Authors
Nathan Garrett
Abstract
With 27 million users, Excel (Microsoft Corporation, Seattle, WA) is the most common business data analysis software. However, audits show that almost all complex spreadsheets have errors.
The author examined textbooks to understand why responsible data analysis is taught. A purposeful sample of 10 textbooks was coded, and then compared against spreadsheet development best practices.
The results show a wide range of approaches, and reveal that none of the 10 books fully cover the methodologies needed to create well-rounded Excel data analysts.
There is a need to re-evaluate the teaching approaches being used in office application courses.
Sample
Summary of the key results:
- Pedagogical approach. A third of the books guide students through problem description, design requirements, data input, visualization, and printing. The second third use illustrative examples and conceptual explanations; describing the value of individual features, giving limitations and work-arounds. The remaining books use a variety of approaches, such as step-by-step explanations with minimal conceptual information.
- Lifecycle. Only two books use a lifecycle development approach, though without explaining why it is important.
- Block structuring. While good practices are sometimes modeled, their justification and explanation are almost entirely absent. Three books separate assumptions from calculations, but only one explains why this is important.
- Documentation. Almost without exception, the books do not show how to document a spreadsheet. Only one explained the need for documentation, or shows formatting as conveying meaning (instead of as decoration).
- Test. None of the books present information on error rates. Only one strongly supported the need to audit an Excel sheet for errors.
Publication
2015, Journal of Education for Business, March, pages 1-6
