Validation of two measures for assessing English vocabulary knowledge on web-based testing platforms: brief assessments.
| Autor: | Drown L; Department of Speech, Language, and Hearing Sciences, University of Connecticut, Storrs, CT, USA., Giovannone N; Department of Speech, Language, and Hearing Sciences, University of Connecticut, Storrs, CT, USA., Pisoni DB; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA., Theodore RM; Department of Speech, Language, and Hearing Sciences, University of Connecticut, Storrs, CT, USA. |
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| Jazyk: | angličtina |
| Zdroj: | Linguistics vanguard : multimodal online journal [Linguist Vanguard] 2023 Sep 13; Vol. 9 (1), pp. 99-111. Date of Electronic Publication: 2023 Sep 13 (Print Publication: 2023). |
| DOI: | 10.1515/lingvan-2022-0116 |
| Abstrakt: | Two measures for assessing English vocabulary knowledge, the Vocabulary Size Test (VST) and the Word Familiarity Test (WordFAM), were recently validated for web-based administration. An analysis of the psychometric properties of these assessments revealed high internal consistency, suggesting that stable assessment could be achieved with fewer test items. Because researchers may use these assessments in conjunction with other experimental tasks, the utility may be enhanced if they are shorter in duration. To this end, two "brief" versions of the VST and the WordFAM were developed and submitted to validation testing. Each version consisted of approximately half of the items from the full assessment, with novel items across each brief version. Participants ( n = 85) completed one brief version of both the VST and the WordFAM at session one, followed by the other brief version of each assessment at session two. The results showed high test-retest reliability for both the VST ( r = 0.68) and the WordFAM ( r = 0.82). The assessments also showed moderate convergent validity (ranging from r = 0.38 to 0.59), indicative of assessment validity. This work provides open-source English vocabulary knowledge assessments with normative data that researchers can use to foster high quality data collection in web-based environments. (© 2023 Walter de Gruyter GmbH, Berlin/Boston.) |
| Databáze: | MEDLINE |
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