Campus Enterprises had formed a partnership with a popular pizza restaurant for the purpose of selling freshly made pizza from specially heated trucks on the campus of a large state university. The trucks arrived at numerous convenient locations throughout the campus at specified times. Freshly made pizzas with the most popular ingredients and in the most popular sizes were available from the trucks at prices comparable to a similar pizza delivered to the buyer’s place of residence.
The manager of Campus Enterprises felt that sales of pizzas from the trucks were less than what he had expected. To help him identify why sales were not up to expectations and what might be done to improve sales, the manager had a survey taken in which more than 400 students were interviewed.
During the interview students were asked such questions as how often they had eaten pizza in the last month, whether they were aware of the Campus Enterprises’ trucks being on campus and how much they knew about the trucks’ pizza products and prices, whether they knew about the pizza trucks’ scheduled times and locations, what their attitudes were toward the idea of a pizza truck on campus how much money they spent each month on meals and other personal items, how often they had purchased pizza from the trucks, and other questions.
The manager of Campus Enterprises was hoping that the survey findings would help him have a better understandings of such things as: how frequent pizza eaters differed from those who ate pizza less frequently; why some students had favorable attitudes toward the pizza truck idea other students had unfavorable attitudes; how students who patronized the pizza trucks differed from those who didn’t; and other things. The manager was wondering how the survey findings would have to be analyzed in order to obtain the information he wanted.
Tabulation and analysis consists of first 4 steps that is preparing the data, entering the data into computer, tabulating the data, and testing the significance of observed differences. If the observed differences in survey data are judged significant researchers may want to undertake further analyses in order to gain a better understanding of their survey findings.
Managers and researchers frequently are interested in gaining a better understanding of the differences that exist between two or more subgroups (e.g. between frequent pizza eaters and infrequent pizza eaters; between students with favorable towards the pizza truck and those with unfavorable attitudes; between students who patronize the pizza trucks and those who do not patronize the pizza trucks). Whenever managers or researchers try to identify characteristics common to one subgroup but not common to the other subgroups, they are trying “explain differences” between the subgroups.
When managers or researchers are trying to explain the differences between two or more subgroups, they are likely to use one of the three methods of analysis discussed – cross tabulation, correlation, and regression analysis.
Characteristics of the Data used when trying to “Explain Differences”:
The data to be analyzed must possess three characteristics when researchers wish to apply the analytical methods in this article.
1. The data must be obtained from descriptive studies, not from experiments.
2. The data must be from large probability samples, usually in excess of 100 and frequently much larger.
3. The data must include measures on a number of variables for each respondent. Researchers will have data reflecting different attitudes, behavior, or opinions as well as such data as monthly household consumption of certain products, television viewing habits, brand awareness, annual income, education, and so on. Such data are called multivariate data.