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Age-of-acquisition ratings for 30,000 English words

(2012) BEHAVIOR RESEARCH METHODS. 44(4). p.978-990
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Abstract
We present age-of-acquisition (AoA) ratings for 30,121 English content words (nouns, verbs, and adjectives). For data collection, this megastudy used the Web-based crowdsourcing technology offered by the Amazon Mechanical Turk. Our data indicate that the ratings collected in this way are as valid and reliable as those collected in laboratory conditions (the correlation between our ratings and those collected in the lab from U.S. students reached .93 for a subsample of 2,500 monosyllabic words). We also show that our AoA ratings explain a substantial percentage of the variance in the lexical-decision data of the English Lexicon Project, over and above the effects of log frequency, word length, and similarity to other words. This is true not only for the lemmas used in our rating study, but also for their inflected forms. We further discuss the relationships of AoA with other predictors of word recognition and illustrate the utility of AoA ratings for research on vocabulary growth.
Keywords
LEXICAL DECISION DATA, Word recognition, IMAGERY, HYPOTHESIS, CONCRETENESS, IMAGEABILITY, TASKS, FAMILIARITY, RECOGNITION, PROJECT, FREQUENCY NORMS, Age of acquisition, Ratings, Amazon Mechanical Turk

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Citation

Please use this url to cite or link to this publication:

MLA
Kuperman, Victor, Hans Stadthagen-Gonzalez, and Marc Brysbaert. “Age-of-acquisition Ratings for 30,000 English Words.” BEHAVIOR RESEARCH METHODS 44.4 (2012): 978–990. Print.
APA
Kuperman, Victor, Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. BEHAVIOR RESEARCH METHODS, 44(4), 978–990.
Chicago author-date
Kuperman, Victor, Hans Stadthagen-Gonzalez, and Marc Brysbaert. 2012. “Age-of-acquisition Ratings for 30,000 English Words.” Behavior Research Methods 44 (4): 978–990.
Chicago author-date (all authors)
Kuperman, Victor, Hans Stadthagen-Gonzalez, and Marc Brysbaert. 2012. “Age-of-acquisition Ratings for 30,000 English Words.” Behavior Research Methods 44 (4): 978–990.
Vancouver
1.
Kuperman V, Stadthagen-Gonzalez H, Brysbaert M. Age-of-acquisition ratings for 30,000 English words. BEHAVIOR RESEARCH METHODS. 2012;44(4):978–90.
IEEE
[1]
V. Kuperman, H. Stadthagen-Gonzalez, and M. Brysbaert, “Age-of-acquisition ratings for 30,000 English words,” BEHAVIOR RESEARCH METHODS, vol. 44, no. 4, pp. 978–990, 2012.
@article{3133349,
  abstract     = {We present age-of-acquisition (AoA) ratings for 30,121 English content words (nouns, verbs, and adjectives). For data collection, this megastudy used the Web-based crowdsourcing technology offered by the Amazon Mechanical Turk. Our data indicate that the ratings collected in this way are as valid and reliable as those collected in laboratory conditions (the correlation between our ratings and those collected in the lab from U.S. students reached .93 for a subsample of 2,500 monosyllabic words). We also show that our AoA ratings explain a substantial percentage of the variance in the lexical-decision data of the English Lexicon Project, over and above the effects of log frequency, word length, and similarity to other words. This is true not only for the lemmas used in our rating study, but also for their inflected forms. We further discuss the relationships of AoA with other predictors of word recognition and illustrate the utility of AoA ratings for research on vocabulary growth.},
  author       = {Kuperman, Victor and Stadthagen-Gonzalez, Hans and Brysbaert, Marc},
  issn         = {1554-351X},
  journal      = {BEHAVIOR RESEARCH METHODS},
  keywords     = {LEXICAL DECISION DATA,Word recognition,IMAGERY,HYPOTHESIS,CONCRETENESS,IMAGEABILITY,TASKS,FAMILIARITY,RECOGNITION,PROJECT,FREQUENCY NORMS,Age of acquisition,Ratings,Amazon Mechanical Turk},
  language     = {eng},
  number       = {4},
  pages        = {978--990},
  title        = {Age-of-acquisition ratings for 30,000 English words},
  url          = {http://dx.doi.org/10.3758/s13428-012-0210-4},
  volume       = {44},
  year         = {2012},
}

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