Measuring Adult Heritage Language Lexical Proficiency for Studies on Facilitative Processing of Gender
Abstract
1. Introduction
1.1. Facilitative Processing, Grammatical Gender, and Individual Differences
1.2. Measuring Lexical Proficiency
1.3. Research Questions and Approach
2. Materials and Methods
2.1. Eye-Tracking Materials
2.2. Materials for Measuring Lexical Proficiency
2.3. Participants
2.4. Procedure
2.5. Data Preparation
3. Results
3.1. Facilitative Use of Grammatical Gender
3.2. Lexical Proficiency Results
3.3. Individual Differences Among HSs: Predicting Facilitative Use of Gender by Lexical Proficiency
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
F | Feminine |
M | Masculine |
PL | Plural |
SG | Singular |
1 | We note that cluster-based permutation analyses are the current best method for testing for an effect of a manipulation between conditions in a VWP study, but they are less reliable for estimating the onset of an identified effect (Ito & Knoeferle, 2022). For this reason, we do not make any claims pertaining to the relative onset of the effect of condition between the two groups; this matter is also not relevant to our current research questions. |
2 | Thank you to an anonymous reviewer for suggesting this analytical approach. |
3 | Given the significant correlations between raw scores on the three measures of lexical proficiency reported in Section 3.2, we calculated the Variance Inflation Factor (VIF) for predictors in every model that contained more than one measure of lexical proficiency, to check for multicollinearity. All VIFs were found to be below 3 (a typical cut-off value is 5), indicating that although there was some inflation of the variance of predictor slopes in these models, high multicollinearity was not a major concern. |
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Condition | Target | Competitor | Distractor |
---|---|---|---|
Same | M SG | M SG | F PL |
Different gender | M SG | F SG | F PL |
Different number | M SG | M PL | F PL |
Different | M SG | F PL | F PL |
Different number or gender | M SG | F SG | M PL |
Effect | Estimate | SE | t | p-Value |
---|---|---|---|---|
Intercept | −1.22 | 0.05 | −23.62 | <0.001 |
Condition | 0.09 | 0.05 | 1.99 | 0.047 |
LexTALE score | 0.15 | 0.05 | 3.27 | 0.002 |
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Fuchs, Z.; Kealey, E.; Eldem-Tunç, E.; Mermelstein, L.; Pham, L.; Runova, A.; Chen, Y.; Oğuz, M.; Hong, S.; Pan, C.; et al. Measuring Adult Heritage Language Lexical Proficiency for Studies on Facilitative Processing of Gender. Languages 2025, 10, 189. https://doi.org/10.3390/languages10080189
Fuchs Z, Kealey E, Eldem-Tunç E, Mermelstein L, Pham L, Runova A, Chen Y, Oğuz M, Hong S, Pan C, et al. Measuring Adult Heritage Language Lexical Proficiency for Studies on Facilitative Processing of Gender. Languages. 2025; 10(8):189. https://doi.org/10.3390/languages10080189
Chicago/Turabian StyleFuchs, Zuzanna, Emma Kealey, Esra Eldem-Tunç, Leo Mermelstein, Linh Pham, Anna Runova, Yue Chen, Metehan Oğuz, Seoyoon Hong, Catherine Pan, and et al. 2025. "Measuring Adult Heritage Language Lexical Proficiency for Studies on Facilitative Processing of Gender" Languages 10, no. 8: 189. https://doi.org/10.3390/languages10080189
APA StyleFuchs, Z., Kealey, E., Eldem-Tunç, E., Mermelstein, L., Pham, L., Runova, A., Chen, Y., Oğuz, M., Hong, S., Pan, C., & Subramony, J. (2025). Measuring Adult Heritage Language Lexical Proficiency for Studies on Facilitative Processing of Gender. Languages, 10(8), 189. https://doi.org/10.3390/languages10080189