-
Grøver, Vibeke & Lawrence, Joshua
(2024).
Exploring Idioms in Shared Book Reading: Impact on Chinese-Norwegian Bilingual Children's Vocabulary Growth. .
-
Lawrence, Joshua & Grøver, Vibeke
(2024).
Ask more, know more: Children’s Wh-questions Predict Language Development. .
-
Yang, Junyi; Grøver, Vibeke & Lawrence, Joshua
(2023).
Idioms exposure in shared book reading and child vocabulary growth: An exploratory study.
-
Lawrence, Joshua; Knoph, Rebecca; Hwang, Jin Kyoung & Hagen, Åste Marie Mjelve
(2022).
Measures of lexical ambiguity for 62,954 words.
-
Knoph, Rebecca & Lawrence, Joshua
(2022).
Vocabulary tasks and target words are not equal: How language learners answer differently.
-
Yang, Junyi; Grøver, Vibeke & Lawrence, Joshua
(2022).
Analyzing Mothers' Use of Chinese Idioms (Chengyu) in Natural Home Settings.
-
Yang, Junyi; Lawrence, Joshua & Grøver, Vibeke
(2022).
Mothers' Linguistic Input During Shared Book Reading and Dual Language Learners' Home Language Skills.
-
Knoph, Rebecca; Hagen, Åste Mjelve; Zhang, Wenjie & Lawrence, Joshua
(2021).
A LURI Comparison: Listening comprehension for Norwegian and Chinese children.
-
Knoph, Rebecca & Lawrence, Joshua Fahey
(2020).
The Impact of word features on lexical decision tasks.
Show summary
This study used multiple linear regression to determine if special features of academic words (from the Academic Word List) could predict reaction times and accuracy on lexical decision tasks. Specifically, we examined five word feature factors: word frequency from different corpora, word completxity (the number of letters, phonemes, morphemes, syllables, etc.), proximity to other words (the number of phonographic, phonologic, and orthographic neighbors), polysemy (the number of senses and meanings for that word), and diversity (the number of contexts in which the word can be found). We not only discovered a trend in which word features predicted longer lexical decision reaction times, but also found that this trend was different for accuracy. That is, features that predicted shorter mean reaction times did not always predict more participants would accurately identify the word as a word. The different regression weights indicate that how we identify and process words might not only be explained by the length or frequency of a word, but that it also depends on the context in which we can find the word and how many different ways we can use the word.
-
Knoph, Rebecca & Lawrence, Joshua Fahey
(2019).
A Factor Analysis of Word Features:
Are Academic Words Different?
Show summary
The current study replicates two previous factor structures for word features (e.g. word frequency and length), but focuses specifically on the structure for academic vocabulary. These words followed a similar but not identical factor structure to words in general. Use of the factor structure can reduce suppression between multicollinear variables.
-
Knoph, Rebecca & Lawrence, Joshua Fahey
(2019).
What’s in a Word?
The Impact of Word Features on Item Difficulty.
Show summary
This study used multiple linear regression to determine the predictability of item difficulty on three types of items (hypernyms, topical associates, and definitions) given specific features of the target words (complexity, diversity, proximity, frequency, and polysemy). We found a trend in which features tend to be related to item difficulty.
-
Lawrence, Joshua Fahey; Hwang, Jin Kyoung; Lin, Grace; Hagen, Åste Mjelve & Jaeggi, Susanne
(2016).
Polysemy and Semantic
Precision: Semantic Measures Extracted from WordNet.
-
Lawrence, Joshua Fahey; Hwang, Jin Kyoung; Lin, Grace & Hagen, Åste Mjelve
(2016).
What Are Important Academic Words For Reading?