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Our assessment research suggests that quantitative business courses that rely primarily on algorithmic problem solving may not produce the deep learning required for addressing real-world business problems. This article illustrates a strategy, supported by recent learning theory, for promoting deep learning by moving students gradually from “well-structured” algorithmic problems with single correct answers to “ill-structured” real-world business problems that may have multiple correct answers and require an argument addressed to a specific audience. We show how these scaffolded communication assignments promote deep learning, and suggest ways that interested faculty can adapt the assignments to their own courses.

Business registers are the foundation upon which all business statistics are compiled. In addition to providing sampling and weighting frames, they are a rich source of information on national and regional economic and industrial structures. The comprehensive coverage of business registers is of particular relevance in the context of tourism, which is a fragmented sector dispersed across a variety of industries. This paper presents a profile of enterprise demography and employment for the tourism industries in Ireland at county level, which corresponds with level 4 of the European Union spatial classification ‘NUTS’. New metrics, entitled ‘Tourism Dependency Ratios’ are derived and mapped. These ratios illustrate how the tourism supply side can be analysed and understood from a spatial perspective. A full-time equivalent (FTE) estimate of employment in the tourism industries is also provided; this is especially important, as it provides a more stable and comparable measure of employment over time.

While information professionals tend to be expert in finding statistical information resources, they tend to be less capable of handling and processing the numerical data that play such an important role in business information. This article reviews some of the basic mathematical and statistical techniques that are intensively used in the economic and business fields, to provide help to information professionals who may work with such material, and reveals how such concepts as series of index numbers and compound annual growth rates can be calculated using tools such as Excel spreadsheet functions. Some of the key elements of the Excel program are set out, including: basic arithmetic; working with percentages; working out CAGR (compound annual growth rate); index numbers; actual numbers; current and constant values; methods of describing data; data ranges; variance and standard deviation; and coefficient of variation. Detailed, worked examples are included to show how the Excel spreadsheets can be used to perform the necessary calculations. The article concludes with some useful statistical data websites, including: UK national statistics (http://www.statistics.gov.uk); Business Information portal (http://www.rba.co.uk); and Biz/ed (http://www.bized.ac.uk).

The emphasis on mechanics in business statistics courses poses a problem for marketing research instructors who teach their course with an application emphasis. Instructors of marketing research could find students ill prepared for the course. As a result, some marketing programs require students to take a research laboratory or break the research course into two parts of 3 credit hours each. This article outlines an alternative. Repositioning (reengineering) the second course in business statistics from a problem-solving orientation to a decision-making emphasis eliminates the need for an additional research course. A 2-year experience teaching the course shows students to be better prepared for the marketing research course. In addition, how to develop and assess key business skills such as communication, problem solving, decision making, and computer use in the course is also discussed. Assessment of outcomes is presently mandated by the American Assembly of Collegiate Schools of Business (AACSB).

In preparing business majors, schools of business have a special responsibility to ensure that students are equipped with the technological and mathematical skills they will need to compete in the worldwide marketplace. This article describes an innovative instructional paradigm for teaching business statistics. This paradigm harnesses the latest classroom technologies and integrates these technologies with established principles of learning. The paradigm also implements the mathematical and technological recommendations put forward by the National Research Council of the National Academy of Science. Evaluation data from both instructor and student perspectives are reported.

With the focus on larger and faster computers and multimedia presentations, the trend in computer-aided instruction implies bigger (or more complex) must be necessarily better. Whereas more powerful applications allow economics instructors to demonstrate some very interesting and often complicated procedures and theories, there is much that can be done with very simple and widely available programs. In this paper, I explain the advantages of introducing undergraduate students to econometrics by helping them design their own statistical packages using a spreadsheet program. Keywords : Economics, instruction, computers, econometrics, statistics, spreadsheets.

This article describes a team-based project developed for undergraduate students in both business communication and business statistics classes in a small, midwestern college. More than 94% of the students endorsed the usefulness of the project, which was designed to help them develop communication competencies in multiple areas: working in teams, writing collaboratively, participating in meetings, and giving and receiving constructive criticism. The project presents a model of collaboration between instructors in business departments.

The influence of students' race and gender on their academic achievement has been the subject of numerous studies along with the development of a number of models to predict students' performance in a particular area of study (e.g., mathematics). These models, even though generally useful, tend to lose their validity when applied to a specific course. In the current investigation, a model has been developed to predict students' success and failure in a particular course (i.e., the first business statistics course). Through the use of discriminant analysis, this model was able to predict students' success and failure with approximately 72% accuracy. The model can be used to identify potential underachievers from the beginning of semesters. With such information, students who have been categorized as possible under-achievers can be monitored closely and provided with additional help in the form of tutorials and special classes, or workshops to improve their performance.

The demand for business information, in relation to its price, is rather perverse in that high price often generates a high demand. The Business Statistics Office, however, produces some inexpensive but useful data currently in the form of Business Monitors but soon to be released online as the BSO Databank.