Teaching your Students how to Create Relevant Data Analysis Objectives and Questions-Dzuranin Slides

Dzuranin Slides

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The document, authored by Ann C. Dzuranin, discusses the importance of teaching students how to create relevant data analysis objectives and questions. Dzuranin outlines the significance of formulating precise and pertinent questions as a foundation for achieving accurate analysis results, emphasizing that "getting the right answers is only possible when you have asked the right questions.” <br /><br />The agenda of the document covers various areas including skill definitions, determining objectives, articulating questions, and evaluating results. It categorizes types of data analysis questions into descriptive, diagnostic, predictive, and prescriptive, illustrating these categories with examples related to different professional practice areas in accountancy, such as financial accounting, auditing, and managerial accounting.<br /><br />Specific steps discussed include defining the problem, generating questions, performing analyses, and evaluating results. Objectives should align with the motivation for the analysis, as illustrated by an example where a business unit operating at a loss is analyzed to identify underlying factors. Good questions should be clear, focused on a single issue, and measurable. The evaluation of results involves checking if the data used and methods applied are appropriate and if the analysis makes sense in relation to the questions posed, addressing concerns of potential biases such as the "What you see is all there is" bias, coined by Daniel Kahneman.<br /><br />Overall, the document provides a comprehensive guide for educators on how to instill essential analysis skills in students, crucial for their future roles in accounting and related fields.

Keywords

data analysis

teaching

objectives

questions

accountancy

evaluation

bias

skills

education

Dzuranin