Ebook Business research method (8th edition): Part 2

Ebook Business research method (8th edition): Part 2 includes the following content: chapter 16 sampling designs and sampling procedures; chapter 17 determination of sample size: a review of statistical theory; chapter 18 fieldwork; chapter 19 editing and coding: transforming raw data into information; chapter 20 basic data analysis: descriptive statistics; chapter 21 univariate statistical analysis; chapter 22 bivariate statistical analysis: differences between two variables; chapter 23 bivariate statistical analysis: measures of association; chapter 24 multivariate statistical analysis; chapter 25 communicating research results: report generation, oral presentation, and follow-up.

U O G IN TC O M ES RN A LE CHAPTER 16 SAMPLING DESIGNS AND SAMPLING PROCEDURES After studying this chapter, you should be able to Explain reasons for taking a sample rather than a complete census Describe the process of identifying a target population and selecting a sampling frame Compare random sampling and systematic (nonsampling) errors Identify the types of nonprobability sampling, including their advantages and disadvantages Summarize the advantages and disadvantages of the various types of probability samples Discuss how to choose an appropriate sample design, as well as challenges for Internet sampling © SUSAN VAN ETTE N Chapter Vignette: Changing Pocketbook Problems for Today’s Families It is easy to ask people what they consider to be the most pressing financial problems they face From low wages, to rising health care and housing costs, to a concern for too much debt, these problems are constantly on the minds of many families today When pressed about which financial problem is most important, some interesting trends occur These trends could not have been captured if not for the work of large-scale sampling of populations Each quarter, the Gallup Corporation develops a representative sample of approximately 1,000 U.S adults, aged 18 and older, to capture public perceptions on a variety of relevant topics, to include financial concerns of the family Since the sample is developed and obtained carefully, it serves as a representation of the population of adults in the U.S who are 18 years or older As a result of this sampling technique, researchers can be 95 percent confident that the responses of the sample are reflective of this national population, with a sampling error of less than percent Using telephone based interviews, the Gallup Corporation asks the respondent to describe “the most important financial problem facing your family today.” Responses are open-ended, and are then coded based upon the theme of the response Interestingly, trends suggest that the most important financial problem facing families can often change over time, and may be reflective of the respondent’s current awareness of the financial challenges of the day For example, when energy and gas prices were at their highest during the summer of 2008, almost one-third (29 percent) of the July 2008 Gallup respondents listed energy and gas prices as their most important problem However, in less than six months (January 2009), energy and gas prices were mentioned by only percent While health care costs was mentioned by 19 percent of families in October 2007, only mentioned health care a year later The implication of these types of changing trends suggest that financial problems facing families evolve over time And, families often look no further than their own pocketbook (or credit card statement) when they consider their greatest financial challenges The use of large-scale representative samples by the Gallup Corporation helped reveal these interesting trends.1 386 93754_16_ch16_p385-411.indd 386 7/14/09 8:29:38 AM Chapter 16: Sampling Designs and Sampling Procedures 387 Introduction Sampling is a familiar part of daily life A customer in a bookstore picks up a book, looks at the cover, and skims a few pages to get a sense of the writing style and content before deciding whether to buy A high school student visits a college classroom to listen to a professor’s lecture Selecting a university on the basis of one classroom visit may not be scientific sampling, but in a personal situation, it may be a practical sampling experience When measuring every item in a population is impossible, inconvenient, or too expensive, we intuitively take a sample Although sampling is commonplace in daily activities, these familiar samples are seldom scientific For researchers, the process of sampling can be quite complex Sampling is a central aspect of business research, requiring in-depth examination This chapter explains the nature of sampling and ways to determine the appropriate sample design Sampling Terminology As seen in the chapter vignette above, the process of sampling involves using a portion of a population to make conclusions about the whole population A sample is a subset, or some part, of a larger population The purpose of sampling is to estimate an unknown characteristic of a population Sampling is defined in terms of the population being studied A population (universe) is any complete group—for example, of people, sales territories, stores, or college students—that shares some common set of characteristics The term population element refers to an individual member of the population Researchers could study every element of a population to draw some conclusion A census is an investigation of all the individual elements that make up the population—a total enumeration rather than a sample Thus, if we wished to know whether more adult Texans drive pickup trucks than sedans, we could contact every adult Texan and find out whether or not they drive a pickup truck or a sedan We would then know the answer to this question definitively sample A subset, or some part, of a larger population population (universe) Any complete group of entities that share some common set of characteristics population element An individual member of a population census An investigation of all the individual elements that make up a population Why Sample? At a wine tasting, guests sample wine by having a small taste from each of a number of different wines From this, the taster decides if he or she likes a particular wine and if it is judged to be of low or high quality If an entire bottle were consumed to decide, the taster may end up not caring care about the next bottle However, in a scientific study in which the objective is to determine an unknown population value, why should a sample rather than a complete census be taken? Pragmatic Reasons Applied business research projects usually have budget and time constraints If Ford Motor Corporation wished to take a census of past purchasers’ reactions to the company’s recalls of defective models, the researchers would have to contact millions of automobile buyers Some of them would be inaccessible (for example, out of the country), and it would be impossible to contact all these people within a short time period A researcher who wants to investigate a population with an extremely small number of population elements may elect to conduct a census rather than a sample because the cost, labor, and time drawbacks would be relatively insignificant For a company that wants to assess salespersons’ satisfaction with its computer networking system, circulating a questionnaire to all 25 of its employees is practical In most situations, however, many practical reasons favor sampling Sampling cuts costs, reduces labor requirements, and gathers vital information quickly These advantages may be sufficient in themselves for using a sample rather than a census, but there are other reasons 93754_16_ch16_p385-411.indd 387 7/14/09 8:29:39 AM S U R V E Y T H I S ! COURTESY OF QUALTRICS.COM How well you think the results collected cted in this survey represent the population of entry-level, businessoriented, recent college graduates? If question one shown in the screenshot does not describe the population to which this survey pertains, describe one that you believe is better represented by this data In other words, work backwards from the data characteristics to infer a population that is well represented Can the data be stratified in a way that would allow it to represent more specific populations? Explain your answer Take a careful look at the choices indicated in the responses shown Does this particular respondent neatly represent a common population? Comment © GEORGE DOYLE T data gathered in conjunction with the The BRM Survey asks students questions related d to job preferences These data may well be e of interest to prospective employers looking ng to hire qualified business people Accurate and Reliable Results As seen in the Research Snapshot on p 390, another major reason for sampling is that most properly selected samples give results that are reasonably accurate If the elements of a population are quite similar, only a small sample is necessary to accurately portray the characteristic of interest Thus, a population consisting of 10,000 eleventh grade students in all-boys Catholic high schools will require a smaller sample than a broader population consisting of 10,000 high school students from coeducational secondary schools A visual example of how different-sized samples produce generalizable conclusions is provided in Exhibit 16.1 All are JPEG images that contain different numbers of “dots.” More dots mean more memory is required to store the photo In this case, the dots can be thought of as sampling units representing the population which can be thought of as all the little pieces of detail that form the actual image The first photograph is comprised of thousands of dots resulting in a very detailed photograph Very little detail is lost and the face can be confidently recognized The other photographs provide less detail Photograph consists of approximately 2,000 dots The face is still very recognizable, but less detail is retained than in the first photograph Photograph is made up of 1,000 dots, constituting a sample that is only half as large as that in photograph The 1,000-dot sample provides an image that can still be recognized Photograph consists of only 250 dots Yet, if you look at the picture at a distance, you can still recognize the face The 250-dot sample is still useful, although some detail is lost and under some circumstances (such as looking at it from a short distance) we have less confidence in judging the image using this sample Precision has suffered, but accuracy has not A sample may on occasion be more accurate than a census Interviewer mistakes, tabulation errors, and other nonsampling errors may increase during a census because of the increased volume of work In a sample, increased accuracy may sometimes be possible because the fieldwork and tabulation of data can be more closely supervised In a field survey, a small, well-trained, closely supervised group may a more careful and accurate job of collecting information than a large group of nonprofessional interviewers who try to contact everyone An interesting case in point is the use of samples by the Bureau of the Census to check the accuracy of the U.S Census If the sample indicates a possible source of error, the census is redone 388 93754_16_ch16_p385-411.indd 388 7/14/09 8:29:39 AM Chapter 16: Sampling Designs and Sampling Procedures 389 EXHIBIT 16.1 A Photographic Example of How Sampling Works Photograph Portrait of young man Photograph 1,000 dots Photograph 2,000 dots Photograph 250 dots Source: Adapted with permission from A D Fletcher and T A Bowers, Fundamentals of Advertising Research (Columbus, OH: Grid Publishing, 1983), pp 60–61 Destruction of Test Units Many research projects, especially those in quality-control testing, require the destruction of the items being tested If a manufacturer of firecrackers wished to find out whether each unit met a specific production standard, no product would be left after the testing This is the exact situation in many research strategy experiments For example, if an experimental sales presentation were presented to every potential customer, no prospects would remain to be contacted after the experiment In other words, if there is a finite population and everyone in the population participates in the research and cannot be replaced, no population elements remain to be selected as sampling units The test units have been destroyed or ruined for the purpose of the research project 93754_16_ch16_p385-411.indd 389 7/14/09 8:29:40 AM Finding Out about Work Is a Lot of Work! surveyed for four months out of the sample of eight months, and then are sampled again for four more months before they are removed from the panel Moreover, the sample is surveyed for each month on a week that contains the 19th of that month Not surprisingly, the cost of conducting the CPS is measured in the millions of dollars The sophistication and detail of the CPS is required to ensure that accurate national statistics are captured on a monthly basis As a result, the CPS is considered one of the standards by which other household surveys are conducted The cost of the CPS, as well as the need for extensive telephone and field staff, really does represent a lot of “work”! Source: U.S Department of Labor, Bureau of Labor Statistics, and U.S Department of Commerce, U.S Census Bureau, Current Population Survey: Design and Methodology, Technical Paper 63RV (2002) © VICKI BEAVER What people for work? How long does it take them to get there? What they earn? These and many other questions are critically important for United States economists and social scientists The U.S Census Bureau and the Bureau of Labor Statistics have jointly asked these questions, every month, for almost 70 years The work of these two Bureaus is captured by the Current Population Survey (CPS) The CPS uses a scientifically derived panel sample of 60,000 households The participating households are © GEORGE DOYLE & CIARAN GRIFFIN R E S E A R C H S N A P S H O T Practical Sampling Concepts Before taking a sample, researchers must make several decisions Exhibit 16.2 presents these decisions as a series of sequential stages, but the order of the decisions does not always follow this sequence These decisions are highly interrelated The issues associated with each of these stages, except for fieldwork, are discussed in this chapter and Chapter 17 Fieldwork is examined in Chapter 18 Defining the Target Population Once the decision to sample has been made, the first question concerns identifying the target population What is the relevant population? In many cases this question is easy to answer Registered voters may be clearly identifiable Likewise, if a company’s 106-person sales force is the population of concern, there are few definitional problems In other cases the decision may be difficult One survey concerning organizational buyer behavior incorrectly defined the population as purchasing agents whom sales representatives regularly contacted After the survey, investigators discovered that industrial engineers within the customer companies rarely talked with the salespeople but substantially affected buying decisions For consumer-related research, the appropriate population element frequently is the household rather than an individual member of the household This presents some problems if household lists are not available At the outset of the sampling process, the target population must be carefully defined so that the proper sources from which the data are to be collected can be identified The usual technique for defining the target population is to answer questions about the crucial characteristics of the population Does the term comic book reader include children under six years of age who not actually read the words? Does all persons west of the Mississippi include people in east bank towns that border the river, such as East St Louis, Illinois? The question to answer is, “Whom we want to talk to?” The answer may be users, nonusers, recent adopters, or brand switchers To implement the sample in the field, tangible characteristics should be used to define the population A baby food manufacturer might define the population as all women still capable of bearing children However, a more specific operational definition would be women between the ages of 12 and 50 While this definition by age may exclude a few women who are capable of childbearing and include some who are not, it is still more explicit and provides a manageable basis for the sample design 390 93754_16_ch16_p385-411.indd 390 7/14/09 8:29:41 AM © GEORGE DOYLE & CIARAN GRIFFIN How Much Does Your Ho Pre Prescription Cost? It Depends on Who You Buy It from Many people are sensitive to the costs of their prescription drugs For some drugs, these costs can make k up up a significant part p of a person’s monthly or yearly budget Generally speaking, speakin however, most people would expect that would cost about the same, no hat their prescriptions prescript matter where they buy them After a number of complaints to h the contrary, the state of Michigan sought to answer that very question The attorney general of the state of Michigan commissioned a targeted survey of 200 pharmacies to capture drug prescription costs for 11 common drugs used by people within the state The survey was further focused on 10 specific communities, to include Detroit and Grand Rapids, as well as the Upper Peninsula of the State of Michigan Since the sample was drawn purposely, there was confidence that the survey would lead to some fruitful insights Not surprisingly, the results confirmed the complaints of customers to the attorney general Prices for the same prescription could vary as much as $100, and the variation may exist even though pharmacies were quite literally “down the block.” Long term, the use of a carefully drawn sample led to a consumer alert from the attorney general’s office—encouraging customers to shop carefully for their prescription drugs in the state Source: May 2007 Prescription Drug Survey Summary, Office of the Attorney General, State of Michigan (May 2007) © BLEND IMAGES/JUPITER IMAGES R E S E A R C H S N A P S H O T The Sampling Frame In practice, the sample will be drawn from a list of population elements that often differs somewhat from the defined target population A list of elements from which the sample may be drawn is called a sampling frame The sampling frame is also called the working population because these sampling frame A list of elements from which a sample may be drawn; also called working population EXHIBIT 16.2 Define the target population Stages in the Selection of a Sample Select a sampling frame Determine if a probability or nonprobability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Conduct fieldwork 391 93754_16_ch16_p385-411.indd 391 7/14/09 8:29:42 AM 392 Part 5: Sampling and Fieldwork units will eventually provide units involved in analysis A simple example of a sampling frame would be a list of all members of the American Medical Association In practice, almost every list excludes some members of the population For example, would a university e-mail directory provide an accurate sampling frame for a given university’s student population? Perhaps the sampling frame excludes students who registered late and includes students who have resigned from the university The e-mail directory also will likely list only the student’s official university e-mail address However, many students may not ever use this address, opting to use a private e-mail account instead Thus, the university e-mail directory could not be expected to perfectly represent the student population However, a perfect representation isn’t always possible or needed Some firms, called sampling services or list brokers, specialize in providing lists or databases that include the names, addresses, phone numbers, and e-mail addresses of specific populations Exhibit 16.3 shows a page from a mailing list company’s offerings Lists offered by companies such as this are compiled from subscriptions to professional journals, credit card applications, warranty card registrations, and a variety of other sources One sampling service obtained its listing of households with children from an ice cream retailer who gave away free ice cream cones on children’s birthdays The children filled out cards with their names, addresses, and birthdays, which the retailer then sold to the mailing list company A valuable source of names is Equifax’s series of city directories Equifax City Directory provides complete, comprehensive, and accurate business and residential information The city directory EXHIBIT 16.3 Mailing List Directory Page 93754_16_ch16_p385-411.indd 392 7/14/09 8:29:43 AM Chapter 16: Sampling Designs and Sampling Procedures records the name of each resident over 18 years of age and lists pertinent information about each household The reverse directory pages offer a unique benefit A reverse directory provides, in a different format, the same information contained in a telephone directory Listings may be by city and street address or by phone number, rather than alphabetical by last name Such a directory is particularly useful when a research wishes to survey only a certain geographical area of a city or when census tracts are to be selected on the basis of income or another demographic criterion A sampling frame error occurs when certain sample elements are excluded or when the entire population is not accurately represented in the sampling frame Election polling that used a telephone directory as a sampling frame would be contacting households with listed phone numbers, not households whose members are likely to vote A better sampling frame might be voter registration records Another potential sampling frame error involving phone records is the possibility that a phone survey could underrepresent people with disabilities Some disabilities, such as hearing and speech impairments, might make telephone use impossible However, when researchers in Washington State tested for this possible sampling frame error by comparing Census Bureau data on the prevalence of disability with the responses to a telephone survey, they found the opposite effect The reported prevalence of a disability was actually higher in the phone survey.2 These findings could be relevant for research into a community’s health status or the level of demand for services for disabled persons As in this example, population elements can be either under- or overrepresented in a sampling frame A savings and loan defined its population as all individuals who had savings accounts However, when it drew a sample from the list of accounts rather than from the list of names of individuals, individuals who had multiple accounts were overrepresented in the sample 393 reverse directory A directory similar to a telephone directory except that listings are by city and street address or by phone number rather than alphabetical by last name sampling frame error An error that occurs when certain sample elements are not listed or are not accurately represented in a sampling frame ■ SAMPLING FRAMES FOR INTERNATIONAL RESEARCH The availability of sampling frames around the globe varies dramatically Not every country’s government conducts a census of population In some countries telephone directories are incomplete, no voter registration lists exist, and accurate maps of urban areas are unobtainable However, in Taiwan, Japan, and other Asian countries, a researcher can build a sampling frame relatively easily because those governments release some census information If a family changes households, updated census information must be reported to a centralized government agency before communal services (water, gas, electricity, education, and so on) are made available.3 This information is then easily accessible in the local Inhabitants’ Register Sampling Units During the actual sampling process, the elements of the population must be selected according to a certain procedure The sampling unit is a single element or group of elements subject to selection in the sample For example, if an airline wishes to sample passengers, it may take every 25th name on a complete list of passengers In this case the sampling unit would be the same as the element Alternatively, the airline could first select certain flights as the sampling unit and then select certain passengers on each flight In this case the sampling unit would contain many elements If the target population has first been divided into units, such as airline flights, additional terminology must be used A unit selected in the first stage of sampling is called a primary sampling unit (PSU) A unit selected in a successive stages of sampling is called a secondary sampling unit or (if three stages are necessary) tertiary sampling unit When there is no list of population elements, the sampling unit generally is something other than the population element In a random-digit dialing study, the sampling unit will be telephone numbers sampling unit A single element or group of elements subject to selection in the sample primary sampling unit (PSU) A term used to designate a unit selected in the first stage of sampling secondary sampling unit A term used to designate a unit selected in the second stage of sampling tertiary sampling unit A term used to designate a unit selected in the third stage of sampling Random Sampling and Nonsampling Errors An advertising agency sampled a small number of shoppers in grocery stores that used Shopper’s Video, an in-store advertising network The agency hoped to measure brand awareness and purchase intentions Investigators expected this sample to be representative of the grocery-shopping 93754_16_ch16_p385-411.indd 393 7/14/09 8:29:43 AM 394 Part 5: Sampling and Fieldwork population However, if a difference exists between the value of a sample statistic of interest (for example, the sample group’s average willingness to buy the advertised brand) and the value of the corresponding population parameter (the population’s average willingness to buy), a statistical error has occurred Two basic causes of differences between statistics and parameters were introduced in an earlier chapter and are described below: random sampling errors systematic (nonsampling) error random sampling error The difference between the sample result and the result of a census conducted using identical procedures An estimation made from a sample is not the same as a census count Random sampling error is the difference between the sample result and the result of a census conducted using identical procedures Of course, the result of a census is unknown unless one is taken, which is rarely done Other sources of error also can be present Random sampling error occurs because of chance variation in the scientific selection of sampling units The sampling units, even if properly selected according to sampling theory, may not perfectly represent the population, but generally they are reliable estimates Our discussion on the process of randomization (a procedure designed to give everyone in the population an equal chance of being selected as a sample member) will show that, because random sampling errors follow chance variations, they tend to cancel one another out when averaged This means that properly selected samples generally are good approximations of the population Still, the true population value almost always differs slightly from the sample value, causing a small random sampling error Every once in a while, an unusual sample is selected because too many atypical people were included in the sample and a large random sampling error occurred Random Sampling Error Random sampling error is a function of sample size As sample size increases, random sampling error decreases Of course, the resources available will influence how large a sample may be taken It is possible to estimate the random sampling error that may be expected with various sample sizes Suppose a survey of approximately 1,000 people has been taken in Fresno to determine the feasibility of a new soccer franchise Assume that 30 percent of the respondents favor the idea of a new professional sport in town The researcher will know, based on the laws of probability, that 95 percent of the time a survey of slightly fewer than 900 people will produce results with an error of approximately plus or minus percent If the survey were conducted with only 325 people, the margin of error would increase to approximately plus or minus percentage points This example illustrates random sampling errors Systematic Sampling Error Systematic (nonsampling) errors result from nonsampling factors, primarily the nature of a study’s design and the correctness of execution These errors are not due to chance fluctuations For example, highly educated respondents are more likely to cooperate with mail surveys than poorly educated ones, for whom filling out forms is more difficult and intimidating Sample biases such as these account for a large portion of errors in marketing research The term sample bias is somewhat unfortunate, because many forms of bias are not related to the selection of the sample We discussed nonsampling errors in Chapter Errors due to sample selection problems, such as sampling frame errors, are systematic (nonsampling) errors and should not be classified as random sampling errors Less Than Perfectly Representative Samples Random sampling errors and systematic errors associated with the sampling process may combine to yield a sample that is less than perfectly representative of the population Exhibit 16.4 illustrates two nonsampling errors (sampling frame error and nonresponse error) related to sample design 93754_16_ch16_p385-411.indd 394 7/14/09 8:29:43 AM Chapter 16: Sampling Designs and Sampling Procedures EXHIBIT 16.4 395 Errors Associated with Sampling Total population Sampling frame Planned sample Random sampling error Respondents (actual sample) Nonresponse error Sampling frame error Source: Adapted from Cox, Keith K and Ben M Enis, The Marketing Research Process (Pacific Palisades, CA: Goodyear, 1972); and Bellenger, Danny N and Barnet A Greenberg, Marketing Research: A Management Information Approach (Homewood, IL: Richard D Irwin, 1978), pp 154–155 The total population is represented by the area of the largest square Sampling frame errors eliminate some potential respondents Random sampling error (due exclusively to random, chance fluctuation) may cause an imbalance in the representativeness of the group Additional errors will occur if individuals refuse to be interviewed or cannot be contacted Such nonresponse error may also cause the sample to be less than perfectly representative Thus, the actual sample is drawn from a population different from (or smaller than) the ideal Probability versus Nonprobability Sampling Several alternative ways to take a sample are available The main alternative sampling plans may be grouped into two categories: probability techniques and nonprobability techniques In probability sampling, every element in the population has a known, nonzero probability of selection The simple random sample, in which each member of the population has an equal probability of being selected, is the best-known probability sample In nonprobability sampling, the probability of any particular member of the population being chosen is unknown The selection of sampling units in nonprobability sampling is quite arbitrary, as researchers rely heavily on personal judgment Technically, no appropriate statistical techniques exist for measuring random sampling error from a nonprobability sample Therefore, projecting the data beyond the sample is, technically speaking, statistically inappropriate Nevertheless, as the Research Snapshot on prescription drug costs shows, researchers sometimes find nonprobability samples best suited for a specific researcher purpose As a result, nonprobability samples are pragmatic and are used in market research probability sampling A sampling technique in which every member of the population has a known, nonzero probability of selection nonprobability sampling A sampling technique in which units of the sample are selected on the basis of personal judgment or convenience; the probability of any particular member of the population being chosen is unknown Nonprobability Sampling Although probability sampling is preferred, we will discuss nonprobability sampling first to illustrate some potential sources of error and other weaknesses in sampling 93754_16_ch16_p385-411.indd 395 7/14/09 8:29:43 AM 660 undisguised questions Straightforward questions that assume the respondent is willing to answer uniform resource locator (URL) A Web site address that Web browsers recognize unit of analysis What or who should provide the data and at what level of aggregation it should be analyzed (organizations, strategic business units, departments, families, individuals ) univariate statistical analysis Tests of hypotheses involving only one variable unobtrusive methods Methods in which research respondents not have to be disturbed for data to be gathered unstructured question A question that does not restrict the respondents’ answers V validity The accuracy of a measure or the extent to which a score truthfully represents a concept value labels Unique labels assigned to each possible numeric code for a response variable piping software Software that allows variables to be inserted into an Internet questionnaire as a respondent is completing it variable Anything that varies or changes from one instance to another; variables can exhibit differences in value, usually in magnitude or strength, or in direction variance A measure of variability or dispersion Its square root is the standard deviation variate A mathematical way in which a set of variables can be represented with one equation 93754_29_glos_b_p648-660.indd 660 Glossary verification Quality-control procedures in fieldwork intended to ensure that interviewers are following the sampling procedures and to determine whether interviewers are cheating visible observation Observation in which the observer’s presence is known to the subject voice-pitch analysis A physiological measurement technique that records abnormal frequencies in the voice that are supposed to reflect emotional reactions to various stimuli W welcome screen The first Web page in an Internet survey, which introduces the survey and requests that the respondent enter a password or pin within-group error or variance The sum of the differences between observed values and the group mean for a given set of observations, also known as total error variance within-subjects design Involves repeated measures because with each treatment the same subject is measured World Wide Web (WWW) A portion of the internet that is a system of computer servers that organize information into documents called Web pages Z Z-test for differences of proportions A technique used to test the hypothesis that proportions are significantly different for two independent samples or groups 7/14/09 9:19:25 AM D O S ENDNOTES Chapter 1 Grapetime, Terry, “Vote for Me,” Marketing Research 16 (Winter 2004), 5; “AFLAC’s Quacking Duck Selected One of America’s Favorite Icons,” Best Review 105 (October 2004), 119; Gage, Jack, “Waddling Through,” Forbes 176 (August 15, 2005), 90 Keyo, Michelle “Web Site of the Week: Jelly Belly: Using Sampling to Build a Customer Database,” Inc Online (1996), http://www.inc.com, December 9, 1996 Penn, Catherine, “New Drinks Include a Health Benefit for 05,” Beverage Industry, 96 (January 2005), 45–54 Jelly Belly Candy Company (March 6, 2008) “April Fools’ Day: Bamboozle Someone with New Jelly Belly BeanBoozled Jelly Beans: A Fools Errand and Silly Celebrations.” Press release “U.S Coffee Makers Perky as Consumption Increases,” Nations Restaurant Business 36 (April 22, 2002), 34; “U.S Specialty Coffee Market in 30 Year Renaissance,” (December 15, 2000), http://www cnn.com Kafka, Peter, “Bean-Counter,” Forbes 175 (February 28, 2005), 78–80 Adapted from “DuPont Employee Survey Finds Eldercare Emerging as Key Work/Life Issues,” PR Newswire (January 2, 2001), 49–93 Garvin, Andrew P., “Evolve Approach to Serve Complex Market,” Marketing News (September 15, 2005), 22 Gibson, Lawrence D., “Quo Vadis Marketing Research?” Marketing Research 12 (Spring 2000), 36–41 10 Matthew, Arnold, “FDA Delays DTC Draft Guidance to Study How 11 12 13 14 15 Consumers Use Brief Summaries,” Medical Marketing and Media 39 (November 2004), 10 Reyes, Sonia, “Ian Friendly: Groove Tube,” BrandWeek (October 16, 2000), M111–M116 Garretson, Judith and Scot Burton, “The Role of Spokescharacters as Advertisement and Package Cues in Integrated Marketing Communications,” Journal of Marketing 69 (October 2005), 118–132 Clancy, Kevin J and Randy L Stone, “Don’t Blame the Metrics,” Harvard Business Review 83 (June 2005), 26–28 Honomichl, Jack, “Growth Stunt,” Marketing News (June 4, 2001), 144 “You Say Tomato, I Say Tomahto,” Express Magazine (Spring 2006), 19 Chapter 2 Collett, Stacy, “External Business Intelligence Can Be a Powerful Addition to Your Data Warehouse, but Beware of Data Overload,” Computerworld (April 15, 2002), 34; “Krispy Kreme to Open Stores in China,” Atlanta Business Chronicle (October 9, 2008), http:// atlanta.bizjournals.com/atlanta/ stories/2008/10/06/daily56.html See Albers, Brad, “Home Depot’s Special Projects Support Team Powers Information Management for Business Needs,” Journal of Organizational Excellence 21 (Winter 2001), 3–15; Songini, Marc L., “Home Depot’s Next IT Project: Data Warehouse,” Computerworld 36 (October 7, 2002), 1–2 LaBahn, Douglass W and Robert Krapfel, “Early Supplier Involvement in Customer New Product Development: A Contingency Model of Component Supplier Intentions,” Journal of Business Research 47 (March 2000), 173–190 Tay, Nicholas S P and Robert F Lusch, “A Preliminary Test of Hunt’s General Theory of Competition: Using Artificial Adaptive Agents to Study Complex and Ill-Defined Environments,” Journal of Business Research 58 (September 2005), 1155–1168 Knapp, Ellen M., “Knowledge Management,” Business and Economic Review (July–September 1998), 3–6 Sherman, D J., D Berkowitz, and W E Soulder, “New Product Development Performance and the Interaction of Cross-Functional Integration and Knowledge Management,” Journal of Product Innovation Management 22 (September 2005), 399–411 Chonko, L B., A J Dubinsky, E Jones, and J A Roberts, “Organizational and Individual Learning in the Sales Force: An Agenda for Sales Research,” Journal of Business Research 56 (December 2003), 935–946 “Benefits of RFID Becoming More Visible,” DSN Retailing Today (August 8, 2005), 22 Hall, Mark, “Seeding for Data Growth,” Computerworld 36 (April 15, 2002), 52 10 Angwein, J and Delaney, K J., “Top Web Sites Build Up Ad Backlog, Raise Rates,” Wall Street Journal (November 16, 2005), A1 11 Gale Annual Directory of Databases, Gale Research Inc.: Detroit 12 Patrick A Moore and Ronald Milliman, “Application of the Internet in Marketing Education,” (paper presented at the Southwest Marketing Association, Houston, Texas, 1995) 13 Geng, X., M B Stinchcombe, and A B Whinston, “Radically New Product Introduction Using On-Line Auctions,” Journal of Electronic Commerce (Spring 2001), 169–189 14 “A Better Web Through Higher Math” Business Week Online (January 2, 2002), http://www.businessweek com (accessed November 12, 2005) 15 Rangaswamy, Arvind and G Lilien, “Software Tools for New Product Development,” Journal of Marketing Research 34 (February 1997), 177–184 16 Desouza, Kevin and Yukika Awazu, “Maintaining Knowledge Management Systems: A Strategic Imperative,” Journal of the American Society for Information Science and Technology 56 (May 2005), 765–768 17 Rangaswamy, Arvind and G Lilien (1997) 18 Close, A G., A Dixit and N Malhotra, “Chalkboards to Cybercourses: The Internet and Marketing Education,” Marketing Education Review 15 (Summer 2005), 81–94 19 Adapted with permission from deJony, Jennifer, “View from the Top,” Technology (1995), downloaded from the Internet July 3, 1998 Chapter 3 Cox, Reavis, Wroe Alderson, and Stanley J Shapiro, Theory in Marketing (Chicago: American Marketing Association, 1964), 20 Babin, B J., D M Hardesty, and T A Suter, “Color and Shopping Intentions: The Effect of Price Fairness and Perceived Affect,” Journal of Business Research 56 (July 2003) 541–551 Robert Dubin, Theory Building (New York: Free Press, 1969), 661 93754_30_EN_p661-667.indd 661 7/14/09 9:19:54 AM 662 Pietrobon, Ricardo, Marcus Taylor, Ulrich Guller, Laurence D Higgins, Danny O Jacobs, and Timothy Carey, “Predicting Gender Differences as Latent Variables: Summed Scores, and Individual Item Responses: A Methods Case Study,” Health and Quality of Life Outcomes (2004), 2–59 Robert Bartels, Marketing Theory and Metatheory (Chicago: American Marketing Association, 1970), 6 Hull, Clark L., A Behavioral System (New York: John Wiley & Sons, 1952), Based on Ellen J Jackofsky, “Turnover and Job Performance: An Integrated Process Model,” Academy of Management Review 9, No (1984), p 78; and Paul Solomon, “Reducing Unwanted Staff Turnovers in Public Accounting: An Action Plan,” Northern California Executive Review (Spring 1986), pp 22–25 Karl Popper, Conjectures and Refutations (London: Routledge and Keagan Paul, 1963) Kerlinger, Fred N., Behavioral Research: A Conceptual Approach (New York: Holt, Rinehart and Winston, 1979), 10 From Tucker, W T., Foundation for a Theory of Consumer Behavior (New York: Holt, Rinehart and Winston, 1967), v–vii 11 Zaltman, Gerald, Christian Pinson, and Reinhart Angelmar, Metatheory and Consumer Research (New York: Holt, Rinehart and Winston, 1972), 12–13 12 From Pirsig, Robert M., Zen and the Art of Motorcycle Maintenance (New York: Harper Collins Publishing), (© 1974), 107–111 Endnotes Chapter 4 This section is based in part on Richard Draft, Management (Hillsdale, IL: Dryden Press, 1994) Zahay, Debra, Abbie Griffin, and Elisa Fredericks, “Sources, Uses, and Forms of Data in the New Product Development Process,” Industrial Marketing Management 33 (October 2004), 658–666 Hara,Yoshika, “New Industry Awaits Human-Friendly Bipeds—‘Personal Robots’ get Ready to Walk on the Human Side,” Electronic Engineering Times (September 16, 2002), 157–159 Bocchi, Joe, Jacqueline K Eastman, and Cathy Owens Swift, “Retaining the Online Learner: Profile of Students in an Online MBA Program and Implications for Teaching Them,” Journal of Education for Business (March/ April 2004), 245–253 Janoff, Barry, “Brands of the Land,” BrandWeek (April 20, 2001), 28 Bocchi, Eastman and Swift (2004); Carr, S., “As Distance Education Comes of Age, the Challenge Is Keeping the Students,” Chronicle of Higher Education (2000), 23, A1; Moskal, P D and C G Dziuban, 93754_30_EN_p661-667.indd 662 10 11 12 13 “Present and Future Directions for Assessing Cybereducation: The Changing Research Paradigm,” in L R Vandervert, L V Chavinina, and R A Cornell, Eds., Cybereducation: The Future of LongDistance Learning (New York: P D Moskal and C G Dziuban, Liebert, 2001), 157–184 Read, Melissa, “Ice Cream Purchases and Murder Rates—Correlation Does Not Imply Causation” Spunlogic Blog (February 5th, 2007), http:// blog.spunlogic.com/index.php/2007/ 02/05/ice-cream-purchases-andmurder-rates-correlation-does-notimply-causation/ Thomas, Jerry W., “Skipping MR a Major Error,” Marketing News (March 4, 2005), 50 Einstein, A and L Infeld, The Evolution of Physics (New York: Simon and Schuster, 1942), 95 Perdue, B C and J O Summers, “Checking the Success of Manipulations in Marketing Experiments,” Journal of Marketing Research 23 (November 1986), 317–326 See, for example, Kwok, S and M Uncles, “Sales Promotion Effectiveness: The Impact of Consumer Differences at an EthnicGroup Level,” Journal of Product and Brand Management 14, no (2005), 170–186 Approximate exchange rates as of January 25, 2009 Crowley, Michael, “Conservatives (Finally) Rejoice,” New Republic (2004) 231, 13–14 10 11 12 Chapter 5 Kinnear, Thomas C and Ann Root, Eds., Survey of Marketing Research (Chicago: American Marketing Association, 1994) Blembach, J and K Clancy, “Boy, Oh Boy!” Adweek 21 (September 25, 2000), 16, Southeastern Edition See Armstrong, J S., “Why Do We Know? Predicting the Interests and Opinions of the American Consumer,” Journal of Forecasting (September 1989), 464 See John, Joby and Mark Needel, “Entry-Level Marketing Research Recruits: What Do Recruiters Need?” Journal of Marketing Education (Spring 1989), 68–73 See Izzo, G Martin and Scott J Vitell, “Exploring the Effects of Professional Education on Salespeople: The Case of Autonomous Agents,” Journal of Marketing Theory & Practice 11 (Fall 2003), 26–38; Loe, Terry and William A Weeks, “An Empirical Investigation of Efforts to Improve Sales Students’ Moral Reasoning,” Journal of Personal Selling and Sales Management 20 (Fall 2000), 243–252 13 14 15 Barnett, Tim and Sean Valentino, “Issue Contingencies and Marketers’ Recognition of Ethical Issues, Ethical Judgments and Behavioral Intentions,” Journal of Business Research 57 (April 2004), 338–346 Robin, D P., R E Reidenbach, and B J Babin, “The Nature, Measurement and Stability of Ethical Judgments in the Workplace,” Psychological Reports 80 (1997), 563–580 Gillin, Donna L., “The Evolution of Privacy Legislation: How Privacy Issues Are Changing Research,” Marketing Research 13 (Winter 2001), 6–7 Jarvis, Steve, “CMOR Finds Survey Refusal Rate Still Rising,” Marketing News 36 (February 4, 2002), Gillin, Donna L., “The Evolution of Privacy Legislation: How Privacy Issues Are Changing Research,” Marketing Research 13 (Winter 2001), 6–7 Spangenberg, E., B Grohmann, and D E Sprott, “It’s Beginning to Smell (and Sound) a Lot Like Christmas: The Interactive Effects of Ambient Scent and Music in a Retail Setting,” The Journal of Business Research 58 (November 2005), 582–589; Michon, Richard, JeanCharles Chebat, and L W Turley, “Mall Atmospherics: The Interaction Effects of the Mall Environment on Shopping Behavior,” Journal of Business Research 58 (May 2005), 576–583 Carrigan, M and M Kirkup, “The Ethical Responsibilities of Marketers in Retail Observational Research: Protecting Stakeholders through the ‘Ethical Research’ Covenant,” International Journal of Retail, Distribution and Consumer Research 11 (October 2001), 411–435 “Marketers Value Honesty in Marketing Researchers,” Marketing News 29 (June 5, 1995), 27 Brennan, M., S Benson, and Z Kearns, “The Effect, of Introductions on Telephone Survey Participation Rates,” International Journal of Market Research 47, no (2005), 65–74 Mack, Beth, “Online Privacy Critical to Research Success,” Marketing News 36 (November 25, 2002), 21 Yang, Yoo S., Robert P Leone, and Dala L Aldaen, “A Market Expansion Ability Approach to Identify Potential Exporters,” Journal of Marketing 56 (January 1992), 84–96 Ackoff, Russell L., The Scientific Method (New York: Wiley, 1962), 71 Majchrzak, A., L P Cooper, and O E Neece, “Knowedge Reuse for Innovation,” Management Science 50 (February 2004), 174–188 Gibson, Lawrence D (1988) Gibson, Lawrence D (1988) Chapman, Randall G (1989) Holbert, N B., “Research: The Ways of Academe and Business,” Business Horizons (February 1976), 38 10 Honomichl, Jack, “ICR/ International Communications Research,” Marketing News 36 (June 11, 2002), 47 11 Excerpt reprinted with permission from Paul E Green, Abba M Krieger, and Terry G Varra, “Evaluating New Products,” Marketing Research: A Magazine of Management and Applications (Winter 1997), 17–18 Chapter Chapter Gibson, Lawrence D., “Defining Marketing Problems: Don’t Spin Your Wheels Solving the Wrong Puzzle,” Marketing Research 10 (Spring 1998), 4–12 “What’s New in Your Industry,” Business China 30 (February 16, 2004), 8–9 Chapman, Randall G., “Problem Definition in Marketing Research Studies,” Journal of Consumer Marketing (Spring 1989), 51–59; Cassidy, Hilary, “Many Paths to Cool, but big Gains for All,” Brandweek 46 (June 20, 2005), S53 Niemi, Wayne, “Schoenfeld to Leave as Vans CEO; As Its Deal with VF Corp Closes, the Skate Brand Gains a New President and a New Focus on Apparel,” Footwear News (July 5, 2004), McLaughlin, Lisa, “The New Roll Model,” Time 164 ( July 26, 2004), 74 Sayre, Shay, Qualitative Methods for Marketplace Research (Thousand Oaks, CA: Sage, 2001) Sayre, Shay, (2001); Morse, Janice M and Lyn Richards, Readme First for a User’s Guide to Qualitative Methods (Thousand Oaks, CA: Sage, 2002) See, for example, May, Carl, “Methodological Pluralism: British Sociology and the Evidence-Based State: A Reply to Payne et al.,” Sociology 39 (July 2005), 519–528; Achenbaum, A A., “When Good Research Goes Bad,” Marketing Research 13 (Winter 2001), 13–15; Wade, K R., “We Have Come Upon the Enemy: And They Are Us,” Marketing Research 14 (Summer 2002), 39; Neill, James, “Qualitative versus Quantitative Research: Key Points in a Classic Debate,” http://wilderdom.com/ research/QualitativeVersusQuantitative Research.html, accessed February 6, 2009 Babin, Barry J., William R Darden, and Mitch Griffin, “Work and/ or Fun: Measuring Hedonic and Utilitarian Shopping Value,” Journal of Consumer Research 20 (March 1994), 644–656 7/14/09 9:19:54 AM Endnotes 10 11 12 13 14 15 16 17 18 19 20 21 22 Stengal, J R., A L Dixon, and C T Allen, “Listening Begins at Home,” Harvard Business Review (November 2003), 106–116 Semon, Thomas T., “You Get What You Pay for: It May Be Bad MR,” Marketing News 36 (April 15, 2002), Thompson, Craig J., “Interpreting Consumers: A Hermeneutical Framework for Deriving Marketing Insights from the Tests of Consumers’ Consumption Stories,” Journal of Marketing Research 34 (November 1997), 438–455; Woodside, Arch G., H M Pattinson, and K E Miller, “Advancing Hermeneutic Research for Interpreting Interfirm New Product Development,” Journal of Business and Industrial Marketing 20 (2005) 364–379 Thompson, Craig J., “Interpreting Consumers: A Hermeneutical Framework for Deriving Marketing Insights from the Tests of Consumers’ Consumption Stories,” Journal of Marketing Research 34 (November 1997), 438–455 (see pp 443–444 for quotation) While we refer to a hermeneutic unit as being text-based here for simplicity, they can actually also be developed using pictures, videotapes, or artifacts as well Software such as Atlas-TI will allow files containing pictures, videos, and text to be combined into a hermeneutic unit Morse, Janice M and Lyn Richards (2002) See Feldman, Stephen P., “Playing with the Pieces: Deconstruction and the Loss of Moral Culture,” Journal of Management Studies 35 ( January 1998), 60–80 “Futurespeak,” American Demographics 26 (April 2004), 44 Louella, Miles, “Living Their Lives,” Marketing (UK) (December 11, 2003), 27–28 Reid, D M., “Changes in Japan’s Post-Bubble Business Environment: Implications for Foreign-Affiliated Companies,” Journal of International Marketing 7, no (1999), 38–63 Morse, Janice M and Lyn Richards (2002) Strauss, A L and J Corbin, Basics of Qualitative Research (Newbury Park, CA: Sage Publications, 1990) Glaser, B G., and Strauss, A L., The Discovery of Grounded Theory: Strategies for Qualitative Research (New York: Aldine Publishing Company, 1967) Geiger, S and D Turley, “Personal Selling as a Knowledge-Based Activity: Communities of Practice in the Sales Force,” Irish Journal of Management 26 (2005), 61–70 Beverland, M., “The Components of Prestige Brands,” Journal of Business Research 59 (February 2006) 251– 258; Beverland, M., “Brand Value, Convictions, Flexibility and New 93754_30_EN_p661-667.indd 663 663 23 24 25 26 27 28 29 30 31 32 33 Zealand Wine,” Business Horizons 47 (September/October 2004), 53–61 Harwood, Jonathan, “Philip Morris Develops Smokeless Cigarette,” Marketing Week 28 (March 31, 2005), Creamer, Mathew, “Slowly, Marketers Learn How to Let Go and Let Blog,” Advertising Age 76 (October 31, 2005), 1–35 Fass, Allison, “Collective Opinion,” Forbes 176 (November 28, 2005), 76–79 O’Loughlin, Sandra, “Real Women Have Lingerie,” Brandweek 46 (November 14, 2005), 22–24 See Palan, K M and R E Wilkes, “Adolescent-Parent Interaction in Family Decision Making,” Journal of Consumer Research 24 (September 1997), 159–170; Haytko, Diana L and Julie Baker, “It’s All at the Mall: Exploring Adolescent Girls’ Experiences,” Journal of Retailing 80 (Spring 2004), 67–83 Godes, David and Dina Mayzlin “Using On-Line Conversations to Study Word-of-Mouth Communications,” Marketing Science 23 (2004), 545–560 Babin, Barry J., William R Darden and James S Boles, “Salesperson Stereotypes, Consumer Emotions, and Their Impact on Information Processing,” Journal of the Academy of Marketing Science 23 (Spring 1995), 94–105 Murphy, Ian, “Aided by Research, Harley Goes Whole Hog,” Marketing News 30 (December 2, 1996), 16–17 Philip Kotler, “Behavioral Models for Analyzing Buyers,” Journal of Marketing (October 1965), pp 37–45 Alsop, Ronald, “Advertisers Put Consumers on the Couch,” Wall Street Journal (May 13, 1998), 19 Huxley, John, “Horrific, but Not the Worst We’ve Suffered.” Sydney Morning Herald (February 11, 2009) 10 11 12 13 14 15 16 Chapter The North American Industry Classification System (NAICS), http:// www.census.gov/eos/www/naics/ “Breakfast Sandwich Boom,” Chain Leader (November 2005), http:// web2.infotrac.galegroup.com; Perlik, Allison,“Fast Starts,” Restaurants & Institutions (January 15, 2006), http://web2.infotrac.galegroup.com Grow, Brian, “Yes, Ma’am, That Part Is in Stock,” BusinessWeek (August 1, 2005), http://web2.infotrac galegroup.com; and Servigistics, “Servigistics Pricing: Maximizing the Profitability of Your Service Network,” http://www.servigistics com, accessed February 7, 2006 Prasso, Sheridan, “Battle for the Face of China,” Fortune (December 12, 2005), http://web2.infotrac galegroup.com Charles, Susan K., “Custom Content Delivery,” Online Magazine 17 (March–April 2004), http://galenet galegroup.com; Fleming, Lee, “Digital Delivery: Pushing Content to the Desktop,” Digital Information Group (January 31, 1997); “How Smart Agents Will Change Selling,” Forbes ASAP (August 28, 1995), 95 “Seeking New Beer Drinkers in the High Andes,” Global Agenda (July 20, 2005), http://galenet.galegroup com; “China Ranked Largest Beer Consumer in 2004,” Kyodo News International (December 15, 2005), http://galenet.galegroup.com This section is based on Levy, Michael and Barton Weitz, Retail Management (Homewood, IL: Richard D Irwin, 1992), 357–358 Data from the “About Us” section of the Capital One Web site, http:// www.capitalone.com, accessed February 9, 2006 Rao, Srikumar S., “Technology: The Hot Zone,” Forbes (November 18, 1996) IBM Business Intelligence Data Mining Product Discovery, http:// www.ibm.com Wasserman,Todd, Gerry Khermouch and Jeff Green, “Mining Everyone’s Business,” Brandweek (February 28, 2000), 34 “Clients: Case Studies,” DataMind Web site, http://www.datamind com, accessed February 6, 2006 Totty, Michael, “Making Searches Work at Work,” Wall Street Journal (December 19, 2005), http://online wsj.com “Hispanic-Owned Businesses: Growth Projections, 2004–2010,” HispanicBusiness.com Store, http:// www.hbinc.com, accessed February 7, 2006 Neff, Jack, “Wal-Mart Takes Stock in RetailLink System,” Advertising Age (May 21, 2001), See Federal Grants Wire, “National Trade Data Bank (NTDB),” http:// www.federalgrantswire.com, accessed February 6, 2006; and STATUSA, “What Information Is Available under GLOBUS and NTBD?” and “GLOBUS & NTDB,” http://www.stat-usa.gov, accessed February 6, 2006 Based on Brown, Warren, “Pain at the Pump Doesn’t Faze NewCar Buyers,” Washington Post (January 29, 2006), http://www washingtonpost.com; Wells, Melanie, “Snowboarding Secrets,” Forbes (February 14, 2005), http://web5 infotrac.galegroup.com; Halliday, Jean, “Automakers Scrap SUVs, Tout Hybrids,” Advertising Age (September 26, 2005), http://web5 infotrac.galegroup.com Chapter “About In-Stat,” In-Stat, http:// www.instat.com/index.asp; Nissen, Keith, “In-Depth Analysis: The Media Phone Has Arrived!” In-Stat, http://www.instat.com/promos/09/ dl/media_phone_3ufewaCr.pdf Vascellaro, Jessica E., “Who’ll Give Me $50 for This Purse from Nana?” Wall Street Journal (December 28, 2005), http://online.wsj.com; “Survey Reveals Majority of Americans Receive Unwanted Gifts,” Survey.com news release (December 19, 2005), http://www survey.com Excerpts from Arlen, Michael J., Thirty Seconds (New York: Farrar, Straus and Giroux, Inc., 1979, 1980), 185–186.This material first appeared in the New Yorker However, the popularity of marketing research has affected the willingness of respondents to participate in surveys People are increasingly refusing to participate Tuckel, Peter and Harry O’Neill, “The Vanishing Respondent in Telephone Surveys,” (paper presented at the 56th annual conference of the American Association of Public Opinion Research [AAPOR], Montreal, Canada, May 17–20, 2001) Cull, William L., Karen G O’Connor, Sanford Sharp, and Suk-fong S.Tang, “Response Rates and Response Bias for 50 Surveys of Pediatricians,” Health Services Research (February 2005), downloaded from http://galenet.galegroup.com Lee, Eunkyu, Michael Y Hu, and Rex S Toh, “Respondent Noncooperation in Surveys and Diaries: An Analysis of Item Non-Response and Panel Attrition,” International Journal of Market Research (Autumn 2004), downloaded from http://web7.infotrac.galegroup.com Douglas Aircraft, Consumer Research (undated), p 13 For an interesting study of extremity bias, see Baumgartner, Hans and Jan-Benedict E M Steenkamp, “Response Styles in Marketing Research: A CrossNational Investigation,” Journal of Marketing Research (May 2001), 143–156 10 Turner, Charles F., Maria A Villarroel, James R Chromy, Elizabeth Eggleston, and Susan M Rogers, “Same-Gender Sex among U.S Adults: Trends across the Twentieth Century and during the 1990s,” Public Opinion Quarterly (Fall 2005), downloaded from http://web7.infotrac.galegroup.com 11 The term questionnaire technically refers only to mail and self-administered surveys, and the term interview schedule is used for interviews by telephone or face-to-face However, we will use questionnaire to refer to all three forms of communications in this book 12 Sobel, Bill, “Poll Reveals Men More Likely Than Women to Keep Their New Year’s Resolutions” (December 7/14/09 9:19:54 AM 664 13 14 15 16 17 29, 2008), http://www.sobelmedia com/2008/12/29/poll-reveals-menmore-likely-than-women-to-keeptheir-new-years-resolutions, accessed March 30, 2009 Ohlemacher, Stephen, “Study Finds That Marriage Builds Wealth,” Yahoo! News (January 18, 2006), http://news.yahoo.com; Charles Pierret, “The National Longitudinal Survey of Youth: 1979 Cohort at 25,” Monthly Labor Review (February 2005), 3–7 The Bureau of Business Practice, Profiles in Quality: Blueprints for Action from 50 Leading Companies (Boston: Allyn and Bacon, 1991), 113 Weisberg, Karen, “Change Maker,” Food Service Director (January 15, 2006), downloaded from http:// web7.infotrac.galegroup.com Gavin, David A., “Competing on the Eight Dimensions of Quality,” Harvard Business Review (November– December 1987), 101–8 Forelle, Charles, “Many Colleges Ignore New SAT Writing Test,” Wall Street Journal (December 7, 2005), http://online.wsj.com; “Kaplan’s New SAT Survey Results,” Kaplan Inc., College Admissions, Kaplan Web site, http://www.kaptest.com, accessed February 14, 2006 Endnotes 10 11 12 13 14 15 16 Chapter 10 Warwick, Donald T and Charles A Lininger, The Sample Survey: Theory and Practice (New York: McGrawHill, 1975), Lockley, L C., “Notes on the History of Marketing Research,” Journal of Marketing (April 1950), 733 Hof, Robert D., “The Power of Us,” BusinessWeek (June 20, 2005), http://web2.infotrac.galegroup.com For a complete discussion of conducting surveys in Hispanic neighborhoods, see Hernandes, Sigfredo A and Carol J Kaufman, “Marketing Research in Hispanic Barrios: A Guide to Survey Research,” Marketing Research (March 1990), 11–27 Curtin, Richard, Stanley Presser, and Eleanor Singer, “Changes in Telephone Survey Nonresponse over the Past Quarter Century,” Public Opinion Quarterly (Spring 2005), http://web3.infotrac.galegroup.com Cuneo, Alice Z., “Researchers Flail as Public Cuts the Cord,” Advertising Age (November 15, 2004), http:// web3.infotrac.galegroup.com See ibid.; and Jon Kamman, “Cell Phones Put Pollsters ‘in a Muddle,’” USA Today (December 31, 2003), http://www.usatoday.com Hembroff, Larry A., Debra Rusz, Ann Rafferty, Harry McGee, and Nathaniel Ehrlich, “The CostEffectiveness of Alternative Advance 93754_30_EN_p661-667.indd 664 17 18 19 20 21 22 Mailings in a Telephone Survey,” Public Opinion Quarterly (Summer 2005), http://web3.infotrac galegroup.com Brennan, Mike, Susan Benson, and Zane Kearns, “The Effect of Introductions on Telephone Survey Participation Rates,” International Journal of Market Research 47, no (2005), 65–74 Dillman, Don A., Mail and Internet Surveys: The Tailored Design Method (New York: John Wiley and Sons, 2000), 173 Schaefer, David R and Don A Dillman, “Development of a Standard E-Mail Methodology: Results of an Experiment,” Public Opinion Quarterly 62, no (Fall 1998), 378 Ibid For a complete discussion of fax surveys, see the excellent article by Dickson, John P and Douglas L Maclachlan, “Fax Surveys: Return Patterns and Comparison with Mail Surveys,” Journal of Marketing Research (February 1996), 108–113 Merriman, Joyce A., “Your Feedback Is Requested,” American Family Physician (October 1, 2005), http://web3.infotrac.galegroup com Dillmann, D A (2000), 369–372 Göritz, Anja S., “Recruitment for On-Line Access Panels,” International Journal of Market Research 46, no 4, (2004), 411–425 Fricker, Scott, Mirta Galesic, Roger Tourangeau, and Ting Yan, “An Experimental Comparison of Web and Telephone Surveys,” Public Opinion Quarterly (Fall 2005), http:// web3.infotrac.galegroup.com See Nielsen, Jakob, “Keep Online Surveys Short,” Alertbox (February 2, 2004), http://www.useit.com; “About Jakob Nielsen,” http:// www.useit.com, accessed February 21, 2006; and Nielsen Norman Group, “About Nielsen Norman Group,” http://www.nngroup.com, accessed February 21, 2006 See Kilbourne, Lawrene, “Avoid the Field of Dreams Fallacy,” Quirk’s Marketing Research Review (January 2005), 70, 72–73 Mary Lisbeth D’Amico, “Call Security,” Wall Street Journal (February 13, 2006), http://online wsj.com For an interesting empirical study, see Akaah, Ishmael P and Edward A Riordan, “The Incidence of Unethical Practices in Marketing Research: An Empirical Investigation,” Journal of the Academy of Marketing Sciences (Spring 1990), 143–152 Based on “Do-Not-Call List Reduces Telemarketing, Poll Finds,” Wall Street Journal (January 12, 2006), http://online.wsj.com Chapter 11 Four Seasons Hotel Chicago, http:// www.fourseasons.com/chicagofs/ dining.html; Mystery Shopping Providers Association, http:// www.mysteryshop.org; Michelson, M., “Taking the Mystery Out of Mystery Shopping,” Mystery Shopping Providers Association, www.mspa-eu.org/about/ MysteryShopping1.ppt Selltiz, Claire, Lawrence S Wrightsman, and Stuart W Cook, Research Methods in Social Relations (New York: Holt, Rinehart and Winston, 1976), 251 Campbell, Angus, Philip E Converse, and Willard L Rodgers, The Quality of American Life (New York: Russell Sage Foundation, 1976), 112 Although weather conditions did not correlate with perceived quality of life, the comfort variable did show a relationship with the index of wellbeing This association might be confounded by the fact that ventilation and/ or air-conditioning equipment is less common in less affluent homes Income was previously found to correlate with quality of life Abrams, Bill, The Observational Research Handbook (Chicago: NTC Business Books, 2000), 2, 105 Adapted with permission from the April 30, 1980, issue of Advertising Age Copyright © 1980 by Crain Communications, Inc “Inside TV Ratings,” Nielsen Media Research, http://www.nielsenmedia com, accessed February 21, 2009 “The Portable People Meter System,” Arbitron, http://www arbitron.com, accessed February 24, 2006 “About the PreTesting Company” and “Television,” PreTesting Company, http://www.pretesting com, accessed February 24, 2006 “Accurate Web Site Visitor Measurement Crippled by Cookie Blocking and Deletion,” Jupiter Media news release, (March 14, 2005), http://www.jupitermedia com; See also Johnson, Steve, “Who’s in Charge of the Web Site Ratings Anyway?” Chicago Tribune (February 26, 2006), sec 1, p 18 10 Kiley, David, “Google: Searching for an Edge in Ads,” BusinessWeek (January 30, 2006), downloaded from http://web3.infotrac.galegroup.com; See also Sanders, Pieter and Bram Lebo, “Click Tracking: A Fool’s Paradise?” Brandweek (June 6, 2005), http://web3.infotrac.galegroup.com 11 Neff, Jack, “Aging Population Brushes Off Coloring,” Advertising Age (July 25, 2005), downloaded from http://web5.infotrac.galegroup com 12 Stringer, Kortney, “Eye-Tracking Technology for Marketers,” Detroit Free Press (August 1, 2005), downloaded from http://galenet galegroup.com 13 Herbert B Krugman’s statement as quoted in “Live, Simultaneous Study of Stimulus, Response Is Physiological Measurement’s Great Virtue,” Marketing News (May 15, 1981), 1, 20 14 Based on “Mazda Turns to EyeTracking to Assist Revamp of European Site,” New Media Age (November 3, 2005), downloaded from http://galenet.galegroup com; and “Persuasion Is the New Focus,” Revolution (February 21, 2006), downloaded from the Media Coverage page of the Syzygy Web site, http://www.syzygy.co.uk 15 Adapted with permission from Rayner, Bruce, “Product Development, Now Hear This!” Electronic Business (August 1997) Chapter 12 Kohlhoff, C and R Steele, “Evaluating SOAP for High Performance Business Applications: Real-Time Trading Systems.” Proceedings of WWW2003, May 20–24, 2003, Budapest, Hungary, accessed from http://staff.it.uts.edu au/~rsteele/EvaluatingSOAP.pdf Based on McNatt, D Brian and Timothy A Judge, “Self-Efficacy Intervention, Job Attitudes, and Turnover: A Field Experiment with Employees in Role Transition,” Human Relations 61, no (June 2008), 783–810, Shadish, William R., Thomas D Cook, and Donald T Campbell, Experimental and Quasi Experimental Designs for Generalized Causal Inference (Geneva, IL: Houghton Mifflin, 2002) Ellingstad, Vernon and Norman W Heimstra, Methods in the Study of Human Behavior (Monterey, CA: Brooks/Cole, 1974) Anderson, Barry F., The Psychological Experiment: An Introduction to the Scientific Method (Belmont, CA: Brooks/Cole, 1971), 28, 42–44 Reitter, Robert N., “Comment: American Media and the SmokingRelated Behaviors of Asian Adolescents,” Journal of Advertising Research 43 (March 2003), 12–13 Lach, Jennifer, “Up in Smoke,” American Demographics 22 (March 2000), 26 Mitchell, Vincent-Wayne and Sarah Haggett, “Sun-Sign Astrology in Market Segmentation: An Empirical Investigation,” Journal of Consumer Marketing 14, no (1997), 113–131 Roethlisberger, F J and W J Dickson, Management and the Worker (Harvard University Press: Cambridge, MA, 1939) 7/14/09 9:19:54 AM Endnotes 10 Shiv, Baba, Ziv Carmon, and Dan Aneley, “Placebo Effects of Marketing Actions: Consumers May Get What They Pay for,” Journal of Marketing Research 42 (November 2005), 383–393 11 Tybout, Alice M and Gerald Zaltman, “Ethics in Marketing Research: Their Practical Relevance,” Journal of Marketing Research 21 (November 1974), 357–368 12 Peterson, Robert A., “On the Use of Students in Social Science Research: Evidence from a Second Order Meta Analysis,” Journal of Consumer Research 28 (December 2001), 450–461 13 Shadish, William R., Thomas D Cook, and Donald T Campbell (2002) 14 Reprinted with permission from Lee Martin, Geoffrey “Drinkers Get Court Call,” Advertising Age (May 20, 1991) Copyright © 1991 Crain Communications, Inc Chapter 13 Babin, Barry J and Jill Attaway, “Atmospheric Affect as a Tool for Creating Value and Gaining Share of Customer,” Journal of Business Research 49 (August 2000), 91–99; Verhoef, P C., “Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development,” Journal of Marketing 67 (October 2003), 30–45 Periatt, J A., S A LeMay, and S Chakrabarty, “The Selling Orientation-Customer Orientation (SOCO) Scale: Cross-Validation of the Revised Version,” Journal of Personal Selling and Sales Management 24 (Winter 2004), 49–54 Anderson, Barry F., The Psychology Experiment (Monterey, CA: Brooks/ Cole, 1971), 26 Kerlinger, Fred N., Foundations of Behavioral Research (New York: Holt, Rinehart and Winston, 1973) Cohen, Jacob, “Things I Have Learned (So Far),” American Psychologist 45 (December 1990), 1304–1312 Arnold, Catherine, “Satisfaction’s the Name of the Game,” Marketing News 38 (October 15, 2004), 39–45 Also, see http://www.theacsi.org In more advanced applications such as those involving structural equations analysis, a distinction can be made between reflective composites and formative indexes See Hair, J F., W C Black, B J Babin, R Anderson, and R Tatham, Multivariate Data Analysis, 6th ed (Upper Saddle River, NJ: Prentice Hall, 2006) Bart, Yakov, Venkatesh Shankar, Fareena Sultan, and Glen L Urban, “Are the Drivers and Role of 93754_30_EN_p661-667.indd 665 665 10 11 12 13 14 15 Online Trust the Same for All Web Sites and Consumers? A LargeScale Exploratory Study,” Journal of Marketing 69 (October 2005), 133–152 Cronbach, Lee J and Richard J Shavelson, “My Current Thoughts on Coefficient Alpha and Successor Procedures,” Educational and Psychological Measurement 64 (June 2004), http://epm.sagepub.com/cgi/ content/short/64/3/391 Hair et al (2006) Wells, Chris, “The War of the Razors,” Esquire (February 1980), Babin, Barry J., William R Darden, and Mitch Griffin, “Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value,” Journal of Consumer Research 20 (March 1994), 644–656 Hair et al (2006) Cox, Keith K and Ben M Enis, The Marketing Research Process (Pacific Palisades, CA: Goodyear, 1972); Kerlinger, Fred N., Foundations of Behavioral Research, 3rd ed (Ft Worth: Holt, Rinehart and Winston, 1986) Headley, Dean E., Brent D Bowen, and Jacqueline R Liedtke This case, originally titled “Navigating through Airline Quality,” was reviewed and accepted for publication by the Society for Case Research Chapter 14 Anhalt, Karen Nickel, “Whiskas Campaign Recruits a Tiny Tiger,” Advertising Age International (October 19, 1998), 41 Breeden, Richard, “Owners, Executives Cite Small Firms’ Advantages,” Wall Street Journal (January 3, 2006), http://online.wsj.com; “SMB State of the Union Study,” AllBusiness com (Winter 2005), news and press page, http://www.allbusiness.com/ press/barometer.pdf Likert, Rensis, “A Technique for the Measurement of Attitudes,” Archives of Psychology 19 (1931), 44–53 Osgood, Charles, George Suci, and Percy Tannenbaum, The Measurement of Meaning (Urbana: University of Illinois Press, 1957) Seven-point scales were used in the original work; however, subsequent researchers have modified the scale to have five points, nine points, and so on Menezes, Dennis and Norbert F Elbert, “Alternative Semantic Scaling Formats for Measuring Store Image: An Evaluation,” Journal of Marketing Research (February 1979), 80–87 Costanzo, Chris, “How Consumer Research Drives Web Site Design,” American Banker (April 19, 2005), http://galenet.galegroup.com “Technology Still Matters to Start-Ups Say Venture Capitalists and Other Industry Influencers,” Roeder-Johnson Corp news release (January 24, 2006), http://finance yahoo.com; “Importance of Unique Technology to Start-Up Companies: A Survey,” Roeder-Johnson Corp (January 2006), http://www roederjohnson.com Chapter 15 10 11 White, Joseph B., “The Price of Safety,” Wall Street Journal (December 5, 2005), http:// online.wsj.com; “J.D Power and Associates Reports: Premium Surround Sound Systems and HD Radio Garner High Consumer Interest Based on Their Market Price, while Consumers Prefer One-Time Fee over the Monthly Fee Associated with Satellite Radio,” J.D Power and Associates news release (August 18, 2005), http:// www.jdpower.com Smith, Robert, David Olah, Bruce Hansen, and Dan Cumbo, “The Effect of Quesionnaire Length on Participant Response Rate: A Case Study in the U.S Cabinet Industry,” Forest Products Journal (November– December 2003), http://galenet galegroup.com “Insurers Question Methods in U.S Treasury Survey on Terror Backstop,” A M Best Newswire (April 12, 2005), http://galenet galegroup.com “Mothers Misunderstand Questions on Feeding Questionnaire,” medical letter on the CDC and FDA (September 5, 2004), http://galenet galegroup.com Donahue, Amy K and Joanne M Miller, “Citizen Preferences and Paying for Police,” Journal of Urban Affairs 27, no (2005): 419–35 Weber, Nathan, “Research: A Survey Shows How Media Influence Our Decorating and Cooking Choices,” HFN, the Weekly Newspaper for the Home Furnishing Network (December 5, 2005), http:// galenet.galegroup.com Payne, Stanley L., The Art of Asking Questions (Princeton, NJ: Princeton University Press, 1951), 185 The reader who wants a more detailed account of question wording is referred to this classic book on that topic Roll, Charles W., Jr and Albert H Cantril, Polls: Their Use and Misuse in Politics (New York: Basic Books, 1972), 106–7 “Hilarious Republican Senate Leadership Survey,” The Misanthropic Principle: The Blog of a Bipolar Misanthrope, http:// misanthropicscott.wordpress com/2008/04/19/hilariousrepublican-senate-leadership-survey/, accessed March 9, 2009 Payne, Stanley L (1951), 102–3 Dillman, Don A., Mail and Internet Surveys: The Tailored Design Method 12 13 14 15 16 17 (New York: John Wiley and Sons, 2000), 357–61 Young, Sarah J and Craig M Ross, “Web Questionnaires: A Glimpse of Survey Research in the Future,” Parks & Recreation 35, no (June 2000), 30 Michel, Matt “Controversy Redux,” CASRO Journal, http://www decisionanalyst.com/publ_art/ contredux.htm, accessed February 8, 2001 Ghaleb Almekhlafi, Abdurrahman, “Preservice Teachers’ Attitudes and Perceptions of the Utility of WebBased Instruction in the United Arab Emirates,” International Journal of Instructional Media 32, no (2005): 269–84 Harzing, Anne-Wil, “Does the Use of English-Language Questionnaires in Cross-National Research Obscure National Differences?” International Journal of Cross Cultural Management 5, no (2005): 213–24 Cateora, Philip R., International Marketing (Homewood, IL: Richard D Irwin, 1990), 387–89 “Hospitals, Feds Design Survey to Identify Culture That Encourages Patient Safety,” Health Care Strategic Management (February 2005), http:// galenet.galegroup.com; “Hospital Survey on Patient Safety Culture,” Agency for Healthcare Research and Quality, http://www.ahrq.gov/qual/ hospculture, accessed March 7, 2006 Chapter 16 Jones, J M., “Debt, Money Woes Are Top Family Financial Problems,” Gallup Inc (March 6, 2009), http:// www.gallup.com Kinne, Susan and Tari D.Topolski, “Inclusion of People with Disabilities in Telephone Health Surveillance Surveys,” American Journal of Public Health 95, no (March 2005): 512–517 Brock, Sabra E., “Marketing Research in Asia: Problems, Opportunities, and Lessons,” Marketing Research (September 1989), 47 Yeganeh, Hamid, Zhan Su, Elie Virgile, and M Chrysostome, “A Critical Review of Epistemological and Methodological Issues in Cross-Cultural Research,” Journal of Comparative International Management (December 2004), http://web2 infotrac.galegroup.com Sigenman, Lee, Steven A.Tuch, and Jack K Martin, “What’s in a Name? Preference for ‘Black’ versus ‘African-American’ among Americans of African Descent,” Public Opinion Quarterly (Fall 2005), http://web2.infotrac.galegroup.com Rideout, Bruce E., Katherine Hushen, Dawn McGinty, Stephanie Perkins, and Jennifer Tate, “Endorsement of the New Ecological Paradigm in Systematic and E-Mail Samples of College Students,” 7/14/09 9:19:54 AM 666 10 11 12 13 14 Journal of Environmental Education (Winter 2005), http://web2.infotrac galegroup.com SurveySite, “What We Do: Quantitative Research,” http:// www.surveysite.com, accessed March 15, 2006 “Frequently Asked Questions about Conducting Online Research: New Methodologies for Traditional Techniques,” Council of American Survey Research Organizations (CASRO) (1998), http://www casro.org Mellinger, Gloria, “World Opinion Research Profiles,” Harris Interactive Inc (July 18, 2000) Ibid “Frequently Asked Questions about Conducting Online Research” (1998) “Internet Sampling Solutions,” Survey Sampling International, http://www.ssisamples.com, accessed March 15, 2006 Based on Gene Mueller, “It’s Hard to Figure Number of Anglers,” Washington Times (March 20, 2005), http://web3.infotrac.galegroup com; Atlantic Coastal Cooperative Statistics Program, “About Us: Committees,” http://www.accsp.org, accessed March 16, 2006; Atlantic States Marine Fisheries Commission, “About Us,” http://www.asmfc.org, accessed March 16, 2006 Material for this case is from Scientific Telephone Samples User’s Manual, Scientific Telephone Samples, Santa Ana, CA Endnotes Based on Gerdes, Geoffrey R., Jack K Walton II, May X Liu, Darrel W Parke, and Namirembe Mukasa,“Trends in the Use of Payment Instruments in the United States,” Federal Reserve Bulletin (Spring 2005), http://web2.infotrac galegroup.com Most of the statistical material in this book assumes that the population parameters are unknown, which is the typical situation in most applied research projects The reasons for this are related to the concept of degrees of freedom, which will be explained later At this point, disregard the intuitive notion of division by n, because it produces a biased estimate of the population variance In practice, most survey researchers will not use this exact formula A modification of the formula, Z ϭ (X Ϫ ␮)/S, using the sample standard deviation in an adjusted form, is frequently used Hayes, William L., Statistics (New York: Holt, Rinehart and Winston, 1963), 193 Wonnacott, Thomas H and Ronald J Wonnacott, Introductory Statistics, 2nd ed (New York: Wiley, 1972), 125 93754_30_EN_p661-667.indd 666 4 Askia, http://www.askia.com, accessed April 4, 2009 Sauerbeck, Laura R., Jane C Khoury, Daniel Woo, Brett M Kissela, Charles J Moomaw, and Joseph P Broderick, “Smoking Cessation after Stroke: Education and Its Effect on Behavior,” Journal of Neuroscience Nursing (December 2005), downloaded from http:// web1.infotrac.galegroup.com This section relies heavily on Interviewer’s Manual, rev ed (Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan, 1976) Ibid., p 11 Ibid., pp 11–13 Reprinted by permission Oliver, Daniel G., Julianne M Serovich, and Tina L Mason, “Constraints and Opportunities with Interview Transcription: Towards Reflection in Qualitative Research,” Social Forces (December 2005), downloaded from http://web1 infotrac.galegroup.com, Viewpoint Learning, http://www viewpointlearning.com, accessed June 22, 2009 Ripley, Birch G “Confessions of an Industrial Marketing Research Executive Interviewer,” Marketing News (September 10, 1976), 20 Eng, Susanna and Gardner, Susan, “Conducting Surveys on a Shoestring Budget,” American Libraries, 36 (February 2005), 38-39 10 Chapter 19 http://www.tobii.com, Tobii, accessed April 17, 2009 Braunsberger, Karin, B R Buckler, and David J Ortinau, “Categorizing Cognitive Responses: An Empirical Investigation of the Cognitive Intent Congruency Between Independent Raters and Original Subject Raters,” Journal of the Academy of Marketing Science 33 (Fall 2005), 620–632 These imputation methods are beyond the scope of this text For more see Hair et al., Multivariate Data Analysis (Upper Saddle River, NJ: Prentice Hall, 2006), 39–73, 709–740 Pope, Jeffrey L., Practical Marketing Research (New York: AMACOM, 1981), 22 © 1998–1999 VNU Business Media Inc Used with permission Chapter 21 Chapter 20 Chapter 18 Chapter 17 Note that the derivation of this formula is (1) E ϭ ZSX; (2) E ϭ _ _ ZS/͙n ; (3)͙n ZS/E; (4) (n) ϭ (ZS/E) Based on Bialik, Carl, “A Survey Probes the Back Seats of Taxis, with Dubious Results,” Wall Street Journal (January 28, 2005), http://online.wsj com; “Taxis Hailed as Black Hole for Lost Cell Phones and PDAs, as Confidential Data Gets Taken for a Ride,” Pointsec Mobile Technologies news release (January 24, 2005), http://www.pointsec.com 11 12 13 Dolliver, Mark, “Plow Under Your Hops and Plant Some Vines,” Adweek 46 (July 25, 2005), 36–38 Fisher, Mark, “Beer Surges in Popularity—At the Expense of Wine,” Dayton Daily News (August 1, 2008), http://www.encyclopedia com/doc/1P2-16950875.html, accessed March 22, 2009 Kirsche, M L., “Targeting Boomers Could Boost Fizzling Out Beer Sales,” Drug Store News 27 (June 2005), 81 Longo, Don, “Drink Up,” Progressive Grocer 84 (October 15 2005), 52–58 “Oprah Again Tops American’s List of Favorite Personalities,” Wall Street Journal (February 3, 2006), http://online.wsj.com/article_print/ SB113889692780763347.html, accessed February 2, 2006 Rasmussen Reports, National Survey of 1,000 Adults (March 17–18 2009), http://www.rasmussenreports com/premium_content/econ_ crosstabs/march_2009/crosstabs_ aig_march_17_18_2009, accessed March 22, 2009 See Dubinsky, Alan J., Rajan Nataraajan, and Wen-Yeh Huang, “Consumers’ Moral Philosophies: Identifying the Idealist and the Relativist,” Journal of Business Research 58 (December 2005), 1690–1701; Deal, Ken, “Deeper into the Trees,” Marketing Research 17 (Summer 2005), 38–40 Adapted from Yavas, Ugur and Emin Babakus, “What Do Guests Look for in a Hotel? A Multi-Attribute Approach,” Services Marketing Quarterly 25, no (2003), 6–14 http://www.wineinstitute.org/ communications/statistics, accessed February 6, 2006 The data analysis tool must be added to the conventional Excel install by unpacking the data tool This can be done by clicking on tools and then clicking on add-ins and following the instructions See http://www microsoft.com for more instructions on how to accomplish this Iuso, Bill, “Concept Testing: An Appropriate Approach,” Journal of Marketing Research 12 (May 1975), 230 Diamon, Sidney, “Market Research Latest Target in Ad Claim,” Advertising Age (January 25, 1982), 52 Reprinted with permission by Crain Communications, Inc Adapted with permission from Prince, Melvin, Consumer Research for Management Decisions (New York: John Wiley and Sons, 1982), 163–166 Technically, the t-distribution should be used when the population variance is unknown and the standard deviation is estimated from sample data However, with large samples, the t-distribution approximates the Z-distribution, so the two will generally yield the same result See a comprehensive statistics text for a more detailed explanation A more complex discussion of the differences between parametric and nonparametric statistics appears in Appendix 22A Kranz, Rick, “Maybach, Rolls Models Are Far Below Predictions,” Automotive News 79 (October 18, 2004) In most cases, low p-values support hypotheses However, if the hypothesis is that the observations will be equal to the theoretical expectations for a given distribution (this would be the null case), then a high p-value would be desired to support the hypothesis Generally, this is not good form for a hypothesis Exceptions to this rule exist One of the most common is when a researcher compares some matrix of values with some alternative matrix of values with a goodness-of-fit test Particularly in advanced applications (beyond the scope of this book), the researcher may wish to test whether or not the two matrices are the same within sampling error In this case, the researcher would need an insignificant p-value (above α) to support the hypothesis Chapter 22 Vermeir, I and P Van Kenhove, “Gender Differences in Double Standards,” Journal of Business Ethics 81 (2008), 281–295 Vermeir and Van Kenhove (2008) Tests for complex experimental designs are covered in Appendix 22B The formula is not shown here but it can be found in most basic statistics books See, for example, Armstrong-Stassen, M., “Designated Redundant but Escaping Lay-Off: A Special Group of Lay-Off Survivors,” Journal of Occupational and Organizational Psychology 75 (March 2002), 1–13 This is the “statistical alternative” hypothesis Sukhdial, Ajay, Damon Aiken, and Lynn Kahle, “Are You Old School? A Scale for Measuring Sports Fans’ Old-School Orientation,” Journal of Advertising Research 42 (July/August 2002), 71–81 7/14/09 9:19:55 AM Endnotes 667 Chapter 23 Chapter 24 1 Greenhaus, J H and N J Beutell, “Sources of Conflict Between Work and Family Roles,” Academy of Management Review, 10, no (1985), 76 Boyar, S L., C P Maertz Jr., A W Pearson, and S Keough, “WorkFamily Conflict: A Model of Linkages Between Work and Family Domain Variables and Turnover Intentions,” Journal of Managerial Issues 15, no (2003), 175 Beutell, N J and U Wittig-Berman, “Predictors of Work-Family Conflict and Satisfaction with Family, Job, Career, and Life,” Psychological Reports 85 (1999), 893–903 For a discussion of the other measures of association, see the appendix to this chapter and J D Gibbons, Nonparametric Methods for Quantitative Analysis (New York: Holt, Rinehart and Winston, 1976) Bott, J P., D J Svyantek, S A Goodman, and D S Bernal, “Expanding the Performance Domain: Who Says Nice Guys Finish Last?” International Journal of Organizational Analysis, 11, no (2003), 137–152 Bagozzi, R P., “Salesforce Performance and Satisfaction as a Function of Individual Difference, Interpersonal and Situational Factors,” Journal of Marketing Research (November 1978), 517–531 Recall that the mean for a standardized variable is equal to For more on this topic, see Hair, J F., W C Black, B J Babin, R Tathum, and R Anderson, Multivariate Data Analysis, 6th ed (Upper Saddle River, NJ: Prentice Hall, 2006) 93754_30_EN_p661-667.indd 667 Goulding, Christina, “Romancing the Past: Heritage Visitors and the Nostalgic Consumer,” Psychology and Marketing 18 (June 2001), 565–592 Tesoriero, H W., “Babes in 80s Toyland,” Time 160 (November 11, 2002), 14 “Nostalgia, Education Hot Trends in Toys,” Mass Market Retailers 21 (February 23, 2004), 47, ThomsonGale Database Betts, Kate, “A 1950s State of Mind,” Time (April 15, 2004), Osborn, Suzanne Barry, “It’s Yesterday Once More: Companies Use Nostalgia to Entice Consumers,” Chain Store Age (June 2001), 32 Peterson, Karyn M., “Entertaining the Future: Licensing Execs on Last Year’s Lessons and the Challenge of What’s Next,” Playthings (February 1, 2009), http://www.playthings.com/ article/CA6635647.html, accessed April 20, 2009 See Holak, S L and W Havlena, “Feelings, Fun and Memories: An Examination of the Emotional Components of Nostalgia,” Journal of Business Research 42, no (1998), 217–226 Muehling, Darrel D and David E Sprott, “The Power of Reflection,” Journal of Advertising 33 (Fall 2004), 25–35 Holak, S L and W Havlena (1998) 10 When the actual regression model is illustrated as an explanation of the actual dependent variable in a population, Yi is used and an error term (ei) is included because the sample parameters cannot be expected to perfectly predict and explain the actual value of the dependent variable in the population 11 12 13 14 15 16 17 When we use a regression equation to represent its ability to predict sample values of the dependent variable from the estimated parameter coefficients, Yˆi is used to represent predicted values of Yi and no error term is included since the actual amount of error in any given observation is unknown School enrollment statistics can often be found using the Internet and either searching through government statistics or examining the Web site for the local school district or school board The constant term has disappeared since it is equal to when the regression coefficients are standardized For more on this topic, see Hair, J F., W C Black, B J Babin, and R Anderson, Multivariate Data Analysis (Upper Saddle River, NJ: Prentice Hall, 2010) Cox, A D., D Cox, and R D Anderson, “Reassessing the Pleasures of Store Shopping,” Journal of Business Research 58 (March 2005), 250–259 Closs, D J., M Swink, and A Nair, “The Role of Information Connectivity in Making Flexible Logistics Programs Successful,” International Journal of Physical Distribution & Logistics Management 35, no (2005), 258–277 Morrison, Mark, A Sweeney, and T Heffernan, “Learning Styles of On-Campus and Off-Campus Marketing Students: The Challenge for Marketing Educators,” Journal of Marketing Education 25 (December 2003), 208–217 Paul E Green, Ronald E Frank, and Patrick J Robinson, “Cluster Analysis in Test-Market Selection,” Management Science 13 (April 1967) Chapter 25 North, Tim, “Business Report Writing Tips,” http://www betterwritingskills.com, downloaded April 28, 2009 The original version of this chapter was written by John Bush, Oklahoma State University, and appeared in William G Zikmund, Business Research Methods (Hinsdale, IL: Dryden Press, 1984) “A Speech Tip,” Communication Briefings 14, no (1995), These guidelines, adapted with permission from Marjorie Brody (President, Brody Communications, 1200 Melrose Ave., Melrose Park, PA 19126), appeared in “How to Gesture when Speaking,” Communication Briefings 14, no 11 (1995), “Tips of the Month,” Communication Briefings 24, no (May 2005), Based on Bridis, Ted, “Study: Shoppers Naïve about Online Pricing,” Information Week ( June 1, 2005), downloaded from http://web2.infotrac.galegroup com; (APPC),”Annenberg Study Shows Americans Vulnerable to Exploitation in the Online and Offline Marketplace,” Annenberg Public Policy Center news release ( June 1, 2005), http://www annenbergpublicpolicycenter.org; Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, downloaded from http:// www.annenbergpublicpolicycenter org 7/14/09 9:19:55 AM INDEX INDEX A Absolute causality, 59 Abstract level, 40 Accuracy coding data and, 474 in descriptive research, 57 of political polls, 430 of questionnaire, 337 of sampling, 388–389, 404–405 ACNielsen BASES system, 87 Claritas, 160, 166, 171, 178 PeopleMeter, 247–248 ScanTrack, 176–177 ACNielsen International, 14 Acquiescence bias, 192–193 Actionable variables, 120 Active data warehousing, 24 Active research, and right to privacy, 91–92 ADI (Area of Dominant Influence), 162 Administrative error, 194–195 Adrenaline, 251–252 Adult beverages, 485–486 Advance notification of mail surveys, 224 Advertising research, 177, 375–377 Advocacy research, 101 AFLAC Insurance, 2–3, 14 Agency for Health Care Research and Quality Hospital survey, 365–370 Aided-recall format, 347, 351 Airline industry, 10–11, 12, 312–313, 321 Alloy Eighth Annual College Explorer study, 318 Alpha (␣), 511 Alternative hypothesis, 510 Alternatives in research process, 62–63 Ambiguity in decision making, 53–54 in question wording, 345–346 of symptoms of business problem, 111 American Kennel Club, 397 American Marketing Association, Code of Ethics, 95, 100 Analysis of variance See ANOVA Anchoring effect, 350 Annenberg Public Policy Center, study by, 629–630 Anonymity of respondents, 212, 220, 230 ANOVA (analysis of variance) applied to regression, 571 description of, 541, 543 for factorial designs, 556–557 F-test and, 545–546 illustration of, 543–544 independent samples t-test and, 542 multivariate, 589, 590, 591 n-way, 589–590 partitioning variance in, 544–545 for randomized-block designs, 555–556 Appendix to research report, 617 Applied business research, 6, Arbitron Portable People Meter, 248–249 Area sample, 401 Arithmetic means of sample, 428 Askia software, 443 Assessment of problems or opportunities, Assumptions made in question wording, 347 Atlanta Braves case study, 637–638 ATLAS.ti software package, 138 Attitude, 315 Attitude measurement behavioral intention, 326–327 choosing scale for, 328–331 importance of, 315–316 rating scales, 317–326 techniques for, 316–317 Attribute, 303 Attribution theory, 38 Australia, brushfires in, 159 Authorization letters, 613 Availability of data, and need for research, 11–12 Average, figuring, 416 Average deviation, 419 B Back translation, 363 Backward linkage, 62 Balanced rating scale, 330 Ballistic theory, 45 Bar charts, 623–625 Basic business research, Basic experimental designs, 271, 278, 280–282 Behavioral differential, 327 Behavioral intention, measurement of, 326–327 Behavioral tracking, 25–26 Benchmarking, 201 Best-fit line, 568 Between-groups variance, 544–545 Between-subjects design, 273 Bias See also Response bias in decision making process, 83 of experimenter, 267 of observer, 243 order, 349 in quota sampling, 397 sample, 189 Bivariate statistical analysis, 509, 530, 532–534 “Blind” experimental administrator, 269 Blind monadic testing, 505 Blocking variables, 258 Blogs, 148, 170 Body language, 461 Body of research report, 615–617 Box and whisker plots, 501, 502 Briefing sessions, 445, 454 See also Debriefing sessions Bristol-Myers, 505 Brown-Forman distiller, 14–15 Budget for research Internet surveys and, 227 mail surveys and, 220 personal interviews and, 212 sampling method and, 405 scientific decision process and, 156 as source of conflict, 82 telephone interviews and, 215 Burt’s Bees, 318 Business-class airfare, 12 Business decisions, information required for, 3–5 See also Decision making process Business ethics, 88 Business-Facts, 160 Business.gov Web site, 616 Business intelligence, 19, 20 Business opportunity, 51 Business orientations, Business problem, 51 Business research definition of, 5–6 determination of need for, 11–13 flaws in, 16 functions of, 23 global, 14–15 managerial value of, 8–11 types of, 6, 7, 54 C Callbacks, 213, 217, 229 Calo Research Services, 448 Campbell’s Soup Company, 245–246 Carrefour, 65 Case studies, 140, 632–638 See also specific case studies Categorical variables, 119, 261 Category scales, 318, 319, 330 Causal inference, 57 Causal research, 16, 57–61, 71, 257 Causation, correlation, and covariance, 561–562 Celebrity endorsements, 491 Cell, 263 Cengage Learning, 31 Census, 387 Central-limit theorem, 425–429 Central location interviews, 217 Certainty, 52 See also Uncertainty CHAID (chi-square automatic interaction detection) software, 492 Change in business situations, 111 Change interviews, 115 Charts, display of data in, 498–499, 619–625 Cheating by interviewers, 194–195, 456 Check boxes, 358, 359 Checklist questions, 341 Children, as subjects, 92–93 China, consumer demand in, 166 Chi-square distribution, 642 Chi-square tests, 522–524, 530, 532–534, 559 Circular-flow process, 61, 62 Claritas (ACNielsen), 160, 166, 171, 178 Classificatory variables, 119 Click-through rate, 249–250 Client sponsors/users, rights and obligations of, 100–101 Climate change, attitudes toward, 350 Closed-ended questions See Fixed-alternative questions Cluster analysis, 597–599 Cluster sampling, 401, 402 Coca-Cola, 110 Code book, 477, 478 Code construction, 472 Codes, definition of, 468 Codes of ethics, 94–95 Coding data code construction, 472 data file, 471–472 description of, 70, 468 devising scheme for, 475–477 editing and, 477 error checking and, 478–479 fixed-alternative responses, 472–474 open-ended responses, 474–477 qualitative responses, 468–471 Coding process, facilitating when editing, 467–468 Coefficient alpha (␣), 306 Coefficient of determination (R2 ), 562 Coffee industry, 4, 73 Cohort effect, 276 Collages, 153 Commercial sources of data, 176–178 Communality values, 596 Communication process, 609–610 668 93754_31_Ind_668-674.indd 668 7/14/09 9:20:25 AM Index Communication technologies, 13–14 Comparative rating scales, 329 Completely randomized design, 283–284 Completeness of data, 21, 466–467 Complex experimental designs, 282–286 Composite measure, 303 Composite scales, 320, 596 Compromise design, 282 CompuStat, 29 Computer-assisted telephone interviews, 218, 474 Computerized survey data processing, 477–478 Computerized voice-activated telephone interviews, 218–219 Computer mapping, 500–501 Concept/construct, 40, 295 Conclusions and recommendations section of report, 617 Concomitant variation, 58 Conditional causality, 59 Confidence interval, 430, 434 Confidence interval estimates, 429–432, 520–521 Confidence level, 434, 511 Confidentiality, 91, 98–100 Confirmatory orientation, 53 Confirmatory research, exploratory research compared to, 136–137 Conflict between management and research, 81–85, 86 Conflict of interest, 100 Confound, 265–266 Confused “don’t know” answers, 467 Consistency, internal, 305–306, 310 Constancy of conditions, 270 Constants, 119 Constant-sum scale, 323 Construct definition of, 40, 296 hypothetical, 315, 317 Construct validity, 308 Consumer Assessment of Health Providers and Systems Hospital Survey, pretesting, 361 Consumer panels, 198, 250 Consumer Point, 160 Consumption patterns, 165, 177, 580 Content analysis, 246–247 Content providers, 32 Content validity, 307–308 Contingency tables, 488–489, 491 Continuous measures, 302–303 Continuous quality improvement stage of total quality management, 201 Continuous variables, 119 Contractors See Suppliers and contractors Contracts, research proposals as, 125–127 Contributory causality, 59 Contrived observation, 244, 245 Control groups, 261 Control of variables, establishing, 269–270 Convenience sampling, 396, 408 Convergent validity, 308–309 Conversational approach to qualitative research, 151–152 Cookies, 34 Cooperation Internet surveys and, 227 telephone surveys and, 216 COPPA (Children’s Online Privacy Protection Act), 92 Corporate Reputation Survey, 339 Corporate social responsibility, 318 Correlation covariance, causation, and, 561–562 partial, 586 Pearson product-moment, 564, 645 93754_31_Ind_668-674.indd 669 669 Correlation coefficient, 559, 561, 563 Correlation matrix, 562–564, 565 Correspondence rules, 295–296 Cost-benefit analysis, and need for research, 12–13 Costs See Budget for research Counterbalancing, 270 Counterbiasing statement, 345 Covariance, 559, 561–562 Cover letters for mail surveys, 222, 223 Criterion validity, 308 Critical values description of, 513–514 of F for ␣ ϭ 01, 644 of F for ␣ ϭ 05, 643 of Pearson correlation coefficient, 645 of T in Wilcoxon matched-pairs signed-rank test, 646 Criticism, research that implies, 82 Cross-checks of data, 163 Cross-functional teams, 85 Cross-sectional studies, 196–197 Cross-tabulations chi-square test, 530, 532–534 contingency tables, 488–489, 491 description of, 488–489 elaboration, refinement, and, 491–492 number of, 492 percentage, 490–491 quadrant analysis, 493 Cross-validation of research results, 15 Curb-stoning, 456 Current Population Survey, 55, 390 Customer discovery, 170 Customer relationship management (CRM), 23–24, 170–171 Custom research, 88 Cyclical business situations, 110–111 D Data characteristics of valuable, 19, 21 cross-checks of, 163 definition of, 19, 20 gathering, 69 input management, 25–28 Internet and, 31–32 processing and analyzing, 70 secondary, 161–163 sources of, 171–178 Data analysis computer programs for, 499–501 definition of, 70 interpretation of, 501–503 stages of, 462–463 Data archives, computerized, 28–30 Database marketing, 170–171 Databases, 24, 28–30 Data collection See Fieldwork Data conversion, 162 Data entry, 477 Data files, 470, 471–472 Data gathering stage of research, 69 Data integrity, 463, 465 Data mining, 169–170 Data processing, computerized, 477–478 Data-processing error, 194 Data quality, 21 Data reduction technique, 595–596 Data specialist company, 28 Data transformation description of, 493 index numbers, 496 problems with, 494–495 rank order, calculation of, 496–498 simple, 493–494 Data warehouses, 24–25, 472 Data warehousing, 24 Data wholesalers, 28 DDB SignBank, 241 Debriefing sessions, 93, 270–271 See also Briefing sessions Deception in research design, 93 Decision making, definition of, 52 Decision making process ambiguity in, 53–54 biases in, 83 certainty in, 52, 53 information for, 19 opportunities and problems in, 51–52, 53–54 research contribution to, 51 stages of, 9–11 uncertainty in, 52–53 Decision situation hypotheses, variables, and, 121 managerial, defining, 64 Decision statements description of, 108 influence of, on objectives and research design, 123 linking with objectives and hypotheses, 66 translating into research objectives, 116–118 Decision support systems (DSS), 23–24, 26, 79 Deductive reasoning, 44 Degrees of freedom (df ), 519 Deland Trucking Company, 107–108 Deliverables, 63 Demand characteristics, 267–269 Demand effect, 267 Demographic data Internet sampling and, 408 question wording and measurement scales for, 382–384 sources of, 177 Dependence techniques of multivariate data analysis ANOVA and MANOVA, 589–590 discriminant analysis, 590, 592 multiple regression, 584–586, 588–589 overview of, 583, 584, 592 Dependent variables, 120, 257, 263–264 Depository institutions, survey of, 412 Depth interviews, 150–151 Descriptive analysis, 486–487 Descriptive research deception in, 93 description of, 16, 55–57, 60, 71 results of, 61 Descriptive statistics, 413 Design of research See also Experimental design; Secondarydata research designs deception in, 93 definition of, 66 influence of decision statements on, 123 planning, 66–68 for surveys, 231–232 Destruction of test units, 389 Determinant-choice questions, 340 Deviation, 419 Diagnosis of problems or opportunities, Diagnostic analysis, 57 DIALOG, 28, 29 Dialog boxes, 230 Direct observation, 242–244 Director of research, 80–81 Discovery orientation, 53 Discrete measures, 301 Discriminant analysis, 590, 592 Discriminant validity, 308–309 Discussion guides for focus groups, 146–147 Disguised experiments, 268–269 Disguised questions, 196 Display of data, tabular and graphic, 498–499, 617–625 Disproportional stratified sampling, 400–401 Dissemination of faulty conclusions, 100 Distortion of data in charts, 620–621 Dog Ownership Survey, 397 Domain, 31 Do-not-call legislation, 91, 214, 236 “Don’t know” answers, editing and tabulating, 467–468 Door-in-the-face compliance technique, 447 Door-to-door interviews, 212–213, 232 Double-barreled questions, 346–347 Dow Jones News Retrieval, 28, 29 Downy-Q Quilt commercial, 506–507 Drinking-related behaviors, 262 Drop-down boxes, 358, 359 Drop-off method, 225 Dummy coding, 469–470 Dummy tables, 127–128 Dummy variables, 585 DuPont, 4–5, 14 E Editing data coding and, 467–468, 477 for completeness, 466–467 in field, 464 in-house, 464–466 overview of, 70, 463–464 pitfalls of, 468 during pretest stage, 468 questions answered out of order, 467 Edward Jones investment firms, 159 E-Lab, LLC, 255 Elaboration analysis, 492 Electronic data interchange (EDI), 30 Electronic interactive media, 208 Electronic toll collection case study, 131 E-mail surveys, 226 Emotion, and scientific decision process, 156 Empire Health Services, 201 Empirical level, 40–41 Empirical testing, 42, 43, 48 Entrapment, 245 Environmental scanning, 33, 165–166 Eos airline, 12 Equifax City Directory, 392–393 Error See also specific types of error designing experiment to minimize, 260–266 with direct observation, 243–244 in prediction, and regression analysis, 569 reporting, 98 in sample selection, training interviewers to avoid, 454 sources of, when studies are rushed, 82 Error checking in coding process, 478–479 Error trapping, 360 Estimation of parameters, 429–432 Ethical dilemma, 88 7/14/09 9:20:25 AM 670 Ethical issues in choosing focus group respondents, 145 client sponsors and, 100–101 in experimentation, 270–271 general rights and obligations, 90 in observation of humans, 245 as philosophical issues, 88–90 professionalism and, 102 researcher and, 94–100 research participants and, 90–94 in surveys, 233 Ethical perceptions, statistical tests on, 529–530 Ethnography, 138–139 Evaluation of course of action, 10–11 of questionnaires, 363 of secondary data, 163 Evaluation research, 10 Executive summary of research project, 614–615 Experiment See also Experimental design creating, 257 deception in, 93 definition of, 59–60 designing to minimize error, 260–266 ethical issues with, 270–271 self-efficacy intervention and job attitude, 257–260 Experimental condition, 258, 269 Experimental design basic, 271, 278, 280–282 complex, 282–286 diagramming, 278 factorial, 271, 284–286, 556–557 field experiments, 272–273 laboratory experiments, 271–272 quasi-experimental designs, 278–280 time series, 282 within-subjects and between-subjects, 273–274 Experimental groups, 261 Experimental treatment, 261–262 Experimental variables, 59 Experimenter bias, 267 Exploratory research confirmatory research compared to, 136–137 description of, 16, 54–55, 60, 71 misuses of, 154–156 objectives and, 64–65 results of, 61 External data, 172 External distributors, 27 External validity, 277–278 Extraneous variables controlling, 269–270 description of, 265, 266 internal validity and, 275–277 Extremity bias, 193 Eye-tracking monitor, 251 E-ZPass case study, 131 F Face validity, 307 Facial coding, 461 Fact-finding, 164–165 Factor analysis, 593–597 Factorial experimental designs, 271, 284–286, 556–557 Factor loading, 594 Factor rotation, 594–595 Fairfax County Public Library, 199 Fax surveys, 225–226 93754_31_Ind_668-674.indd 670 Index Federal Reserve survey, 412 Federal Trade Commission (FTC), 95–96, 214 FedWorld Web site, 175 Feedback, and personal interviews, 209–210 Field editing, 464 Field experiments, 272–273 Field in data file, 470 Field interviewing service, 444 Field notes, 152 Fieldwork Askia software and, 443 description of, 443–445 management of, 453–454 Fieldworkers, 444, 455–457 Filter questions, 350, 466 Financial databases, 29 FIX (Financial Information eXchange), 256 Fixed-alternative questions description of, 339, 363 precoding responses to, 472–474 recording responses to, 450 types of, 340–341 using, 339–340 FleetBoston bank, 113 Flowchart plan for questionnaire, 351 Focus blog, 148 Focus group interviews advantages of, 141–144 as diagnostic tools, 148 disadvantages of, 149–150 discussion guides for, 146–147 environment for sessions, 145 flexibility of, 143 nonverbal communications and, 242 online, 148–149 piggybacking and multiple perspectives, 143 scrutiny and, 143–144 speed and ease of, 142–143 uses of, 144 videoconferencing and, 148 Focus groups, 65, 144–146 FocusVision system, 148 Follow-up to mail surveys, 223 to research, 627 Follow-up questions, 448 Food and Drug Administration (FDA), Foot-in-the-door compliance technique, 447 Forced answering software, 360 Forced-choice rating scales, 330–331 Ford Motor Company, 33, 84, 110 Forecast analyst, 79 Forecasting sales, 167–168 Format of research reports, 611–617 Forward linkage, 61–62 Free-association techniques, 152–154 Frequency-determination questions, 340 Frequency distribution description of, 413–415 of sample means, 428 Frequency tables, 488 F-statistic, manual calculation of, 552–554 F-test, 545–546, 571–572, 590 Full enumeration method of sampling, 402 Funded business research, 127 Funnel technique, 349 Furnace employees, attitudes of, 314–315 G Gale Research Database, 31 Gallup Corporation, sampling by, 386 Garbage observation projects, 245–246 General linear model, 584 Geographical databases, 28 Geographic areas, estimating market potential for, 166–167 Geographic hierarchy in urbanized areas, 404 Geographic Information System (GIS), 87 Gestures during oral presentations, 626 Global business research description of, 14–15 mail surveys and, 225 personal interviews and, 214 questionnaires for, 362–363 sampling frames for, 393 sources of data for, 178–179 telephone interviews and, 219 Global information systems, 23 Global positioning satellite (GPS) systems, 25–26, 27, 105, 248 Goodness-of-fit, 522–524, 530, 532–534 Goods and services, question wording and measurement scales for, 378–382 Google, 32, 33, 171–172, 339 Government sources of data, 175 Grand mean, 544 Grants, peer review process for, 96 Graphical representations of data charts, 498–499, 619–625 descriptive analysis and, 500–501 in reports, 617–618 tables, 498–499, 618–619 Graphical user interface (GUI) software, 356 Graphic rating scales, 323–325, 360 Grounded theory, 139–140 Grouping variables, 536 H Hand washing, 244 Happy-face scale, 325 Harley-Davidson, 11, 153 Harm, protection of participants from, 94, 96 Harris Interactive Inc., 407–408 Harvard Cooperative Society case study, 37 Hawthorne effect, 268 Health club industry, 97 Healthy house, attitudes toward, 325 Heavy equipment case study, 122 Hermeneutics, 138 Hermeneutic unit, 138 Hidden observation, 240, 245 Hidden skip logic, 360 Hidden Valley Ranch, 273 Histograms, 487–488 History effect, 275–276 Home Depot, 19, 27 Horizon Research Services, 446 Host, 31 Human subjects review committee, 94, 96 Hypothesis See also Hypothesis testing clarity in, 121–123 decision situations, variables and, 121 decision statements, objectives, and, 66 definition of, 42 null, 510 testing, 7–8 variables and, 296 Hypothesis testing applications of, 526 chi-square test for goodnessof-fit, 522–524 as critical skill, 526 description of, 509 example of, 513–515 parametric compared to nonparametric, 517–518 procedure for, 509–510 of proportion, 525 significance levels and p-values, 510 simple regression and, 574 Type I and Type II errors, 515–516 univariate, using t-distribution, 521–522 Hypothetical constructs, 315, 317 I IBM, 19 Idealism, ethical, 89–90 Identification of problems or opportunities, Image profiles, 321 Implementation of course of action, 9–10 Importance-performance analysis, 493 Imputing missing value, 466 Incentives for fieldworkers, 457 to respond, 216, 222 Inconsistency, checking data for, 464–466 Independent samples t-test, 534–538, 542 Independent variables, 120, 257, 260–263, 491 Index measure, 303, 331 Index numbers, 496 Index of retail saturation, 168–169 Inductive reasoning, 44–45 Inferential statistics, 413 Information for business decisions, 3–5, 19 completeness of, 21, 466–467 definition of, 19, 20 valuable, characteristics of, 19, 21 Information technology, 33–34 Informed consent, 90–91, 94 In-house editing, 464–466 In-house interviewers, 444 In-house research, 76–77 Initial quality improvement stage of total quality management, 201 Input management, 25–28 In-Stat, 185–186 Instrumentation effect, 276 Integrity of data, 463, 465 Interaction effect, 260 Interactive help desk, 360 Interactive media, 32–33, 208 Interactive questionnaires, software to make, 360–361 Interdependence techniques of multivariate statistical analysis cluster analysis, 597–599 factor analysis, 593–597 multidimensional scaling, 599–600 overview of, 583, 584, 601 Internal and proprietary data, 171–172 Internal consistency, 305–306, 310 Internal records, 25 Internal Revenue Service, research proposal for, 124 Internal validity, 274–277, 278 Internet data access and, 31 data collection and, 31–32 description of, 30–31 navigating, 32 privacy issues with, 92, 102 research reports on, 626 as source of data, 172, 174 7/14/09 9:20:25 AM Index Internet 2, 34 Internet surveys advantages and disadvantages of, 227–230, 232 initial contact, 447 layout of, 356–360 sampling and, 406–409 Interpretation of data analysis, 501–503 Interquartile range, 501 Interrogative techniques, 114 Intersubjective certifiability, 135 Interval scale, 300 Interviewer bias, 193 Interviewer error, 194 Interviewer influence, 211–212 Interviewers briefing sessions for, 454 cheating by, 194–195, 456 instructions for, 352 training for, 445 Interviewing basic principles of, 452 required practices for, 452–453 total quality management for, 455 Interview process, 113–114 Interviews See also Fieldwork; Focus group interviews; Interviewers; Interviewing; Personal interviews; Telephone interviews change, 115 combining direct observation with, 244 depth, 150–151 door-to-door, 212–214 as interactive communication, 208 semi-structured, 151–152 shopping mall intercepts, 212–214, 232 terminating, 451 verification of, by reinterviewing, 457 Intranet, 34 Introduction section of research report, 615 Intuit, 188 Intuitive decision making, 83 Inverse relationship, 561 Investing behavior, and peer pressure, 294 iPhone, 34 Isolation of subjects, 269 Item nonresponse, 211, 466 J Jack Daniels, 14, 15 J D Power and Associates, 87, 335 Jelly Belly brand, 3–4, Job attitude, and self-efficacy intervention, 257–260 Jobs in business research director of research, 80–81 large firms, 79–80 mid-sized firms, 78–79 research generalist, 85 salaries for, 81 small firms, 78 Johnson & Johnson, 339 Judgment, determining sample size on basis of, 438 Judgment sampling, 396 K Kaplan, Inc., 205 Keying mail surveys, 225 Keyword search, 32 Kia Motors, 165 Kiosk interactive surveys, 230–231 Kish method of sampling, 402 Knowledge, definition of, 22 93754_31_Ind_668-674.indd 671 671 Knowledge management, 22 Krispy Kreme, 18–19 L Laboratory experiments, 271–272 Laddering, 150 Ladder of abstraction, 40, 41 Ladder scales, 324 Latent construct, 41–43 Layout for questionnaires Internet, 356–360 traditional, 352–356 Leading questions, 344–345 Legitimate “don’t know” answers, 467 Length of mail surveys, 221 of personal interviews, 210 of questionnaires, 233 of telephone interviews, 217 Letters of authorization, 613 cover, for mail surveys, 222, 223 of transmittal, 613, 614 Level of precision, determination of after data collection, 439 Level of scale measurement, and selection of statistical techniques, 517 Libraries, as sources of data, 172 Likert scales, 303, 318–319, 341 Limited research service companies, 88 Line graphs, 622–623 List brokers, 392 List-wise deletion, 467 Literature review, 65 Loaded questions, 344–345 Longitudinal studies, 197–198, 200 Love, as hypothetical construct, 317 M Macy’s, 110 Magnitude of error, 434 Mail surveys, 219–221, 232 Main effect, 259 Mall intercept interviews, 213–214, 232 Management conflict between research and, 81–85, 86 research as facilitating, 8–9 Managerial action standard, 123 Manager of decision support systems, 79 Manipulation definition of, 59 of independent variables, 260–263 Manipulation check, 275 Manual calculation of F-statistic, 552–554 Marginals, 489 Marginal tabulation, 488 Market-basket analysis, 169 Marketing Information Systems, Inc., 505 Marketing-oriented firms, Market segments, and descriptive research, 56 Market-share data, 176–177 Market tracking, 165 Mark-sensed questionnaires, 477 Marriott Corporation, 78, 79–80 Mars M&M characters, 55 Matching subjects, 265 Maturation effect, 276 Maxjet, 12 Mazda Motor Europe, 255 MBA degree, market for, 50–51 McDonald’s, 60, 373 Mean description of, 415–417 sample size for questions involving, 433–435 Mean absolute deviation, 419 Mean squared deviation, 420 Measurement See also Attitude measurement; Scale measurement concepts and, 295 criteria for evaluating, 305 operational definitions and, 295–296 overview of, 293–295 reliability of, 305–307, 309 sensitivity of, 309 validity of, 307–309 Measure of association, 559–560 Measures of central tendency mean, 415–417 median, 416, 418 mode, 416, 418 strengths and weaknesses of, 439 Measures of dispersion range, 418–419 standard deviation, 419–421 Mechanical observation, 247–249 Median, 416, 418 Median split, 494–495 Media phones, 185–186 Media sources of data, 175–176 Memory, questions that may tax, 347–348 Mere-measurement effect, 195, 196 Methodology, selection of, 67 Microsoft, 339 MINITAB, 499 Misrepresentation of research, 93, 98, 99, 620–621 Missing data, handling, 479 Mixed-mode surveys, 231 Mobile phone interviews, 214–215 Mobile surveys, 207 Mode, 416, 418 Model building, 166–169 Moderators of focus groups, 145–146 Moderator variables, 492 Monadic rating scales, 329 Money See Budget for research Moral standards, 88 Mortality effect, 277 Mr Peanut, 55 Multicollinearity, 588 Multidimensional scaling, 599–600 Multiple-grid questions, 352 Multiple regression analysis example of, 585 interpreting results of, 588–589 overview of, 584–585 purposes of, 600 R2 in, 586 regression coefficients in, 585–586 statistical significance in, 586, 588 Multistage area sampling, 402–404 Multivariate analysis of variance (MANOVA), 589, 590, 591 Multivariate statistical analysis classifying techniques of, 582–584 definition of, 509, 581 Music for mobile phones, 165 social networking sites and, 228 Mutually exclusive response alternative, 341 Mystery shoppers/diners, 238, 244 N National Assessment of Adult Literacy, 210 National Do Not Call Registry, 214, 215, 236 Negative relationship, 561 NeoTech Mobile-Trak, 248 Netflix, 136 Net promoter surveys, 188 Networking, 30 Neural networks, 169 Neuroco, 251 Neutral questions, asking, 449 No contacts, 190 Nominal scales, 297–298 Noninteractive media, 208 Nonparametric statistics, 517–518 Nonprobability sampling comparison of techniques of, 404 convenience, 396, 408 description of, 395 judgment (purposive), 396 quota, 396–397 snowball, 398 Nonrespondent error, 462 Nonrespondents, 190 Nonresponse error mail surveys and, 222 overview of, 189–191 sampling and, 394–395 Nonsampling error, 264, 393–395 Nonspurious association, 58–59 Nonverbal communications, 241–242 “No opinion” option, 467–468 Normal curve, area under, 640 Normal distribution, 421–424 Norman Estates wine, 56 Nostalgia, trend toward, 580–581 Nuisance variables, 264 Null hypothesis, 510 Numerical scales, 322 Nutrition labels, N-way ANOVA, 589–590 O Objectives decision statements and, 116–118, 123 defining, 63–66, 113–114 survey questions and, 363 writing, 120–121 Objectivity, by researchers, 98 Observable phenomena, 239 Observation, definition of, 239 Observation of human behavior complementary evidence and, 242 direct observation, 242–244 ethical issues in, 245 overview of, 241–242 Observation techniques content analysis, 246–247 direct, 242–244 in ethnography, 138–139 example of, 67–68 limitations of, 240 mechanical, 247–252 mystery shoppers/diners, 238, 244 nature of, 240 for physical phenomena, 245–246 in qualitative research, 152–153 Observer bias, 243 OLS See Ordinary least-squares method of regression analysis Olson Zaltman Associates, 449 One-group pretest-posttest design, 279 One-shot design, 279 One-tailed test, 521 One-way ANOVA, 541 Online focus groups, 148–149 Open-ended boxes, 358, 359 Open-ended response questions coding responses to, 474–477 description of, 338–339, 363 recording responses to, 450 Operational definition description of, 295–296 example of, 297 of target population, 390 7/14/09 9:20:25 AM 672 Operationalizing variables, 42–43 Optical scanning systems, 477 Opt-in lists, 409 Oral presentation of research results, 625–626 Order bias, 349 Ordinal scales, 299–300 Ordinary least-squares method of regression analysis equations, arithmetic behind, 578–579 hypothesis testing and, 574 interpreting regression output, 572–573 overview of, 569–570 plotting regression line, 573–574 statistical significance of model, 570–572 Organizational structure of business research, 77–80 Outlier, 501 Outside agencies, research by, 76, 77 Outside vendors, 27 Ownership, question wording and measurement scales for, 377–378 P Paging layout, 357 Paired comparisons, 327–328 Paired samples t-test, 538–540 Pair-wise deletion, 467 Panel samples, 406, 407–408 Pantry audits, 246 Parameter estimates, 429–432, 566–567 Parametric statistics, 517–518 Parlin, Charles Coolidge, 245–246 Partial correlation, 586 Participant-observation, 138 Participants, rights and obligations of, 90–94, 96 See also Respondents; Subjects Participation gaining for interview, 447 Internet surveys and, 227 personal interviews and, 211 in surveys, Partitioning variance in ANOVA, 544–545 Passive research, and right to privacy, 92 Path estimate, 566 Pearson product-moment correlation, 564, 645 Peer pressure, and investing behavior, 294 Peer review process, 96 Percentage cross-tabulations, 490–491 Percentage distribution, 413–414 Perceptual map, 600 Performance-monitoring research, 10 Personal interviews advantages of, 209–211, 232 description of, 209 disadvantages of, 211–212, 232 initial contact, 445 layout of pages from, 355–356 questions for, 341–342 Personnel See Jobs in business research Petabyte, 472 Phenomenology, 137–138 Philip Morris, 144 Photographs, sampling of, 388, 389 Physical phenomena, observation of, 245–246 Physiological reactions, measurement of, 251–252 Pie charts, 498, 621–622 Piggyback, 143 Pilot studies, 65 93754_31_Ind_668-674.indd 672 Index Pivot questions, 350 Placebo, 93, 269 Placebo effect, 269 Planning tools, research proposals as, 125 Plug value, 466 Point estimates, 429 Pointsec Mobile Technologies, 442 Political polls, accuracy of, 430 Pooled estimate of the standard error, 535 Population, 387 See also Target population Population distribution, 424–425, 426 Population element, 387 Population mean, calculation of, 428 Population parameters, 413 Population size, and sample size, 435, 439 Pop-up boxes, 358 Posttest-only control group design, 281–282 PowerPoint, 10/20/30 rule of, 626 Precoding fixed-alternative responses, 472–474 Preliminary tabulation, 362 Pretest of CAHPS Hospital Survey, 361 description of, 65 editing questionnaires and, 468 of questionnaires, 361–362 surveys and, 233 Pretesting effect, 276 Pretest-posttest control group design, 280–281 Previous research, investigation of, 65 Price promotions at bars, and intoxication, 262 Pricing decisions, 110–111 Primary sampling units, 393 Principles of good interviewing, 452–453 Privacy on Internet, 102 participant right to, 91–92, 101 PRIZM, 160, 171, 178 Probability, definition of, 415 Probability distribution, 415 Probability sampling cluster, 401, 402 comparison of techniques of, 405 description of, 395 multistage area, 402–404 proportional compared to disproportional, 400–401 sample size and, 438–439 simple random, 398–399 stratified, 400 systematic, 399 Probing definition of, 114 personal interviews and, 210 when no response given, 448–449, 450 Problem, definition of, 112 Problem definition business decision and, 112–116 description of, 108 gaps in performance and, 112 importance of, 108 quality of, 109–111 steps in process of, 112, 113 symptoms and, 116, 117 time spent on, 123 unit of analysis and, 119 variables and, 119–120 writing decision statements and objectives, 116–118 writing objectives and questions, 120–121 Procter & Gamble, 135 Producers of data, 173–178 Production-oriented firms, Product-oriented firms, Product usage, question wording and measurement scales for, 377–378 Projective techniques, 153 Propensity-weighting method, 408 Proportion definition of, 415 hypothesis test of, 525 sample size for, 435–438 Z-test for comparing, 540–541 Proportional stratified sampling, 400–401 Proposal See Research proposal Proposition, 42 Proprietary business research, 25 Protection of participants from harm, 94, 96 Pseudo-research, 96–97 Psychogalvanometer, 252 Psychology of consumption, 580 Public opinion research, 177 Pull technology, 33 Pupilometer, 252 Pure research, Purposive sampling, 396 Push buttons, 357 Push polls, 97 Push technology, 33, 34, 92 P-values, 510, 512 Q Quadrant analysis, 493 Qualitative analysis, 133 Qualitative data, 136 Qualitative research case studies, 140 conversations, 151–152 definition of, 133 depth interviews, 150–151 ethnography, 138–139 focus group interviews, 141–150 free-association/sentence completion method, 152–154 grounded theory, 139–140 misuses of, 154–156 orientations to, 137 phenomenology, 137–138 quantitative research compared to, 135–136, 156 techniques of, 141, 142 uses of, 133–134 Qualitative responses, coding, 468–471 Quality of data, 21 definition of, 199 Quality dimensions for goods and services, 202 Quantified electroencephalography (QEEG), 251 Quantitative data, 136 Quantitative research, 134–136, 156 Quasi-experimental designs, 278–280 Questionnaires See also Questions; Surveys about car features, 335 about climate change, 350 Agency for Health Care Research and Quality case study, 365–370 completeness of, and personal interviews, 211 constructing, 343–349 development stage for, 336 evaluation of, 363 flowchart plan for, 351 for global markets, 362–363 layout for, 352–361 mark-sensed, 477 McDonald’s Spanish language, 373 pretesting and revising, 361–362 quality and design considerations, 336–337 response rates to, 221–225 sample, 484 sample of completed page from, 451 self-administered electronic, 225–231 self-administered mail, 219–225 sequence of questions in, 349–351 software to make interactive, 360–361 travel case study, 371–372 types of, 195–196 wording and measurement scales for, 375–384 Questions ambiguity in wording of, 345–346 assumptions made in, 347 burdensome, and memory, 347–348 complexity of, 363 double-barreled, 346–347 filter, 350, 466 to generate variance, 348–349 language for, 343 leading and loaded, 344–345 multiple-grid, 352 neutral, asking, 449 objectives of research and, 363 open-ended compared to fixedalternative, 338–341, 363 pivot, 350 for probing, 450 repeating, 448–449 rules for asking, 447–448 sample codes for, 482–483 selection of statistical techniques and, 516 for self-administered, telephone, and personal interview surveys, 341–342 sensitive or potentially embarrassing, 363 skip, 354, 356, 466 Quota sampling, 396–398 R Radio buttons, 358, 359 Radio frequency identification (RFID) tags, 22, 23 Raising Cane’s case study, 131 Random, definition of, 398 Random digit dialing, 217 Random digits, table of, 639 Random error, and sample size, 432–433 Randomization, 264–265, 280 Randomized-block design, 284, 555–556 Randomness, definition of, 398 Random sampling nonsampling errors and, 393–395 simple, 398–399 of Web site visitors, 407 Random sampling error, 188, 203, 394, 438 Range, 418–419 Ranking preferences, 327–328, 331 Ranking task in attitude measurement, 316 Rank order, calculation of, 496–498 Rating scales advantages and disadvantages of, 326 balanced or unbalanced, 330 category, 318, 319, 330 category labels, 329–330 composite, 320, 596 constant-sum, 323 forced-choice, 330–331 7/14/09 9:20:25 AM Index graphic, 323–325, 360 Likert, 303, 318–319, 341 monadic compared to comparative, 329 numerical, 322 ranking scales compared to, 331 semantic differential, 320–321, 328, 341 simple attitude, 317 single measure compared to index measure, 331 Stapel, 322–323, 341 summated, 318–319 Thurstone, 325 Rating task in attitude measurement, 316 Ratio scales, 300–301 Raw data, 462 Raw regression estimates, 567 Real-time data capture, 229 Recording responses, 449–450 Records in data files, 470 Recruited ad hoc samples, 408 Refusals, 190 Regression analysis See also Multiple regression analysis equation for, 564, 566 errors in prediction, 569 ordinary least-squares method of, 569–574 overview of, 564 parameter estimate choices, 566–567 visual estimation of simple model, 567–568 Regression coefficients in multiple regression analysis, 585–586 Reinterviewing, verification by, 457 Relativism, ethical, 89–90 Relevance of data, 21, 35 of questionnaire, 336–337 Relevant, definition of, 120 Reliability of measurement, 305–307, 309 of sampling, 388–389 Reluctant “don’t know” answers, 467 Repeated measures, 263 Replication, 154 Reports format of, 611–617 graphic aids for, 617–625 on Internet, 626 oral presentation of, 625–626 tips for writing, 608 Representative samples Internet surveys and, 228 telephone interviews and, 217 Research, definition of, 5–6 Research analysts, 78 Research assistants/associates, 78–79 Research design See also Experimental design deception in, 93 definition of, 66 influence of decision statements on, 123 planning, 66–68 secondary-data, 161–163, 171–179 for surveys, 231–232 Researcher-dependent research, 133 Researchers as communicators, 609–610 rights and obligations of, 94–100 Research firms, largest, 79 Research follow-up, 627 Research generalist, 85 Research methodology section of report, 616 Research objectives, 63 See also Objectives 93754_31_Ind_668-674.indd 673 673 Research process alternatives in, 62–63 challenges in, 75–76 defining objectives, 63–66 drawing conclusions and preparing report, 70 gathering data, 69 overview of, 61–62 planning design, 66–68 processing and analyzing data, 70 sampling, 68–69 Research program strategy, 70–71 Research project, 70–71 Research proposal as anticipating research outcomes, 127–128 basic points addressed by, 126 as contract, 125–127 description of, 124 as planning tool, 125 Research questions, 121–123 Research reports See Reports Research suppliers, 86 Resources See Budget for research Respondent error definition of, 189 nonresponse error, 189–191 response bias, 191–194 Respondents See also Participants, rights and obligations of; Subjects anonymity of, 212, 220, 230 choosing for focus groups, 145 definition of, 186 Response bias, 191–194 Response latency, 243 Response rates description of, 221–222 Internet surveys and, 230 for mail surveys, increasing, 222–225 Responses, recording, 449–450 Results, presentation of, 98, 99 Results section of report, 616 Retail Forward, 87 Return on investment for research, 615 ReTweetability Index, 497 Reverse coding, 304 Reverse directory, 393 Reverse recoding, 319–320 Revising questionnaires, 361–362 Ringtones, 165 R J Reynolds, 110 Robot technology, 55 Roeder-Johnson Corporation, 333 Rolling Rock beer, 68 Royal Bee electric fishing reel, 236–237 Rule of parsimony, 595 S Sales, mixing with research, 95–96 Salesperson input, 25 Sample attrition, 277 Sample bias, 189 Sample distribution, 424–425, 426 Sample selection error, 194 Sample size determining on basis of judgment, 438 population size and, 435, 439 probability sampling and, 438–439 for proportions, 435–438 for questions involving means, 433–435 random error and, 432–433 Sample statistics, 413 Sample survey, 186 Sampling accuracy and reliability of, 388–389, 404–405 description of, 68–69, 387 Internet surveys and, 406–409 nonprobability, 395–398 pragmatic reasons for, 387 probability, 398–404, 408 random, 393–395, 398–399, 407 selection of method of, 404–406 sequential, 434 stages in, 391 stratified, 400, 438–439 target population, defining, 390, 408 training interviewers to avoid errors in, 454 verification of plan for, 455–456 Sampling distribution of sample mean, 424–425, 426, 427 Sampling frame error, 393, 394–395 Sampling frames, 391–393, 411 Sampling interval, 399 Sampling services, 392 Sampling units, 393 SAS, 499–500, 538, 595 Scale measurement determining which to use, 310 influence of, on multivariate data analysis, 583 interval scale, 300 nominal scale, 297–298 ordinal scale, 299–300 overview of, 296–297 ratio scale, 300–301 types of, 298, 299 Scales See also Rating scales; specific types of scales description of, 295 mathematical and statistical analysis of, 301–303 Scale values, computing, 303–304 Scanner-based consumer panels, 250 Scanner data, 26–27, 28–29 Scantel Research, 14 Scarborough Research, 433 Schönbrunn Palace case study, 373–374 Schwinn bicycles, 141 Scientific decision processes, 155–156 Scientific method, 7–8, 45–47 Scientific Telephone Samples, 411 Scrolling layout, 357 Search engine, 32 Secondary data, 161–163, 171–179 Secondary-data research designs, 164–170 Secondary sampling units, 393 Security issues with Internet surveys, 230 Selection effect, 277 Selection of course of action, 9–10 Self-administered questionnaires electronic, 225–231 by mail, 219–225 Self-efficacy intervention and job attitude, 257–260 Self-selection bias, 191 Semantic differential scales, 320–321, 328, 341 Semi-structured interviews, 151–152 Send.com ad, 83 Sensitivity of measurement, 309 Sentence completion method, 152 Sequence of questions in questionnaires, 349–351 Sequential sampling, 434 Service monitoring, 97–98 Significance level, 510–512 Silent probe, 449 Simple (bivariate) linear regression, 564 Simple-dichotomy questions, 340 Single-source data, 26–27, 178 Site analysis techniques, 168–169 Situation analysis, 112–113 Skip questions, 354, 356, 466 Smart agent software, 33 SMART car, 116 Snowball sampling, 398 SOAP (Simple Object Access Protocol), 256 Social desirability bias, 193–194 Social networking, 152, 228, 497 Software See also SPSS Askia, 443 ATLAS.ti, 138 CHAID, 492 for data analysis, 499–501 GUI, 356 to make questionnaires interactive, 360–361 SAS, 499–500, 538, 595 smart agent, 33 Sorting task in attitude measurement, 316, 328 Sources of data, 171–178 Speed Internet surveys and, 227 telephone interviews and, 215 Split-ballot technique, 345 Split-half method, 306 Sponsorship of mail surveys, 224 SPSS (Statistical Package for the Social Sciences) correlation matrix, 565 cross-tabulation output, 500 data file stored in, 471 data storage terminology in, 470 factor analysis in, 595 MANOVA, conducting, 591 popularity of, 499 regression results, obtaining in, 587 reverse coding scales in, 305 Spyware, 92 Squishing error, 347–348 Standard deviation, 419–421, 434 Standard error, pooled estimate of, 535 Standard error of the mean, 425 Standardized distribution curve, 424 Standardized normal distribution, 421–422 Standardized normal tables, 422 Standardized regression coefficient (␤ ), 566 Standardized regression estimates, 567 Standardized research services, 87–88 Standardized value, computation of, 423 Stapel scales, 322–323, 341 Starbucks, 4, 5, 14 Static group design, 279–280 Statistical Abstract of the United States, 28 Statistical base, 490 Statistical databases, 28–29 Statistical software packages, 499–500 See also SAS; SPSS Statistical techniques determining when to use, 547 selection of, 516–518 Statistics, 413, 440 Status bar, 357 St Louis Community College, 213 Stratified sampling, 400, 438–439 String characters, 470 Structuration theory, 41 7/14/09 9:20:25 AM 674 Structured qualitative responses, coding, 469–470 Structured questions, 196 Students adjustment to college by, 406 as subjects, 277–278 weight gain by, 511 Subjective research, 135 Subjects See also Participants, rights and obligations of; Respondents children as, 92–93 description of, 258 matching, 265 students as, 277–278 Summary of research project, 614–615 Summated scales, 303–304, 318–319 Supervision of fieldworkers, 455–457 Suppliers and contractors client sponsors and, 100 limited research service companies, 88 standardized research services, 87–88 syndicated service, 86–87 top 25 global firms, 89 Surveys See also Questionnaires administrative error in, 194–195 advantages of, 187–188 categories of error in, 189 consumer panels, 198 cross-sectional studies, 196–197 description of, 66 ethical issues in, 233 longitudinal studies, 197–198 mobile, 207 participation in, pretesting and, 233 random sampling error in, 188 research designs for, 231–232 respondent error in, 189–194 rule-of-thumb estimates for error, 195 systematic error in, 189 total quality management, 200–203 uses of, 186–187 Survey Sampling International, 409 SurveySite, 407 Susceptibility to influence, 294, 297 SUV sales, 116, 182 Symptoms of business problem ambiguity of, 111 description of, 51–52 identifying, 114–115 identifying relevant issues from, 116, 117 as scattered or widespread, 111 Syndicated service, 86–87 Systematic error, 189, 195, 203, 264 Systematic sampling, 399 Systematic sampling error, 394 Syzygy research firm, 255 T TABH, Inc case study, 636–637 Table of contents, 613 Tables contingency, 488–489, 491 display of data in, 498–499, 618–619 dummy, 127–128 frequency, 488 standardized normal, 422 two-way contingency, 490 Tables, statistical area under normal curve, 640 chi-square distribution, 642 critical values of F for ␣ ϭ 01, 644 critical values of F for ␣ ϭ 05, 643 93754_31_Ind_668-674.indd 674 Index critical values of Pearson correlation coefficient, 645 critical values of T in Wilcoxon matched-pairs signed-rank test, 646 random digits, 639 t-distribution for given probability levels, 641 Tabulations, 362, 475–476, 488 See also Cross-tabulations Tachistoscope, 272 Tallying, 488 Target population, 69, 390, 408 T-distribution calculating confidence interval estimate using, 520–521 description of, 518–520 for given probability levels, 641 univariate hypothesis test using, 521–522 Technology and lifestyle, attitude survey regarding, 333–334 Telemarketing, 91–92 Telephone interviews automated surveys of teens, 218 central location, 217 characteristics of, 215–217 computer-assisted, 218 computerized voice-activated, 218–219 description of, 214 initial contact, 445 layout of page from, 353 mobile phone, 214–215 precoded format for, 473, 474 questions for, 341–342 with skip questions, 354 Telescoping error, 347–348 Television monitoring, 247–249 Temporal sequence, 58 Terminating interviews, 451 Tertiary sampling units, 393 Testing effect, 276 Test-market, 59–60, 271, 273 Test of differences, 530, 531 Test-retest method, 306–307 Test tabulation, 475–476 Test units, 264–266, 389 Test variables, 536 Texas Instruments, 255 Text-message surveys, 231 Thematic apperception test, 153–154 Themes, and case studies, 140 Theory building, 44–45 definition of, 39 goals of, 39 graphical presentation of, 43, 44 practical value of, 47 verifying, 43–44 Thomas and Dorothy Leavey Library, 459 Thurstone scales, 325 Time constraints mail surveys and, 221 need for research and, 11 sampling method and, 405–406 scientific decision process and, 155–156 Time for research, 82 Timeliness of data, 21 Time series designs, 282 Title page of report, 613 Titles of questionnaires, 352 Tobii Eye Tracker system, 461 Tooheys beer, 289 Totally exhaustive response alternative, 341 Total quality management, 198–203, 455 Total variability, 546 Toyota, 318 Tracking mechanisms case study, 105 Tracking studies, 198 Trade association sources of data, 176 Traffic cameras, 248 Training for interviewers, 445, 454 Transmittal letters, 613, 614 Travel questionnaire case study, 371–372 Trend analysis, 165 T-test for comparing two means, 534–540 description of, 518 independent samples, 534–538, 542 one- and two-tailed, 521 paired samples, 538–540 type of question and, 516 TV-Cable Week (magazine), 13 Twitter, 497 Two-tailed test, 521 Two-way ANOVA, partitioning sum of squares for, 556–557 Two-way contingency tables, 490 Type I and Type II error, 515–516 Type of research, and uncertainty, 60–61 U Umbria Communications, Buzz Report, 170 Unaided recall, 347 Unbalanced rating scales, 330 Uncertainty in decision making, 52–53 type of research and, 60–61 Undisguised questions, 196 Uniform resource locator (URL), 32 United Airlines survey, 10–11 Unit of analysis, determining, 119 Univariate statistical analysis, 509 Universal Product Code (UPC), 28–29, 176 Universe, 387 Unobtrusive methods of data gathering, 69, 92 Unrestricted samples, 407 Unstructured qualitative responses, coding, 468–469 Unstructured question, 196 Urbanized areas, geographic hierarchy in, 404 Usability assessment of Web site, 322 U.S Department of the Interior telephone survey, 481 Utah Jazz case study, 605–606 V Validity external, 277–278 internal, 274–277 of measurement, 307–309 Value labels, 471 Vangard AccuSpeech and Mobile Voice Platform, 477 Vans shoes, 132 Variable piping software, 360 Variables blocking, 258 categorical, 119, 261 concept values and, 296 decision situations, hypotheses and, 121 definition of, 42, 119 dependent, 120, 257, 263–264 dummy, 585 establishing control of, 269–270 experimental, 59 extraneous, 265, 266, 269–270, 275–277 grouping, 536 hypotheses and, 121, 296 independent, 120, 257, 260–263, 491 moderator, 492 nuisance, 264 operationalizing, 42–43 selection of statistical techniques and, 516–517 test, 536 types of, 119–120 Variance See also ANOVA covariance, 559, 561–562 dispersion and, 420 partitioning, 544–545 wording questions to generate, 348–349 Variate, definition of, 581 Vendors of data, 172 Verification by reinterviewing, 457 of sampling plan, 455–456 of theory, 43–44 Vidal Sassoon, Inc., 505 Videoconferencing, and focus groups, 148 Video databases, 29–30 Visible observation, 240 Visual aids, and personal interviews, 211 See also Graphical representations of data Visual estimation of simple regression model, 567–568 Voice-pitch analysis, 252 W Walker Information Group, 205 Wal-Mart, data warehouse of, 472 Wang Laboratories, 83 Water, bottled, trend for, 176 Web sites Business.gov, 616 description of, 32 FedWorld, 175 random sampling of visitors to, 407 statistical resources, 517 traffic to, monitoring, 249–250 usability assessment, 322 Welcome screen, 227 “Why” follow-up questions, 448 Wilcoxon matched-pairs signed-rank test, 646 Within-group error, 545 Within-subjects design, 273 Wording questions, 337–342, 375–384 Work-family conflict, 558 Working population, 391–393 World Wide Web (WWW), 32 Y Yankelovich Partners, 452 Yoplait Go-Gurt, 8–9 Z Z-distribution, 520 Zogby International, 430 Z-test, 520, 540–541 7/14/09 9:20:25 AM ... 198 160 March 199 175 April 20 0 181 May 20 0 1 92 June 20 0 20 0 July 20 0 20 0 August 20 1 20 2 September 20 1 21 3 October 20 1 22 4 November 20 2 24 0 December 20 2 26 1 Average 20 0 20 0 ■ THE RANGE The simplest... (4 Ϫ 3 .25 ) ϭ 75 5 625 (3 Ϫ 3 .25 ) ϭ Ϫ .25 0 625 (2 Ϫ 3 .25 ) ϭϪ1 .25 1.5 625 (5 Ϫ 3 .25 ) ϭ 1.75 3.0 625 (3 Ϫ 3 .25 ) ϭ Ϫ .25 0 625 (3 Ϫ 3 .25 ) ϭ Ϫ .25 0 625 (1 Ϫ 3 .25 ) ϭ? ?2. 25 5.0 625 (5 Ϫ 3 .25 ) ϭ 1.75 3.0 625 nϭ8... formula gives (1.96 )2( 0.6)(0.4) n ϭ ᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐ 0.03 52 (3.8416)(0 .24 ) ϭ ᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐᎐ 0.00 122 5 0. 922 ϭ ᎐᎐᎐᎐᎐᎐᎐᎐ 0.00 122 5 ϭ 753 93754_17_ch17_p4 12- 4 42. indd 436 7/14/09 8:31 :22 AM