Secondary Sources Of Information

 “A few months of field work collecting data can save several hours in the library”

Secondary data (or secondary information) is information that has been collected by persons or agencies for purposes other than the solution of the marketing research problem at hand. These data may have been collected from sources within the researcher’s firm or from sources outside the firm. The key point is that the data were collected for some other project, or reason, than the current one.

In contrast, primary data is data collected for the researcher’s current research project. Primary data is often collected from a respondent, an individual who provides information either passively through the observation of his or her behavior, or actively through verbal response. Researchers using primary data must be concerned with information obtained by asking questions, by observing behavior or by examining the results of past behavior.

In addition to primary and secondary data, there exists commercial data sold in the form of syndicated services. These data are collected by commercial marketing research firms or industry associations and, as such, have characteristics of both primary and secondary data. Since these data relate to ongoing concerns of a marketer they can be viewed as primary data.

However, the commercial agency did not design its service solely to provide information for one company’s specific project. Thus, there are elements of secondary data. It should be clear that distinctions between primary and secondary commercial data may be minimal

In this chapter, we discuss the reasons for obtaining secondary information, types of secondary information, sources of external secondary data, and syndicated services that provide commercial data. Data, in all their forms, are the heart of research. Secondary research can help provide a clearer picture of a problem so that researchers and managers can make the necessary critical decisions.

Reasons For Obtaining Secondary Information

As a general rule, no research project should be conducted without a search of secondary information sources. This search should be conducted early in the problem investigation stage and prior to any organized collection of information from primary sources. There are several reasons for this.

Secondary Information May Solve the Problem

If adequate data are available from secondary sources, primary data collection will not be required. For example, Campbell Soup Co. based a long running advertising campaign on the theme “soup is good food.” This theme emerged from federal government data pertaining to eating habits, nutritional health, and related topics collected over a period of 15 years. 

1959 : Campbell's Soup

Secondary Information Search Costs Substantially Less

Comprehensive search of secondary sources can almost always be made in a fraction of the time and cost required for the collection of primary information. This is particularly true today with online access to research publications and databases. Searching for secondary research helps you avoid duplicating primary research and optimizes research expenditures by acquiring only information that cannot be found else where. Many marketing problems do not warrant expenditures for primary information collection, but are worth the time and cost of secondary information.

Secondary Information Has Important Supplementary Uses

When secondary information cannot solve the research problem, it can provide invaluable supplemental uses  :

1. Defining the problem and formulating hypotheses about its solution. The analysis of available secondary data will almost always provide a better understanding of the problem and its context, and will frequently suggest solutions not considered previously.
2. Planning the collection of primary data. An examination of the methods and techniques employed by other investigators in similar studies may be useful in planning the present one. It may also be of value in establishing classifications that are compatible with past studies so that trends may be more readily analyzed.
3. Defining the population and selecting the sample. Past information and samples may help establish classifications for current primary information collection.

The researcher must be careful when using only secondary data. To be useful, secondary data must be available, relevant to the information needs (which includes being timely), accurate, and sufficient to meet data requirements for the problem at hand. Often, little is known about the reliability of secondary research studies. It is important that the researcher know how the secondary data being considered for use were collected, if the data is reliable, and if the right techniques were used.

Example : One company wants to do a segmentation analysis on foreign markets with a particular emphasis on examining demographics (Albaum, Duerr, & Strandskov, 2005, Chap. 5). The company is considering using the official government census of the population. However, they become aware that the data are not available from all markets in equal quantity, aggregation, and detail, and the reliability of data is not the same.

What one gets from a census depends on what was on the census form in the first place typically a mixture of traditional questions and new items of interest to public policy makers and civil servants at the time. Some countries publish information about noncitizens, and others collect data on religion—both of these topics are ignored in U.S. Censuses. Income is one of the major dimensions of U.S. segmentation research, but many highly developed nations ignore the income question in their censuses.

In short, one cannot always expect to find the same range of data topics that you are interested in. More over, the data may not use the same categories when showing relevant distributions of demographic variables, such as age. In short, comparability and equivalence issues arise, and these can hinder your effectiveness in using secondary data.

Types Of Secondary Information

Secondary information falls into two categories, data that is available within the company (internal data) and that which must be obtained from outside sources (external data).

Internal Secondary Information

All companies collect information in the everyday course of conducting business. Orders are received and filled, costs are recorded, warranty cards are returned, sales people’s reports are submitted, engineering reports are made—all are collected for other purposes, but may be useful to the researcher (Andreasen, 1988, pp. 77–89). The key, of course is knowing where they are and how to access them. In order to do this efficiently, the firm must have an effective marketing information system.

Example : Spectra Physics Lasers Division (producing laser grocery store scanners) regularly performs customer satisfaction studies. Where as these studies are primary research to the Retail Systems organization, they are internal secondary information to other divisions that may want to look at them. Also, they can be secondary data to Retail Systems should they be used at a much later date for aiding in decision making or for purposes other than those originally intended when the studies were done.

External Secondary Information

External secondary information is available in staggering assortments and volumes. It also is applicable to all of the major types of marketing research projects and is mainly concerned with the noncontrollable aspects of the problem :

• Total market size
• Market characteristics
• Competitor products, prices, promotional efforts, and distribution methods

As an example, a consumer goods company is considering whether it should establish a direct selling operation. Direct selling is defined as personal contact between a sales person and a consumer away from a fixed business location such as a retail store. The Direct Selling Association (DSA) provides secondary information in the form of a regular survey of the industry. Some types of information on industry statistics and sales force demographics are available on a regular basis. Some of the types of industry statistics are the following :

• Estimated U.S. sales
• Estimated U.S. salespeople
• Percent of sales by major product groups
• Location of sales
• Percent of sales by census region
• Sales strategy
• Compensation structure by percent of firms
• Compensation structure by percent of sales dollars
• Compensation structure by percent of sales people

The following are some of the sales force demographics available :

• Gender
• Age Education
• Independent contractor/employee status
• Hours per week dedicated to direct selling
• Average time spent on direct selling tasks
• Main reasons for becoming a direct sales representative 
• Percent of sales people by distributor ship type

Age, education, average time spent on direct selling tasks, and main reason for becoming a direct sales representative data are from DSA’s National Sales force Survey. (Direct Selling Association, 2003;)

Sources Of External Secondary Data

The major original sources of external secondary information are :

1. Government (supranational, federal, state, and local)

2. Trade associations and trade press

3. Periodicals and professional journals

4. Institutions (e.g., universities)

5. Commercial services

Government Data Sources

The federal government is by far the largest single source of this type of data. Both governmental and trade sources are so important that the experienced researcher will be thoroughly familiar with them in his or her field of specialization. Periodicals and research publications of universities and research institutes frequently provide valuable information. Commercial services of many types are available that are highly useful for specific research problems.

Market performance studies on consumer products, for example, will normally provide such demographic information as the number of consumers (or consuming units) by age group, income class, gender, and geographic area. Such data are usually available on a reasonably recent basis from censuses conducted by federal, state, local, and, when needed, supranational governments.

Often, a good first source is the Statistical Abstract of the United States, available online from the Bureau of the Census http://www.census.gov/compendia/statab/. This reference abstracts data from original reports and gives some useful material on social, political, and economic matters. The source is a good reference to the more detailed data in the original sources.

The State and Metropolitan Area Data Book is a publication of the Bureau of the Census that is available online in PDF format. It provides detailed comparative data on states, metropolitan areas and their component counties, and central cities. It covers information about numerous topics relevant for both B2C and B2B marketing, including population, income, labor force, commercial office space, banking, health care, housing, and so forth. 

The Census of Population http://www.census.gov/population/www/ and the Census of Housing http://www.census.gov/hhes/www/housing.html taken by the U.S. Department of Commerce every 10 years are the most comprehensive of such censuses. Updates of various census measurements based on smaller yearly surveys are available in Current Population Reports  and Current Construction and Housing Reports Many other up-to-date estimates are made periodically by governmental and non–governmental agencies.

Data from the U.S. Census Bureau is available online for custom data analysis, on CDROM, and in report form as downloadable PDF files. There are, however, private companies that make such data available for a fee in more processed form, which, in effect, adds value to the Census Bureau data. The company previously mentioned, GeoLytics (http://geolytics.com/), markets a line of census data products and a variety of custom data retrieval services.

􀁸 Demographic reports (and maps)

• Custom data sets and reports Area segmentation
• Area to area correspondence files
• Banking and realtor tract level data and maps

􀁸 Services

• Geocoding (GPS) addresses
• Custom-built databases
• Normalized data for across census comparisons

Other companies include census data in mapping software that is used for geographic market analysis. This type of software is potentially useful for such applications as retail site analysis, real estate site reports, direct marketing, database creation, and so forth. One supplier is Scan/US, Inc. (http://www.scanus.com ), whose software product Scan/US Streets and Data U.S.A. includes maps for the entire United States that include all types of demographics.

Private Data Sources

Private organizations are another source of demographic information useful to marketers. To illustrate, SRDS  publishes The Lifestyle Market Analyst. This annual provides demographic and lifestyle information for 210 Designated Market Areas (DMAs) in the United States. As shown in Table 3.1 and Figure 3.1, this market data can be accessed in graphic and tabular formats for demographic and lifestyle variables:

• Demographic categories for each DMA: Start with a specific demographic segment, such as dualincome households, and identify lifestyles and geographic locations.
• Most popular lifestyles for each DMA: Specify a lifestyle and then identify what other interests
• frequently appeal to those consumers and what demographic information corresponds to that profile.

Nielsen’s Claritas division, a provider of solutions for geographic, lifestyle and behavioral target marketing, has developed a demographic widget that is available as a free download for personal electronics.

Market size studies (e.g., size in sales dollars or units) often are conducted by trade associations, media, firms in the industry, and private research organizations. These studies are published and made available to interested parties. Industry type studies may be concerned with such types of information as total market size, market characteristics, market segments and their size and characteristics, and similar types of information.

Example: Mediamark Research, Inc. conducts a single source continuing survey, primarily aimed at the advertising industry that provides demographics, lifestyles, product usage, and exposure to all advertising media data. One part of this study is a series of studies on specific products/services that is published as syndicated reports. 

Information on new products and processes is available from such sources as patent disclosures, trade journals, competitors’ catalogs, testing agencies, and the reports of governmental agencies, such as the Food and Drug Administration, the Department of Agriculture, and the National Bureau of Standards.

Starch Readership Reports

The best way to create print ads for the future, and for the long term, is to get feedback on a constant basis in order to find out what works and what doesn’t.

Each year, Starch measures over 25,000 ads in over 400 magazine issues. On the most basic level you get raw readership scores the percent of readers who saw the ad and read the copy. Then the data are put

into a context: The ad is ranked not only against other ads in the issue but also against other ads in its product category over the past two years. These norms are a fast and easy way to judge the performance of your ad over time and against the competition.

The Benefits of Starch Ad Readership 

In Depth Analysis

Campaign analyses inform clients not only about the scores of the ads but also why they performed as they did and what can be done to improve the ads. Moreover Starch also is unique in its ability to tell clients about the best positions in various publications (e.g., whether farforward positioning is superior to ads in the back of the book).

Extra Questions

• To give you information on advertising like ability, persuasiveness and intent to purchase
• Many times, if you ask a publisher to Starch an issue your ads will appear in, they will assume the cost and pass on the data to you for free

The Starch Ad Readership Program

Through the Book, Recognition Method

• One-to-one in-person interview
• Generally, 100–200 sample, but can be more if client desires
• Sample approximates readership of publication, but is not representative
• Reports present data on 

            o Noted: percent who saw any part of the ad

            o Associated: percent who saw advertiser’s name

            o Read Some: percent who read any of the copy

            o Read Most: percent who read more than half the copy

• Most reports also offer indexed scores, based on ads of the same size, color, product category

Internet Databases

The Internet has become the staple of research and provides access to most commercial electronic databases. Thousands of such databases are available from numerous subscription systems, such as DIALOG (http://www.dialog.com ), LexisNexis (http://www.lexisnexis.com/), or Dow Jones News/Retrieval http://www.dowjones.com/Products_Services/ElectronicPublishing/EnterpriseMedia.htm.

In general, there are five categories of commercial databases :

1. Bibliographic databases that index publications
2. Financial databases with detailed information about companies
3. Statistical databases of demographic, econometric, and other numeric data for forecasting and doing projections
4. Directories and encyclopedias offering factual information about people, companies, and organizations
5. Full text databases from which an entire document can be printed out.

The advantages of such current databases are obvious. All that is needed is personal computer with internet access or a CD-ROM/DVD.

Computerized databases have led to an expanded focus on database marketing. Database marketing has been defined as an extension and refinement of traditional direct marketing, but uses databases to target direct response advertising efforts and tracks response and/or transactions. In database marketing, the marketer identifies behavioral, demographic, psychographic, sociological, attitudinal, and other information on individual consumers/households that are already served or that are potential customers. Data may come from secondary and/or primary sources. Qualtrics clients are increasingly APIs (Application Programming Interface) to link and integrate customer databases with survey data and respondent panels. APIs can be used to link and integrate data from multiple sources in real time. Thus, information in database profiles is augmented by new contact and survey data, and can be viewed in dashboards that report current information and can be used to better target and predict market response. Databases can be used to estimate market size, find segments and niches for specialized offerings, and even view current customer use and spending (Morgan, 2001). In short, it helps the marketer develop more specific, effective, and efficient marketing programs.

Today, data mining is in high demand as a research focus. Data mining involves sifting through large volumes of data to discover patterns and relationships involving a company’s products and customers. Viewed as a complement to more traditional statistical techniques of analysis, two of the more powerful data mining techniques are neural networks and decision trees (Garver, 2002). Further discussion of data mining techniques is beyond the scope of this text, but good discussions are found in Berry & Linoff (1997, 2002) and Dehmater & Hancock (2001).

Elkind and Kassel (1995) provided essential guidelines for attaining market knowledge from online sources :

• Develop an online research plan. The plan will outline all the key areas of inquiry and will provide a systematic pathway to search, retrieve, and arrive at the desired data, information, and knowledge.
• Clearly define your information needs, knowledge gaps, and issue to be resolved. One of the best  ways is to do a knowledge inventory or review to determine what you already know or have in your possession, both primary and secondary research.
• Focus the search. Start by applying the learning from your knowledge inventory and specify the new areas that are critical to your project. The focus can be further enhanced by specifying key hypotheses regarding possible findings, information categories relevant to the issue, and other criteria such as product categories, consumer targets, market areas, time frames, and so on.
• Search across multiple sources. Don’t expect to find what you need in single pieces of data or sources of information. You only rarely will find what you need in one place
• Integrate information from the multiple sources. Use techniques of trend analysis to identify patterns that emerge when various information elements are combined; for example, content analysis, stakeholder analysis, paradigm shift, trendlines, critical path analysis, sector analysis (technological, social/cultural, occupational, political, economic), or other analytic techniques that facilitate integration of diverse data and information and identification of underlying patterns.
• Search for databases that contain analyses rather than limiting the search to just data or information. Many of the professional online database producers and vendors offer thousands of full text articles and resources that contain analyses. You may be able to find material that already provides some interpretation that may be helpful.
• Enhance the robustness of your data or information through multiple source validation. You can increase confidence in the validity of the findings of your secondary searches by looking for redundant patterns that cut across different sources and studies

Syndicated Service

Some of the aforementioned commercial services are examples of what are called syndicated services. Research organizations providing such services collect and tabulate specialized types of marketing information on a continuing basis for purposes of sale to a large number of firms. In general, syndicated data are made available to all who wish to subscribe. Reports are made available on a regular basis (for example, weekly, monthly, quarterly). Since these data are not collected for a particular firm for use in a specific research problem situation, they can properly be viewed as secondary data. Syndicated services are widely used in such areas as movement of consumer products through retail outlets, direct measures of consumer purchases, social trends and lifestyles, and media readership, viewing, and listening.

The syndicated Survey of American Consumers (based on surveying more than 25,000 adults) by Mediamark Research, Inc. provides an illustration of syndicated services. This survey provides data useful for detecting a marketer’s best prospects by providing answers to such questions as the following:

• How many customers are there for the products or services we market? Is the size of the market growing? Stabilizing? Or shrinking?
• Who are the customers? How old are they? What do they earn? Where do they live?
• How do customers differ in terms of how often and how much they buy? Who are the heaviest purchasers of the product?
• What brands are customers buying? How have shares of the market changed? What differences are there among brand buyers?
• What’s the best way of reaching prospects? Which media vehicles and formats are most efficient in delivering the message to the customer?

Mediamark is able to profile American consumers on the basis of more than 60 demographic characteristics and covers usage of some 500 product categories and services and 6,000 brands.

Types of Syndicated Services

Syndicated data may be obtained by personal interviews, direct observation, self reporting and observation, or use of certain types of mechanical reporting or measuring devices. One of the most widely used approaches is the continuous panel, which refers to a sample of individuals, households, or firms from whom information is obtained at successive time periods. Continuous panels are commonly used for the following purposes:

1. As consumer purchase panels, which record purchases in a consumer diary and submit them periodically.
2. As advertising audience panels, which record programs viewed, programs listened to, and publications read.
3. As dealer panels, which are used to provide information on levels of inventory, sales, and prices.

Such panels have been established by many different organizations, including the federal government, various universities, newspapers, manufacturers, and marketing research firms. These types of panels furnish information on at regular intervals on continuing purchases of the products covered.

For example, typical consumer panels might report the type of product purchased by brand, weight or quantity of unit, number of units, the kind of package or container, price per unit, whether a special promotion was in effect, store name, and date and day of week of purchase. Data are recorded in diaries, either online or are mailed in each month.

One of the largest consumer panels is maintained by NPD Research This panel comprises 13,000 families and is national in coverage. NPD also maintains self-contained panels in 29 local markets. 

Advertising audience panels are undoubtedly more widely publicized than other panels. It is from these panels that television and radio program ratings are derived. These panels are operated by independent research agencies rather than the media both for reasons of economy and to avoid any question of partisanship.

For example, ACNielsen uses a metering device that provides information on what TV shows are being watched, how many households are watching, and which family members are watching. The type of activity is recorded automatically; household members merely have to indicate their presence by pressing a button. The sample is about 5,000 households. In local markets, the sample may be 300 to 400 households.

Single source data tracks TV viewing and product purchase information from the same household. Mediamark’s national survey and IRI’s Behavior Scan are examples of such single source data. The single source concept was developed for manufacturers who wanted comprehensive information about brand sales and share, retail prices, consumer and trade promotion activity, TV viewing, and household purchases.

The information obtained from the types of syndicated services described previously has many applications. The changes in level of sales to consumers may be analyzed directly without the problem of determining changes in inventory levels in the distribution channel. Trends and shifts in market composition may be analyzed both by type of consumer and by geographic areas. A continuing analysis of brand position may be made for all brands of the product class. Analyses of trends of sales by package or container types may be made. The relative importance of types of retail outlets may be determined. Trends in competitor pricing and special promotions and their effects can be analyzed along with the effects of the manufacturer’s own price and promotional changes. Heavy purchasers may be identified and their associated characteristics determined. Similarly, innovative buyers may be identified for new products and an analysis of their characteristics made to aid in the prediction of the growth of sales. Brand switching and brand loyalty studies may be made on a continuing basis. One reported use of this syndicated service has been to design products for specific segments.

The products from syndicated services are continually changing with client needs and new technological opportunities.

Summary

This chapter has been concerned with secondary information and sources of such information. We started with some reasons why secondary information is essential to most marketing research projects. Then, various sources and types of secondary information internal and external were discussed in some depth. Also given more than cursory treatment was syndicated data, a major type of service provided by commercial agencies.

Rerferences

ACNielsen: http://en-us.nielsen.com

ACNielsen – Claritas Demographic Widget, http://www.claritas.com/target-marketing/nielsenclaritas-

demographic-widget.jsp

Albaum, G., Duerr, E., & Strandskov, J. (2005). International marketing and export management

(5th ed.). Harlow, UK: Pearson Education.

Andreasen, A. (1988). Cheap but good marketing research. Homewood, IL: Dow-Jones Irwin.

Berry, M., & Linoff, G. (1997). Data mining techniques: For marketing, sales, and customer

support. New York: Wiley.

Berry, M., & Linoff, G. (2002). Mining the Web: Transforming customer data. New York:

Wiley.

Campbells 1959 attribute commercial http://www.youtube.com/watch?v=qMJtLP8jMWQ

Campbells 1985 Soup is good food commercial http://www.youtube.com/watch?v=4PtZYlcyXfI

Dehmater, R., & Hancock, M. (2001). Data mining explained: A manager’s guide to customer

centric business intelligence. Burlington, MA: Digital Press

DIALOG http://www.dialog.com

Direct Selling Association, 2003 http://www.dsa.org/pubs/

Dow Jones News/Retrieval

http://www.dowjones.com/Products_Services/ElectronicPublishing/EnterpriseMedia.htm

Elkind, F., & Kassel, A. (1995, July 31). A marketer’s guide for navigating the information

superhighway. Marketing News, 29, 2ff.

Garver, M. S. (2002, September 16). Try new data mining techniques. Marketing News, 36, 31–

33.  

IRI: infores.com 

LexisNexis http://www.lexisnexis.com

Morgan, M. S. (2001, October 8). Research boosts database’s power. Marketing News, 35, 16.

NFO: mysurveys.com

NPD Research http://www.npd.com

NPD Research, Inc. http://www.npdor.com

RoperASW. (2002). Available at RoperASW.com. Accessed January 9, 2002.

SRDS Life Style Market Analyst, http://www.srds.com/frontMatter/ips/lifestyle/index.html

SRDS Graphical Reports,

http://www.srds.com/frontMatter/ips/lifestyle/images/LMAA_overview-with-map.jpg

Synovate: synovate.com

U.S. Census Bureau, Statistical Abstract, http://www.census.gov/compendia/statab/

U.S. Census of Population, http://www.census.gov/population/www/

U.S. Census of Housing, http://www.census.gov/hhes/www/housing.html

U.S. Current Population Reports, http://www.census.gov/main/www/cprs.html

U.S. Current Construction and Housing Reports, http://www.census.gov/prod/www/abs/conshou.

html

Defining The Research Design And Controlling Research Errors

A research design specifies the methods and procedures for acquiring the information needed to structure and solve the research problem. The overall operational design for a research project stipulates what information is to be collected, from what sources, and by what procedures. A good research design ensures that the information obtained is relevant to the research problem, and that it is collected by objective and economical procedures. A research design might be described as a series of advance decisions that, taken together, form a master plan or model for conducting a research investigation.

Research designs vary depending on the type of study. Generally designs are associated with three types of studies, those that focus on providing exploratory research, descriptive research and causal research. Each will be described in turn. 

Exploratory Studies

The major purposes of exploratory studies are for the identification of problems, the precise formulation of problems (including the identification of relevant variables), and the formulation of new alternative courses of action.

An exploratory study is often the first in a series of projects. That is, an exploratory study is often used as an introductory phase of a larger study, and its results are used to bring focus to the larger study and to develop specific techniques that will be used. Thus flexibility is a key to designing and conducting exploratory studies.

We can distinguish three separate tasks that are usually included in exploratory studies and that are typically conducted in the sequence listed :

• A search of secondary information sources
• Interviews with persons knowledgeable about the subject area
• The examination of analogous situations

Search Secondary Sources

Secondary sources of information are the “literature” on the subject. It is the rare research problem for which there is no relevant information to be found by a relatively quick and inexpensive search of the literature. Secondary information sources are not limited to external sources. Searches should also be made of company records.

Interview Knowledgeable Persons

Having searched secondary sources, it is usually desirable to talk with persons who are well informed in the area being investigated, including company executives, experts, consumers and mavens, and users outside the organization

A widely used technique in exploratory research is the focus group. In focus group interviews, a group of knowledgeable people participate in a joint interview that does not use a structured question-and-answer methodology. The group, usually consisting of 8 to 12 people (but may have as few as 5 or as many as 20), is purposely selected to include individuals who have a common background, or similar buying or use experience, as related to the problem being researched. The interviewer or moderator of the focus group session works with the client to develop a general discussion outline that typically includes such topics as usage experience, problems with use, and how decisions are made. The objective is to foster involvement and interaction among the group members during the interview that will lead to spontaneous discussion and the disclosure of attitudes, opinions, and information on present or prospective buying and use behavior.

Focus groups are used primarily to identify and define problems, provide background information, and generate hypotheses. Focus groups typically do not provide solutions for problems. Areas of application include detecting trends in lifestyles, examining new product or service concepts, generating ideas for improving established products or services, developing creative concepts for advertising, and determining effective means of marketing products or services.

If the sole purpose is to create ideas, then individual interviews may be a better alternative than focus groups. Limited research on this issue conducted more than 20 years ago suggests that the number and quality of ideas generated may be greater from such interviews (Fern, 1982).

More specific uses of focus groups include :

1.	Identifying and understanding consumer language relating to the product category in question. What terms do they use? What do they mean?
2.	Identifying the range of consumer concerns. How much variability is there among consumers’ perception of the product, and in the considerations leading them to accept or reject the product?
3.	Identifying the complexity of consumer concerns. Do a few simple attitudes govern consumer reaction toward the product, or is the structure complex, involving many contingencies?
4.	Identifying specific methodological or logistical problems that are likely to affect either the cost of the subsequent research, or one’s ability to generate meaningful, actionable findings.

An example of focus group usage might be to determine the reasons for the decline in a product’s overall rating, as reported in a syndicated research report

Examine Analogous Situations

It is also logical that a researcher will want to examine analogous situations to determine what else can be learned about the nature of the problem and its variables. Analogous situations include case histories and simulations. More discussion of the use of focus groups is given in Chapter 4

Descriptive Studies

Much research is concerned with describing market characteristics or functions. For example, a market potential study may describe the number, distribution, and socioeconomic characteristics of potential customers of a product. A market-share study finds the share of the market received by both the company and its major competitors. A sales analysis study describes sales by territory, type of account, size or model of product, and the like. In marketing, descriptive studies are also made in the following areas :

-	Product research: identification and comparison of functional features and specifications of competitive products
-	Promotion research: description of the demographic characteristics of the audience being reached by the current advertising program
-	Distribution research: determining the number and location of retailers handling the company’s products that are supplied by wholesalers versus those supplied by the company’s distribution centers
-	Pricing research: identifying competitors’ prices by geographic area

These examples of descriptive research cover only a few of the possibilities. Descriptive designs, often called observational designs by some researchers, provide information on groups and phenomena that already exist; no new groups are created (Fink, 2003).

One example of a descriptive study is one conducted by a school-employees credit union  in order to gain information useful to provide better service to its members. Management knew very little about the members, other than that they were school employees, family members of employees, or former employees. In addition, the credit union knew very little about member awareness and use of, and attitudes toward individual services available to them. Consequently, investigators undertook a study to answer the following research questions :

-	What are the demographic and socioeconomic characteristics of primary members?
-	How extensively are existing services being used, and what are members’ attitudes toward such services?
-	What is the degree of interest in specific new services?

Although associations can be used only to make inferences, and not establish a causal relationship, they are often useful for predictive purposes. It is not always necessary to understand causal relations in order to make accurate predictive statements. Descriptive information often provides a sound basis for the solution of marketing problems, even though it does not explain the nature of the relationship involved. The basic principle involved is to find desirable behavior correlates that are measurable when the predictive statement is made.

Causal Studies

Although descriptive information is often useful for predictive purposes, where possiblewe would like to know the causes of what we are predicting—the “reasons why.” Further, we would like to know the relationships of these causal factors to the effects that we are predicting.mIf we understand the causes of the effects we want to predict, we invariably improve our ability  both to predict and to control these effects

Bases for Inferring Causal Relationships

There are three types of evidence that can be used for drawing inferences about causal relationships:

1. Associative variation

2. Sequence of events

3. Absence of other possible causal factors

In addition, the cause and effect have to be related. That is, there must be logical implication (or theoretical justification) to imply the specific causal relation.

Associative Variation

Associative variation, or “concomitant variation,” as it is often termed, is a measure of the extent to which occurrences of two variables are associated. Two types of associative variation may be distinguished :

-	Association by presence: A measure of the extent to which the presence of one variable is associated with the presence of the other
-	Association by change: A measure of the extent to which a change in the level of one variable is associated with a change in the level of the other.

It has been argued that two other conditions may also exist, particularly for continuous variables:

(a) the presence of one variable is associated with a change in the level of other; and (b) a change  in the level of one variable is associated with the presence of the other (Feldman, 1975).

Sequence of Events

A second characteristic of a causal relationship is the requirement that the causal factor occur first; the cause must precede the result. In order for salesperson retraining to result in increased sales, the retraining must have taken place prior to the sales increase.

Absence of Other Possible Causal Factors

A final basis for inferring causation is the absence of any other possible causes other than the one(s) being investigated. If it could be demonstrated, for example, that no other factors present could have caused the sales increase in the third quarter, we could then logically conclude that the salesperson training must have been responsible.

Obviously, in an after-the-fact examination of an uncontrolled result such as an increase

in detergent sales, it is impossible to clearly rule out all causal factors other than salesperson retraining. One could never be completely sure that there were no competitor-, customer-, or company-initiated causal factors that would account for the sales increase.

Conclusions Concerning Types of Evidence

No single type of evidence, or even the combination of all three types considered, can ever conclusively demonstrate that a causal relationship exists. Other unknown factors may exist. However, we can obtain evidence that makes it highly reasonable to conclude that a particular relationship exists. Exhibit 2.1 shows certain questions that are necessary to answer.

EXHIBIT 2.1 Issues in Determining Causation

Several questions arise when determining whether a variable X has causal power over another variable, Y :

1.	What is the source of causality—does X cause Y, or does Y cause X?
2.	What is the direction of causality—does X positively influence Y, or is the relationship negative?
3.	Is X a necessary and sufficient cause—or necessary, but not sufficient cause—of Y? Is X’s causation deterministic or probabilistic?
4.	Which value of the believed cause exerts a causal influence—its presence or absence?
5.	Are the causes and effects the states themselves or changes in the states? Is the relationship static or dynamic?

In the end, the necessary conditions for causality to exist are a physical basis for causality, a cause that temporally precedes the effect (even for associative variation), and a logical reason to imply the specific causal relation being examined. (Monroe and Petroshius, n.d.).

SOURCES OF MARKETING INFORMATION

There are five major sources for obtaining marketing information. In this section we briefly describe each as an introduction to subsequent chapters that describe some of these sources in more depth.

• Secondary sources
• Respondents
• Natural experiments
• Controlled experiments
• Simulation

Sources of Information

Secondary information is information that has been collected by persons or agencies for purposes other than the solution of the problem at hand.

If a furniture manufacturer, for example, needs information on the potential market for furniture in the Middle Atlantic States, many government and industry sources of secondary information are available.

The federal government collects and publishes information on the numbers of families, family formation, income, and the number and sales volume of retail stores, all by geographic area. It also publishes special reports on the furniture industry. Many state and local governments collect similar information for their respective areas.

The trade associations in the furniture field collect and publish an extensive amount of information about the industry. Trade journals are also a valuable source of secondary information, as are special studies done by other advertising media.

Private research firms collect specialized marketing information on a continuing basis and sell it to companies. These so-called syndicated services, particularly those for packaged consumer goods, are becoming more sophisticated as they are increasingly becoming based on scanner data. Technology advancements are having a measurable impact on the availability of secondary data.

Information from Respondents

A second major source of information is obtained from respondents. Asking questions and observing behavior are primary means of obtaining information whenever people’s actions are being investigated or predicted.

The term respondent literally means “one who responds or answers.”Both verbal and behavioral responses should be considered.

In this book we shall consider both the information obtained from asking people questions, and that provided by observing behavior (or the results of past behavior) to comprise information from respondents.

Information from Natural and Controlled Experiments

As described earlier, three types of evidence provide the bases for drawing inferences about causal relationships. While both natural and controlled experimental designs are capable of providing associative variation and sequence of events, only controlled experiments can provide reasonably conclusive evidence concerning the third type of evidence, the absence of other possible producers.

A natural experiment is one in which the investigator intervenes only to the extent required for measurement. That is, there is no manipulation of an assumed causal variable. The investigator merely looks at what has happened. As such, the natural experiment is a form of ex post facto research. In this type of study, the researcher approaches data collection as if a controlled experimental design were used. The variable of interest has occurred in a natural setting, and the researcher looks for respondents who have been exposed to it and also, if a control group is desired, respondents who have not been exposed.

Measurements can then be made on a dependent variable of interest. For example, if the impact of a television commercial on attitudes were desired, the investigator would conduct a survey of people after the commercial was shown. Those who saw the commercial would constitute the experimental group, and those who did not see it would be a type of control group. Differences in attitudes could be compared as a crude measure of impact. Unfortunately, one can never be sure whether the obtained relationship is causal or non-causal, since the attitudes may be affected by the presence of other variables. For a brief discussion of natural experiments, see Anderson (1971).

In controlled experiments, investigator intervention is required beyond that needed for measurement purposes. Specifically, two kinds of intervention are required :

1. Manipulation of at least one assumed causal variable

2. Random assignment of subjects to experimental and control groups

The researcher conducts the experiment by assigning the subjects to an experimental group where the causal variable is manipulated, or to a control group where no manipulation of the causal variable occurs. The researcher measures the dependent variable in both situations and then tests for differences between the groups. Differences between the groups, if present, are attributed to the manipulation variable.

Field experiments are increasingly being completed using online survey instruments. For example, researchers often use the advanced branching logic, randomization, question block presentation, question timing, and java scripting capabilities of Qualtrics.com to conduct time and cost effective field experiments.

Simulation

The expense and time involved in the personal interviews often associated with field experimentation may preclude it as a source of information for a particular operational situation. In such cases it may be desirable to construct a model of the operational situation and to experiment with it instead of venturing into a real-world situation. The manipulation of such models is called simulation.

Simulation can be defined as a set of techniques for manipulating a model of some realworld process to find numerical solutions that represent the real process being modeled. Models that are environmentally rich (that is, that contain many complex interactions and nonlinear relationships among the variables, probabilistic components, time dependencies, etc.) are usually too difficult to solve by standard analytical methods such as calculus or other mathematical programming techniques. Rather, the analyst views a simulation model as a limited imitation of the process or system under study and attempts to run the system on a computer to see what would happen if a particular policy were put into effect.

Simulations may be used for research, instruction, decision-making, or some combination of these applications. During the past 50 or more years, simulations have been developed for such marketing decision- making applications as marketing systems, marketing-mix elements (new-product, price advertising, and sales-force decisions), and interviewing costs in marketing surveys.

TYPES OF ERRORS AFFECTING RESEARCH DESIGNS

The marketing research process (and research design) involves the management of error. Potential errors can arise at any point from problem formulation through report preparation, and rarely will a research project be error-free. Consequently, the research designer must adopt a strategy for managing and minimizing this error. As we shall see in the next section of this chapter, there are alternative strategies that can be followed.

The objective underlying any research project is to provide information that is as accurate as possible. Maximizing accuracy requires that total study errors be minimized. Total study error has two components—sampling error and non-sampling error—and can be expressed as follows :

Total error = Sampling error = Non-sampling error

Total error is usually measured as total error variance, also known as the mean-squared error

(Assael & Keon, 1982):

(Total error)²  = ( Sampling error)² = ( Non Sampling error)²

Sampling error refers to the variable error resulting from the chance specification of population from elements according to the sampling plan. Since this introduces random variability into the precision with which a sample statistic is calculated, it is often called random sampling error. Exhibit 2.2 gives an illustration of how total error is assessed.

It is important to know all the sources of error that contribute to inaccuracy, and to assess the impact of each. As an example, consider the figure below, which shows components of error in a mstudy designed to estimate the size of the personal computer market (Lilien, Brown, & Searls, 1991).

When estimating the market, adjustments are made for each source of error. The components are then combined mathematically to create the total error. For purposes of simplicity, total error is shown here as the sum of the component errors. In actuality, total error would be smaller, as it is usually based on the square roots of summed squares of component errors. Assessing the individual components of total error is highly judgmental and subjective, but it is worth the effort.

Non-sampling error consists of all other errors associated with a research project. Sucherrors are diverse in nature and are often thought of as resulting in some sort of bias, whichimplies systematic error. Bias can be defined simply as the difference between the true value ofthat which is being measured and the researchers’ estimate of the true value. However, there canbe a random component of non-sampling error. For example, misrecording a response during data collection would represent a random error, whereas using a loaded question would be a systematic error. Non-sampling errors have both non response and response based origins.

To a large extent these major error components are inversely related. Increasing the sample size to reduce sampling error can increase non-sampling error in that, for example, there are more instances where such things as recording errors can occur, and the impact of biased (i.e., nonobjective) questions and other systematic errors will be greater. Thus, this inverse relationship lies at the heart of our concern for total error.

Ideally, efforts should be made to minimize each component. Considering time and cost limitations this can rarely be done. The researcher must make a decision that involves a tradeoff between sampling and non-sampling errors. Unfortunately, very little is known empirically about the relative size of the two error components, although there is some evidence that non-sampling error tends to be the larger of the two. In a study comparing several research designs and data collection methods, Assael and Keon (1982) concluded that non-sampling error far outweighs random sampling error in contributing to total survey error. As an introduction, Exhibit 2.3 briefly defines eight major types of errors that can influence research results.

EXHIBIT 2.3 Types of Errors in the Research Process

Different types of errors can influence research results :

-	Population specification: noncorrespondence of the required population to the population selected by the researcher
-	Sampling: noncorrespondence of the sample selected by probability means and the representative sample sought by the researcher
-	Selection: noncorrespondence of the sample selected by nonprobability means and the sought representative sample
-	Frame: noncorrespondence of the sought sample to the required sample
-	Nonresponse: noncorrespondence of the achieved (or obtained) sample to the selected sample
-	Surrogate information: noncorrespondence of the information being sought by the researcher and that required to solve the problem
-	Measurement: noncorrespondence of the information obtained by the measurement process and the information sought by the researcher
-	Experimental: noncorrespondence of the true (or actual) impact of, and the impact attributed to, the independent variable(s)

Population Specification Error

This type of error occurs when the researcher selects an inappropriate population or universe from which to obtain data.

Examples: Cessna Aircraft conducts an online survey to learn what features should be added to a proposed corporate jet. They consider conducting a survey of purchasing agents from major corporations presently owning such aircraft. However, they believe that that this would be an inappropriate research universe; since pilots are most likely play a key role in the purchase decision.

Similarly, packaged goods manufacturers often conduct surveys of housewives, because they are easier to contact, and it is assumed they decide what is to be purchased and also do the actual purchasing. In this situation there often is population specification error. The husband may purchase a significant share of the packaged goods, and have significant direct and indirect influence over what is bought.

Sampling Error

Sampling error occurs when a probability sampling method is used to select a sample, but the resulting sample is not representative of the population concern.

Example: Suppose that we collected a random sample of 500 people from the general adult population and upon analysis found it to be composed only of people aged 35 to 55. This sample would not be representative of the general adult population. Sampling error is affected by the homogeneity of the population being studied and sampled from and by the size of the sample.

In general, the more homogeneous the population (meaning smaller variance on any given characteristic of interest), the smaller the sampling error; as sample size increases, sampling error decreases. If a census were conducted (i.e., all elements of the population were included) there would be no sampling error.

Selection Error

Selection error is the sampling error for a sample selected by a nonprobability method.

Example: 

Interviewers conducting a mall intercept study have a natural tendency to select those respondents who are the most accessible and agreeable whenever there is latitude to do so. Such samples often comprise friends and associates who bear some degree of resemblance in characteristics to those of the desired population.

Selection error often reflects people, who are most easily reached, better dressed, and have better kept homes or more pleasant personalities. Samples of these types rarely are representative of the desired population

Frame Error

A sampling frame is the source for sampling that represents all the members of the population. It is usually a listing of the prospective respondents to be sampled

Example: 

Consider the sample frame for a shopper intercept study at a shopping mall. This sample frame includes all shoppers in the mall during the period of data collection. A commonly used frame for consumer research is the telephone directory. This frame introduces error because many elements of the population are not included in the directory (unlisted phone numbers, new arrivals), some elements are listed more than once, and nonpopulation elements are also included (businesses, people who have left the area).

A perfect frame identifies each member of the population once, but only once, and does not include members not in the population of interest.

Nonresponse Error

Nonresponse error can exist when an obtained sample differs from the original selected sample. There are two ways in which nonresponse can occur: (a) noncontact (the inability to contact all members of the sample); and (b) refusal (nonresponse to some or all items on the measurement instrument). Errors arise in virtually every survey from the inability to reach respondents.

Example: 

In telephone surveys, some respondents are inaccessible because they are not at home (NAH) for the initial call or call-backs. Others have moved or are away from home for the period of the survey. Not-at-home respondents are typically younger with no small children, and have a much higher proportion of working wives than households with someone at home. People who have moved or are away for the survey period have a higher geographic mobility than the average of the population. Thus, most surveys can anticipate errors from non-contact of respondents.

Refusals may be by item or for the entire interview. Income, religion, sex, and politics are topics that may elicit item refusals. Some respondents refuse to participate at all because of time requirements, health issues, past experiences in which an “interviewer” turned out to be a telemarketer, or other reasons. Refusals can also be specific to the method of data collection, as in nonresponse to a mail and email questionnaires or using caller ID to screen and avoid telephone surveys. Nonresponse to mail and email questionnaires sometimes runs as high as 90 percent of the initial mailing, even after several successive mailings

The amount of effort involved in data collection is another possible way to affect nonresponse error. However, little research has been done to examine the impact of effort.

Example : 

In a national telephone survey, a so-called five-day “standard” survey was compared to a “rigorous” survey conducted over an eight-week period (Keeter, Miller, Kohut, Groves, & Presser, 2000). Response rates were significantly different; the rigorous survey generated about two-thirds greater response. But the two surveys produced similar results. Most of the statistically significant differences were for demographic items. Very few differences were found on substantive variables.

Nonresponse is also a potential problem in business-to-business and within organization research situations. Although specific respondents are individuals, organizations are not, as they are differentiated and hierarchical. These characteristics may affect organizational response to survey requests

Tomaskovic-Devey, Leiter, and Thompson (1994) in a study of organizational response, stated the likelihood that an organizational respondent will respond is a function of three characteristics of the respondent :

1.	Authority to respond: The degree to which a designated respondent has the formal or informal authority to respond to a survey request
2.	Capacity to respond: Organizational practices and the division of labor and information affect the assembly of relevant knowledge to reply adequately
3.	Motive to respond: Both individual and organizational motivations to provide information (or not provide information) about the organization

Surrogate Information Error

In many research situations, it is necessary to obtain information that acts as a surrogate for that which is required. The necessity to accept substitute information arises from either the inability or unwillingness of respondents to provide the information requested Decision-oriented behavioral research is always concerned with the prediction of behavior. This limits most marketing research projects to using proxy information, since one cannot observe future behavior. Typically, researchers obtain one or more kinds of surrogate information believed to be useful in predicting behavior.

Examples : 

One may obtain information on past behavior because it is believed that there is sufficient stability in the underlying behavior pattern to give it reasonably high predictive validity. One may ask about intended behavior as a means of prediction. Or one may obtain information about attitudes, level of knowledge, or socioeconomic characteristics of the respondent in the belief that, individually or collectively, they have a high degree of association with future behavior.

Since the type of information required is identified during the problem-formulation stage of the research process, minimizing this error requires an accurate problem definition.

Measurement Error

Measurement error is generated by the measurement process itself, and represents the difference between the information generated and the information wanted by the researcher. Such error can potentially arise at any stage of the measurement process, from the development of an instrument through the analysis of the findings.

In the transmittal stage, errors may be due to the faulty wording of questions orpreparation of nonverbal materials, unintentional interviewer modification of the question’swording, or the way in which a respondent interprets the question. In the response phase, errorsmay occur because the respondent gives incorrect information, the interviewer interprets itincorrectly, or recording errors occur. One aspect of this regards form; form-related errorsconcern psychological orientation toward responding differently to different item formats and include:

1.	Leniency: the tendency to rate something too high or too low
2.	Central tendency: reluctance to give extreme scores
3.	Proximity: giving similar responses to items that occur close to one another (Yu, Albaum, & Swenson, 2003, p. 217)

In the analysis stage, errors of incorrect editing and coding, descriptive summarization, and inference can contribute substantially to measurement error. Measurement error is particularly troublesome for the researcher, since it can arise from many different sources and take on many different forms.

Experimental Error

When an experiment is conducted, the researcher attempts to measure the impact of one or more manipulated independent variables on some dependent variable of interest, while controlling for the influence of all other (i.e., extraneous) variables. Unfortunately, control over all possible extraneous variables is rarely possible. Consequently, what may be measured is not the effect of the independent variables but the effect of the experimental situation itself.

METHODS FOR DEALING WITH POTENTIAL ERRORS

For any research design, recognizing that potential errors exist is one thing, but doing\something about them is another matter. There are two basic approaches for handling potential errors:

1. Minimize errors through precision in the research design

2. Measure or estimate the error or its impact

Minimize Error

Two different approaches can be taken to minimize total error. The first uses the research design to minimize errors that may result from each of the individual error components. Much of the material in Chapters 3 through 9 of this book discusses effective research methods, and as such, involves techniques designed to minimize individual errors. This is consistent with our view that research design innately involves error management. However, this approach is often limited by the budget allotted to a project.

The second approach recognizes that individual error components are not necessarily independent of each other. Thus, attempts to minimize one component may lead to an increase in another. Reducing sampling error by increasing sample size, for example, leads to potentially greater non-sampling error. This means that the research designer must trade off errors when developing a research design that minimizes total error. For a fixed project budget, therefore, it may be prudent for the research designer to choose a smaller sample size (which will increasesampling error) if the cost savings by doing this can develop techniques that will reduce nonresponse and/or improve the measurement process. If the reduction in these nonsampling errors exceeds the increase in sampling error, there will be a reduction in total error.

Estimate or Measure Error

Estimating or measuring individual components and total error is not easy, primarily due to the nature of non-sampling errors. There is a body of accepted sampling theory that allows the researcher to estimate sampling error for a probability sample, but nothing comparable exists for non-sampling errors. Consequently, subjective or judgmental estimates must be made.

As a final note, even though the researcher has designed a project to minimize error, it is almost never completely eliminated. Consequently, the error that exists for every project must be estimated or measured. This is recognized for sampling error when probability samples are used, though non-sampling errors typically are ignored. Although estimating or measuring errors is better than ignoring them, there may be times when ignoring non-sampling error may not be that bad. For example, if non-sampling error is viewed as a multiple of sampling error, ignoring nonsampling errors up to an amount equal to one-half of sampling error reduces a .95 confidence level only to .92 (Tull & Albaum, 1973). However, ignoring a non-sampling error equal in amount to sampling error reduces the .95 level to .83.

CHOOSING A RESEARCH DESIGN

The overview of research designs and sources of error just presented should make it apparent that, given a specified problem, many competing designs can provide relevant information. Each design will have an associated expected value of information and incurred cost.

Suppose, for example, that a researcher is assigned to determine the market share of the ten leading brands of energy drinks. There are many possible ways of measuring market share of energy drink brands, including questioning a sample of respondents, observing purchases at a sample of retail outlets, obtaining sales figures from a sample of wholesalers, obtaining sales figures from a sample of retailers and vending machine operators, obtaining tax data, subscribing to a national consumer panel, subscribing to a national panel of retail stores, and, possibly, obtaining data directly from trade association reports or a recent study by some other investigative agency. Though lengthy, this listing is not exhaustive.

The selection of the best design from the alternatives is no different in principle from choosing among the alternatives in making any decision. The associated expected value and cost of information must be determined for each competing design option. If the design is such that the project will yield information for solving more than one problem, the expected value should be determined for all applicable problems and summed. The design with the highest, positive, net expected payoff of research should be selected.

SUMMARY

In this chapter we dealt with a subject of single most importance to the research project: the research design. We described what a research design is, discussed the classes of designs, and examined major sources of marketing information that various designs employ. Finally, we considered the errors that affect research designs. Presenting these topics as an introduction and overview, we deal with the topics in more depth in the next several chapters. These chapters deal with major sources of marketing information—respondents and experimentation—and the means of obtaining and analyzing research information

REFERENCES

Anderson, B. F. (1971). The psychology experiment: An introduction to the scientific method (2nd ed.). Belmont, CA: Brooks/Cole.

Assael, H., & Keon, J. (1982, Spring). Non-sampling vs. sampling errors in survey research. Journal of Marketing, 46, 114–123.

Feldman, J. (1975). Considerations in the use of causal-correlational technique in applied psychology. Journal of Applied Psychology, 60, 663–670.

Fern, E. F. (1982). The use of focus groups for idea generation: The effects of group size, acquaintanceship, and moderator on response quantity and quality. Journal of Marketing Research, 19 (February), 1–13.

Fink, A. (2003). How to design surveys (2nd ed.). Thousand Oaks, CA: Sage.

Keeter, S., Miller, C., Kohut, A., Groves, R. M., & Presser, S. (2000). Consequences of reducing non-response in a national telephone survey. Public Opinion Quarterly, 64, 125–148.

Lilien, G., Brown, R., & Searls, K. (1991, January 7). How errors add up. Marketing News, 25, 20–21.

Monroe, K. B., & Petroshius, S. M. (n.d.). Developing causal priorities. Unpublished working paper, College of Business, Virginia Polytechnic Institute and State University.

Tomaskovic-Devey, D., Leiter, J., & Thompson, S. (1994). Organizational survey response. Administrative Science Quarterly, 39, 439–457.

Tull, D. S., & Albaum, G. S. (1973). Survey research: A decisional approach. New York: Intext Educational Publishers.

Yu, J., Albaum, G., & Swenson, M. (2003). Is a central tendency error inherent in the use of semantic differential scales in different cultures? International Journal of Market Research, 45 (2), 213–228.