Predicting Bilingual Performance: Effects of Subjective and Objective Measures of Bilingual Profile
Year: 2026
Type: article
Study design: Observational Study
Abstract: Aims and objectives: This study seeks to test the inferential properties of self-reported measures of language profile on performance. We hypothesise that well-powered dimension reduction techniques (in this study, principal component analysis or PCA) will perform well in predictive performance on language processing tests. Method: The Language Experience and Proficiency Questionnaire was administered to 262 Norwegian–English bilingual respondents. A subset of 64 participants also completed the following measures of individual differences: (a) LexTALE as a proficiency measure, (b) a morphosyntactic error judgement task, (c) two verbal fluency tasks (semantic and phonemic), (d) a non-linguistic task-switching measure of domain-general executive control. Data and Analyses: The sample size permitted a well-powered PCA of 26 variables, identifying seven principal components: (a) English Use, (b) English Later AoA, (c) English Learning and Proficiency, (d) English Media Exposure, (e) Foreign Pronunciation in English, (f) Language Mixing and (g) Overall Language Proficiency. Scores were extracted for the subset of 64 participants, and regression analyses were conducted for each objective task using the self-report-based component scores as predictors. All components predicted performance on at least one task, except for English Later AoA and English Media Exposure. These results demonstrate consistent relationships between self-reported and behavioural measures and underscore the utility of PCA component loadings as inferential predictors in bilingualism research. Originality: The study shows clear and sensible relationships between subjective measures and performance even following dimension reduction. The study highlights the importance of individual differences and lends further credence to the use of self-reported measures for bilingual research. Significance/Implications: Given the variability across bilingual populations, we advocate for the broader inferential use of self-reported measures and application of PCA in multilingual research to better capture the underlying dimensions of language experience and proficiency and their predictive abilities.
Institution: University of Agder
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
OpenAlex ID: W7221503012