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Presenting the Results - Summary Communication skills are important to the marketing research process which involves the presentation of the research proposal and the research results. An effective presentation involves several elements. The audier.:-: should be clearly identified so that the presentation will be on target. The presentation should include an introduction with an overview of the presentation structure, a body, and a summary. Motivation can be provided by relating the presentation to the research objectives and purpose, by focusing on the most interesting findings, and by having an interesting presentation style. The use of specific examples and visual material can communicate the presentation more effectively and interestingly. The presenter should discuss those elements of methodology that affect interpretation Several guidelines can help improve oral presentations. Reading tends to be boring and should be avoided. Visual aids such as transparencies and handouts can...

Presenting the Results - Oral Presentation The ability to communicate orally is extremely important to effective management in general and to the marketing research function in particular What can be done to ensure the oral presentation is as effective as possible The following five suggestions will be discussed in this section. 1. Don't read 2. Use visual aids 3. Make sure the start is positive 4. Avoid distracting the audience 5. Involve the audience Don't Read Not everyone will agree with this first suggestion; however, it is the f conviction of these authors that the risks and disadvantages of rea ' overwhelm the advantages. The biggest problem with reading is that it is usually boring for the reader and for the audience. Very few can make a script sound interesting, and those few usually do even better without a script. Further, it is necessary to develop the ability to communicate orally in front of a group without a script, in preparation for those o...

Presenting the Results - Guidelines to Successful Presentations The objective of this chapter is to help readers avoid making presentaticr^ that are ineffective because they are dull, confusing, or irrelevant. Have ■: _ been exposed lately to any that hit the jackpot, that are all three? Presentations can be written, oral, or both. Later in the chapter, some tips on making oral presentations will be offered. First, however, several guidelines wtl be presented and discussed that apply to both types of presentations In general, a presenter should: 1. Communicate to a specific audience 2. Structure the presentation 3. Create audience interest 4. Be specific and visual 5. Address validity and reliability issues Each of these guidelines will be discussed in turn. Communicate to a Specific Audience The first step is to know the audience and its background and objectives Most effective presentations seem like a conversation or a memo to a particular person, as opposed to an...

Introducing a Third Variable - Summary In Chapter 13, we introduced measures of association between two variables, which can provide evidence of causal relationships. The task for the analyst in a descriptive study is to exert the mental effort required to employ theory, common sense, and data analysis to explore competing explanations for associations. This chapter explores four explanations that become pnssi-ble when a third variable is introduced. The first, termed spurjous association,involves the possibility that two variables—such as advertising and intention—-are both caused by a third variable, usage. Spurious association can be detected by developing association measures for subgroups defined by the spurious variable (i. e., users and nonusers). The problem is to identify the spurious variable.

Introducing a Third Variable - Direction of Causation Issue If a causal link between two variables is thought to exist, a reasonable question is: which variable is the causal (or independent) variable and which is the "caused" (or dependent) variable? Such a question arose when the task was to distinguish between: INTERVENING VARIABLE SPURIOUS ASSOCIATION BETWEEN A AND I BETWEEN A AND I A     I A->U-+1 and \/ U In some situations there is a reciprocal causal relationship. Attitude-can influence purchase, for example. However, the act of purchasing and using a product or service can affect the attitude, which again affects purchase. Even when a reciprocal causal relationship exists, it might be useful to determine the direction of the dominant flow of influence. Does the attitude-to-purchase direction have a greater effect than the purchase-to-attitude direction, for example? One approach to determining the direction of causation is to draw on logic and prev...

Introducing a Third Variable - Interactive Causal Relationships The introduction of a third variable also might suggest some interactions. Sales (S) might be influenced by distribution (D), but only if the packagingand display (P) is appealing and capable of attracting the shoppers' attention. Interactions, which are present when associations between two variables are affected by the presence or absence of a third variable, were explored in the context of factorial experimental designs (recall Figures 10-1 and 14-5). In the context of cross-tabulations this possibility is explored fat development cross-tabs for subgroups defined by the third variable. Suppose that a segmentation study is attempting to identify the heaij users of a certain library. In Figure 15-3 it is shown that 28 percent of those below age 40 use it, while only 20 percent of those over age 40 use it. 7: explore the relationship further the variable of sex is introduced and the analysis is repeated for men a...

Introducing a Third Variable - Intervening Variables An intervening variable is conceptually very different from a variable causing spurious association: SPURIOUS ASSOCIATION                     INTERVENING VARIABLE BETWEEN A AND B                              BETWEEN A AND B A<-C->B                                                      A->C->B However, from a data analysis viewpoint the two cases are indistinguishable. In each case, the association between A and B will disappear if the analyst controls for C. In the first case, it is because C caused both A and B or is associated with both A and B. In the second, it is because C intervened between A and B; that is, A causes C, which in turn causes B. The t...

Introducing a Third Variable - Spurious Association Association measures, by themselves, do not demonstrate causation. This statement merits repeating because it is so easy to forget or suppress in the context of data analysis. Association measures do not demonstrate causation because they can be the result of extraneous variables. For example, the number of churches in a community is associated with the number liquor stores. Yet, few would maintain that churches tend to spawn liquor stores (or the reverse). The fact is that this is a spurious association because of the third variable, community size, which influences both the number of churches and the number of liquor stores. Another example is the fact that the amount of damage at a fire is associated with the number of fire trucks, only because a large fire both attracts fire trucks and causes substantial damage. If you add fire trucks, you don't increase the damage. A major task of data analysis is to help the researcher ...

Introducing a Third Variable - Causal Relationships Causation, strictly speaking, means that a change in one variable will produce a change in another. In this context, the definition will be broadened somewhat to include the concept of a precondition influencing a variable of interest. Thus, we could conceive that credit-card usage is partly determined by a persons' sex. In this case, sex could be conceptualized as causal in nature, despite the fact that it would be impossible to take a group of people and change their sex to observe if a change in credit-card usage was "produced." The weaker term, "influence," often will be used when it is more appropriate than the term "cause," but the logic of the analysis normally will remain the same. Given the causation concept, that a change in one variable will produce a change in another, it is reasonable to conclude that, if two variables are causally linked, they should be associated. Thus, an obvious d...

Introducing a Third Variable In the HMO study it was found that the greatest interest in the proposed HMO was among the low-income respondents (see Figure 13-3 in Chapter 13). An appropriate question concerns why this association was found. Perhaps it was because higher-income people are more satisfied with their existing medical programs. They may be satisfied because they can afford good health care or because they eat better and are healthier. Or perhaps the interest in the HMO by the low-income respondents might be because the low-income people are students, who have needs and characteristics other than income that make them want the HMO. In fact, maybe the association between income and intentions to enroll has nothing to do with income but was just due to occupation. Data analysis, particularly in descriptive studies, should be guided by such inquiries. Each analysis and potential analysis should be exposed to penetrating questions. The process involves mental effort, attempt...

Hypothesis Testing - Appendix Measures of Association for Nominal Variables We saw earlier in this chapter that the chi-square statistic is seriously flawed as a measure of the association of two variables. The nub of the problem is that the computed value of chi-square can tell us whether there is an association or a relationship but gives us only a weak indication of the strength of the association. The principal purpose of this appendix is to describe a measure, Goodman and Kruskal's Tau, which overcomes many of the problems of chi-square. First we look at some efforts to correct the problems of the chi-square measure. To illustrate these measures we return to Table 14-1 which is reproduced here. According to the chi-square test (x2 = 20), the relationship between location and attendance in Table 14-1 is highly significant. That is, there is a probability of less than .001 that the observed relationship could have happened by chance. Now we wish to know whether there is a suf...

Hypothesis Testing - Summary Hypothesis testing involves four steps: (1) develop evidence supporting.ii judgment; (2) conceptualize the null hypothesis; (3) determine the ^probability of obtaining the evidence if the null hypothesis were true; and (4) calculate the probability—that is, the p-value—of the hypothesis test. There are several points worth remembering about hypothesis testing: 1. Hypothesis testing is a screening test. If the evidence does not pass this test, it may not be worth much attention. If it does pass this test, then it might at least be worth further analysis. 2. Hypothesis testing really measures the sample size. A large sample nearly always will yield "statistically significant" results, and a small enough sample probably will not be statistically significant. Thus, the test really does no more than provide a measure of sample size. 3. Hypothesis testing does not establish whether the null hypothesis is true or false: it only quantifies how per...

Hypothesis Testing - Difference Between Means To illustrate the hypothesis test appropriate for the difference between sample means, consider the following pricing experiment. Three prices are under consideration for a new product: 39 cents, 44 cents, and 49 cents. To determine the influence that the various price levels will have on sales, three samples of five supermarkets are randomly selected from the geographic area of interest. Each sample is assigned one of the three price levels. Figure 14-3 shows the resulting sales levels in both graphic and tabular form. The 390 stores, the first row, had sales of 8, 12, 10, 9, and 11, averaging 10 units. The 44<f stores, the second row, averaged 8 units; and the 49<t stores, the third row, averaged 7 units. Obviously, the determination of the optimal price will require an extensive analysis involving a host of considerations. However, before the analysis begins, it is appropriate to consider the hypothesis that price levels have no...

Hypothesis Testing When an interesting, relevant, empirical finding emerges from data analysis based on a sample, a simple yet penetrating hypothesis test question should occur to every manager and researcher as a matter of course: Does the empirical finding represent only a sampling accident? For example, suppose a study was made of wine consumption. Data analysis revealed that a random sample of 100 California residents consumes more wine per family than a random sample of 100 New York residents. It could be that the observed difference was only caused by sampling error; in actuality, there maybe no difference between the two populations. If the difference found in the two samples could be caused by sampling fluctuations, then it makes little sense to spend additional time on the results or to base decisions on them. If, on the other hand, the results are not simply caused by sampling variations, then there is reason to consider the results further. The hypothesis test question i...

Fundamentals of Data Analysis - Summary The first data analysis phase is to edit and code the data. Editing involves identifying omissions, ambiguities, inconsistencies, lack of cooperation. axicLineligible respondents. Coding involves deciding how the responses are going to be entered. There are a variety of data analysis techniques available. The most basic is to analyze each question by itself, A frequency distribution provides the moaLcomplete. jrrfprmajrion and often leads to decisions to combine response categories. Reporting only the sample means or percentages is the other principal approach. The usual next step is to_tabu]ate questions among subgroups and involves two of the questions from the questionnaire. Thus, the sample mean or.the fre,quen_cy.diMribution is obta for subgroups such as transit users and transit nonusers. Another technique involves the association between two intervally scaled variables and is termed correlation analysis. The sample correlation is a nu...

Fundamentals of Data Analysis - Presenting the Results Eventually the researcher must develop some conclusions from the data analysis and present the results. The presentation, whether oral, written, or both, can be critical to the ultimate ability of the research to influence decisions. We will address this in Chapter 16, where we provide several guidelines that will lead to effective presentations and where we also offer some special tips for making oral presentations.

Fundamentals of Data Analysis - Measuring Association—a Recap A basic procedure in data analysis is determining whether or not two variables are associated. Three methods for exploring association have just been presented. Which is used depends on the nature of the variables involved. Figure 13-5 summarizes the methods of measuring association. If both of the variables are nominally scaled—in that they serve to label or identify categories such as heavy users, light users, or nonusers—then the approach is cross-tabulation. If one of the variables is intervally scaled, such as age or income (that is, scales objects and has a constant unit of measurement), then the difference between means is employed. If both variables are intervally scaled, then the appropriate association measure is the sample correlation.

Fundamentals of Data Analysis - Sample Correlation The correlation measures the degree to which there is an aggoci^inn_be-tween two intervally scaled variables. A positive correlation will reflect a tendency for a high value of one variable to be associated with a high value in the second. A negative correlation reflects an association between a high value on one variable and a low value on the second variable. Of course, one or both of the intervally scaled variables could be used to define categories such as age and income in Figure 13-3, but that would sacrifice information. If the data base included an entire population, such as all adults in California, the measure would be termed the population correlation. If. however, it is based on a sample, it is termed a sample correlation. If two variables are plotted on a two-dimensional graph, termed a scatter diagram, the sample correlation reflects the tendency for the points to cluster systematically about a straight line rising or...