Validation of two measures for assessing English vocabulary knowledge on web-based testing platforms
Long-form assessments
Bibliographic Data
| ID | 21226648 |
|---|---|
| Authors | Lee Drown (0000-0002-1189-1799, University of Connecticut), Nikole Giovannone (University of Connecticut), David B Pisoni (0000-0002-8046-3157, Indiana University Bloomington), Rachel Théodore (0000-0002-4601-8340, University of Connecticut, corresponding author), Rachel M Theodore (University of Connecticut) |
| Year | 2024 |
| Volume | 9 |
| Issue | 1 |
| Pages | 113-124 |
| Publication date | 2024-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Linguistics Vanguard (JOURNAL) |
| Journal identifiers | ISSN: 2199-174X • E-ISSN: 2199-174X |
| Publisher | Walter de Gruyter GmbH (PUBLISHER • DE) |
| DOI | 10.1515/lingvan-2022-0115 |
| PMID | 38173913 |
| OpenAlex | W4386638466 |
| Language | EN |
| Citations received | 3 |
| References cited | 30 |
The goal of the current work was to develop and validate web-based measures for assessing English vocabulary knowledge. Two existing paper-and-pencil assessments, the Vocabulary Size Test (VST) and the Word Familiarity Test (WordFAM), were modified for web-based administration. In Experiment 1, participants ( n = 100) completed the web-based VST. In Experiment 2, participants ( n = 100) completed the web-based WordFAM. Results from these experiments confirmed that both tasks (1) could be completed online, (2) showed expected sensitivity to English frequency patterns, (3) exhibited high internal consistency, and (4) showed an expected range of item discrimination scores, with low frequency items exhibiting higher item discrimination scores compared to high frequency items. 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
Linguistics · Natural language processing · Normative · Vocabulary · Web application · World Wide Web · Computer Science · Natural Language Processing Techniques · Second Language Acquisition and Learning · Text Readability and Simplification · Artificial Intelligence
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| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |