Evaluating potential language attrition via the Supplemental Language Attrition Questionnaire

Aims and Objectives/Purpose/Research Questions: This paper introduces the Supplemental Language Attrition Questionnaire (SLAQ), a standardized instrument that evaluates whether and to what extent an individual’s previously acquired language may have undergone attrition. Current language background questionnaires identify patterns of language acquisition and use but rarely include variables thought to influence attrition. We ask whether the SLAQ shows evidence of construct and incremental validity.Design/Methodology/Approach: The SLAQ was developed through extensive review of attrition research and expert feedback. It collects self-report data about acquisition environment, peak proficiency, attitude, motivation, period of disuse, and current contact. This paper compares SLAQ responses to responses from two existing language background questionnaires and evaluates its reliability and validity.Data and Analysis: Participants completed either the LHQ (n = 36) or LEAP-Q (n = 22) and the SLAQ. Twenty-seven participants reported attrition of at least one language. Quantitative SLAQ items were analyzed using polychoric correlation matrices, item–total correlations, and descriptive statistics.Findings/Conclusions: Results indicate that existing questionnaires fail to capture attrition, with more than half of the attrited languages not mentioned in LHQ/LEAP-Q responses. Additionally, SLAQ items demonstrated minimal overlap and strong internal consistency. Thus, the SLAQ provides a comprehensive account of language experience that is tailored to attrition contexts.Originality: This is the first validated background instrument for language attrition. Its branching design and short average completion time (7.7 minutes) make the SLAQ an unobtrusive addition to multilingualism research designs.Significance/Implications: The SLAQ offers a standardized method for evaluating attrition, which contributes to cross-study comparability, improves participant classification, and supports more accurate models of multilingual experience. Its adoption has the potential to strengthen research on attrition, bilingualism, and language assessment practices.