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Before analyzing the data, the researcher should ensure that the units of measurement are comparable across countries or cultural units. For example, the data may have to be adjusted to establish currency equivalents or metric equivalents. Furthermore, standardization or normalization of the data may be necessary to make meaningful comparisons and achieve consistent results.

A Worldwide Scream for Ice Cream

As of 2009, Haagen-Dazs ( had become a global phenomenon, available in more than SO countries. How did this come about? The strategy for whetting foreign appetites is simple. Marketing research conducted in several European (e.g., Britain, France, and Germany) and several Asian (e.g., Japan, Singapore, and Taiwan) countries revealed that consumers were hungry for a high-quality ice cream with premium image and were willing to pay a premium price for it. These consistent findings emerged after the price of ice cream in each country was standardized to have a mean of zero and a standard deviation of unity. Standardization was desirable, because the prices were specified in different local currencies and a common basis was needed for comparison across countries. Also, in each country, the premium price had to be defined in relation to the prices of competing brands. Standardization accomplished both these objectives

Based on these findings, Haagen-Dazs first introduced the brand at a few high-end retailers; then built company-owned stores in high-traffic areas: and finally rolled into convenience stores and supermarkets. Hungry for a quality product, British consumers shelled out $S a pint-double or triple the price of some home brands. “It is easily the largest selling ice cream shop in the world under a trademark name,” says John Riccitiello, senior vice president for international sales. In the United States, Haagen-Dazs remains popular although faced with intense competition and health consciousness. This added to the impetus to enter the foreign markets

The data analysis could be conducted at three levels:

(1) individual. (2) with country or cultural unit, and (3) across countries or cultural units. Individual-level analysis requires that the data from each respondent be analyzed separately. For example, one might compute a correlation coefficient or run a regression analysis for each respondent. This means that enough data must be obtained from each individuaJ to allow analysis at the individual level, which is often not feasible. Yet it has been argued that in international marketing or cross-cultural research, the researcher should possess a sound knowledge of the consumer in each culture. This can best be accomplished by individual-level analysis

In within-country or cultural-unit analysis, the data are analyzed separately for each country or cultural unit. This is also referred to as intracultural analysis. This level of analysis is quite similar to that conducted in domestic marketing research. The objective is to gain an understanding of the relationships and patterns existing in each country or cultural unit. In across-countries analysis, the data of all the countries are analyzed simultaneously. Two approaches to this method are possible. The data for all respondents from all the countries can be pooled and analyzed. This is referred to as pancultural analysis. Alternatively, the data can be aggregated for each country and these aggregate statistics analyzed. For example, one could compute means of variables for each country, and then compute correlations on these means. This is referred to as cross-cultural analysis. The objective of this level of analysis is to assess the comparability of findings from one country to another. The similarities as well as the differences between countries should be investigated. When examining differences, not only differences in means but also differences in variance and distribution should be assessed. All the statistical techniques that have been discussed in this book can be applied to within-country or across-country analysis and, subject to the amount of data available, to individual-level analysis as well.

Ethics in Marketing Research 

Ethical issues that arise during the data preparation and analysis step of the marketing research process pertain mainly to the researcher. While checking, editing, coding, transcribing, and cleaning, researchers should try to get some idea about the quality of the data. An attempt should be made to identify respondents who have provided data of questionable quality. Consider, for example, a respondent who checks the “7” response to all the 20 items measuring attitude toward spectator sports on a 1-t0-7 Liken-type scale. Apparently, this respondent did not realize that some of the statements were negative whereas the others were positive. Thus, this respondent indicates an extremely favorable attitude toward spectator spurts on all the positive statements and an extremely negative attitude on the statements that were reversed. Decisions whether such respondents should be discarded, that is, not included in the analysis, can raise ethical concerns. A good rule of thumb is to make such decisions during the data preparation phase before conducting any analysis.

In contrast, suppose the researcher conducted the analysis without first attempting to identify unsatisfactory respondents. The analysis, however, does not reveal the expected relationship, that is, the analysis does not show that attitude toward spectator sports influences attendance at spectator sports. The researcher then decides to examine the quality of data obtained. In checking the questionnaires, a few respondents with unsatisfactory data are identified. In addition to the type of unsatisfactory responses mentioned earlier, there were other questionable patterns as well. To illustrate, some respondents had checked all responses as “4,” the “neither agree nor disagree” response, to all the 20 items measuring attitude toward spectator sports. When these respondents are eliminated and the reduced data set analyzed, the expected results are obtained, showing a positive influence of attitude on attendance at spectator sports. Discarding respondents after analyzing the data raises ethical concerns, particularly if the report does not state that the initial analysis was inconclusive. Moreover, the procedure used to identify unsatisfactory respondents and the number of respondents discarded should be clearly disclosed.

Posted on November 30, 2015 in Data Preparation

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