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LexTALE Scoring Calculator

This calculator computes scores for LexTALE (Lemhöfer & Broersma, 2012) and LexTALE-type vocabulary tests using three different scoring methods. Enter your test results below to calculate the original LexTALE score, Ghent score (Brysbaert, 2013), and Normalised Ghent score (Wen et al., 2024).

Calculated Scores

Original LexTALE Score (%correctAV):
Ghent Score:
Normalised Ghent Score (%):

Signal Detection and other info

d′ (d-prime):
Hit Rate:
False Alarm Rate:
% Correct Words:
% Correct Nonwords:
Overall % Correct:
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Scoring Methods Explained

1. Original LexTALE Score (%correctAV)

The original scoring method proposed by Lemhöfer & Broersma (2012) calculates the average of the percentage correct for words and nonwords separately. This corrects for the unequal proportion of words and nonwords (typically 2:1 ratio) and penalises both "yes" bias and "no" bias equally.

\[ \%\text{correct}_{\text{AV}} = \frac{\left(\frac{\text{Correct Words}}{N_{\text{words}}} \times 100\right) + \left(\frac{\text{Correct Nonwords}}{N_{\text{nonwords}}} \times 100\right)}{2} \]
Note: The score ranges from 0% to 100%. However, if a participant responds "Yes" to all items, the score would be 50% (100% words correct, 0% nonwords correct). The same happens when a participant responds "No" to all items. This is a limitation addressed by the Ghent score.

CEF Level Classification (English LexTALE only)

For the original English LexTALE, Lemhöfer & Broersma (2012) provide cut-off scores to classify participants according to the Common European Framework of Reference for Languages:

  • < 60%: Lower proficiency (B1 and below)
  • 60% – 80%: Upper-intermediate (B2)
  • > 80%: Advanced (C1/C2)

2. Ghent Score

The Ghent score, introduced by Brysbaert (2013), adjusts the calculation by taking into account incorrect responses to nonwords (false alarms) rather than correct rejections. This provides a score that can range from negative values to positive values, better reflecting guessing behaviour.

\[ \text{Ghent Score} = N_{\text{yes to words}} - \frac{N_{\text{words}}}{N_{\text{nonwords}}} \times N_{\text{yes to nonwords}} \]

When the word:nonword ratio is 2:1, this simplifies to:

\[ \text{Ghent Score} = N_{\text{yes to words}} - 2 \times N_{\text{yes to nonwords}} \]

3. Normalised Ghent Score

The normalised Ghent score, proposed by Wen et al. (2024), divides the Ghent score by the number of word items to produce a percentage score with a fixed range of −100% to +100%, regardless of the number of items in the test. This allows for better comparison across different LexTALE-type tests.

\[ \text{Normalised Ghent Score} = \frac{N_{\text{yes to words}} - \frac{N_{\text{words}}}{N_{\text{nonwords}}} \times N_{\text{yes to nonwords}}}{N_{\text{words}}} \times 100\% \]

4. d′ (d-prime) — Signal Detection Measure

d′ is a measure from signal detection theory that quantifies the ability to discriminate between words (signals) and nonwords (noise). It is calculated as the difference between the z-transformed hit rate and false alarm rate. Higher d′ values indicate better discrimination ability.

\[ d' = z(\text{Hit Rate}) - z(\text{False Alarm Rate}) \]

Where:

\[ \text{Hit Rate} = \frac{\text{Correct Words}}{N_{\text{words}}} \quad \text{and} \quad \text{False Alarm Rate} = \frac{\text{Incorrect Nonwords}}{N_{\text{nonwords}}} \]
Note: When the hit rate is 1.0 or the false alarm rate is 0.0, the z-score would be infinite. In such cases, the standard correction is applied: values are adjusted to 1/(2N) or 1−1/(2N) respectively (Macmillan & Creelman, 2005).

Native Speaker Detection (LexCHI)

For LexCHI, Wen et al. (2024) conducted a receiver operator characteristic (ROC) curve analysis to determine a cut-off score that can distinguish native from non-native Chinese speakers. The analysis yielded:

  • Cut-off score: 70% (Normalised Ghent Score)
  • Sensitivity: 0.957 (correctly identifies native speakers)
  • Specificity: 0.898 (correctly identifies non-native speakers)
  • AUC: 0.974 (near-perfect discrimination)

If a participant has a Normalised Ghent Score lower than 70%, it is very likely they are not a native speaker of Chinese. This cut-off can be used as a screening test when recruiting native Chinese speakers for online studies.

Available LexTALE-type Tests

The following table lists the LexTALE and LexTALE-type vocabulary tests currently available:

Language Test Name Words Nonwords α Reference OSF Online version
Arabic LexArabic 60 30 .92 Alzahrani (2024)
Basque Basque LexTALE 50 25 de Bruin et al. (2017)
Chinese LexCHI 40 20 .96 Wen et al. (2024) osf.io/dh3ty
Chinese (single character) LEXTALE_CH 60 30 .95 Chan & Chang (2018) osf.io/qdy4n
Dutch Dutch LexTALE 40 20 lextale.com
English LexTALE 40 20 Lemhöfer & Broersma (2012) lextale.com
Estonian LexEst 60 30 .96 Lõo et al. (2025) osf.io/y42xv lexest.ut.ee
Finnish Lexize 60 30 .97 Salmela et al. (2021)
French LEXTALE_FR 56 28 .96 Brysbaert (2013)
German German LexTALE 40 20 lextale.com
Italian LexITA 45 21 .96 Amenta et al. (2020)
Japanese LexJP 60 30 .94 Suzukida & Saito (2025)
Korean LexKo 40 20 .92 Chang et al. (2025) osf.io/rf39u
Malay LexMAL 60 30 .97 Lee et al. (2024) osf.io/8y4ft lexmal.org
Portuguese LextPT 60 30 .97 Zhou & Li (2022)
Sicilian LexSIC 50 25 .97 Kupisch et al. (2023) osf.io/nwrq5
Spanish Lextale-Esp 60 30 .96 Izura et al. (2014)
Venetan LexVEN 50 25 .98 Ferin et al. (2023) osf.io/f8vpu

Note: α = Cronbach's alpha reliability coefficient from the final validation study (where available). All tests maintain a 2:1 word:nonword ratio.

Stimuli, instructions and other information about LexTALE and LexTALE-type tests can generally be found in the paper's Appendix/Supplementary Information, on OSF, online, or requested by contacting the corresponding author.

References

  • Alzahrani (2024). LexArabic: A receptive vocabulary size test to estimate Arabic proficiency. Behavior Research Methods, 56, 5529–5556. https://doi.org/10.3758/s13428-023-02286-z
  • Amenta, S., Badan, L., & Brysbaert, M. (2020). LexITA: A quick and reliable assessment tool for Italian L2 receptive vocabulary size. Applied Linguistics, 42(2), 292–314. https://doi.org/10.1093/applin/amaa020
  • Brysbaert, M. (2013). LEXTALE_FR: A fast, free, and efficient test to measure language proficiency in French. Psychologica Belgica, 53(1), 23–37. https://doi.org/10.5334/pb-53-1-23
  • Chan, I. L., & Chang, C. B. (2018). LEXTALE_CH: A quick, character-based proficiency test for Mandarin Chinese. Proceedings of the 42nd Annual Boston University Conference on Language Development, 1, 114–130.
  • Chang, C. B., Ahn, S., & Kim, Y. (2025). LexKO: A quick, reliable lexical test of Korean language proficiency. Behavior Research Methods, 57, article number 317. https://doi.org/10.3758/s13428-025-02806-z
  • de Bruin, A., Carreiras, M., & Duñabeitia, J. A. (2017). The BEST dataset of language proficiency. Frontiers in Psychology, 8, 522. https://doi.org/10.3389/fpsyg.2017.00522
  • Ferin, M., Gyllstad, H., Venagli, I., Zordan, A., & Kupisch, T. (2023). LexVEN: A quick vocabulary test for proficiency in Venetan. Isogloss. Open Journal of Romance Linguistics, 9(1), 1–31. https://doi.org/10.5565/rev/isogloss.374
  • Izura, C., Cuetos, F., & Brysbaert, M. (2014). Lextale-Esp: A test to rapidly and efficiently assess the Spanish vocabulary size. Psicológica, 35(1), 49–66. https://www.redalyc.org/articulo.oa?id=16930557004
  • Kupisch, T., Arona, S., Besler, A., Cruschina, S., Ferin, M., Gyllstad, H., & Venagli, I. (2023). LexSIC : A quick vocabulary test for dialect proficiency in Sicilian. Isogloss. Open Journal of Romance Linguistics, 9(1), 1–24. https://doi.org/10.5565/rev/isogloss.302
  • Lee, S. T., van Heuven, W. J. B., Price, J. M., & Leong, C. X. R. (2024). LexMAL: A quick and reliable lexical test for Malay speakers. Behavior Research Methods, 56(4), 3306–3324. https://doi.org/10.3758/s13428-023-02202-5
  • Lemhöfer, K., & Broersma, M. (2012). Introducing LexTALE: A quick and valid Lexical Test for Advanced Learners of English. Behavior Research Methods, 44(2), 325–343. https://doi.org/10.3758/s13428-011-0146-0
  • Lõo, K., Leppik, K., Malmi, A., Bleive, A., & Bertram, R. (2025). Introducing LexEst: a quick and efficient vocabulary test for assessing vocabulary knowledge in L2 Estonian. Applied Psycholinguistics, 46, e39. https://doi.org/10.1017/S0142716425100301
  • Macmillan, N. A., & Creelman, C. D. (2005). Detection Theory: A User's Guide (2nd ed.). Lawrence Erlbaum Associates.
  • Salmela, R., Lehtonen, M., Garusi, S., & Bertram, R. (2021). Lexize: A test to quickly assess vocabulary knowledge in Finnish. Scandinavian Journal of Psychology, 62(6), 806–819. https://doi.org/10.1111/sjop.12768
  • Suzukida, Y., & Saito, K. (2025). LexJP: A test to efficiently assess vocabulary knowledge of Japanese. Research Methods in Applied Linguistics, 4(2), 100208. https://doi.org/10.1016/j.rmal.2025.100208
  • Wen, Y., Qiu, Y., Leong, C. X. R., & van Heuven, W. J. B. (2024). LexCHI: A quick lexical test for estimating language proficiency in Chinese. Behavior Research Methods, 56(3), 2333–2352. https://doi.org/10.3758/s13428-023-02151-z
  • Zhou, C., & Li, X. (2022). LextPT: A reliable and efficient vocabulary size test for L2 Portuguese proficiency. Behavior Research Methods, 54(6), 2625–2639. https://doi.org/10.3758/s13428-021-01731-1