primingHeidPrevRT {languageR} | R Documentation |
Primed lexical decision latencies for neologisms ending in -heid
Description
Primed lexical decision latencies for Dutch neologisms ending in the
suffix -heid, with information on RTs to preceding trials added
to the data already in primingHeid.
Usage
data(primingHeidPrevRT)
Format
A data frame with 832 observations on the following 17 variables.
Subject
- a factor with subjects as levels.
Word
- a factor with words as levels.
Trial
- a numeric vector for the rank of the trial in its
experimental list.
RT
- a numeric vector with log-transformed lexical decision
latencies.
Condition
- a factor coding the priming treatmen,
with levels
baseheid
(prime is the base word) and
heid
(the prime is the neologism)
Rating
- a numeric vector for subjective frequency estimates.
Frequency
- a numeric vector for
log-transformed frequencies of the whole word.
BaseFrequency
- a numeric vector for the log-transformed
frequencies of the base word.
LengthInLetters
- a numeric vector coding orthographic length
in letters.
FamilySize
- a numeric vector for the log-transformed
count of the word's morphological family.
NumberOfSynsets
- a numeric vector for the number of synonym
sets in WordNet in which the base is listed.
ResponseToPrime
- a factor with levels
correct
and
incorrect
for the response to the prime.
RTtoPrime
- a numeric vector for the log-transformed
reaction time to the prime.
RTmin1
- a numeric vector for
reaction time in ms to the item preceding the target.
RTmin2
- a numeric vector for
reaction time in ms to the item preceding the target by two trials.
RTmin3
- a numeric vector for
reaction time in ms to the item preceding the target by three trials.
RTmin4
- a numeric vector for
reaction time in ms to the item preceding the target by four trials.
References
De Vaan, L., Schreuder, R. and Baayen, R. H. (2007) Regular morphologically
complex neologisms leave detectable traces in the mental lexicon, The
Mental Lexicon, 2, in press.
Examples
## Not run:
data(primingHeidPrevRT)
library(lme4, keep.source=FALSE)
primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition +
log(RTmin1) + (1|Subject) + (1|Word), data = primingHeidPrevRT)
pvals.fnc(primingHeid.lmer)$summary
## End(Not run)
[Package
languageR version 0.953
Index]