quasif: Simulated data set with subjects and items requiring quasi-F...

In languageR: Data Sets and Functions with Analyzing Linguistic Data: A Practical Introduction to Statistics

Simulated data set with subjects and items requiring quasi-F ratios

Description

Simulated lexical decision latencies with SOA as treatment, traditionally requiring an analysis using quasi-F ratios, as available in Raaijmakers et al. (1999).

Usage

data(quasif)

Format

A data frame with 64 observations on the following 4 variables.

Subject

a factor coding subjects.

RT

a numeric vector for simulated reaction times in lexical decision.

Item

a factor coding items.

SOA

a factor coding SOA treatment with levels long and short.

Source

Raaijmakers, J.G.W., Schrijnemakers, J.M.C. & Gremmen, F. (1999) How to deal with "The language as fixed effect fallacy": common misconceptions and alternative solutions, Journal of Memory and Language, 41, 416-426.

Examples

## Not run: 
data(quasif)
items.quasif.fnc(quasif)

## End(Not run)

languageR documentation built on June 10, 2025, 9:08 a.m.