small amount of primary data by inter-
cepting students near the registration window. In return, Michelle is
works selling all major makes of automobiles, they also produce a asked to purchase a booth at the Cal Poly career fair.
monthly “white paper” that is sold via their website. This off-the- As a result, Michelle obtains some basic information from stu-
dents.The information results in a small data set consisting of the
shelf research is purchased by other research firms and by compa- following observations for 100 undergraduate college students in
Pomona, California:
nies within the auto industry itself. This month, they would like
to produce a white paper analyzing the viability of college students
attending schools located in small college towns as a potentially
underserved market segment.
TABH management assigns a junior analyst named Michelle
Gonzalez to the project. Lacking time for a more comprehensive
Variable Description
Sex
Color Student’s sex dummy coded with 1 5 female and 0 5 male
Major
Grade Color of a student’s car as listed on the registration form
Finance
Residence Student’s major field of study (Business, Liberal Arts (LA), or Engineering (ENG))
Animal
Student’s grade record reported as the mode (A, B, or C)
Whether the student financed the registered car or paid for it with cash, coded 0 5 cash payment and 1 5 financed
Whether the student lives on campus or commutes to school, coded 0 5 commute and 1 5 on campus
Michelle asks each student to quickly draw a cartoon about the type of car they would like to purchase. Students are told to depict
the car as an animal in the cartoon. Although Michelle expects to interpret these cartoons more deeply when time allows, the initial
coding specifies what type of animal was drawn by each respondent. When Michelle was unsure of what animal was drawn, a second
researcher was conferred with to determine what animal was depicted. Some students depicted the car as a dog, some as a cat, and
some as a mule.
The purpose of the white paper is to offer car dealers considering • How do different segments view a car?
new locations a comparison of the profile of a small town university • What types of automobiles would be most in demand?
with the primary market segments for their particular automobile.
For instance, a company specializing in small pickup trucks appeals Questions
to a different market segment than does a company specializing in 1. What types of tests can be performed using the data that may at
two-door economy sedans. Many small towns currently do not
have dealerships, particularly beyond the “Big 3.” Although TABH least indirectly address the primary research question?
cannot predict with certainty who may purchase the white paper, 2. What do you think the primary conclusions of the white paper
it particularly wants to appeal to companies with high sales growth
in the United States, such as Kia (http://www.kia.com), Hyundai will be based on the data provided?
(http://www.hyundaiusa.com), and potentially European auto 3. Assuming a small college town lacked an auto dealership
dealerships currently without significant U.S. distribution, such as
Smart (http://www.smart.com), among others. TABH also hopes (beyond Ford, GM, and Chrysler), what two companies should
the white paper may eventually lead to a customized project for one be most interested in this type of location? Use the Internet
of these companies. Thus, the general research question is: if necessary to perform some cursory research on different car
companies.
What are the automobile market segment characteristics of students 4. What are the weaknesses in basing decisions on this type of
attending U.S. universities in small towns? research?
5. Are there key issues that may diminish the usefulness of this
This question can be broken down into a series of more specific research?
questions: 6. What kinds of themes might emerge from the cartoon drawings?
7. Are there any ethical dilemmas presented in this case?
• What segments can be identified based on identifiable character-
istics of students?
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
642 Part 7: Comprehensive Cases with Computerized Databases
The Atlanta Braves
CASE A visit to Turner Field, the Atlanta Braves’ state-of- problem spending money when they’re getting value.We have one
5 the-art ballpark, feels like a trip back to the future. of the highest payrolls in baseball, and I needed to find new ways to
The stadium blends 1940s tradition with twenty-first sustain our revenues.”
century convenience. The Braves’ marketing campaign reflects the Turner Field’s main entry plaza opens three hours before
games—compared to two hours for the rest of the ballpark—and
charm and nostalgia of baseball’s past, but it has a futuristic slogan: stays open for about two hours after games. On weekends, there is
live music.
“Turner Field: Not just baseball. A baseball theme park.”
Everyone’s invited—186 $1 “skyline seats” are available for each
Fans love the fact that they’re closer to the action at Turner Field. game—and that buck gets you anywhere, from the open-air porch
at the Chop House restaurant (which specializes in barbecue, bison
It’s only 45 feet from either first or third base to the dugouts, with the dogs, Moon Pies, and Tomahawk lager) to the grassy roof at Coke’s
Sky Field, where fans can keep cool under a mist machine.
stands just behind. Besides that, there’s a Braves Museum and Hall of
Interactive games in Scouts Alley range from $1 to $4, and the
Fame with more than 200 artifacts. Cybernauts will find Turner Field chroma-key studios in the East and West Pavilions cost $10–20,
where fans can have their picture inserted into a baseball card or
awesome because it’s a ballpark that makes them a part of the action. into a photo of a great moment in Braves history. Admission to the
museum is $2. And it should come as no surprise that there are seven
At the stadium, built originally for the 1996 Olympics and converted ATMs located throughout the ballpark.
for baseball after the Games, there are interactive games to test fans’ One of the Braves’ key marketing objectives is to help build a new
generation of baseball fans.The stadium was planned so that fans will
hitting and pitching skills, and their knowledge of baseball trivia; find something to love and learn at every turn.The minute a fan’s ticket
is torn, that person becomes part of what’s happening at Turner Field.
electronic kiosks with touch screens and data banks filled with scout-
Questions
ing reports on 300 past and present Braves, along with the Braves’ 1. What are the key elements of the Turner Field marketing effort?
2. What aspect of the planning of Turner Field, home of the
Internet home page; a dozen 27-inch television monitors mounted
Atlanta Braves, may have been influenced by research using
above the Braves’ Clubhouse Store, broadcasting all the other major s econdary data?
3. What role should business research play in a sporting organiza-
league games in progress, with a video ticker-tape screen underneath tion such as the Atlanta Braves, both in making capital decisions
and in supporting everyday operational maters?
spitting out up-to-the-minute scores and stats; a sophisticated com- 4. Suppose an executive for the Braves wishes to know whether
the stadium has caused employees (including ticket takers, park-
munications system, with four miles of fiber-optic cable underneath ing attendants, ushers, security personnel, team employees, etc.)
to be more committed to the Braves organization than when
the playing field that will allow World Series games to be simulcast they were playing in an old-fashioned stadium. What would a
potential research design involve and what data collection and
around the globe, as well as special black boxes placed throughout the statistical tests, if any, could be useful?
stadium to allow as many as 5,500 cell-phone calls an hour.
The marketing of Turner Field is aimed at many types of fans. It
is not enough just to provide nine innings of baseball.
Turner Field’s theme-park concept was the brainchild of Braves
President Stan Kasten. In the early 1990s, as the Braves grew into
one of the best teams in baseball, Kasten increasingly became frus-
trated while watching fans flock to Atlanta–Fulton County Stadium
a few hours before games, with little to do but eat overcooked hot
dogs and watch batting practice.
As Kasten saw it, they spent too much time milling on the club-
level concourse and too little time spending money.What if he could
find a way for families to make an outing of it, bring the amenities
of the city to Hank Aaron Drive, and create a neighborhood feel in a
main plaza at the ballpark? “I wanted to broaden fans’ experience at
the ballpark and broaden our fan base,” Kasten says.“People have no
Knowing the Way
CASE (Download the data sets for this case from just to break even.The Swamp Palace has sought help from the
6 www.cengagebrain.com or request them from Marketivity Group to help them address the long-term viability of
your instructor.) The Swamp Palace Museum is the park. Initially, the Swamp Palace conducts exploratory research
employing a participant-observer technique in which trained inter-
an interactive museum that teaches visitors the ways of life on the viewers pretend to be park guests and engage in dialog with museum
patrons.After employing interpretive techniques to the data gathered
swamps of the southern United States. Visitors can visit over one in these interviews, the Marketivity Group reports the exploratory
results to management. A report emphasizes these key findings:
hundred exhibits demonstrating the ecology of the swamp and 1. Patrons who complain tend to base their complaints over defi-
the habits of the animals and insects that call the swamp home. ciencies in quality. Happy patrons voice nothing indicating a
low-quality theme.
Additionally, the museum includes several fast-food and full ser- 2. Patrons also express a theme around value. Unhappy patrons
believe the value offered by the park is low based in part on
vice restaurants, the opportunity for swimming, and several thrill what is perceived as a high admission price.
rides. The park covers over 40 acres and includes miles and miles of (Continued )
pathways.
The park was originally supported with one-time government
funding but now it has to become self-supporting. After five years
of operation, the park has not lacked for visitors but has struggled
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Comprehensive Cases 643
Case 6 (Continued )
3. Patrons express the difficulty in getting around in the park as a 1. Do patrons who use a mobile phone navigation app report
key theme. Even happy patrons joke about how difficult it is to higher service quality and have an improved experience relative
find their way around. to those who do not?
4. As groups get larger, at least one member of the group was 2. Do patrons who use the mobile phone app have a greater likeli-
unhappy about having to accompany the others to the park. hood of upgrading to a season pass?
After further subsequent discussions, Marketivity is hired to 3. Do patrons who use a coupon report more positive price
undertake a further study aimed at helping in addressing these deci- perceptions?
sion statements:
4. Do patrons who use the mobile phone app have a greater likeli-
• In what ways can Swamp Palace use technology to improve a hood of upgrading to a season pass?
customer’s ability to effectively navigate around the museum?
5. What factors contribute to improved value perceptions?
• In what ways can Swamp Palace increase return visits by customers?
• Is participation in online coupon programs an effective way of Marketivity implements a quasiexperimental design over a one
week period in August. A sample of 200 visitors are randomly
increasing patronage and value? intercepted before entering the park. Approximately half are given
the opportunity to download a free navigation app for their cell
Several technologies are considered as ways of enhancing value. One phone. Similarly, about half are invited to go to a kiosk and down-
is a mobile phone app that will provide oral and visual navigation load a coupon from the Internet. The park provides Marketivity
aids around the park. For instance, if someone says “take me to the with employees to intercept the patrons and explain the research
Blind Bayou Bar,” the phone will give directions using prominent procedures. Upon exiting the park, the patrons are taken to a
museum landmarks. Second, Swamp Palace is considering subscrib- desk where they fill out a short questionnaire. The employee then
ing to an Internet coupon program that would provide patrons keys the data into the computer. The variables in the data set are
with discounts. Marketivity translates these statements into several described in the table below:
research questions including the following:
Name Description* Values
Wayf A variable indicating whether the patron was provided the mobile phone app on entering the park Yes or No
Groupon A variable indicating whether the patron used a Groupon discount to enter the park 1 5 Yes
2 5 No
SQ1 Employees at SPM offer high quality service. 5-point Likert (SD to SA)
SQ2 The attractions at SPM are high in quality. 5-point Likert (SD to SA)
SQ3 The food quality at SPM is very good. 5-point Likert (SD to SA)
SQ4 The service at SPM is excellent overall. 5-point Likert (SD to SA)
SQ5 The quality of SPM is very good. 5-point Likert (SD to SA)
VAL1 The time I spent at SPM was truly a joy. 5-point Likert (SD to SA)
VAL2 I enjoyed being engaged in exciting activities during my visit to SPM. 5-point Likert (SD to SA)
VAL3 While at SPM, I was able to forget my problems. 5-point Likert (SD to SA)
VAL4 I think SPM offers guests a lot of value. 5-point Likert (SD to SA)
PriceP The admission price is very fair. 5-point Likert (SD to SA)
Use the terms below to describe your feelings about your overall experience at the museum:
FEEL1 Favorable ------- Unfavorable 7-point semantic differential
FEEL2 Exciting ------- Boring 7-point semantic differential
FEEL3 Happy ------- Sad 7-point semantic differential
FEEL4 Delighted ------- Terrible 7-point semantic differential
UPGRADE Whether respondent agreed to upgrade their ticket to a season pass 1 5 No
2 5 Yes
3 5 Undecided
Gender Sex of respondent 1 5 Female
2 5 Male
Age Age group 1 5 less than 18
2 5 19–24
3 5 25–35
4 5 36–45
5 5 46 or more
Others How many others were with the patron 0 5 None
151
252
353
4 5 more than 3
*Notes: Missing values in the data set are indicated by either an empty cell (sometimes with a . in the cell) or by the numeral 9. SPM stands for Swamp
Place Museum, SD 5 Strongly Disagree (1), and SA 5 Strongly Agree (5).
(Continued )
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644 Part 7: Comprehensive Cases with Computerized Databases
Case 6 (Continued ) 4. List an additional research question that can be addressed with a
one-way ANOVA. Conduct the test.
Questions
1. Run frequencies on Gender, Others, and Age. Are any problems 5. List an additional research question that can be addressed with a
GLM model. Conduct the test.
evident with coding? Take any necessary corrective actions.
2. Compute a composite scale for the 5 SQ items and the 4 VAL 6. Summarize the implications for the decision statements that arise
from the tests above. Make sure you cover whether the park
items. Compute a coefficient alpha for each of the resulting ser- should invest in the navigation system and coupon technologies.
vice quality and value scales.
3. Perform an appropriate test of each research question RQ1,
RQ2, RQ3, RQ4, and RQ5.
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GLOSSARY OF FREQUENTLY
USED SYMBOLS
Greek Letters
(alpha) level of significance or probability of a Type I error
b (beta) probability of a Type II error or slope of the regression line
m (mu) population mean
(rho) population Pearson correlation coefficient
(sigma) take the sum of
p (pi) population proportion
(sigma) population standard deviation
x2 chi-square statistic
English Letters
df number of degrees of freedom
F F-statistic
n sample size
p sample proportion
Pr( ) probability of the outcome in the parentheses
r sample Pearson correlation coefficient
r 2 coefficient of determination (squared correlation coefficient)
R2 coefficient of determination (multiple regression)
S sample standard deviation (inferential statistics)
Sx estimated standard error of the mean
Sp estimated standard error of the proportion
S2 sample variance (inferential statistics)
t t-statistic
X variable or any unspecified observation
X sample mean
Y any unspecified observation on a second variable, usually the dependent variable
Yˆ predicted dependent variable score
Z standardized score (descriptive statistics) or Z-statistic
645
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GLOSSARY
A a specific organization. It attempts to expand the limits of
k nowledge in general and is not aimed at solving a particular
Absolute causality Means the cause is necessary and sufficient to pragmatic problem.
bring about the effect. Basic experimental design An experimental design in which
only one variable is manipulated.
Abstract level In theory development, the level of knowledge Behavioral differential A rating scale instrument similar to a
expressing a concept that exists only as an idea or a quality apart semantic differential, developed to measure the behavioral
from an object. intentions of subjects toward future actions.
Between-groups variance The sum of differences between the
Acquiescence bias A tendency for respondents to agree with all group mean and the grand mean summed over all groups for a
or most questions asked of them in a survey. given set of observations.
Between-subjects design Each subject receives only one
Administrative error An error caused by the improper t reatment combination.
a dministration or execution of the research task. Bivariate statistical analysis Tests of hypotheses involving two
variables.
Advocacy research Research undertaken to support a specific Blocking variables A categorical (less than interval) variable that
claim in a legal action or represent some advocacy group. is not manipulated like an experimental variable but is included
in the statistical analysis of experiments.
Analysis of variance (ANOVA) Analysis involving the Box and whisker plots Graphic representations of central
investigation of the effects of one treatment variable on an tendencies, percentiles, variabilities, and the shapes of frequency
interval-scaled dependent variable—a hypothesis-testing distributions.
technique to determine whether statistically significant Briefing session A training session to ensure that each interviewer
differences in means occur between two or more groups. is provided with common information.
Business ethics The application of morals to behavior related to
Applied business research Research conducted to address a the exchange environment.
specific business decision for a specific firm or organization. Business intelligence The subset of data and information
that actually has some explanatory power enabling effective
Attitude An enduring disposition to consistently respond in a d ecisions to be made.
given manner to various aspects of the world, composed of Business opportunity A situation that makes some potential
affective, cognitive, and behavioral components. competitive advantage possible.
Business problem A situation that makes some significant
Attribute A single characteristic or fundamental feature of an n egative consequence more likely.
object, person, situation, or issue. Business research The application of the scientific method in
searching for the truth about business phenomena. These
B activities include defining business opportunities and problems,
generating and evaluating ideas, monitoring performance, and
Back translation Taking a questionnaire that has previously understanding the business process.
been translated into another language and having a second,
independent translator translate it back to the original language.
Backward linkage Implies that later steps influence earlier stages
of the research process.
Balanced rating scale A fixed-alternative rating scale with an
equal number of positive and negative categories; a neutral
point or point of indifference is at the center of the scale.
Basic business research Research conducted without a specific
decision in mind that usually does not address the needs of
646
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Glossary 647
C Coefficient of determination (R2) A measure obtained by
squaring the correlation coefficient; the proportion of the total
Callbacks Attempts to recontact individuals selected for a sample variance of a variable accounted for by another value of another
who were not available initially. variable.
Case studies The documented history of a particular person, Cohort effect Refers to a change in the dependent variable
group, organization, or event. that occurs because members of one experimental group
e xperienced different historical situations than members of other
Categorical variable A variable that indicates membership in experimental groups.
some group.
Communication process The process by which one person or
Category scale A rating scale that consists of several response source sends a message to an audience or receiver and then
categories, often providing respondents with alternatives to receives feedback about the message.
indicate positions on a continuum.
Comparative rating scale Any measure of attitudes that asks
Causal inference A conclusion that when one thing happens, respondents to rate a concept in comparison with a benchmark
another specific thing will follow. explicitly used as a frame of reference.
Causal research Allows causal inferences to be made; seeks to Completely randomized design An experimental design that
identify cause-and-effect relationships. uses a random process to assign subjects to treatment levels of an
experimental variable.
Cell Refers to a specific treatment combination associated with an
experimental group. Composite measures Assign a value to an observation based on a
mathematical derivation of multiple variables.
Census An investigation of all the individual elements that make up
a population. Composite scale A way of representing a latent construct by
summing or averaging respondents’ reactions to multiple items
Central location interviewing Telephone interviews conducted each assumed to indicate the latent construct.
from a central location allowing firms to hire a staff of
p rofessional interviewers and to supervise and control the quality Computer-assisted telephone interviewing (CATI) Technology
of interviewing more effectively. that allows answers to telephone interviews to be entered directly
into a computer for processing.
Central-limit theorem The theory that, as sample size increases,
the distribution of sample means of size n, randomly selected, Concept (or construct) A generalized idea about a class of objects
approaches a normal distribution. that has been given a name; an abstraction of reality that is the
basic unit for theory development.
Checkboxes In an Internet questionnaire, small graphic boxes, next
to answers, that a respondent clicks on to choose an answer; Concept A generalized idea that represents something of meaning.
typically, a check mark or an X appears in the box when the Conclusions and recommendations section The part of the
respondent clicks on it.
body of a report that provides opinions based on the results and
Checklist question A fixed-alternative question that allows the suggestions for action.
respondent to provide multiple answers to a single question by Concomitant variation One of three criteria for causality; occurs
checking off items. when two events “covary,” meaning they vary systematically.
Conditional causality Means that a cause is necessary but not
Chi-square (2) test One of the basic tests for statistical sufficient to bring about an effect.
significance that is particularly appropriate for testing Confidence interval estimate A specified range of numbers
hypotheses about frequencies arranged in a frequency or within which a population mean is expected to lie; an
contingency table. e stimate of the population mean based on the knowledge
that it will be equal to the sample mean plus or minus a small
Choice A measurement task that identifies preferences by requiring sampling error.
respondents to choose between two or more alternatives. Confidence level A percentage or decimal value that tells how
confident a researcher can be about being correct; it states the
Classificatory variable Another term for a categorical variable long-run percentage of confidence intervals that will include the
because it classifies units into categories. true population mean.
Confidentiality The information involved in a research study will
Click-through rate Proportion of people who are exposed to not be shared with others.
an Internet ad who actually click on its hyperlink to enter the Conflict of interest Occurs when one researcher works for two
w ebsite; click-through rates are generally very low. competing companies.
Confound A confound means that there is an alternative
Cluster analysis A multivariate approach for grouping observations explanation beyond the experimental variables for any observed
based on similarity among measured variables. differences in the dependent variable.
Constancy of conditions Means that subjects in all experimental
Cluster sampling An economically efficient sampling technique groups are exposed to identical conditions except for the
in which the primary sampling unit is not the individual element d iffering experimental treatments.
in the population but a large cluster of elements; clusters are Constant Something that does not change; is not useful in
selected randomly. addressing research questions.
Constant-sum scale A measure of attitudes in which respondents
Code book A book that identifies each variable in a study and are asked to divide a constant sum to indicate the relative
gives the variable’s description, code name, and position in the importance of attributes; respondents often sort cards, but the
data matrix. task may also be a rating task.
Codes Rules for interpreting, classifying, and recording data in
the coding process; also, the actual numerical or other character
symbols assigned to raw data.
Coding The process of assigning a numerical score or other
c haracter symbol to previously edited data.
Coefficient alpha () The most commonly applied estimate of a
multiple-item scale’s reliability. It represents the average of all
possible split-half reliabilities for a construct.
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648 Glossary
Construct A term used to refer to concepts measured with Critical values The values that lie exactly on the boundary of the
multiple variables. region of rejection.
Construct validity Exists when a measure reliably measures Cross-checks The comparison of data from one source with data
and truthfully represents a unique concept; consists of several from another source to determine the similarity of independent
components including face validity, content validity, criterion projects.
validity, convergent validity, and discriminant validity.
Cross-functional teams Employee teams composed of individuals
Consumer panel A longitudinal survey of the same sample of from various functional areas such as engineering, production,
individuals or households to record their attitudes, behavior, or finance, and marketing who share a common purpose.
purchasing habits over time.
Cross-sectional study A study in which various segments of a
Content analysis The systematic observation and quantitative population are sampled and data are collected at a single moment
description of the manifest content of communication. in time.
Content providers Parties that furnish information on the World Cross-tabulation The appropriate technique for addressing
Wide Web. research questions involving relationships among multiple less-
than interval variables; results in a combined frequency table
Content validity The degree that a measure covers the breadth of displaying one variable in rows and another in columns.
the domain of interest.
Cross-validate To verify that the empirical findings from one
Contingency table A data matrix that displays the frequency of culture also exist and behave similarly in another culture.
some combination of possible responses to multiple variables;
cross-tabulation results. Curbstoning A form of interviewer cheating in which an
interviewer makes up the responses instead of conducting an
Continuous measures Measures that reflect the intensity of a actual interview.
concept by assigning values that can take on any value along
some scale range. Custom research Research projects that are tailored specifically to
a client’s unique needs.
Continuous variable A variable that can take on a range of values
that correspond to some quantitative amount. Customer discovery Involves mining data to look for patterns
identifying who is likely to be a valuable customer.
Contributory causality Means that a cause need be neither
necessary nor sufficient to bring about an effect. Customer relationship management (CRM) The part of the
DSS that addresses exchanges between the firm and its customers.
Contrived observation Observation in which the investigator
creates an artificial environment in order to test a hypothesis. D
Control group A group of subjects to whom no experimental Data analysis The application of reasoning to understand the data
treatment is administered. that have been gathered.
Convenience sampling The sampling procedure of obtaining Data conversion The process of changing the original form of the
those people or units that are most conveniently available. data to a format suitable to achieve the research objective; also
called data transformation.
Convergent validity Concepts that should be related to one
another are in fact related; highly reliable scales contain Data entry The activity of transferring data from a research project
convergent validity. to computers.
Conversations An informal qualitative data-gathering approach in Data Facts or recorded measures of certain phenomena (things).
which the researcher engages a respondent in a discussion of the Data file The way a data set is stored electronically in spreadsheet-
relevant subject matter.
like form in which the rows represent sampling units and the
Cookies Small computer files that a content provider can save onto columns represent variables.
the computer of someone who visits its website. Data integrity The notion that the data file actually contains the
information that the researcher promised the decision maker he
Correlation coefficient A statistical measure of the covariation, or or she would obtain, meaning in part that the data have been
association, between two at-least interval variables. edited and properly coded so that they are useful to the decision
maker.
Correlation matrix The standard form for reporting correlation Data mining The use of powerful computers to dig through
coefficients for more than two variables. volumes of data to discover patterns about an organization’s
customers and products; applies to many different forms of
Correspondence rules Indicate the way that a certain value on a analysis.
scale corresponds to some true value of a concept. Data quality The degree to which data represent the true situation.
Data reduction technique Multivariate statistical approaches that
Counterbalancing Attempts to eliminate the confounding effects summarize the information from many variables into a reduced
of order of presentation by requiring that one-fourth of the set of variates formed as linear combinations of measured
subjects be exposed to treatment A first, one-fourth to treatment variables.
B first, one-fourth to treatment C first, and finally one-fourth to Data transformation Process of changing the data from their
treatment D first. original form to a format suitable for performing a data analysis
addressing research objectives.
Counterbiasing statement An introductory statement or preamble Data warehouse The multitiered computer storehouse of current
to a potentially embarrassing question that reduces a respondent’s and historical data.
reluctance to answer by suggesting that certain behavior is not Data warehousing The process allowing important day-to-day
unusual. operational data to be stored and organized for simplified access.
Covariance The extent to which two variables are associated
systematically with each other.
Cover letter Letter that accompanies a questionnaire to induce the
reader to complete and return the questionnaire.
Criterion validity The ability of a measure to correlate with
other standard measures of similar constructs or established
criteria.
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Glossary 649
Data wholesalers Companies that put together consortia of data Discrete measures Measures that take on only one of a finite
sources into packages that are offered to municipal, corporate, number of values.
and university libraries for a fee.
Discriminant analysis A statistical technique for predicting the
Data-processing error A category of administrative error that probability that an object will belong in one of two or more
occurs because of incorrect data entry, incorrect computer mutually exclusive categories of the dependent variable, based
programming, or other procedural errors during data analysis. on several independent variables.
Database A collection of raw data arranged logically and organized Discriminant validity Represents the uniqueness or
in a form that can be stored and processed by a computer. distinctiveness of a measure; a scale should not correlate too
highly with a measure of a different construct.
Database marketing The use of customer databases to promote
one-to-one relationships with customers and create precisely Discussion guide A focus group outline that includes written
targeted promotions. introductory comments informing the group about the focus
group purpose and rules and then outlines topics or questions to
Debriefing Research subjects are fully informed and provided be addressed in the group session.
with a chance to ask any questions they may have about the
experiment. Disguised questions Indirect questions that assume the purpose of
the study must be hidden from the respondent.
Decision making The process of developing and deciding among
alternative ways of resolving a problem or choosing from among Disproportional stratified sample A stratified sample in which
alternative opportunities. the sample size for each stratum is allocated according to
analytical considerations.
Decision statement A written expression of the key question(s)
that the research user wishes to answer. Do-not-call legislation Restricts any telemarketing effort from
calling consumers who either register with a no-call list or who
Decision support system (DSS) A computer-based system request not to be called.
that helps decision makers confront problems through direct
interaction with databases and analytical software programs. Door-in-the-face compliance technique A two-step process for
securing a high response rate. In step 1 an initial request, so large
Deductive reasoning The logical process of deriving a conclusion that nearly everyone refuses it, is made. Next, a second request
about a specific instance based on a known general premise or is made for a smaller favor; respondents are expected to comply
something known to be true. with this more reasonable request.
Degrees of freedom (df ) The number of observations minus Door-to-door interviews Personal interviews conducted at
the number of constraints or assumptions needed to calculate a respondents’ doorsteps in an effort to increase the participation
statistical term. rate in the survey.
Deliverables The term used often in consulting to describe Double-barreled question A question that may induce bias
research objectives to a research client. because it covers two issues at once.
Demand characteristic Experimental design element or procedure Drop-down box In an Internet questionnaire, a space-saving
that unintentionally provides subjects with hints about the device that reveals responses when they are needed but
research hypothesis. otherwise hides them from view.
Demand effect Occurs when demand characteristics actually affect Drop-off method A survey method that requires the interviewer
the dependent variable. to travel to the respondent’s location to drop off questionnaires
that will be picked up later.
Dependence techniques Multivariate statistical techniques that
explain or predict one or more dependent variables. Dummy coding Numeric “0” or “1” coding where each number
represents an alternate response such as “female” or “male.”
Dependent variable A process outcome or a variable that is
predicted and/or explained by other variables. Dummy tables Tables placed in research proposals that are exact
representations of the actual tables that will show results in the
Depth interview A one-on-one interview between a professional final report with the exception that the results are hypothetical
researcher and a research respondent conducted about some (fictitious).
relevant business or social topic.
Dummy variable The way a dichotomous (two group)
Descriptive analysis The elementary transformation of raw data independent variable is represented in regression analysis by
in a way that describes the basic characteristics such as central assigning a 0 to one group and a 1 to the other.
tendency, distribution, and variability.
E
Descriptive research Describes characteristics of objects, people,
groups, organizations, or environments; tries to “paint a picture” E-mail surveys Surveys distributed through electronic mail.
of a given situation. Editing The process of checking the completeness, consistency,
Descriptive statistics Statistics that summarize and describe the and legibility of data and making the data ready for coding and
data in a simple and understandable manner. transfer to storage.
Elaboration analysis An analysis of the basic cross-tabulation
Determinant-choice question A fixed-alternative question that for each level of a variable not previously considered, such as
requires the respondent to choose one response from among subgroups of the sample.
multiple alternatives. Electronic data interchange (EDI) Type of exchange that
occurs when one company’s computer system is integrated with
Diagnostic analysis Seeks to diagnose reasons for business another company’s system.
outcomes and focuses specifically on the beliefs and feelings Empirical level Level of knowledge that is verifiable by experience
consumers have about and toward competing products. or observation.
Dialog boxes Windows that open on a computer screen to
prompt the user to enter information.
Direct observation A straightforward attempt to observe and
record what naturally occurs; the investigator does not create an
artificial situation.
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650 Glossary
Empirical testing Examining a research hypothesis against reality Factor rotation A mathematical way of simplifying factor analysis
using data. results so as to better identify which variables “load on” which
factors; the most common procedure is varimax.
Environmental scanning Entails all information gathering
designed to detect changes in the external operating Factorial design A design that allows for the testing of the effects
environment of the firm. of two or more treatments (experimental variables) at various
levels.
Error trapping Using software to control the flow of an Internet
questionnaire—for example, to prevent respondents from Fax survey A survey that uses fax machines as a way for
backing up or failing to answer a question. respondents to receive and return questionnaires.
Ethical dilemma Refers to a situation in which one chooses Field A collection of characters that represents a single type of
from alternative courses of actions, each with different ethical data—usually a variable.
implications.
Field editing Preliminary editing by a field supervisor on the
Ethnography Represents ways of studying cultures through same day as the interview to catch technical omissions, check
m ethods that involve becoming highly active within that legibility of handwriting, and clarify responses that are logically
culture. or conceptually inconsistent.
Evaluation research The formal, objective measurement and Field experiments Research projects involving experimental
appraisal of the extent a given activity, project, or program has manipulations that are implemented in a natural environment.
achieved its objectives or whether continuing programs are
presently performing as projected. Field interviewing service A research supplier that specializes in
gathering data.
Experiment A carefully controlled study in which the researcher
manipulates a proposed cause and observes any corresponding Field notes The researcher’s descriptions of what actually happens
change in the proposed effect. in the field; these notes then become the text from which
meaning is extracted.
Experimental condition One of the possible levels of an
experimental variable manipulation. Fieldworker An individual who is responsible for gathering data in
the field.
Experimental group A group of subjects to whom an
experimental treatment is administered. Filter question A question that screens out respondents who are
not qualified to answer a second question.
Experimental treatment The term referring to the way an
experimental variable is manipulated. Fixed-alternative questions Questions in which respondents are
given specific, limited-alternative responses and asked to choose
Experimental variable Represents the proposed cause and is the one closest to their own viewpoint.
controlled by the researcher by manipulating it.
Focus blog A type of informal, “continuous” focus group
Exploratory research Conducted to clarify ambiguous established as an Internet blog for the purpose of collecting
situations or discover ideas that may be potential business qualitative data from participant comments.
opportunities.
Focus group A small group discussion about some research
External data Data created, recorded, or generated by an entity topic led by a moderator who guides discussion among the
other than the researcher’s organization. participants.
External validity The accuracy with which experimental results Focus group interview An unstructured, free-flowing interview
can be generalized beyond the experimental subjects. with a small group of around six to ten people. Focus groups
are led by a trained moderator who follows a flexible format
Extraneous variables Variables that naturally exist in the encouraging dialogue among respondents.
environment that may have some systematic effect on the
dependent variable. Foot-in-the-door compliance technique A technique for
obtaining a high response rate, in which compliance with a large
Extremity bias A category of response bias that results because or difficult task is induced by first obtaining the respondent’s
some individuals tend to use extremes when responding to compliance with a smaller request.
questions.
Forced answering software Software that prevents respondents
Eye-tracking monitor A mechanical device used to observe eye from continuing with an Internet questionnaire if they fail to
movements; some eye monitors use infrared light beams to answer a question.
measure unconscious eye movements.
Forced-choice rating scale A fixed-alternative rating scale that
F requires respondents to choose one of the fixed alternatives.
F-test A procedure used to determine whether there is more Forecast analyst Employee who provides technical assistance
variability in the scores of one sample than in the scores of such as running computer programs and manipulating data to
another sample. generate a sales forecast.
F-test (regression) A procedure to determine whether more Forward linkage Implies that the earlier stages of the research
variability is explained by the regression or unexplained by the process influence the later stages.
regression.
Free-association techniques Record respondents’ first (top-of-
Face validity A scale’s content logically appears to reflect what was mind) cognitive reactions to some stimulus.
intended to be measured.
Frequency distribution A set of data organized by summarizing
Factor analysis A prototypical multivariate, interdependence the number of times a particular value of a variable occurs.
technique that statistically identifies a reduced number of factors
from a larger number of measured variables. Frequency table A table showing the different ways respondents
answered a question.
Factor loading Indicates how strongly a measured variable is
correlated with a factor. Frequency-determination question A fixed-alternative question
that asks for an answer about general frequency of occurrence.
Frugging Fund-raising under the guise of research.
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Glossary 651
Funded business research Refers to basic research usually Hypothetical constructs Variables that are not directly observable
performed by academic researchers that is financially supported but are measurable through indirect indicators, such as verbal
by some public or private institution, as in federal government expression or overt behavior.
grants.
I
Funnel technique Asking general questions before specific
questions in order to obtain unbiased responses. Idealism A term that reflects the degree to which one bases one’s
morality on moral standards.
G
Image profile A graphic representation of semantic differential
General linear model (GLM) A way of explaining and predicting data for competing brands, products, or stores to highlight
a dependent variable based on fluctuations (variation) from comparisons.
its mean. The fluctuations are due to changes in independent
variables. Importance-performance analysis Another name for quadrant
analysis.
Global information system An organized collection of computer
hardware, software, data, and personnel designed to capture, Impute To fill in a missing data point through the use of a
store, update, manipulate, analyze, and immediately display statistical algorithm that provides a best guess for the missing
information about worldwide business activity. response based on available information.
Goodness-of-fit (GOF) A general term representing how In-house editing A rigorous editing job performed by a
well some computed table or matrix of values matches some centralized office staff.
population or predetermined table or matrix of the same size.
In-house interviewer A fieldworker who is employed by the
Grand mean The mean of a variable over all observations. company conducting the research.
Graphic aids Pictures or diagrams used to clarify complex points
In-house research Research performed by employees of the
or emphasize a message. company that will benefit from the research.
Graphic rating scale A measure of attitudes that allows
Independent samples t-test A test for hypotheses which
respondents to rate an object by choosing any point along a compares the mean scores for two groups comprised of some
graphic continuum. interval- or ratio-scaled variable using a less-than interval
Grounded theory Represents an inductive investigation in which classificatory variable.
the researcher poses questions about information provided by
respondents or taken from historical records; the researcher asks Independent variable A variable that is expected to influence the
the questions to him or herself and repeatedly questions the dependent variable in some way.
responses to derive deeper explanations.
Index measures An index assigns a value based on how much of
H the concept being measured is associated with an observation.
Indexes often are formed by putting several variables together.
Hawthorne effect People will perform differently from normal
when they know they are experimental subjects. Index numbers Scores or observations recalibrated to indicate
how they relate to a base number.
Hermeneutic unit Refers to a text passage from a respondent’s
story that is linked with a key theme from within this story Index of retail saturation A calculation that describes the
or provided by the researcher. relationship between retail demand and supply.
Hermeneutics An approach to understanding phenomenology that Inductive reasoning The logical process of establishing a general
relies on analysis of texts through which a person tells a story proposition on the basis of observation of particular facts.
about him or herself.
Inferential statistics Using statistics to project characteristics from
Hidden observation Observation in which the subject is unaware a sample to an entire population.
that observation is taking place.
Information completeness Having the right amount of
Histogram A graphical way of showing a frequency distribution in information.
which the height of a bar corresponds to the observed frequency
of the category. Information Data formatted (structured) to support decision
making or define the relationship between two facts.
History effect Occurs when some change other than the
experimental treatment occurs during the course of an Informed consent When an individual understands what the
experiment that affects the dependent variable. researcher wants him or her to do and consents to the research
study.
Host Where the content for a particular website physically resides
and is accessed. Instrumentation effect A nuisance that occurs when a change in
the wording of questions, a change in interviewers, or a change
Human subjects review committee Carefully reviews proposed in other procedures causes a change in the dependent variable.
research designs to try to make sure that no harm can come to
any research participant. Interaction effect Differences in dependent variable means due to
a specific combination of independent variables.
Hypothesis Formal statement of an unproven proposition that is
empirically testable. Interactive help desk In an Internet questionnaire, a live, real-
time support feature that solves problems or answers questions
Hypothesis test of a proportion A test that is conceptually respondents may encounter in completing the questionnaire.
similar to the one used when the mean is the characteristic of
interest but that differs in the mathematical formulation of the Interactive medium A medium, such as the Internet, that a
standard error of the proportion. person can use to communicate with and interact with other
users.
Interdependence techniques Multivariate statistical techniques
that give meaning to a set of variables or seek to group things
together; no distinction is made between dependent and
independent variables.
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652 Glossary
Internal and proprietary data Secondary data that originate L
inside the organization.
Laboratory experiment The researcher has more complete
Internal consistency Represents a measure’s homogeneity or the control over the research setting and extraneous variables.
extent to which each indicator of a concept converges on some
common meaning. Ladder of abstraction Organization of concepts in sequence from
the most concrete and individual to the most general.
Internal validity Exists to the extent that an experimental variable
is truly responsible for any variance in the dependent variable. Laddering A particular approach to probing, asking respondents
to compare differences between brands at different levels that
Internet A worldwide network of computers that allows users produces distinctions at the attribute level, the benefit level, and
access to information from distant sources. the value or motivation level.
Internet survey A self-administered questionnaire posted on a Latent construct A concept that is not directly observable or
website. measurable, but can be estimated through proxy measures.
Interpretation The process of drawing inferences from the analysis Leading question A question that suggests or implies certain answers.
results. Likert scale A measure of attitudes designed to allow respondents
Interquartile range A measure of variability. to rate how strongly they agree or disagree with carefully
Interrogative techniques Asking multiple what, where, who, constructed statements, ranging from very positive to very
negative attitudes toward some object.
when, why, and how questions. Literature review A directed search of published works, including
Intersubjective certifiability Different individuals following the periodicals and books, that discusses theory and presents
empirical results that are relevant to the topic at hand.
same procedure will produce the same results or come to the Loaded question A question that suggests a socially desirable
same conclusion. answer or is emotionally charged.
Interval scales Scales that have both nominal and ordinal Longitudinal study A survey of respondents at different times, thus
properties, but that also capture information about differences in allowing analysis of response continuity and changes over time.
quantities of a concept from one observation to the next.
Interviewer bias A response bias that occurs because the presence M
of the interviewer influences respondents’ answers.
Interviewer cheating The practice by fieldworkers of filling in Mail survey A self-administered questionnaire sent to respondents
fake answers or falsifying interviews. through the mail.
Interviewer cheating The practice of filling in fake answers or
falsifying questionnaires while working as an interviewer. Main effect The experimental difference in dependent variable
Interviewer error Mistakes made by interviewers failing to record means between the different levels of any single experimental
survey responses correctly. variable.
Intranet A company’s private data network that uses Internet
standards and technology. Mall intercept interviews Personal interviews conducted in a
Introduction section The part of the body of a research report shopping mall.
that discusses background information and the specific objectives
of the research. Manager of decision support systems Employee who supervises
Inverse (negative) relationship Covariation in which the the collection and analysis of sales, inventory, and other periodic
association between variables is in opposing directions. As one customer relationship management (CRM) data.
goes up, the other goes down.
Item nonresponse Failure of a respondent to provide an answer to Managerial action standard A specific performance criterion
a survey question. upon which a decision can be based.
Item nonresponse The technical term for an unanswered
question on an otherwise complete questionnaire resulting in Manipulation check A validity test of an experimental
missing data. manipulation to make sure that the manipulation does produce
differences in the independent variable.
J
Manipulation Means that the researcher alters the level of the
Judgment (purposive) sampling A nonprobability sampling variable in specific increments.
technique in which an experienced individual selects the
sample based on personal judgment about some appropriate Marginals Row and column totals in a contingency table, which
characteristic of the sample member. are shown in its margins.
K Market tracking The observation and analysis of trends in industry
volume and brand share over time.
Keyword search Takes place as the search engine searches
through millions of web pages for documents containing the Market-basket analysis A form of data mining that analyzes
keywords. anonymous point-of-sale transaction databases to identify
coinciding purchases or relationships between products
Knowledge A blend of previous experience, insight, and data that purchased and other retail shopping information.
forms organizational memory.
Marketing-oriented Describes a firm in which all decisions are
Knowledge management The process of creating an inclusive, made with a conscious awareness of their effect on the customer.
comprehensive, easily accessible organizational memory, which
is often called the organization’s intellectual capital. Maturation effects Effects that are a function of time and the
naturally occurring events that coincide with growth and
experience.
Mean A measure of central tendency; the arithmetic average.
Measure of association A general term that refers to a number of
bivariate statistical techniques used to measure the strength of a
relationship between two variables.
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Glossary 653
Measurement The process of describing some property of a Nonprobability sampling A sampling technique in which units
phenomenon of interest, usually by assigning numbers in a of the sample are selected on the basis of personal judgment or
reliable and valid way. convenience; the probability of any particular member of the
population being chosen is unknown.
Median A measure of central tendency that is the midpoint; the
value below which half the values in a distribution fall. Nonrespondent error Error that the respondent is not responsible
for creating, such as when the interviewer marks a response
Median split Dividing a data set into two categories by placing incorrectly.
respondents below the median in one category and respondents
above the median in another. Nonrespondents People who are not contacted or who refuse to
cooperate in the research.
Mixed-mode survey Study that employs any combination of
survey methods. Nonresponse error The statistical differences between a survey
that includes only those who responded and a perfect survey that
Mode A measure of central tendency; the value that occurs most often. would also include those who failed to respond.
Model building The use of secondary data to help specify
Nonspurious association One of three criteria for causality;
relationships between two or more variables; can involve the means any covariation between a cause and an effect is true and
development of descriptive or predictive equations. not simply due to some other variable.
Moderator A person who leads a focus group interview and
ensures that everyone gets a chance to speak and contribute to Normal distribution A symmetrical, bell-shaped distribution that
the discussion. describes the expected probability distribution of many chance
Moderator variable A third variable that changes the nature of a occurrences.
relationship between the original independent and dependent
variables. Nuisance variables Items that may affect the dependent measure
Monadic rating scale Any measure of attitudes that asks but are not of primary interest.
respondents about a single concept in isolation.
Moral standards Principles that reflect beliefs about what is ethical Numerical scale An attitude rating scale similar to a semantic
and what is unethical. differential except that it uses numbers, instead of v erbal
Mortality effect (sample attrition) Occurs when some subjects d escriptions, as response options to identify response
withdraw from the experiment before it is completed. positions.
Multicollinearity The extent to which independent variables in
a multiple regression analysis are correlated with each other; O
high multicollinearity can make interpreting parameter estimates
difficult or impossible. Observation The systematic process of recording the behavioral
Multidimensional scaling A statistical technique that measures patterns of people, objects, and occurrences as they are
objects in multidimensional space on the basis of respondents’ witnessed.
judgments of the similarity of objects.
Multiple regression analysis An analysis of association in which Observer bias A distortion of measurement resulting from the
the effects of two or more independent variables on a single, cognitive behavior or actions of a witnessing observer.
interval-scaled dependent variable are investigated simultaneously.
Multiple-grid question Several similar questions arranged in a Online focus group A qualitative research effort in which a
grid format. group of individuals provides unstructured comments by
Multistage area sampling Sampling that involves using a entering their remarks into an electronic Internet display board
combination of two or more probability sampling techniques. of some type.
Multivariate analysis of variance (MANOVA) A multivariate
technique that predicts multiple continuous dependent variables Open-ended boxes In an Internet questionnaire, boxes where
with multiple categorical independent variables. respondents can type in their own answers to open-ended
Multivariate statistical analysis Statistical analysis involving three questions.
or more variables or sets of variables.
Mutually exclusive No overlap exists among the fixed-alternative Open-ended response questions Questions that pose some
categories problem and ask respondents to answer in their own words.
N Operationalization The process of identifying scales that
correspond to variance in a concept that will be involved in a
Neural networks A form of artificial intelligence in which a research process.
computer is programmed to mimic the way that human brains
process information. Operationalizing The process of identifying the actual
measurement scales to assess the variables of interest.
No contacts People who are not at home or who are otherwise
inaccessible on the first and second contact. Opt in To give permission to receive selected e-mail, such as
questionnaires, from a company with an Internet presence.
Nominal scales Represent the most elementary level of
measurement in which values are assigned to an object for Optical scanning system A data processing input device that
identification or classification purposes only. reads material directly from mark-sensed questionnaires.
Nonparametric statistics Appropriate when the variables Oral presentation A spoken summary of the major findings,
being analyzed do not conform to any known or continuous conclusions, and recommendations, given to clients or line
distribution. managers to provide them with the opportunity to clarify any
ambiguous issues by asking questions.
Order bias Bias caused by the influence of earlier questions in a
questionnaire or by an answer’s position in a set of answers.
Ordinal scales Ranking scales allowing things to be arranged based
on how much of some concept they possess.
Outlier A value that lies outside the normal range of the data.
Outside agency An independent research firm contracted by the
company that actually will benefit from the research.
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654 Glossary
P Population element An individual member of a population.
Population parameters Variables in a population or measured
P-value Probability value, or the observed or computed
significance level; p-values are compared to significance levels characteristics of the population.
to test hypotheses. Preliminary tabulation A tabulation of the results of a pretest
Paired comparisons A measurement technique that involves to help determine whether the questionnaire will meet the
presenting the respondent with two objects and asking the objectives of the research.
respondent to pick the preferred object; more than two objects Pretest A small-scale study in which the results are only preliminary
may be presented, but comparisons are made in pairs. and intended only to assist in design of a subsequent study.
Pretesting Screening procedure that involves a trial run with a
Paired-samples t-test An appropriate test for comparing the group of respondents to iron out fundamental problems in the
scores of two interval variables drawn from related populations. survey design.
Primary sampling unit (PSU) A term used to designate a unit
Parametric statistics Involve numbers with known, continuous selected in the first stage of sampling.
distributions; when the data are interval or ratio scaled and the Probability sampling A sampling technique in which every
sample size is large, parametric statistical procedures are appropriate. member of the population has a known, nonzero probability of
selection.
Partial correlation The correlation between two variables after Probability The long-run relative frequency with which an event
taking into account the fact that they are correlated with other will occur.
variables too. Probing An interview technique that tries to draw deeper and
more elaborate explanations from the discussion.
Participant-observation Ethnographic research approach Problem definition The process of defining and developing a
where the researcher becomes immersed within the culture that decision statement and the steps involved in translating it into more
he or she is studying and draws data from his or her observations. precise research terminology, including a set of research objectives.
Problem Occurs when there is a difference between the current
Percentage distribution A frequency distribution organized into conditions and a more preferable set of conditions.
a table (or graph) that summarizes percentage values associated Product-oriented Describes a firm that prioritizes decision making
with particular values of a variable. in a way that emphasizes technical superiority in the product.
Production-oriented Describes a firm that prioritizes efficiency
Performance-monitoring research Refers to research that and effectiveness of the production processes in making decisions.
regularly, sometimes routinely, provides feedback for evaluation Projective technique An indirect means of questioning enabling
and control of business activity. respondents to project beliefs and feelings onto a third party, an
inanimate object, or a task situation.
Personal interview Face-to-face communication in which an Proportion The percentage of elements that meet some criterion.
interviewer asks a respondent to answer questions. Proportional stratified sample A stratified sample in which
the number of sampling units drawn from each stratum is in
Phenomenology A philosophical approach to studying human proportion to the population size of that stratum.
experiences based on the idea that human experience itself is Propositions Statements explaining the logical linkage among
inherently subjective and determined by the context in which certain concepts by asserting a universal connection between
people live. concepts.
Proprietary business research The gathering of new data to
Piggyback A procedure in which one respondent stimulates investigate specific problems.
thought among the others; as this process continues, increasingly Pseudo-research Conducted not to gather information for marketing
creative insights are possible. decisions but to bolster a point of view and satisfy other needs.
Psychogalvanometer A device that measures galvanic skin
Pilot study A small-scale research project that collects data from response, a measure of involuntary changes in the electrical
respondents similar to those to be used in the full study. resistance of the skin.
Pull technology Consumers request information from a web page
Pivot question A filter question used to determine which version and the browser then determines a response; the consumer is
of a second question will be asked. essentially asking for the data.
Pupilometer A mechanical device used to observe and record
Placebo A false experimental condition aimed at creating the changes in the diameter of a subject’s pupils.
impression of an effect. Push button In a dialog box on an Internet questionnaire, a small
outlined area, such as a rectangle or an arrow, that the respondent
Placebo A false experimental effect used to create the perception clicks on to select an option or perform a function, such as submit.
that some effect has been administered. Push poll Telemarketing under the guise of research.
Push technology Sends data to a user’s computer without a
Placebo effect The effect in a dependent variable associated with request being made; software is used to guess what information
the psychological impact that goes along with knowledge of might be interesting to consumers based on the pattern of
some treatment being administered. previous responses.
Plug value An answer that an editor “plugs in” to replace blanks
or missing values so as to permit data analysis; choice of value is
based on a predetermined decision rule.
Point estimate An estimate of the population mean in the form of
a single value, usually the sample mean.
Pooled estimate of the standard error An estimate of the
standard error for a t-test of independent means that assumes the
variances of both groups are equal.
Pop-up boxes In an Internet questionnaire, boxes that appear
at selected points and contain information or instructions for
respondents.
Population (universe) Any complete group of entities that share
some common set of characteristics.
Population distribution A frequency distribution of the elements
of a population.
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Glossary 655
Q Relativism A term that reflects the degree to which one rejects
moral standards in favor of the acceptability of some action.
Quadrant analysis An extension of cross-tabulation in which This way of thinking rejects absolute principles in favor of
responses to two rating-scale questions are plotted in four situation-based evaluations.
quadrants of a two-dimensional table.
Relevance A characteristic of data reflecting how pertinent these
Qualitative business research Research that addresses business particular facts are to the situation at hand.
objectives through techniques that allow the researcher to
provide elaborate interpretations of phenomena without Reliability An indicator of a measure’s internal consistency.
depending on numerical measurement; its focus is on Repeated measures Experiments in which an individual
discovering true inner meanings and new insights.
subject is exposed to more than one level of an experimental
Qualitative data Data that are not characterized by numbers, treatment.
and instead are textual, visual, or oral; focus is on stories, visual Replication The same interpretation will be drawn if the study
portrayals, meaningful characterizations, interpretations, and is repeated by different researchers with different respondents
other expressive descriptions. following the same methods.
Report format The makeup or arrangement of parts necessary to a
Quantitative business research Business research that addresses good research report.
research objectives through empirical assessments that involve Research analyst A person responsible for client contact, project
numerical measurement and analysis. design, preparation of proposals, selection of research suppliers,
and supervision of data collection, analysis, and reporting
Quantitative data Represent phenomena by assigning numbers in activities.
an ordered and meaningful way. Research assistants Research employees who provide technical
assistance with questionnaire design, data analyses, and similar
Quasiexperimental designs Experimental designs that do not activities.
involve random allocation of subjects to treatment combinations. Research design A master plan that specifies the methods
and procedures for collecting and analyzing the needed
Quota sampling A nonprobability sampling procedure that information.
ensures that various subgroups of a population will be Research follow-up Recontacting decision makers and/or
represented on pertinent characteristics to the exact extent that clients after they have had a chance to read over a research
the investigator desires. report in order to determine whether additional information or
clarification is necessary.
R Research generalist An employee who serves as a link between
management and research specialists. The research generalist acts
Radio button In an Internet questionnaire, a circular icon, as a problem definer, an educator, a liaison, a communicator,
resembling a button, which activates one response choice and and a friendly ear.
deactivates others when a respondent clicks on it. Research methodology section The part of the body of a report
that presents the findings of the project. It includes tables, charts,
Random digit dialing Use of telephone exchanges and a table and an organized narrative.
of random numbers to contact respondents with unlisted phone Research objectives The goals to be achieved by conducting
numbers. research.
Research program Numerous related studies that come together
Random sampling error A statistical fluctuation that occurs to address multiple, related research objectives.
because of chance variation in the elements selected for a Research project A single study that addresses one or a small
sample. number of research objectives.
Research proposal A written statement of the research design.
Random sampling error The difference between the sample result Research questions Express the research objectives in terms of
and the result of a census conducted using identical procedures. questions that can be addressed by research.
Research report An oral presentation or written statement of
Randomization The random assignment of subject and treatments research results, strategic recommendations, and/or other
to groups; it is one device for equally distributing the effects of conclusions to a specific audience.
extraneous variables to all conditions. Research suppliers Commercial providers of research services.
Researcher-dependent Research in which the researcher must
Randomized-block design A design that attempts to isolate the extract meaning from unstructured responses such as text from
effects of a single extraneous variable by blocking out its effects a recorded interview or a collage representing the meaning of
on the dependent variable. some experience.
Respondent error A category of sample bias resulting from
Ranking A measurement task that requires respondents to rank some respondent action or inaction such as nonresponse or
order a small number of stores, brands, or objects on the basis of response bias.
overall preference or some characteristic of the stimulus. Respondents People who verbally answer an interviewer’s
questions or provide answers to written questions.
Rating A measurement task that requires respondents to estimate Response bias A bias that occurs when respondents either
the magnitude of a characteristic or quality that a brand, store, or consciously or unconsciously tend to answer questions with a
object possesses. certain slant that misrepresents the truth.
Ratio scales Represent the highest form of measurement in that
they have all the properties of interval scales with the additional
attribute of representing absolute quantities; characterized by a
meaningful absolute zero.
Raw data The unedited information gathered from a respondent in
the exact form as provided by that respondent.
Record A collection of related fields that represents the responses
from one sampling unit.
Refusals People who are unwilling to participate in a research
project.
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656 Glossary
Response latency The amount of time it takes to make a choice Secondary data Data that have been previously collected for some
between two alternatives; used as a measure of the strength of purpose other than the one at hand.
preference.
Secondary sampling unit A term used to designate a unit
Response rate The number of questionnaires returned or selected in the second stage of sampling.
completed divided by the number of eligible people who were
asked to participate in the survey. Selection effect Sample bias from differential selection of
respondents for experimental groups.
Results section The part of the body of a report that presents
the findings of the project. It includes tables, charts, and an Self-administered questionnaires Surveys in which the
organized narrative. respondent takes the responsibility for reading and answering the
questions.
Reverse coding Changing the value of a response to a scale so it
is opposite of the original value. For example, a scale from 1–5 Self-selection bias A bias that occurs because people who feel
is reversed so 1=5, 2=4, 3=3, 4=2, and 5=1. This if often done strongly about a subject are more likely to respond to survey
so negative items in a scale are scored in the same direction as questions than people who feel indifferent about it.
positive items.
Semantic differential A measure of attitudes that consists of a
Reverse directory A directory similar to a telephone directory series of seven-point rating scales that use bipolar adjectives to
except that listings are by city and street address or by phone anchor the beginning and end of each scale.
number rather than alphabetical by last name.
Sensitivity A measurement instrument’s ability to accurately
Rule of parsimony The rule of parsimony suggests that an measure variability in stimuli or responses.
explanation involving fewer components is better than one
involving more. Significance level A critical probability associated with
a statistical hypothesis test that indicates how likely an
S inference supporting a difference between an observed value
and some statistical expectation is true. The acceptable level
Sample A subset, or some part, of a larger population. of Type I error.
Sample bias A persistent tendency for the results of a sample to
Simple (bivariate) linear regression A measure of linear
deviate in one direction from the true value of the population association that investigates straight-line relationships between
parameter. a continuous dependent variable and an independent variable
Sample distribution A frequency distribution of a sample. that is usually continuous, but can be a categorical dummy
Sample selection error An administrative error caused by variable.
improper sample design or sampling procedure execution.
Sample statistics Variables in a sample or measures computed Simple random sampling A sampling procedure that assures each
from sample data. element in the population of an equal chance of being included
Sample survey A more formal term for a survey. in the sample.
Sampling distribution A theoretical probability distribution of
sample means for all possible samples of a certain size drawn Simple-dichotomy (dichotomous) question A fixed-alternative
from a particular population. question that requires the respondent to choose one of two
Sampling frame A list of elements from which a sample may be alternatives.
drawn; also called working population.
Sampling frame error An error that occurs when certain sample Single-source data Diverse types of data offered by a single
elements are not listed or are not accurately represented in a company; usually integrated on the basis of a common variable
sampling frame. such as geographic area or store.
Sampling Involves any procedure that draws conclusions based on
measurements of a portion of the population. Site analysis techniques Techniques that use secondary data to
Sampling unit A single element or group of elements subject to select the best location for retail or wholesale operations.
selection in the sample.
Scales A device providing a range of values that correspond to Situation analysis The gathering of background information to
different values in a concept being measured. familiarize researchers and managers with the decision-making
Scanner data The accumulated records resulting from point-of- environment.
sale data recordings.
Scanner-based consumer panel A type of consumer panel in Smart agent software Software capable of learning an Internet
which participants’ purchasing habits are recorded with a laser user’s preferences and automatically searching out information in
scanner rather than a purchase diary. selected websites and then distributing it.
Scientific method A set of prescribed procedures for
establishing and connecting theoretical statements about Snowball sampling A sampling procedure in which initial
events, for analyzing empirical evidence, and for predicting respondents are selected by probability methods and additional
events yet unknown; techniques or procedures used to respondents are obtained from information provided by the
analyze empirical evidence in an attempt to confirm or initial respondents.
disprove prior conceptions.
Search engine A computerized directory that allows anyone to Social desirability bias Bias in responses caused by respondents’
search the World Wide Web for information using a keyword desire, either conscious or unconscious, to gain prestige or
search. appear in a different social role.
Sorting A measurement task that presents a respondent
with several objects or product concepts and requires the
respondent to arrange the objects into piles or classify the
product concepts.
Split-ballot technique Using two alternative phrasings of the
same question for respective halves of a sample to elicit a more
accurate total response than would a single phrasing.
Split-half method A method for assessing internal consistency by
checking the results of one-half of a set of scaled items against
the results from the other half.
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
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Glossary 657
Spyware Software placed on a computer without consent or T
knowledge of the user.
t-distribution A symmetrical, bell-shaped distribution that is
Standard deviation A quantitative index of a distribution’s contingent on sample size; has a mean of 0 and a standard
spread, or variability; the square root of the variance for a deviation equal to 1.
distribution.
t-test A hypothesis test that uses the t-distribution. A univariate
Standard error of the mean The standard deviation of the t-test is appropriate when the variable being analyzed is interval
sampling distribution. or ratio.
Standardized normal distribution A purely theoretical Tabulation The orderly arrangement of data in a table or other
probability distribution that reflects a specific normal curve for summary format showing the number of responses to each
the standardized value, z. response category; tallying.
Standardized regression coefficient () The estimated Tachistoscope Device that controls the amount of time a subject
coefficient indicating the strength of relationship between an is exposed to a visual image.
independent variable and dependent variable expressed on a
standardized scale where higher absolute values indicate stronger Telephone interviews Personal interviews conducted by
relationships (range is from 21 to 1). telephone, the mainstay of commercial survey research.
Standardized research service Companies that develop a unique Television monitoring Computerized mechanical observation
methodology for investigating a business specialty area. used to obtain television ratings.
Stapel scale A measure of attitudes that consists of a single Temporal sequence One of three criteria for causality; deals with
adjective in the center of an even number of numerical the time order of events—the cause must occur before the effect.
values.
Tertiary sampling unit A term used to designate a unit selected
Statistical base The number of respondents or observations in the third stage of sampling.
(in a row or column) used as a basis for computing percentages.
Test tabulation Tallying of a small sample of the total number
Status bar In an Internet questionnaire, a visual indicator that of replies to a particular question in order to construct coding
tells the respondent what portion of the survey he or she has categories.
completed.
Test units The subjects or entities whose responses to the
Stratified sampling A probability sampling procedure in which experimental treatment are measured or observed.
simple random subsamples that are more or less equal on
some characteristic are drawn from within each stratum of the Test-market An experiment that is conducted within actual
population. market conditions.
String characters Computer terminology to represent formatting Test-retest method Administering the same scale or measure to
a variable using a series of alphabetic characters (nonnumeric the same respondents at two separate points in time to test for
characters) that may form a word. stability.
Structured question A question that imposes a limit on the Testing effects A nuisance effect occurring when the initial
number of allowable responses. measurement or test alerts or primes subjects in a way that affects
their response to the experimental treatments.
Subjective Results are researcher-dependent, meaning different
researchers may reach different conclusions based on the same Tests of differences The investigation of hypotheses stating
interview. that two (or more) groups differ with respect to measures on a
variable.
Subjects The sampling units for an experiment, usually human
respondents who provide measures based on the experimental The scientific method The way researchers go about using
manipulation. knowledge and evidence to reach objective conclusions about
the real world.
Sugging Selling under the guise of research.
Summated scale A scale created by simply summing (adding Thematic apperception test (TAT) A test that presents subjects
with an ambiguous picture(s) in which consumers and products
together) the response to each item making up the composite are the center of attention; the investigator asks the subject to
measure. tell what is happening in the picture(s) now and what might
Survey A research technique in which a sample is interviewed happen next.
in some form or the behavior of respondents is observed and
described in some way. Themes Identified by the frequency with which the same term
Symptoms Observable cues that serve as a signal of a problem (or a synonym) arises in the narrative description.
because they are caused by that problem.
Syndicated service A research supplier that provides standardized Theory A formal, logical explanation of some events that includes
information for many clients in return for a fee. predictions of how things relate to one another.
Systematic error Error resulting from some imperfect aspect
of the research design that causes respondent error or from a Thurstone scale An attitude scale in which judges assign scale
mistake in the execution of the research. values to attitudinal statements and subjects are asked to respond
Systematic or nonsampling error Occurs if the sampling to these statements.
units in an experimental cell are somehow different than the
units in another cell, and this difference affects the dependent Time series design Used for an experiment investigating long-
variable. term structural changes.
Systematic sampling A sampling procedure in which a starting
point is selected by a random process and then every nth number Timeliness Means that the data are current enough to still be
on the list is selected. relevant.
Total quality management A business philosophy that
emphasizes market-driven quality as a top organizational
priority.
Total variability The sum of within-group variance and between-
groups variance.
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
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658 Glossary
Totally exhaustive A category exists for every respondent among Variable Anything that varies or changes from one instance to
the fixed-alternative categories. another; can exhibit differences in value, usually in magnitude or
strength, or in direction.
Tracking study A type of longitudinal study that uses successive
samples to compare trends and identify changes in variables Variable piping software Software that allows variables to
such as consumer satisfaction, brand image, or advertising be inserted into an Internet questionnaire as a respondent is
awareness. completing it.
Type I error An error caused by rejecting the null hypothesis Variables Anything that may assume different numerical values; the
when it is true; has a probability of alpha. Practically, a Type I empirical assessment of a concept.
error occurs when the researcher concludes that a relationship
or difference exists in the population when in reality it does Variance A measure of variability or dispersion. Its square root is
not exist. the standard deviation.
Type II error An error caused by failing to reject the null Variate A mathematical way in which a set of variables can be
hypothesis when the alternative hypothesis is true; has a represented with one equation.
probability of beta. Practically, a Type II error occurs when a
researcher concludes that no relationship or difference exists Verification Quality-control procedures in fieldwork intended
when in fact one does exist. to ensure that interviewers are following the sampling
procedures and to determine whether interviewers are
U cheating.
Unbalanced rating scale A fixed-alternative rating scale that Visible observation Observation in which the observer’s presence
has more response categories at one end than the other, is known to the subject.
resulting in an unequal number of positive and negative
categories. Voice-pitch analysis A physiological measurement technique that
records abnormal frequencies in the voice that are supposed to
Undisguised questions Straightforward questions that assume the reflect emotional reactions to various stimuli.
respondent is willing to answer.
W
Uniform resource locator (URL) A website address that web
browsers recognize. Welcome screen The first web page in an Internet survey, which
introduces the survey and requests that the respondent enter a
Unit of analysis A study indicates what or who should provide the password or PIN.
data and at what level of aggregation.
Within-group error or variance The sum of the differences
Univariate statistical analysis Tests of hypotheses involving only between observed values and the group mean for a given set of
one variable. observations; also known as total error variance.
Unobtrusive methods Methods in which research respondents do Within-subjects design Involves repeated measures because with
not have to be disturbed for data to be gathered. each treatment the same subject is measured.
Unstructured question A question that does not restrict the World Wide Web (WWW) A portion of the Internet that is
respondents’ answers. a system of computer servers that organize information into
documents called web pages.
V
Z
Validity The accuracy of a measure or the extent to which a score
truthfully represents a concept. Z-test for differences of proportions A technique used to test
the hypothesis that proportions are significantly different for two
Value labels Unique labels assigned to each possible numeric code independent samples or groups.
for a response.
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
ENDNOTES
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Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
660 Endnotes
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Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Endnotes 661
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76–79. Pushing Content to the Desktop,” the Pump Doesn’t Faze New-Car communications in this book.
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.survey.com.
3 Excerpts from Arlen, Michael J.,
Thirty Seconds (New York: Farrar,
Straus and Giroux, Inc., 1979, 1980),
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
662 Endnotes
15 Weisberg, Karen,“Change Maker,” 11 Schaefer, David R. and Don 3 Campbell,Angus, Philip E. Converse, Chapter 12
Food Service Director ( January 15, A. Dillman,“Development of a and Willard L. Rodgers, The Quality
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Quarterly 62, no. 3 (Fall 1998), 378. Although weather conditions did Performance Business Applications:
16 Gavin, David A.,“Competing on not correlate with perceived quality Real-Time Trading Systems.”
the Eight Dimensions of Quality,” 12 Ibid. of life, the comfort variable did Proceedings of WWW2003,
Harvard Business Review (November– 13 For a complete discussion of fax show a relationship with the index May 20–24, 2003, Budapest,
December 1987), 101–8. of wellbeing. This association might Hungary, accessed from http://
surveys, see the excellent article be confounded by the fact that staff.it.uts.edu.au/~rsteele/
17 Forelle, Charles,“Many Colleges by Dickson, John P. and Douglas ventilation and/or air-conditioning EvaluatingSOAP.pdf.
Ignore New SAT Writing Test,” Wall L. Maclachlan,“Fax Surveys: Return equipment is less common in less
Street Journal (December 7, 2005), Patterns and Comparison with Mail affluent homes. Income was previously 2 Based on McNatt, D. Brian and
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New SAT Survey Results,” Kaplan (February 1996), 108–113. Intervention, Job Attitudes, and
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1 Warwick, Donald T. and Charles A. On-Line Access Panels,” International Age. Copyright © 1980 by Crain Cook, and Donald T. Campbell,
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Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Endnotes 663
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
664 Endnotes
13 Based on Gene Mueller,“It’s Hard Nursing (December 2005), Pet-Ownership-2011-06-10.pdf, will be equal to the theoretical
to Figure Number of Anglers,” downloaded from http://web1 accessed August 4, 2011. expectations for a given distribution
Washington Times (March 20, 2005), .infotrac.galegroup.com. 6 Rasmussen Reports, National Survey (this would be the null case), then
http://web3.infotrac.galegroup 3 This section relies heavily on of 1,000 Adults (March 17–18 2009), a high p-value would be desired to
.com;Atlantic Coastal Cooperative Interviewer’s Manual, rev. ed. (Ann http://www.rasmussenreports.com/ support the hypothesis. Generally, this
Statistics Program,“About Us: Arbor, MI: Survey Research Center, premium_content/econ_crosstabs/ is not good form for a hypothesis.
Committees,” http://www.accsp.org, Institute for Social Research, march_2009/crosstabs_aig_ Exceptions to this rule exist. One
accessed March 16, 2006;Atlantic University of Michigan, 1976). march_17_18_2009, accessed March of the most common is when a
States Marine Fisheries Commission, 4 Ibid., p. 11. 22, 2009. researcher compares some matrix of
“About Us,” http://www.asmfc.org, 5 Ibid., pp. 11–13. Reprinted by 7 See Dubinsky,Alan J., Rajan values with some alternative matrix
accessed March 16, 2006. permission. Nataraajan, and Wen-Yeh Huang, of values with a goodness-of-fit test.
6 Oliver, Daniel G., Julianne “Consumers’ Moral Philosophies: Particularly in advanced applications
14 Material for this case is from Scientific M. Serovich, and Tina L. Mason, Identifying the Idealist and the (beyond the scope of this book), the
Telephone Samples User’s Manual, “Constraints and Opportunities with Relativist,” Journal of Business Research researcher may wish to test whether
Scientific Telephone Samples, Santa Interview Transcription: Towards 58 (December 2005), 1690–1701; or not the two matrices are the
Ana, CA. Reflection in Qualitative Research,” Deal, Ken,“Deeper into the Trees,” same within sampling error. In this
Social Forces (December 2005), Marketing Research 17 (Summer 2005), case, the researcher would need an
Chapter 17 downloaded from http://web1 38–40. insignificant p-value (above ) to
.infotrac.galegroup.com. 8 Adapted from Yavas, Ugur and Emin support the hypothesis.
1 Based on Gerdes, Geoffrey R., 7 Viewpoint Learning, http://www Babakus,“What Do Guests Look
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“Conducting Surveys on a Shoestring to the conventional Excel install by 2 Vermeir and Van Kenhove (2008).
2 Most of the statistical material in this Budget,” American Libraries, 36 unpacking the data tool. This can be 3 Tests for complex experimental
book assumes that the population (February 2005), 38-39. done by clicking on tools and then
parameters are unknown, which is clicking on add-ins and following the designs are covered in Appendix 22B.
the typical situation in most applied Chapter 19 instructions. See http://www 4 The formula is not shown here but it
research projects. .microsoft.com for more instructions
1 http://www.sensorylogic.com, on how to accomplish this. can be found in most basic statistics
3 The reasons for this are related to accessed August 4, 2011. 11 Iuso, Bill,“Concept Testing:An books.
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Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Endnotes 665
“Expanding the Performance Year’s Lessons and the Challenge of 12 The constant term has disappeared 2 The original version of this
Domain:Who Says Nice Guys What’s Next,” Playthings (February 1, since it is equal to 0 when chapter was written by John
Finish Last?” International Journal 2009), http://www.playthings.com/ the regression coefficients are Bush, Oklahoma State University,
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Performance and Satisfaction as a “Feelings, Fun and Memories:An J. F.,W. C. Black, B. J. Babin, and 1984).
Function of Individual Difference, Examination of the Emotional R. Anderson, Multivariate Data
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8 For more on this topic, see Hair, J. F., Journal of Advertising 33 (Fall 2004), Pleasures of Store Shopping,” Journal (President, Brody Communications,
W. C. Black, B. J. Babin, R.Tathum, 25–35. of Business Research 58 (March 2005), 1200 Melrose Ave., Melrose Park,
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Analysis, 6th ed. (Upper Saddle River, 10 When the actual regression model to Gesture when Speaking,”
NJ: Prentice Hall, 2006). is illustrated as an explanation of 15 Closs, D. J., M. Swink, and A. Nair, Communication Briefings 14, no. 11
the actual dependent variable in “The Role of Information (1995), 4.
Chapter 24 a population, Yi is used and an Connectivity in Making Flexible
error term (ei ) is included because Logistics Programs Successful,” 5 “Tips of the Month,”
1 Goulding, Christina,“Romancing the sample parameters cannot be International Journal of Physical Communication Briefings 24, no. 7
the Past: Heritage Visitors and the expected to perfectly predict and Distribution & Logistics Management 35, (May 2005), 1.
Nostalgic Consumer,” Psychology and explain the actual value of the no. 4 (2005), 258–277.
Marketing 18 ( June 2001), 565–592. dependent variable in the population. 6 Based on Bridis,Ted,“Study:
When we use a regression equation 16 Morrison, Mark,A. Sweeney, and Shoppers Naïve about Online
2 Tesoriero, H.W.,“Babes in 80s to represent its ability to predict T. Heffernan,“Learning Styles of Pricing,” Information Week ( June 1,
Toyland,” Time 160 (November 11, sample values of the dependent On-Campus and Off-Campus 2005), downloaded from http://
2002), 14. variable from the estimated parameter Marketing Students:The Challenge web2.infotrac.galegroup
coefficients, Yˆi is used to represent for Marketing Educators,” Journal of .com; (APPC),”Annenberg Study
3 “Nostalgia, Education Hot Trends predicted values of Yi and no error Marketing Education 25 (December Shows Americans Vulnerable to
in Toys,” Mass Market Retailers 21 term is included since the actual 2003), 208–217. Exploitation in the Online and
(February 23, 2004), 47,Thomson- amount of error in any given Offline Marketplace,”Annenberg
Gale Database. observation is unknown. 17 Zachary Paul Neal, “Culinary Public Policy Center news release
11 School enrollment statistics can often Deserts, Gastronomic Oases: ( June 1, 2005), http://www
4 Betts, Kate,“A 1950s State of Mind,” be found using the Internet and A Classification of U.S. Cities,” .annenbergpublicpolicycenter
Time (April 15, 2004), 4. either searching through government (2006), Urban Studies, 43(1): 1–21. .org; Turow, Joseph, Lauren
statistics or examining the website Feldman, and Kimberly
5 Osborn, Suzanne Barry,“It’s Yesterday for the local school district or school Chapter 25 Meltzer,“Open to Exploitation:
Once More: Companies Use board. American Shoppers Online
Nostalgia to Entice Consumers,” 1 North,Tim,“Business Report and Offline,”APPC report, June
Chain Store Age ( June 2001), 32. Writing Tips,” http://www 2005, downloaded from http://www
.betterwritingskills.com, downloaded .annenbergpublicpolicycenter.org.
6 Peterson, Karyn M.,“Entertaining April 28, 2009.
the Future: Licensing Execs on Last
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
INDEX
Page numbers followed by f indicate data transformation, Business research, definition of, 5 Business research, role of, 2–18, 15f
figures. 491–494, 492f Business research, human side of, applied business research, 5–6
basic business research, 5–6
A descriptive analysis, 73–104 benefits vs. costs, 11–12, 12f
484–485, 485f business research jobs communication technologies,
Absolute causality, 56 12–13
Abstract level, 40 graphic data methods, director of research, 79 course of action
Acquiescence bias, 192 496–497, 496f large firm, 78f, 79 evaluation, 9–10
Administrative error, 193 midsize firms, 77–78 evaluation research, 9
Advocacy research, 99 interpretation, 500 small firms, 77 performance-monitoring
Analysis of variance (ANOVA), rank order, 494–496, 495f client sponsor, rights of research, 9
tabular data methods, open relationship, 99 selection, 8–9
542–547, 543f, 544f, privacy, 100 data availability, 10–11, 12f
545f, 591–594 496–497, 496f conflict sources decision, nature of, 11, 12f
for complex experimental designs, tabulation, 486 criticism, 80 definition of, 5
556–559 Basic experimental design, 274 intuitive decision making, global business research, 13–14
ANOVA. See Analysis of Behavioral differential, 324 managerial value of, 7–9
variance Berra,Yogi, 529 81–82 nature of, 4–7
Applied business research, 5–6 Between-groups variance, 545 money, 80 need for, 10–12
Aristotle, 492 Between-subjects design, 270 past experience, 81–82 opportunities identification, 8
Attitude, 311 Bierce,Ambrose, 615 reduction of, 82–83, 84f problems identification, 8
Attitude measurement, 310–332 Bivariate statistical analysis, 507, time, 80–81 scientific method, 6–7, 6f
behavioral attention, cross-functional teams, 83–84 in the twenty-first century, 12–14
323–324 528–559, 530f, 560–581 custom research, 86 time constraints, 10, 12f
as hypothetical constructs, analysis of variance, 542–547, descriptive research, 92
311–312 ethical issues in, 87–101 Business research process, 48–72
measurement scale selection, 543f, 544f, 545f general rights, 88–89 alternatives to, 60
326–329 cross-tabulation tables, organizational structure, 76–85 ambiguity, 50–51
ranking, 324–325 researcher, rights of casual research, 54–58
ranking scales, 313–322, 323f 531–533 confidentiality, 98 certainty, 49
techniques for, 312–313 f-test, 546–547 honesty in errors, 98 data analysis
Attribute, 299 measure of association, 561 honesty in results, 96–97, 97f coding, 68
paired samples t-test, 539 mixing sales, 93–94 editing, 68
B regression analysis, 566–577, 570f, pseudo-research, 94–95 data gathering, 67
push polls, 95–96 decision linking, 64
Back translation, 361 574f, 575f service monitoring, 96 decision making, 49, 51f
Backward linkage, 59 simple correlation coefficient, research participant, rights of explanatory research, 62
Balanced rating scale, 327 active research, 89–90 managerial decision situation, 62
Basic business research, 5–6 561–566, 562f, 564f, 565f experimental designs, 91 pilot studies, 63
Basic data analysis, 483–505 test of difference, 529–530 passive research, 90–91 previous research, 62–63
t-test, 534–538, 537f protection from harm, 92–93 reports, 68
computer programs, z-test, 540–541 research suppliers research design, 64–66
497–499, 498f Blake,William, 258 syndicated service, 85 “best,” 65–66
Blocking variables, 255 standardized research
cross-tabulation, 486–491 Box and whisker plots, 498
Briefing session, 443 service, 85
Buffett,Warren, 81
Business ethics, 88
Business intelligence, 17
Business opportunity, 49
Business problems, 49
666
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Index 667
research method, 65 Composite measures, 300, 300f Data-processing error, 193 blocking variables, 255
research objectives, 60–64, 61f, Composite scale, 316–317 Data quality, 20 classification of
Comprehensive cases, 636–644 Data reduction technique, 596
63–64 Computer-assisted telephone Data transformation, 491–494, 492f alternative designs, 276–278, 277f
research program strategy, 68–69 Data warehousing, 24 complex experimental design,
sampling, 66–67 interviewing (CATI), 216 Data wholesalers, 27
stages in, 59–68, 60f Concept, definition of, 292 Debriefing, 91 278–282
types of Conclusion section, 619 Decision statement, 107 time series design, 278, 279f
Concomitant variation, 55 Decision support systems (DSS), control, 266
descriptive research, 53–54 Conditional causality, 56 demand characteristics, 263–266
explanatory research, 52–53 Confidence interval estimate, 427 23–24, 23f, 25f designing of
uncertainty and, 58–59, 58f Confidence level, 427 Deductive reasoning, 43 dependent variable, 260
uncertainty, 50 Confidentiality, 89 Degrees of freedom, 517 independent variable, 257–260
Conflict of interest, 98 Deliverables, 60 test units, 260–263
C Confound, 262 Demand characteristics, 263–266 ethical issues in, 267
Constancy of conditions, 266 Demand effect, 263–267 experimental subjects, 255
Callbacks, 211 Constant, 118 Dependence techniques, 584 external validity, 273–274
Carroll, Lewis, 345 Constant-sum scale, 320 Dependent variable, 119 Hawthorne effect, 264
Case studies, 139–140 Construct, 293 Depth interviews, 149–150, 151f interaction effect, 257
Categorical variable, 119 Construct validity, 304 Descriptive analysis, 484–485, 485f main effect, 256–257
Category scales, 314–315 Consumer panel, 197 Descriptive research, 53–54 practical design issues
CATI. See Computer-assisted Content analysis, 243 Descriptive statistics, 410 field experiments, 268–270,
Content providers, 31 Determination-choice question, 339
telephone interviewing Content validity, 304 Diagnostic analysis, 54 269f
Causal inference, 55 Contingency table, 487 Dialog boxes, 227 laboratory experiments,
Causal research Continuous variable, 119 Direct observation, 239
Continuous measures, 299 Discrete measures, 298 267–268
causality, 55 Contributory causality, 57 Discriminant analysis, 592–594 subjects, 255
concomitant variation, 55 Contrived observation, 241 Discriminant validity, 305 validity issues, 271–274
degrees of, 56–57 Control group, 258 Discussion guide, 145 Experimental treatment, 258
experiments, 57–58, 57f Convenience sampling, 392–393 Disguised question, 194 Experimental variable, 57
nonspurious association, Convergent validity, 305 Disproportional sampling, 397–398 Explanatory research, 52–53
Cookies, 33 Do-not-call legislation, 90 External data, 171
55–56, 56f Correlation coefficient, 561 Door-in-the-face compliance External validity, 273
temporal sequence, 55 Correlation matrix, 565–566 Extraneous variables, 262–263
Cell, 259 Correspondence rules, 293 technique, 445 Extremity bias, 192
Census, 385 Cosby, Bill, 121 Door-to-door interviews, 210 Eye-tracking monitor, 247
Central-limit theorem, 424–426, Counterbalancing, 266 Double-barreled questions, 344–345
Counterbiasing statement, 343 Drop-down box, 356 F
424f, 425f, 426f Covariance, 561 Drop-off method, 223
Central location interviewing, 215 Cover letter, 220, 221f Drucker, Peter, 138 Face validity, 303
Checkboxes, 356 Criterion validity, 304 DSS. See Decision support systems Factor analysis, 595–599, 596f
Checklist question, 339 Critical values, 512 Dummy coding, 466 Factorial design, 280–282, 281f
Chi-square test, 521–524 CRM. See Customer relationship Dummy tables, 127 Factor loading, 596
Choice, 313 Dummy variable, 587 Factor rotation, 596
Click-through rate, 245 management Fax surveys, 223–224
Cluster analysis, 599–601, 599f, 600f Cross-checks, 162 E Field editing, 461
Cluster sampling, 398 Cross-functional teams, 84–85 Field experiments, 268–270, 269f
Code book, 475 Cross-sectional study, 195–197 Editing, 458–482 Field interviewing service, 442
Coding, 458–482 Cross-tabulation, 486–491, 487f coding process facilitation, 464 Fieldwork, 441–456, 442
Cross-validation research, 14 for completeness, 463–464
code book, 475 Curbstoning, 453 field editing, 461 good interviewing principles
code construction, 469–470 Customer discovery, 169 in-house editing, 461 basics, 449–450
coding qualitative responses, Customer relationship management pitfalls of, 465 required practices, 450
465–468, 466f, 467f (CRM), 23 Einstein,Albert, 41, 395 in-house training
coding scheme, 472–475 Custom research, 86 Elaboration analysis, 490 initial contact, 443–444
computerized survey data Electronic data interchange, 29 probing, 446–447
D E-mail survey, 224 question asking, 445–446
processing, 476 Emerson, Ralph Waldo, 611 response recording, 447–449
data file, 468–469 Data, definition of, 17 Empirical level, 40 telephone interviews, 444
definition of, 465 Data analysis, 68 Empirical testing, 41–42, 42f
dummy coding, 466 Data analysis, stages of, 459–460, 459f Environmental scanning, 32 management of, 451–452
error checking, 476 Database marketing, 170 Error trapping, 359 nature of, 442
open-ended questions, 471–472, Databases, 24 Ethical dilemma, 88 supervision for, 452–454
Data conversion, 161 Ethnography, 138 team for, 442–443
473f–475f Data entry, 476 Evaluation research, 9 Filter question, 348
precoding fixed-alternative Data file, 467 Experimental condition, 255 Fixed-alternative questions, 336, 338
Data integrity, 460 Experimental group, 258 Focus blog, 147
questions, 470–471, 470f Data mining, 168–170 Experimental research, 253–286, 256f Focus group, 63
Coefficient alpha, 302 Focus group interview, 142, 146f
Coefficient of determination, 564 Foot-in-the-door compliance
Cohort effect, 272
Collages, 152 technique, 445
Communication process, 611 Forced answering software, 359
Comparative rating scale, 327
Completely randomized design,
278–282
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
668 Index
Forced-choice rating scale, 328 data warehousing, 24 J Multistage area sampling,
Forecast analyst, 78 decision support system, 23–24, 399–400, 400f
Forward linkage, 59 Judgment sampling, 393–394
Free-association techniques, 23f, 25f Multivariable analysis of variance
electronic data interchange, 29 K (MANOVA), 591–594
151–153 external distributors, 26–27, 28f
Frequency-determination financial databases, 28 Kerry, John, 67 Multivariate statistical analysis, 507,
global information system, 22 Keyword search, 31 582–609
question, 339 information, 18 Knowledge, definition of, 21
Frequency distribution, input management, 24 Knowledge management, 21–22 analysis of dependence, 586–594
internal records, 24 analysis of interdependence,
411–412, 411f Internet and L
Frugging, 93–94 595–602, 596f, 599f,
F-statistic, manual calculation of, accessing data, 30 Laboratory experiments, 267–268 600f, 601f
collecting data, 30–31 Laddering, 151 ANOVA, 591–594
553–555 definition of, 29 Ladder of abstraction, 40, 40f definition of, 583
F-test, 546–547 hosting, 30 Landon, Alf, 67 MANOVA, 591–594
Funded business research, 126 information technology, 32–33 Lang, Andrew, 98 measurement scales, 585
Funnel technique, 347 interactive media, 31–32 Latent construct, 40 techniques for, 584
Internet2, 33 Leading question, 342 Mutually exclusive, 339
G Intranet, 33 Levenstein, Aaron, 563
navigating of, 31 Lewin, Kurt, 38 N
General linear model (GLM), 585 smart agent software, 33 Likert scale, 315–316
GLM. See General linear model usability and, 30–31 Literature review, 63 Neural networks, 169
Global information systems, 22 knowledge management, 21–22 Loaded question, 342 Newton, Isaac, 160
Global research firm, 87f networks, 29 Longitudinal studies, 196–197, No contacts, 189
Goethe, Johann Wolfgang, 258 outside vendors, 26–27, 28f Nominal scales, 293–295
Goodness-of-fit, 521–524 proprietary business research, 24 196f, 199f Nonparametric statistics, 516
Grand mean, 545 salesperson input, 25 Lubbock, John, 241 Nonprobability sampling, 392, 402f
Graphic aids, 620 scanner data, 26
Graphic rating scales, 321–322, 321f statistical databases, 27–28 M convenience sampling, 392–393
Grounded theory, 139 valuable information judgment sampling, 393–394
Gudder, S., 583 completeness, 20–21 Mail survey, 217–219 quota sampling, 394–395
quality, 20 Main effect, 256 snowball sampling, 395
H relevance, 19–20 Mall intercept interviews, 211 Nonrespondent error, 459
timeliness, 20 Managerial action standard, 122 Nonrespondents, 188f, 189
Happer, Marion, 74 video databases, 29 Manager of decision support Nonresponse error, 188f, 189
Hawthorne effect, 264 Informed consent, 89 Nonspurious association, 55–56
Hermeneutics, 137 In-house editing, 461 systems, 78 Normal distribution, 418–422
Hermeneutic unit, 137 In-house research, 74 Manipulation, 57 Nuisance variables, 261
Hidden observation, 237 Instrumentation effect, 272 Manipulation check, 271 Numerical scales, 319
Histogram, 485 Interaction effect, 257 MANOVA. See multivariable analysis
History effect, 271–272 Interactive help desk, 359 O
Human subjects review Interactive medium, 31 of variance
Interdependence techniques, 584 Marginal, 487 Observation, 152, 236–238
committee, 93 Internal and proprietary data, Market-basket analysis, 169 Observation methods, 235–252, 237f
Hypothesis, 41, 42f Marketing-oriented research, 7
Hypothesis test of a 170–171 Market tracking, 164 in business research, 236–238
Internal consistency, 302 Marx, Groucho, 242 content analysis, 243
proportion, 524 Internal validity, 271 Maturation effect, 272 of human behavior, 238f
Hypothetical constructs, 311–312 Internet, 29–33 McGovern, George, 80
Internet2, 33 Mean, 413 complementary evidence,
I Internet hosting, 30 Measurement, definition of, 288, 289f 238–239
Internet questionnaires, Measurement concepts, 288–309
Idealism, 88 Measure of association, 561 ethical issues in, 241–242
Importance-performance analysis, 355–359, 357f Mechanical observation, 243–249 interviewing, 241
Internet survey, 225 Median, 415 mechanical observation
491, 491f Interpretation, 500 Median split, 493 physiological reactions,
Impute, 463 Interquartile range, 498 Mixed-mode survey, 229
Independent sample t-test, 534 Interrogative techniques, 113 Mode, 415 247–249
Independent variable, 119 Intersubjective certifiability, 134 Model building, 165–168 scanner-based research,
Index numbers, 494 Interval scales, 297 Moderator, 145
Index of retail saturation, 168 Interviewer bias, 192 Moderator variable, 490 246–247
Inductive reasoning, 44 Interviewer cheating, 193, 453 Monadic rating scale, 327 television monitoring, 244
Inferential statistics, 410 Interviewer error, 193 Moral standards, 88 website traffic, 245
Information, definition of, 17 Intranet, 33 Mortality effect, 273 nature of, 237–238
Information completeness, 20–21 Introduction section, 618 Multicollinearity, 590 of physical objects, 242–243
Information systems, 17–36 Inverse (negative) relationship, 563 Multidimensional scaling, 601, 601f Observer bias, 241
Item nonresponse, 209, 463 Multiple-grid question, 350 Ogilvy, David, 341
behavioral tracking, 26 Multiple regression analysis, 586–590 Online focus group, 147
business intelligence, 18 Open-ended boxes, 356
computerized data archives, Open-ended response questions,
27, 27f 336–338
data, 18 Operationalizing, 42, 292
databases, 24 Optical scanning system, 476
Opt-in lists, 405
Oral presentation, 628–629
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Index 669
Order bias, 347 as contract, 125–126 accuracy, 335–336 use of graphic aids
Ordinal scales, 295–296 funded business research, 126 best layout, 350–359 charts, 622–628, 623f, 624f,
Ordinary least-squares (OLS) outcome anticipation, best question sequence, 625f, 626f, 627f
tables, 620–621, 620f, 621f,
analysis method, 571 126–127, 127f 347–350, 349f 622f
Outlier, 499 as a planning tool, 123–124, 124f for global markets, 361
Outside agency, 74 time spent on, 123 guidelines for, 341–347 Research suppliers, 85
Production-oriented research, 7 hospital questionnaire, 365f–368f Respondent error, 188f, 189
P Product-oriented research, 7 McDonald's Spanish language Respondents, 185
Proportional sampling, 397–398 Response bias, 190
Paired comparisons, 324–325 Proportions, 412 questionnaire, 371f Response latency, 239
Paired-samples t-test, 539 Propositions, 41 pretesting, 360–361 Response rate, 219
Parametric statistics, 516 Proprietary business research, 24 question wording Results section, 619
Parker, Dorothy, 136 Protective technique, 152 Reverse coding, 300, 316
Partial correlation, 588 Pseudo-research, 94–95 advertising, 373–375 Reverse directory, 389
Participant-observation, 138 Psychogalvanometer, 248 for children, 381 Rule of parsimony, 596
Pasteur, Louis, 236 Pull technology, 32 for demographics, 380
Performance-monitoring research, 9 Pupilometer, 248 for education, 381 S
Personal interviews, 206–212 Push button, 356 for goods, 376–380
Phenomenology, 136–137 Push polls, 95–96 for income, 381–382 Sample, 385
Piggyback, 142 Push technology, 32 for marital status, 381 Sample bias, 188, 188f
Pilot study, 63 P-value, 509, 510f ownership, 376 Sample distribution, 422
Pirandello, Luigi, 328 product usage, 376 Sample selection error, 193
Pirsig, Robert M., 44–46 Q for services, 376–380 Sample size determination, 409–440
Pivot question, 348 relevancy, 335
Placebo, 91, 265 Quadrant analysis, 491, 491f travel questionnaire, 369f–371f central limit theorem, 424–426,
Placebo effect, 265 Qualitative business research, 132 Quota sampling, 394–395 424f, 425f, 426f
Plug value, 463 Qualitative data, 135
Point estimate, 427 Qualitative research tools, 131–158 R data usability
Pooled estimate of the standard central tendency measures,
case studies, 139–140 Radio button, 356 413–415, 414f
error, 535 common techniques in, 141f Random digit dialing, 215 dispersion measures, 415–418,
Pope,Alexander, 422 Randomization, 261 416f, 418f
Popper, Karl R., 38, 43 collages, 152 Randomized-block design, 280, 280f frequency distribution,
Population distribution, 422 conversations, 150 Random sampling error, 188, 188f, 411–412, 411f
Population element, 385 depth interviews, 149–150, 151f proportions, 412
Population parameters, 410 discussion guide, 145 390–391
Population (universe), 385 focus group interview, Ranking, 313 factors for, 431–433
Pop-up boxes, 358 Rank order, 494–496, 495f judgment, 436
Preliminary tabulation, 360 141–143, 146f Rating, 313 normal distribution, 418–422
Pretest, 63 free-association method, Ration scales, 298–299 parameter estimation, 427–430
Pretesting, 231 Ratio scales, 297 population distribution, 422
Primary sampling unit, 390 151–153 Raw data, 459 population size influence, 433
Probability sampling, 392, 402f group composition, 143–144 Record, 467 for proportions, 433–434
group moderator, 145 Refusals, 189 random error, 430–431, 430f
cluster sampling, 398 interactive media and, 147 Relativism, 88 sample distribution, 422
disproportional sampling, 397–398 laddering, 151 Relevance, of information, 19–20 for sample proportions,
multistage area sampling, observation, 152 Reliability, 301–302
online vs. face-to-face, 148 Repeated measures, 260 435–436, 435f
399–400, 400f protective technique, 152 Replication, 154 sampling distribution, 422
proportional sampling, 397–398 scientific decision process, Report format, 613–620, 614f types of, 423f
simple random sampling, 396 Research analyst, 78 Sample statistics, 410
stratified sampling, 397 154–155 Research assistants, 78 Sample survey, 185
systematic sampling, 396 social networking, 151 Research design, 64–66 Sampling, 66–67
Probing, 114 thematic appreciation test, Researcher-dependent, 132 Sampling designs, 384–408
Problem definition, 106–130 Research generalist, 83 accurate results, 386
problem complexity, 108f 152–153 Research methodology section, 618 appropriateness of design, 401–403
contrasting methods, Research objectives, 60, 120–121 Internet sampling, 404–405
dramatic changes, 110 Research organizations, list of, 75f nonprobability sampling,
situation frequency, 109–110 134–135, 135f Research project, 68–69
symptoms ambiguity, 110–111 environmental conditions, Research proposal, 123–127 392–395, 402f
process of, 112f Research questions, 120–121 nonsampling errors, 390–391
business decision, 112–115 144–145 Research report, 613 practical concepts, 389f
interview, 113 ethnography, 138 Research results communication,
managerial statements, exploratory research, 153–155 sampling frame, 388–390
exploratory vs. confirmatory 610–634 target population, 387–388
116–118, 117f communication model, 611–613, probability sampling,
relevant variables, 118–120, 120f research, 135–136
research objectives, 120–121 grounded theory, 139 611f, 612f 395–400, 402f
steps, 111 phenomenology, 136–137 Internet reports, 629 probability vs. nonprobability, 392
symptoms, 116f uses of, 132–133 Intranet reports, 629 random sampling, 390–391
unit of analysis, 118 vs. quantitative research, 133–134 oral presentation, 628–629 reasons for, 385–386
research proposal Quantitative business research, report format, 613–620, 614f terminology, 385
test unit destruction, 386–387
133–134 Sampling distribution, 422
Quantitative data, 135
Quasiexperimental design, 274–276
Questionnaire design, 333–372
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
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670 Index
Sampling frame, 388–390 Simple (bivariate) linear callbacks, 211 understanding of, 42–44
Sampling frame error, 390 regression, 568 central location interviewing, verifying theory, 43
Sampling unit, 390 Thoreau, Henry David, 196
Santayana, George, 212 Simple-dichotomy question, 338 215 Thurstone interval scale, 322
Scale measurement, 294f, 296f Simple random sampling, 396 disadvantages of, 209–210 Timeliness, of information, 20
Single-source data, 177 door-to-door interviews, 210 Time series design, 278, 279f
criteria for Site analysis techniques, 168 mail intercept interviews, 211 Totally exhaustive, 339
coefficient alpha, 302 Situation analysis, 112 telephone interviews, 212–215 Total quality management,
internal consistency, 302 Snowball sampling, 395
reliability, 301–302 Social desirability bias, 192 characteristics, 212–215 197–201
split-half method, 302 Sophocles, 514 quality management, Tracking study, 197
test-retest method, 302–303 Sorting, 313 Traditional questionnaires, 350–354,
Spencer, Herbert, 335 197–201, 201f
index measures, 299–301 Spinoza, Benedict, 42 respondent communication, 351f, 352f, 353f, 354f
interval scales, 297 Split-ballot technique, 343–344 T-test, 517, 534–538
mathematical analysis of, 298–299 Split-half method, 302 205–234 Twain, Mark, 174
nominal scales, 293–295 Spyware, 91 selection of, 229–231 Type I error, 514
ordinal scales, 295–296 Standard deviation, 417 self-administered Type II error, 514
ratio scales, 297–298 Standard error of the mean, 422
reliability vs. validity, 305 Standardized normal distribution, questionnaires, 217f U
sensitivity, 305–306 e-mail survey, 224
validity, 303–305 419, 419f, 420f, 421f fax surveys, 223–224 Unbalanced rating scale, 328
Scales, 292 Standardized regression Internet survey, 225 Undisguised question, 194
Scaling concepts, 288–309 kiosk interactive surveys, Uniform resource locator (URL), 31
Scanner-based consumer panel, 247 coefficient, 569 Unit of analysis, 118
Scanner data, 26 Standardized research service, 85 228–229 Univariate statistical analysis,
Scientific method, 6–7, 6f, 44 Stapel scales, 319–320, 320f mail survey, 217–219, 220–223
Search engine, 31 Statistical base, 488 response rate, 219, 222f 506–527
Secondary data, 160 Status bar, 356 text message surveys, 229 chi-square test, 521–524
Secondary data research, 159–182 Stratified sampling, 397 survey methods goodness-of-fit, 521–524
advantages of, 160 String characters, 467 consumer panel, 197 hypothesis testing, 507–515, 510f,
disadvantages of, 160–162 Structured question, 194 longitudinal studies, 196–197,
global research, 177–179, 179f Subjective, 133–134 511f, 512f
objectives of, 164f Subjects, 255 196f, 199f hypothesis test of a
Sugging, 93–94 temporal classification,
database marketing, 170 Summated scales, 300 proportion, 524
data mining, 168–170, 168f Survey, 65 195–197 t-distribution, 517–521, 518f
environmental scanning, Survey research using surveys, 185–187 technique choice, 514–516
Symptoms, 49 Unobtrusive methods, 67
164–165 advantages of, 186–187, 230f Syndicated service, 85 Unstructured question, 194
fact-finding, 162–165, 163f disadvantages of, 230f Syrus, Publius, 65 URL. See Uniform resource locator
market potential, 166, 166f errors in, 188f Systematic error, 188, 188f, 261
model building, 165–168 Systematic sampling, 396 V
sales forecasting, 167–168 administrative error, 193
trend analysis, 164 data protecting error, 193 T Validity, 303–305
sources of, 170–177 deliberate falsification, 190–191 Value labels, 467
books, 174 interviewer cheating, 193 Tabulation, 486 Variable piping software, 358
commercial sources, 175–177 interviewer error, 193 Tachistoscope, 268 Variables, 42, 118
for external data, 171 no contacts, 189 TAT. See Thematic appreciation test Variance, 417
government resources, 174 nonrepsonse error, 188f, 189 T-distribution, 517–521, 518f Variate, 583
for internal and proprietary random sampling error, 188, 188f Telephone interviews, 212–215 Visible observation, 237
refusals, 189 Television monitoring, 244 Voice-pitch analysis, 249
data, 171 respondent error, 188f, 189 Temporal sequence, 55
Internet, 173, 173f response bias, 190 Tertiary sampling unit, 390 W
libraries, 171, 172f sample bias, 188, 188f Testing effect, 272
media resources, 174–175 sample selection error, 193 Test-market, 57 Washington, Booker T., 461
producers, 174 self-selection bias, 190 Test of differences, 529–530 Welcome screen, 225
single-source data, 177 systematic error, 188, 188f Test-retest method, 302–303 Wilde, Oscar, 324
trade association sources, 175 types of, 192 Test tabulation, 472 Wilson,Woodraw, 498
vendors, 174 unconscious Test units, 260–263 Within-groups variance, 545
Secondary sampling unit, 390 Thematic appreciation test (TAT), Within-subject design, 270
Selection effect, 273 misinterpretation, 191 World Wide Web (WWW), 31
Self-selection bias, 190 ethical issues in, 231 152–153 WWW. See World Wide Web
Semantic differential, 317–318, external customers, 198 Themes, 139–140 Wycherley,William, 148
internal customers, 198 Theory, definition of, 38
317f, 318f interviews, 206 Theory building, 37–47, 43f Z
Sensitivity, 305–306 noninteractive media, 206
Shelley, Percy Bysshe, 489 overview of, 184–204 goals of, 38–39 Z-test of differences of proportion,
Significance level, 509 personal interviews research concepts, 39–41 540–541
Simple attitude scales, 313–314 research constructs, 39–41
advantages of, 207–209 research hypotheses, 41–42
research propositions, 41–42
scientific method, 44–46
theory, definition of, 38
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.