Bridging the Quantitative Skills Gap: Teaching Simple Linear Regression via Simplicity and Structured Replication

Cook, S., Dawson, Peter and Watson, Duncan (2025) Bridging the Quantitative Skills Gap: Teaching Simple Linear Regression via Simplicity and Structured Replication. In: Handbook for Economics Lecturers. The Economics Network.

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Abstract

The quantitative skills (QS) gap among graduates is a pressing challenge highlighted in numerous reports emphasising the growing demand for data expertise in the workforce. This chapter addresses this issue by focusing on the teaching of Simple Linear Regression (SLR), a prominent feature of quantitative methods training and a cornerstone of econometrics education. To tackle the QS gap, we propose a pedagogical framework that employs simplified, bespoke examples within a replication-based approach to support the teaching and learning of SLR. By simplifying arithmetic complexity, the method emphasises the core principles of SLR, enhancing both conceptual understanding and student engagement. An evaluation based on consideration of key pedagogical issues such as cognitive load theory, active learning, self-efficacy, and technology-enhanced learning (TEL) provides strong, and often unexpected, support for the proposed approach. Despite championing ‘simplicity’, the use of real-world data, as frequently prioritised in quantitative methods education, is not rejected. Instead, a balanced and context-sensitive integration of simplified and real-world datasets is suggested. This balance allows students to develop essential skills in data literacy and quantitative reasoning, creating a strong foundation for advanced learning. The proposed approach not only addresses the QS gap but also contributes to advancing effective teaching practices in economics education.

Item Type:	Book Section
Faculty \ School:	Faculty of Social Sciences > School of Economics
Depositing User:	LivePure Connector
Date Deposited:	26 Nov 2025 16:30
Last Modified:	26 Nov 2025 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101115
DOI:	10.53593/n4229a

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