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 |
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| Language: | English |
| Authors: | Ali H. Al-Hoorie (ORCID |
| 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 |
| 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. |
|---|---|
| ISSN: | 2083-5205 2084-1965 |
