2012
DOI: 10.3758/s13428-012-0210-4
|View full text |Cite|
|
Sign up to set email alerts
|

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

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 th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

27
1,001
6
1

Year Published

2014
2014
2024
2024

Publication Types

Select...
7
3

Relationship

1
9

Authors

Journals

citations
Cited by 1,035 publications
(1,035 citation statements)
references
References 45 publications
27
1,001
6
1
Order By: Relevance
“…We employed several well-established lexicons, such as Emolex (Mohammad and Turney, 2010) (10), Hedonometer (Dodds et al, 2011) (1), DAL (Whissell, 1989) (3), Warriner's Norms (Warriner et al, 2013) (3), Age of Adquisition (Kuperman et al, 2012) (1), Bristol familiarity and imaginary norms (Stadthagen-Gonzalez and Davis, 2006) (2), and WWBP lexicons (Schwartz et al, 2016(Schwartz et al, , 2013World Well-Being Project, 2017) which includes: PERMA (10), OCEAN (5), time-oriented (3) and affect-intensity lexicons (2). We also used MentalDisLex (Zirikly et al, 2016) (1) (2), determiners (1), word counts (1), mean word length (1), number of webpage links (1), lexical diversity (1)(mean fraction of different words among 100 random subsamples of 10 words) and the fraction of words semantically similar to several keywords 1 (8).…”
Section: Body Content Features (68 Features)mentioning
confidence: 99%
“…We employed several well-established lexicons, such as Emolex (Mohammad and Turney, 2010) (10), Hedonometer (Dodds et al, 2011) (1), DAL (Whissell, 1989) (3), Warriner's Norms (Warriner et al, 2013) (3), Age of Adquisition (Kuperman et al, 2012) (1), Bristol familiarity and imaginary norms (Stadthagen-Gonzalez and Davis, 2006) (2), and WWBP lexicons (Schwartz et al, 2016(Schwartz et al, , 2013World Well-Being Project, 2017) which includes: PERMA (10), OCEAN (5), time-oriented (3) and affect-intensity lexicons (2). We also used MentalDisLex (Zirikly et al, 2016) (1) (2), determiners (1), word counts (1), mean word length (1), number of webpage links (1), lexical diversity (1)(mean fraction of different words among 100 random subsamples of 10 words) and the fraction of words semantically similar to several keywords 1 (8).…”
Section: Body Content Features (68 Features)mentioning
confidence: 99%
“…The situation is rapidly improving for the English language, where age-of-acquisition ratings have been collected for 30,000 words (Kuperman, Stadthagen-Gonzalez, & Brysbaert, 2012), affective ratings for 14,000 words (Warriner, Kuperman, & Brysbaert, 2013), and concreteness ratings for 40,000 words (Brysbaert, Warriner, & Kuperman, in press). The main reason for this improvement is that in English, one can make use of Amazon Mechanical Turk, a service created by the company Amazon where Internet users can earn a small amount of money by doing so-called Human Intelligence Tasks.…”
Section: Introductionmentioning
confidence: 99%
“…dinosaur, weird). Nonetheless, since we wished to create a test that could also be applied in higher grades, we selected additional words with irregular spellings whose spoken word forms are acquired by the age of 13 (Kuperman, Stadthagen-Gonzalez & Brysbaert, 2012).…”
Section: Diagnostic Spelling Test Irregular Words (Disti)mentioning
confidence: 99%