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Causal Language and Statistics Instruction: Evidence from a Randomized Experiment

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Title: Causal Language and Statistics Instruction: Evidence from a Randomized Experiment
Language: English
Authors: Jennifer Hill (ORCID 0000-0003-4983-2206), George Perrett, Stacey A. Hancock (ORCID 0000-0002-8540-2492), Le Win, Yoav Bergner (ORCID 0000-0001-7738-4290)
Source: Statistics Education Research Journal. 2024 23(1).
Availability: International Association for Statistical Education and the International Statistical Institute. PO Box 24070, 2490 AB The Hague, The Netherlands. Tel: +31-70-3375737; Fax: +31-70-3860025; e-mail: isi@cbs.nl; Web site: https://iase-web.org/ojs/SERJ
Peer Reviewed: Y
Page Count: 27
Publication Date: 2024
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305D200019
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Statistics Education, Causal Models, Statistical Inference, College Students, Attribution Theory, Teaching Methods, Language Usage, Linguistics, Data Interpretation, Introductory Courses, Vignettes
ISSN: 1570-1824
1570-1824
Abstract: Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2024
Accession Number: EJ1435713
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Jennifer+Hill%22">Jennifer Hill</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4983-2206">0000-0003-4983-2206</externalLink>)<br /><searchLink fieldCode="AR" term="%22George+Perrett%22">George Perrett</searchLink><br /><searchLink fieldCode="AR" term="%22Stacey+A%2E+Hancock%22">Stacey A. Hancock</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8540-2492">0000-0002-8540-2492</externalLink>)<br /><searchLink fieldCode="AR" term="%22Le+Win%22">Le Win</searchLink><br /><searchLink fieldCode="AR" term="%22Yoav+Bergner%22">Yoav Bergner</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7738-4290">0000-0001-7738-4290</externalLink>)
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  Data: Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.
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      – Text: English
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        PageCount: 27
    Subjects:
      – SubjectFull: Statistics Education
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