MARKET RESEARCH 1. Discuss the importance of attitude measurement, and describe tow different approaches to measuring people's attitudes toward a given object.

There is a growing need among today's marketers to better understand their customer's attitudes and feelings toward the company's products, services, and delivery systems. Some researchers view "attitude" as a derived composite outcome of the interaction between a person's beliefs (i.e., cognitive thoughts) and expressed emotions (i.e., affective feelings) regarding those beliefs. Knowing these interactions can be helpful in predicting a person's behavior (i.e., conative action). Not all researchers accept this trilogy approach to measuring attitudes; some simply see attitudes as a global indicator of a person's feelings (i.e., affect = attitude) toward an object or behavior. No matter the approach, these is significant diagnostic value to both researchers and practitioners in understanding the different scale measurements used to capture people's belief structures versus emotional feelings versus behavior tendencies.
Tell how to correctly design and text Likert, semantic differential, and behavior intention scales, and explain their strengths and weaknesses.

Likert scale designs uniquely use a set of agreement/disagreement scale descriptors to capture a person's attitude toward a given object or behavior. Contrary to popular belief, a Likert scale format does not measure a person's complete attitude, only the cognitive structure. Semantic differential scale format are exceptional in capturing a person's perceptual image profile about a given object or behavior. This scale format is unique in that it uses a set of bipolar scales to measure several different yet interrelated factors (both cognitive and affective) of a given object or behavior.

Multiattribute affect scales use scale point descriptors that consist of relative magnitudes of an attitude (e.g., "very important," "somewhat important," "not too important," " not at all important," or "like very much," "like somewhat," neither like nor dislike," "dislike somewhat," "dislike very much"). With respect to behavior intention scale formats, the practitioner is interested in obtaining some idea of the likelihood that people (e.g., actual or potential consumers, customers, buyers) will demonstrate some type of predictable behavior toward purchasing an object or a service. The scale point descriptors like "definitely would," "probably would," "probably would not," and "definitely would not," are normally used in an intentions scale format. If the information objective is that of collecting raw data that can directly predict some type of marketplace behavior, then behavior intention scales should be used in the study. In turn, if the objective is understanding the reasons why certain types of marketplace behavior take place, then it is necessary to incorporate scale measurement formats that capture both the person's cognitive belief structures and feelings.
Discuss the differences between noncomparative and comparative scale designs as well as the appropriateness of rating and ranking scale measurements.

The main difference is that comparative scale measurements require the respondent to do some type of direct comparison between the attributes of the scale from the same known reference point whereas noncomparative scales rate each attribute independently of the other attributes making up the scale measurement. The data from comparative scales must be interpreted in relative terms and only activate the assignment and order scaling properties. Noncomparative scale data are treated as interval or ration, and more advanced statistical procedures can be employed in analyzing the data structures. One benefit of comparative scales is that they allow for identifying small differences between the attributes, constructs, or objects. In addition, their comparative scale designs require fewer theoretical assumptions and are easier for respondents to understand and respond to than are many of the noncomparative scale designs. However, noncomparative scales provide opportunity for greater insights into the constructs and their components.
Identify and discuss the critical aspects of consumer attitudes and other marketplace phenomena that require measurement to allow us to make better decisions.

In order for organizations to make informed decisions regarding their suppliers, customers, competitors, employees, or organizational members, they must gather detailed, accurate information. The selection of a supplier may rest partially on their history of on-time delivery, reputation for quality, and experience within the industry. Information concerning the preferences, purchase behavior, shopping patterns, demographics, and attitudes of consumers can be vital to the success or failure of an organization.

Similarly, in-depth profiles of competitors may reveal opportunities or challenges facing the company and can lead to coherent plans designed to create a significant competitive advantage. If consumers prefer a competitor's product, then it would be quite valuable to understand through the use of proper measurement techniques why such preferences exist.
Discuss the overall rules of measurement and explain the differences between single versus multiple measures of a construct as well as direct versus indirect measures.

No single set of rules exists for all measurements; however, certain standards can be applies to the measurement process. For example, the rules for correctly using a thermometer to measure the temperature of water would be quite different from the rules for the use of a telescope to measure the distance to a star. Even so, the rules must be explicit and detailed so as to allow consistent application of the instrument.

Explain the advantages and disadvantages of using quantitative, descriptive survey research designs to collect primary raw data.
Some of the main advantages of using survey designs to collect primary raw data from respondents are ability to accommodate large sample sizes' generalizability of results; ability to distinguish small differences between diverse samples groups; ease of administering and recording questions and answers; increased capabilities of using advanced statistical analysis; and abilities of tapping into latent factors and relationships. In contrast, the main disadvantages of survey research designs tends to focus on potential difficulties of developing accurate survey instruments; inaccuracies in construct and scale measurements of factors; and limits to the depth of the data structures. In addition, researchers can lack control over long time frames and potentially low response rates, among other problems.Discuss the many types of survey methods available to researchers. Identify and discuss the factors that drive the choice of survey methods.
Survey methods are generally divided into three generic types. One is the person-administered survey, in which there is significant face-to-face interaction between the interviewer and the respondent. Second is the telephone-administered survey. In these surveys the telephone is used to conduct the question-and-answer exchanges. Computers are now used in many ways in telephone interviews, especially in management functions, data recording, and telephone-number selection. Third is the self-administered survey. In these surveys, there is little, if any, actual face-to-face contact between the researcher and prospective respondent. The respondent reads the questions and records his or her answers. Most of the emerging technology survey methods are self-administrated, although some, such as virtual reality, will require human intervention.

There are three major factors affecting the choice of survey method: situational characteristics, task characteristics, and respondent characteristics. With situational factors, consideration must be given to such elements as available resources, completion time frame, and data quality requirements. Also, the researcher must consider the overall task requirements and ask questions like, "How difficult are the tasks?," "What stimuli will be needed to evoke responses?," "How much information is needed from the respondent?," and "To what extent do the questions deal with sensitive topics?" Finally, the researchers must be concerned about the diversity of the prospective respondents, the likely incidence rate, and the degree of survey participation. Maximizing the quantity and quality of data collected while minimizing the cost and time of the survey generally requires the researcher to make trade-offs.Explain how the electronic revolution is affecting the administration of survey research designs.
With the increasing advances in telecommunication and computer technologies, numerous new, fast techniques are available to researchers for collecting primary raw data from people. The range of new techniques continues to grow and includes such methods as computer-assisted telephone interviewing methods; fully automated self-administered techniques; and electronic mail, fax, and Internet surveys. There is little doubt that the time requirements of collecting data will significantly decrease with these new methods.Identify and describe the strengths and weaknesses of each type of survey method.
It is important to remember that all methods have strengths as well as weaknesses. No single method is the best choice under all circumstances. Nor is the information researcher limited to a single method. Innovative combinations of survey methods can produce excellent results, as the strengths of one method can be used to overcome the weakness of another.Identify and explain the types of errors that occur in survey research.
The researcher needs to evaluate the errors in the research results. All errors are either random sampling errors or nonsampling errors. By far the greatest amount of error that can reduce data quality comes from nonsampling (or systematic) error sources. Three major sources of error are respondent error (i.e. nonresponse errors and response biases); measurement and design error (i.e., construct development, scale measurement, and survey instrument design errors); and administrative errors (i.e., data processing, interviewer and sample design errors). In survey research, systematic errors decrease the quality level of the data being collected. Discuss the characteristics, benefits, and weaknesses of observational techniques, and explain how these techniques are used to collect primary data.
Observational techniques are used by researchers in all types of research designs (exploratory, descriptive, causal). In addition to the general advantages of observation, major benefits are the accuracy of collecting data on actual behavior as it unfolds, reduction of confounding factors, and the amount of detailed behavioral data that can be recorded. The unique limitations of using observation methods are lack of generalizability of the data, inability of explaining current behaviors or events, and the complexity of setting and recording the behavior.Describe and explain the importance of and differences between the variables used in experimental research design.
In order to conduct causal research, the researcher must understand the four key types of variables involving experimental designs (independent, dependent, extraneous, control) as well as randomization of test subjects and the role that theory plays in creating experiments. The most important goal of any experiment if to determine what, if any, relationships exist among different variables (independent, dependent). Functional, or cause-effect, relationships require systematic change in one variable as another variable changes.Explain the theoretical importance and impact of internal, external, and construct validity measures in experiments and interpreting functional relationships.
Experimental designs are developed to control for contamination, which may serve to confuse the true relationship being studied. While a variety of issues exist regarding the concept of contamination, internal validity, external validity, and construct validity are at the center of discussion. Internal validity refers to the level of exact conclusions the researcher draws about a demonstrated functional relationship. The question is, are the experimental results truly due to the experimental variables? External validity is concerned with the interaction of experimental manipulations with extraneous factors causing a researcher to suspect the generalizability of the results to other settings. Construct validity is important in the process of correctly identifying and understanding both the independent and dependent variables included in an experimental design.

Several techniques unique to the experimental design are used to control for problems of internal and external validity. These techniques center on the use of control groups, pre-experimental measures, exclusion of subjects, matching subjects into groups and randomization of group members. These dimensions, built into the experimental design, provide true power for controlling contamination.Discuss the three major types of experimental designs used in marketing research. Explain the pros and cons of using causal designs as a means of assessing relationship outcomes.
Pre-experimental designs fail to meet internal validity criteria due to a lack of group comparisons. Despite this weakness, three designs are still used quite frequently in marketing research: the one-shot study; the one-group, pretest-posttest design; and the static group comparison.
True experimental designs ensure equivalence between experimental and control groups by random assignment of subjects into groups. Three forms of true experimental designs exist: pretest-posttest, control group; posttest-only, control group; and the Solomon Four Group.
Quasi-experimental designs are appropriate when the researcher can control some of the variables but cannot establish true randomization of groups. While a multitude of these designs exist, two of the most common forms are the nonequivalent control group and the separate-sample, pretest-posttest.Explain what test markets are, the importance and difficulties of executing this type of research designs, and how the resulting data structures are used by researchers and marketing practitioners.
Test Markets are a specific type of field experiment and are commonly conducted in natural field settings. Most common in the marketing research field are traditional test markets, controlled test markets, electronic test markets, simulated test markets, and virtual test markets. Data gathered from test markets provide both researchers and practitioners with invaluable information concerning customers' attitudes, preferences, purchasing habits/patterns, and demographic profiles. This information is very useful in predicting new product/service acceptance levels and advertising and image effectiveness, as well as in evaluating current marketing mix strategies. |
Chapter 3 (14) Identify and discuss the critical factors that can contribute to directly improving the accuracy of surveys, and explain why questionnaire development is not a simple process.
Questionnaire development is much more than just writing a set of questions and asking people to answer them. Designing good surveys goes beyond just developing reliable and valid scale measurements. There are a number of design factors, systematic procedural steps, and rules of logic that must be considered in the development process. In addition, the process requires knowledge of sampling plans, construct development, scale measurement, and types of data. It is important to remember that a questionnaire is a set of questions/scales designed to generate enough raw data to allow the researcher and decision maker to generate information to solve the business problem.Discuss the theoretical principles of questionnaire design, and explain why a questionnaire is more than just asking a respondent some questions.
Many researchers, unaware of the underlying theory, still believe that questionnaire designing is an art rather than a science. Questionnaires are, however, hierarchial structures consisting of four different components: words, questions, formats, and hypotheses. Most surveys are descriptive instruments that rely heavily on the collection of state-of-being or state-of-behavior data; others are predictive instruments that focus on collecting state-of-mind and state-of-intention data that allow for predicting changes in people's attitudes and behaviors as well as testing hypotheses.Identify and explain the communication roles of questionnaires in the data collection process.
Good questionnaires allow researchers to gain a true report of the respondent's attitudes, preferences, beliefs, feelings, behavioral intentions, and actions/reactions in a holistic manner, not just a fragment. Through carefully worded questions and clear instructions, a researcher has the ability to control a respondent's thoughts and ensure objectivity. By understanding good communication principles, researchers can avoid bad questioning procedures that might result in either incomprehensible information requests, unanswerable questions, or leading questions that obscure, prohibit, or distort the meaning of a person's responses.Explain why the type of information needed to address a decision maker's questions and problems will substantially influence the structure and content of questionnaires.
Once research objectives are transformed into information objectives, determining the specific information requirements plays a critical role in the development of questionnaires. For each information objective, the researcher must be able to determine the types of raw data (state of being, mind, behavior, or intentions); types of question/scale measurement formats (nominal, ordinal, interval, or ratio); types of question structures (open-ended and closed-ended); and the appropriate selection of scale point descriptors. Researchers must be aware of the impact that different data collection methods (personal, telephone, self-administered, computer-assisted, etc.,) have on the wording of both questions and response choices.List and discuss the 11 steps in the questionnaire development process, and tell how to eliminate some common mistakes in questionnaire designs.
Using their knowledge of construct development and scale measurement development (Chapter 12) and attitude measurement (Chapter 13), researchers can follow an 11-step process to develop scientific survey instruments. Refer back to Exhibit 14.4 which lists these steps.Discuss and employ the "flowerpot" approach in developing scientific questionnaires.
The flowerpot approach serves as a unique framework or blueprint for integrating different sets of questions and scale measurements into scientific structure for collecting high-quality raw data. This ordered approach helps researchers make critical decisions regarding (1) construct development (2) the appropriate dimensions and attributes of objects, (3) question/scale measurement formats, (4) wording of actual questions and directives, and (5) scale points and descriptors. Following the flowerpot approach assures that the data flow will correctly go from general information level down to a more specific level.Discuss the importance of cover letters, and explain the guidelines to help eliminate common mistakes in cover letter designs.
While the main role of any cover letter should be that of winning over a prospective respondent, a set of secondary roles ranges from initial introduction with a person to communicating the legitimacy and other important factors about the study. There are 10 critical factors that should be included in most, if not all, cover letters. Including these will help the researcher counteract the three major reasons that prospective respondents use to avoid participating in self-administered survey and personal interviews. A lottery-based incentive or compensation system can significantly improve a prospective respondent's willingness to participate. |

|