ChoiceTheory: ChoiceTheory

In egaudrain/catRpillar: Functions to Process Categorisation Data According to Different Approaches

Description

Calculate a sensitivity and bias measure for categorisation data according to the Choice Theory. This applies to data where an item pertaining to a category is presented, and the participant answers a category by choosing amongst a set of choices.

Usage

ChoiceTheory(dat, grouping, presented = "Presented", responded = "Responded")

Arguments

dat	A data.frame with the responses in long format (one row per trial).
grouping	The factors used to group the d' calculation by (you probably at least want your participant id there). This should be specified as a vector of strings.
presented	A string containing the name of the data.frame field containing the names of the presented categories. (Defaults to "Presented")
responded	A string containing the name of the data.frame field containing the names of the responded categories. (Defaults to "Responded")

Value

A data.frame with the grouping factors, the presented factor, and fields log.alpha.i (which is the sensitivity measure) and bias... for each pair of category (because the bias measure is relative).

References

The calculation are made according to Macmillan and Creelman (2005), p.247.

egaudrain/catRpillar documentation built on Dec. 20, 2021, 3:22 a.m.