Spreadsheet Error Research Articles

Retraction Statement: Plio‐Pleistocene Indonesian Throughflow Variability Drove Eastern Indian Ocean Sea Surface Temperatures

Retraction: Smith, R. A., Castañeda, I. S., Groeneveld, J., De Vleeschouwer, D., Henderiks, J., Christensen, B. A., et al. (2020). Plio‐Pleistocene Indonesian Throughflow variability drove Eastern Indian Ocean sea surface temperatures. Paleoceanography and Paleoclimatology, 35, e2020PA003872. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020PA003872. The above article from Paleoceanography and Paleoclimatology, published online on 22 October 2020, has been retracted by agreement among the authors, the journal Editor‐in‐Chief, the American Geophysical Union, and Wiley Periodicals LLC. The retraction has been agreed because the authors identified a spreadsheet error in the calculation of the Long‐Chain Diol Index (LDI). Additionally, errors in the calculation of alkenone and GDGT concentrations were identified that change the values but not the overall trends. The authors note that because they mainly focused their interpretations on the TEX86‐derived sea‐surface temperature (SST) record, the overall conclusions of the manuscript remain unchanged. The authors intend to submit a new manuscript to reflect the corrected information.

Corrigendum to “Puddled and zero-till unpuddled transplanted rice are each best suited to different environments – An example from two diverse locations in the Eastern Gangetic Plains of Bangladesh” [Field Crops Res. 262 (2021) 108031

The authors regret to report some mistakes (AE for treatment N) in the published Table 3 (resulting from a spreadsheet error) and associated changes necessary to results and discussion section of the paper. The corrected Table 3 and associated text are below (words to be removed in sections ‘3.3 and 4.4’ are shown with bold, words to be added are italicised). There are no changes to paper conclusions or abstract. The authors would like to apologize for any inconvenience caused. Table 3 Interaction of establishment method and N rate on agronomic efficiency of N (AE) and partial factor productivity of N (PFP) of rice at Rajshahi in 2016 and 2017, and at Dinajpur in 2017 and 2018. Values are means of three replicates. [Table presented] PTR = Puddled transplanted rice; ZT UPTR = Zero-till unpuddled transplanted rice; ZT RUPTR = Zero-till rainfed unpuddled transplanted rice; N = 45 kg N ha; N = 90 kg N ha; LSD = Least significance difference; NS = Not significant. 3.3. N use efficiency There was no significant effect of rice establishment method and N rate on AE in any season and location, except for N rate at Dinajpur Rajshahi in the second season (Table 3). In general, AE decreased increased with increasing N rate at Rajshahi, however, the trend was opposite at Dinajpur. 4.4. Effect of establishment method and N fertilization on N use efficiency There was a decreasing trend in both AE and PFP with increasing N rate, irrespective of the establishment method, suggesting that the recommended N management for both the PTR and ZT UPTR rice was similar in both the sites. The AE was generally greater where 45 kg N ha was applied than in the 90 kg N ha treatment. Comparatively lower AEwith increasing N rate at Dinajpur may be due to the higher N losses at the higher N rate resulting from N leaching during deep drainage in the light-textured soil.

Improvement of Spreadsheet Quality through Reduction of End-User Overconfidence: Case Study

This paper is prompted by and based on earlier research into developers' overconfidence as one of the main causes of spreadsheet errors. Similar to related research, the aim of the paper was to ascertain the existence of overconfidence, and then examine the possibility of its reduction by means of experimental treatment designed for the needs of the research. A quasi-experiment was conducted to this end, in which 62 students of the Faculty of Economics of the University of Novi Sad participated, divided into the experimental and control group. Participants of both groups developed domain free spreadsheets in two iterations each. After the first iterations, students in the experimental group were subjected to experimental treatment: they attended lectures on spreadsheet errors taxonomies supported by real-life examples, and about spreadsheet best practices in the area of spreadsheet error prevention. Results showed that spreadsheet developers who were informed about spreadsheet error taxonomies and spreadsheet best practices create more accurate spreadsheets and are less self-confident in terms of accuracy of their spreadsheets.

The Reinhart-Rogoff controversy as an instance of the ‘emerging contrary result’ phenomenon

The article proposes a new approach to the Reinhart–Rogoff affair. In contrast to the two explanations of the controversy put forward in existing literature, I argue that Growth in a Time of Debt and its criticism published by Herndon, Ash and Pollin (Does high public debt constantly stifle economic growth? A critique of Reinhart and Rogoff) exemplify the ‘emerging contrary result’ phenomenon (emerging recalcitrant result, ERR). Three arguments support this hypothesis. First, the infamous spreadsheet error did not cause the findings to differ. On the contrary, the results differed mostly due to employing alternative averaging schemes. Second, the cliometric techniques employed by both research teams are justified to a similar degree. Third, the pattern in the cliometric literature focused on the 90%-threshold hypothesis suggests that the Reinhart–Rogoff controversy exemplifies an ERR phenomenon caused by submission/publication bias.

Spreadsheet Error Types and Their Prevalence in a Healthcare Context

Spreadsheets are commonly used to inform decision making across many business sectors, despite the fact that research performed in the financial sector has shown that they are quite error-prone. However, few studies have investigated spreadsheet errors and their impact in other domains, like the healthcare sector. This article derives a lifecycle-stage classification scheme of spreadsheet error types based on an aggregation of, and extension of, existing classifications. Based on these classifications, a case study is then presented, performed to investigate the prevalence of these spreadsheet error types in an Irish healthcare setting. Results reveal that more than 90% of the spreadsheets studied contained ‘bottom-line' errors and the average cell-error rate was 13%. There was also a correlation between increased perceived impact of the spreadsheets and the number of errors identified. Recommendations from this research include providing spreadsheet training and guidelines for developers and users, and systematically managing and auditing spreadsheet development and use.

Spreadsheets and the Violence of Forms: Tracking Organisational and Domestic Use

Spreadsheets and the Violence of Forms: Tracking Organisational and Domestic Use

Erratum: Corrigendum: A global strategy for road building

Nature 513, 229–232 (2014); doi:10.1038/nature13717 In this Letter, as a result of an inadvertent spreadsheet error, four values presented in Table 1 were slightly inflated. These relate to the proportions of Earth’s total land surface located within the ‘conserve’, ‘agriculture’, ‘conflict’ and ‘low-tension’ zones.

An Alternative Fit through Problem Representation in Cognitive Fit Theory

This paper uses cognitive fit theory to analyze the problem solving process in spreadsheet analyses. Cognitive fit theory proposes the formation of mental representation as a part of the problem solving process. However, there is little research examining mental representation, which is a key concept in cognitive fit theory. This study examines the formation of mental representation and proposes an alternative mechanism of cognitive fit between different problem representations and their corresponding mental representations when the task is invariant, but the problem representation changes. Mental representation is then empirically assessed based on the application of Hick’s law, which states that the response time of users making a choice varies with the logarithm of the number of possible choices. Therefore, this study contributes to research on cognitive fit theory by proposing an alternative fit and by demonstrating a feasible approach for identifying mental representations. It contributes to spreadsheet research by showing how problem representations affect task performance in the case of spreadsheet error correction.

Technical Note: Detecting and Subcategorizing Hard-coding Errors in Bioenergy-relevant Spreadsheets Using Visual Basic for Applications (VBA)

Electronic spreadsheets play an indispensable role in the simulation, modeling, and analysis of bioenergy systems, and their results have the ability to affect decision-making significantly. Prior research has shown that spreadsheets are highly error-prone, and that a large percentage of these errors are difficult to detect. To that end, we developed computer code (implemented in Visual Basic for Applications, running under Microsoft Excel) to detect a particularly insidious form of spreadsheet error: the hard-coding error. These errors are defined as the presence of one or more unreferenced numerical values in a cell formula. The code was used to audit six engineering spreadsheets relevant to bioenergy systems, three developed in our lab (and reported on in other sessions at the AIM), and three in the public domain. The preliminary audit results were analyzed to understand the nature and distribution of hard-coding errors. The preponderance and diversity of hard-coding errors in these spreadsheets motivated us to subcategorize them. Together, the hard-coding error detection program and sub-categorization program provide a robust and rapid means of detecting and categorizing multiple types of hard-coding errors. Use of these programs could increase the reliability of spreadsheet software used in simulation, modeling, and analysis of bioenergy systems.

Revising the Panko–Halverson taxonomy of spreadsheet errors

Error taxonomies are useful because different types of errors have different commission and detection rates and because error mitigation techniques often are only useful for some types of errors. In the early 1990s, Panko and Halverson developed a spreadsheet error taxonomy. This paper updates that taxonomy to reflect human error research more fully. The taxonomy focuses on quantitative errors during development and testing but notes that qualitative errors are very important and that errors occur in all stages of the system development life cycle.

A Review of Spreadsheet Error Reduction Techniques

Full text not available from this repository.The definitive version is available at http://aisel.aisnet.org/cais/vol25/iss1/34/

The Role of Visualization Tools in Spreadsheet Error Correction from a Cognitive Fit Perspective

Errors in spreadsheets pose a serious problem for organizations and academics. This has resulted in ongoing efforts to devise measures for reducing errors or efficient ways of correcting them. Visualization tools are often advertised as means for improving spreadsheet error correction performance. This study investigates the role of visualization tools in spreadsheet error correction. For this purpose, this study proposes a framework for classifying activities associated with spreadsheet error correction. The framework is to highlight the activities that are important for correcting different types of spreadsheet errors, and to show how different visualization tools can aid error correction by effectively supporting these activities. By identifying “chaining” as one of the most important activities from the framework, this study uses cognitive fit theory to examine the effects of a visualization tool that supports chaining on spreadsheet error correction performance. Experimental methodology is used to test the outcome of cognitive fit between the error correction task and the visualization tool. The results of the experiment highlight the importance of cognitive fit between the type of task and the visualization tool for attaining better performance. This study also provides guidelines for designing and developing tools for spreadsheet error correction.

Factors Affecting the Ability to Detect Spreadsheet Errors

ABSTRACTThe extensive computational and formatting capabilities of today's spreadsheets enable users to create sophisticated analytical models with professionally formatted outputs. But good‐looking reports can mask a host of input errors, formula mistakes, and computational problems. This article examines the subject of spreadsheet error detection in detail and describes an experiment designed to identify those factors influencing the error‐detection capabilities of a sample of spreadsheet users.

Spreadsheets and Sarbanes-Oxley: Regulations, Risks, and Control Frameworks

The Sarbanes–Oxley Act of 2002 (SOX) forced corporations to examine their spreadsheet use in financial reporting. Corporations do not like what they are seeing. Surveys conducted in response to SOX show that spreadsheets are used widely in corporate financial reporting. Spreadsheet error research, in turn, shows that nearly all large spreadsheets contain multiple errors and that errors of material size are quite common. The first round of Sarbanes-Oxley assessments confirmed concerns about spreadsheet accuracy. Another concern is spreadsheet fraud, which also exists in practice and is easy to perpetrate. Unfortunately, few organizations maintain effective controls to deal with either errors or fraud. This paper examines spreadsheet risks for Sarbanes-Oxley (and other regulations) and discusses how general and IT-specific control frameworks can be used to address the control risks created by spreadsheets.

REDUCING SPREADSHEET ERROR RATES

This study looks at the impact of spreadsheet training that includes emphasis on spreadsheet errors and their consequences in reducing spreadsheet error rates. The same spreadsheet modeling task was given to students exposed to this “error awareness” training as well as similar students exposed to structural design training. Empirical results indicate that students exposed to the “error awareness” approach produced fewer errors than students exposed to the other training method. Additionally, the study indicates that more knowledge of the problem domain is associated with fewer spreadsheet errors.

Effects of error factors and prior incremental practice on spreadsheet error detection: an experimental study

Previous research has shown that spreadsheet errors are common and are not easily detected. In this paper, an experiment was conducted to examine the rate of detection of both quantitative errors and qualitative errors in two domain-free spreadsheets. A detailed list and explanation of the types of common spreadsheet errors are presented. Results showed that the ability to detect various types of errors appears to be dependent on the type and prominence of errors as well as prior incremental practice with spreadsheet error detection. Implications of the findings are discussed.

Using a structured design approach to reduce risks in end user spreadsheet development

Computations performed using end-user developed spreadsheets have resulted in serious errors and represent a major control risk to organizations. Literature suggests that factors contributing to spreadsheet errors include developer inexperience, poor design approaches, application types, problem complexity, time pressure, and presence or absence of review procedures. We explore the impact of using a structured design approach for spreadsheet development. We used two field experiments and found that subjects using the design approach showed a significant reduction in the number of ‘linking errors,’ i.e., mistakes in creating links between values that must connect one area of the spreadsheet to another or from one worksheet to another in a common workbook. Our results provide evidence that design approaches that explicitly identify potential error factors may improve end-user application reliability. We also observed that factors such as gender, application expertise, and workgroup configuration also influenced spreadsheet error rates.