| Title |
On the predictive validity of various corpus-based frequency norms in L2 English lexical processing
|
|---|---|
| Published in |
Behavior Research Methods, January 2018
|
| DOI | 10.3758/s13428-017-1001-8 |
| Pubmed ID | |
| Authors |
Xiaocong Chen, Yanping Dong, Xiufen Yu |
| Abstract |
The predictive validity of various corpus-based frequency norms in first-language lexical processing has been intensively investigated in previous research, but less attention has been paid to this issue in second-language (L2) processing. To bridge the gap, in the present study we took English as a case in point and compared the predictive power of a large set of corpus-based frequency norms for the performance of an L2 English visual lexical decision task (LDT). Our results showed that, in general, the frequency norms from SUBTLEX-US and WorldLex-Blog tended to predict L2 performance better in reaction times, whereas the frequency norms from corpora with a mixture of written and spoken genres (CELEX, WorldLex-Blog, BNC, ANC, and COCA) tended to predict L2 accuracy better. Although replicated in both low- and high-proficiency L2 English learners, these patterns were not exactly the same as those found in LDT data from native English speakers. In addition, we only observed some limited advantages of the lemma frequency and contextual diversity measures over the wordform frequency measure in predicting L2 lexical processing. The results of the present study, especially the detailed comparisons among the different corpora, provide methodological implications for future L2 lexical research. |
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| Country | Count | As % |
|---|---|---|
| Canada | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
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| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
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|---|---|---|
| Researcher | 6 | 18% |
| Student > Ph. D. Student | 4 | 12% |
| Student > Master | 4 | 12% |
| Professor | 4 | 12% |
| Student > Doctoral Student | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 12 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Linguistics | 12 | 35% |
| Arts and Humanities | 2 | 6% |
| Social Sciences | 2 | 6% |
| Business, Management and Accounting | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 13 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 February 2018.
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#14,283,318
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#1,268
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#216,471
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Outputs of similar age from Behavior Research Methods
#16
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