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Causality and Ability Beliefs: An Introduction to Confounders and Colliders

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Title: Causality and Ability Beliefs: An Introduction to Confounders and Colliders
Language: English
Authors: Ali H. Al-Hoorie (ORCID 0000-0003-3810-5978), Phil Hiver (ORCID 0000-0002-2004-7960)
Source: Studies in Second Language Learning and Teaching. 2025 15(2):227-249.
Availability: Adam Mickiewicz University Department of English Studies. Faculty of Pedagogy and Fine Arts, Ul. Nowy Swiat 28-30, 62-800 Kailsz, Poland. e-mail: ssllt@amu.edu.pll; Web site: http://pressto.amu.edu.pl/index.php/ssllt
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Attribution Theory, Second Language Learning, Second Language Instruction, Self Efficacy, Individual Differences, Language Research, Research Design, Graphs, Statistical Inference
ISSN: 2083-5205
2084-1965
Abstract: Causal inference is a fundamental goal of many research endeavors, including scholarship in the field of language education and learning. Randomized controlled trials are considered an ideal design to test causal claims, but not all claims can be subjected to experimental treatment due to ethical and practical constraints. In this article, we provide an overview of the conditions under which causal inference may be made from observational data. This includes recognition of the role of confounders and colliders; the former are common causes of the independent and dependent variables and must be controlled, while the latter are common effects and must not be controlled. We illustrate these ideas with two examples involving ability beliefs and demonstrate them through directed acyclic graphs. We discuss the implications of this approach to causal inference from observational data, specifically in individual differences in language learning research, highlighting the need for explicit modeling of causal relationships and the risk of the atheoretical inclusion of variables, whether as controls, predictors, or covariates.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1478474
Database: ERIC
FullText Text:
  Availability: 0
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  Data: <searchLink fieldCode="AR" term="%22Ali+H%2E+Al-Hoorie%22">Ali H. Al-Hoorie</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3810-5978">0000-0003-3810-5978</externalLink>)<br /><searchLink fieldCode="AR" term="%22Phil+Hiver%22">Phil Hiver</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2004-7960">0000-0002-2004-7960</externalLink>)
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  Data: Adam Mickiewicz University Department of English Studies. Faculty of Pedagogy and Fine Arts, Ul. Nowy Swiat 28-30, 62-800 Kailsz, Poland. e-mail: ssllt@amu.edu.pll; Web site: http://pressto.amu.edu.pl/index.php/ssllt
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  Data: Causal inference is a fundamental goal of many research endeavors, including scholarship in the field of language education and learning. Randomized controlled trials are considered an ideal design to test causal claims, but not all claims can be subjected to experimental treatment due to ethical and practical constraints. In this article, we provide an overview of the conditions under which causal inference may be made from observational data. This includes recognition of the role of confounders and colliders; the former are common causes of the independent and dependent variables and must be controlled, while the latter are common effects and must not be controlled. We illustrate these ideas with two examples involving ability beliefs and demonstrate them through directed acyclic graphs. We discuss the implications of this approach to causal inference from observational data, specifically in individual differences in language learning research, highlighting the need for explicit modeling of causal relationships and the risk of the atheoretical inclusion of variables, whether as controls, predictors, or covariates.
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        Type: general
      – SubjectFull: Second Language Learning
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      – SubjectFull: Second Language Instruction
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      – SubjectFull: Self Efficacy
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      – SubjectFull: Statistical Inference
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      – TitleFull: Causality and Ability Beliefs: An Introduction to Confounders and Colliders
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