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<p>Regression analysis is a cornerstone of quantitative research across the social and behavioral sciences, yet its teaching is often constrained by students’ persistent misconceptions about correlation and causation and the substantial cognitive demands of statistical learning. Standard approaches emphasize statistical techniques and prediction, leaving students ill-prepared to evaluate the assumptions and design logic necessary for proper interpretation. This essay advocates a pedagogical framework that integrates experimental design principles into regression instruction. Drawing on cognitive theories, including schema theory, situated learning, and cognitive load theory, we argue that embedding concepts such as randomization, control, temporal precedence, and confounding into regression instruction can help students develop richer causal inference schemas for interpreting regression analysis results. Further, building on conceptual change research, we contend that addressing students’ naïve causal theories requires instruction that explicitly challenges and restructures their prior beliefs, fostering self-regulated reasoning and correcting faulty assumptions.</p>

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From Association to Causation: Integrating Experimental Design Principles into Regression Education

  • 期刊论文

  • 发表日期：

    2026年3月28日

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  • Cody Ding

摘要

Regression analysis is a cornerstone of quantitative research across the social and behavioral sciences, yet its teaching is often constrained by students’ persistent misconceptions about correlation and causation and the substantial cognitive demands of statistical learning. Standard approaches emphasize statistical techniques and prediction, leaving students ill-prepared to evaluate the assumptions and design logic necessary for proper interpretation. This essay advocates a pedagogical framework that integrates experimental design principles into regression instruction. Drawing on cognitive theories, including schema theory, situated learning, and cognitive load theory, we argue that embedding concepts such as randomization, control, temporal precedence, and confounding into regression instruction can help students develop richer causal inference schemas for interpreting regression analysis results. Further, building on conceptual change research, we contend that addressing students’ naïve causal theories requires instruction that explicitly challenges and restructures their prior beliefs, fostering self-regulated reasoning and correcting faulty assumptions.